CN118275938A - Transformer winding short-circuit fault detection method, device, platform, medium and equipment - Google Patents
Transformer winding short-circuit fault detection method, device, platform, medium and equipment Download PDFInfo
- Publication number
- CN118275938A CN118275938A CN202410693057.8A CN202410693057A CN118275938A CN 118275938 A CN118275938 A CN 118275938A CN 202410693057 A CN202410693057 A CN 202410693057A CN 118275938 A CN118275938 A CN 118275938A
- Authority
- CN
- China
- Prior art keywords
- sampling
- magnetic flux
- short
- circuit fault
- winding
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004804 winding Methods 0.000 title claims abstract description 203
- 238000001514 detection method Methods 0.000 title claims abstract description 132
- 230000004907 flux Effects 0.000 claims abstract description 158
- 238000000034 method Methods 0.000 claims abstract description 61
- 238000005070 sampling Methods 0.000 claims description 184
- 230000005355 Hall effect Effects 0.000 claims description 34
- 230000005672 electromagnetic field Effects 0.000 claims description 30
- 239000000523 sample Substances 0.000 claims description 15
- 238000006243 chemical reaction Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 10
- 238000005259 measurement Methods 0.000 description 9
- 230000008859 change Effects 0.000 description 8
- 230000000694 effects Effects 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000005856 abnormality Effects 0.000 description 3
- 230000036541 health Effects 0.000 description 3
- 238000009413 insulation Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 229910052802 copper Inorganic materials 0.000 description 2
- 239000010949 copper Substances 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 238000012805 post-processing Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 239000004020 conductor Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000002500 effect on skin Effects 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000010191 image analysis Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000012731 temporal analysis Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000000700 time series analysis Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Landscapes
- Testing Of Short-Circuits, Discontinuities, Leakage, Or Incorrect Line Connections (AREA)
Abstract
The application relates to the field of transformer fault detection and discloses a method, a device, a platform, a medium and equipment for detecting a transformer winding short-circuit fault. The method is characterized in that the space leakage magnetic field characteristics between the turns in the healthy state and the short-circuit fault state are analyzed, one or more of the current density, the magnetic flux density and the magnetic flux phase angle of each turn in the winding are analyzed, and whether the turns are faulty or not is judged by comparing the data with the data under the condition of no fault. The application does not need to detect the specific value of the current, thus solving the problem that the existing detection method can not accurately judge the position and the degree of the short-circuit fault under the condition of small turn-to-turn short-circuit fault current.
Description
Technical Field
The application relates to the field of rolling image analysis, in particular to a method, a device, a platform, a medium and equipment for detecting short-circuit faults of a transformer winding.
Background
Power transformers are one of the most important devices in power transmission, distribution systems. As the capacity and load of transformers increases, the safe operation and reliability of these transformers has become a research hotspot. The transformer consists of an iron core, windings, an insulation protection system and the like, and the most fragile component is the insulation system. Monitoring the winding condition to make some protection measurements in advance is therefore necessary to prevent a severe transformer failure. Power transformers are affected by various faults, with turn-to-turn short circuit faults accounting for 80% of all faults. The causes of these faults are partial discharge inside the transformer, overload, ageing of the windings, loosening.
In general, winding fault detection methods can be classified into two types, namely, lossy measurement and lossless measurement. Invasive measurements require the search coil or magnetic field sensor to be mounted at different locations inside the transformer to measure the internal magnetic field. The measurement locations are typically the inter-winding air gap, the outside of the windings and the core ends. Non-invasive measurement is to provide a sensor outside the transformer to measure low amplitude, stray magnetic flux radiated from the end of the transformer, either electrical signal or internally and externally, to detect transformer failure. However, in the dry type transformer, since the inter-turn short fault current is small and the fault transient is complicated, it is difficult to determine a specific degree and position of the inter-turn short by the frequency response. In particular, the transformer in the running state causes the original fault signal to be distorted, and the short circuit position is more difficult to accurately judge.
Disclosure of Invention
In view of the above, the application provides a method, a device, a medium and equipment for detecting short-circuit faults of a transformer winding, which solve the problem that the existing detection method can not accurately judge the position and the degree of the short-circuit faults under the condition of small turn-to-turn short-circuit fault current.
According to an aspect of the present application, there is provided a transformer winding short-circuit fault detection method, including:
Setting a plurality of sampling points on a transformer winding based on preset sampling point positions in at least one sampling area, wherein the sampling area comprises at least one of an inner side of an inner winding, an outer side of an outer winding and a gap between the inner winding and the outer winding;
Acquiring reference detection signal distribution data aiming at the preset sampling point position in each sampling area under the condition of no faults, wherein reference detection signals corresponding to the reference detection signal distribution data comprise at least one of the following: current density, magnetic flux density, and magnetic flux phase angle;
Based on the types of the reference detection signals, respectively acquiring detection signals corresponding to sampling points in each sampling area, and respectively determining detection signal distribution data of each detection signal corresponding to each sampling area based on the detection signals;
And comparing the detection signal distribution data of each detection signal corresponding to each sampling area with the reference detection signal distribution data respectively, and identifying the short-circuit fault of the transformer winding according to the comparison result.
Optionally, the identifying the short-circuit fault of the transformer winding according to the comparison result includes:
If the deviation between the detected current density distribution data and the reference current density distribution data is larger than a first preset threshold value, determining that a first recognition result is that a first short-circuit fault exists in the transformer winding, and the turn line at the position where the detected current density and the reference current density deviate is a short-circuit fault turn line; and/or the number of the groups of groups,
If the deviation between the detected magnetic flux density distribution data and the reference magnetic flux density distribution data is larger than a second preset threshold value, determining that a second identification result is that a second short circuit fault exists in the transformer winding, and the turn line at the position where the detected magnetic flux density and the reference magnetic flux density deviate is the short circuit fault turn line; and/or the number of the groups of groups,
If the deviation between the detected magnetic flux phase angle distribution data and the reference magnetic flux phase angle distribution data is larger than a third preset threshold value, determining that a third identification result is that a third short-circuit fault exists in the transformer winding;
and determining a short-circuit fault identification result corresponding to the transformer winding according to at least one of the first identification result, the second identification result and the third identification result.
Optionally, the method further comprises:
Determining the fault degree of the transformer winding according to the ratio of the number of the short-circuit fault turns to the total number of turns on the transformer winding, wherein the fault degree of the transformer winding is positively related to the ratio;
And/or the number of the groups of groups,
And if the deviation between the detected magnetic flux phase angle distribution data and the reference magnetic flux phase angle distribution data is larger than a fourth preset threshold value, and the difference value between the detected magnetic flux density of each sampling point and the reference magnetic flux density of the corresponding position is smaller than a fifth preset threshold value, judging that the fault degree of the transformer winding is mild.
Optionally, the acquiring the detection signals corresponding to the sampling points in each sampling area includes:
In the sampling area, a Hall effect sensor arranged at the position of the preset sampling point is utilized to collect sampling signals corresponding to the sampling point, and the current density corresponding to the sampling point is determined according to the magnetic field intensity in the sampling signals;
And calculating the magnetic flux density of the position of the sampling point according to the current density.
Optionally, the acquiring the detection signals corresponding to the sampling points in each sampling area includes:
Detecting electromagnetic field changes of the preset sampling point positions by using the Hall effect sensor, and determining the current electromagnetic field peak time according to the electromagnetic field changes;
and determining the magnetic flux density phase angle corresponding to the sampling point according to the time difference between the current electromagnetic field peak time and the electromagnetic field peak time under the fault-free condition.
Optionally, after the sampling signal corresponding to the sampling point is acquired, the method further includes:
amplifying the sampling signal and converting the amplified sampling signal into a digital signal format.
According to another aspect of the present application, there is provided a transformer winding short-circuit fault detection apparatus, the apparatus comprising:
a sampling point setting module, configured to set a plurality of sampling points on a transformer winding based on preset sampling point positions in at least one sampling area, where the sampling area includes at least one of an inner side of an inner winding, an outer side of an outer winding, and a gap between the inner winding and the outer winding;
The reference data acquisition module is used for acquiring reference detection signal distribution of the preset sampling point positions in each sampling area under the condition of no faults, wherein the reference detection signals corresponding to the reference detection signal distribution data comprise at least one of the following: current density, magnetic flux density, and magnetic flux phase angle;
The sampling module is used for respectively acquiring detection signals corresponding to sampling points in each sampling area based on the types of the reference detection signals and respectively determining detection signal distribution data of each detection signal corresponding to each sampling area based on the detection signals;
And the judging module is used for respectively comparing the detection signal distribution data of each detection signal corresponding to each sampling area with the reference detection signal distribution data and identifying the short circuit fault of the transformer winding according to the comparison result.
Optionally, the judging module is configured to:
If the deviation between the detected current density distribution data and the reference current density distribution data is larger than a first preset threshold value, determining that a first recognition result is that a first short-circuit fault exists in the transformer winding, and the turn line at the position where the detected current density and the reference current density deviate is a short-circuit fault turn line; and/or the number of the groups of groups,
If the deviation between the detected magnetic flux density distribution data and the reference magnetic flux density distribution data is larger than a second preset threshold value, determining that a second identification result is that a second short circuit fault exists in the transformer winding, and the turn line at the position where the detected magnetic flux density and the reference magnetic flux density deviate is the short circuit fault turn line; and/or the number of the groups of groups,
If the deviation between the detected magnetic flux phase angle distribution data and the reference magnetic flux phase angle distribution data is larger than a third preset threshold value, determining that a third identification result is that a third short-circuit fault exists in the transformer winding;
and determining a short-circuit fault identification result corresponding to the transformer winding according to at least one of the first identification result, the second identification result and the third identification result.
Optionally, the judging module is further configured to:
Determining the fault degree of the transformer winding according to the ratio of the number of the short-circuit fault turns to the total number of turns on the transformer winding, wherein the fault degree of the transformer winding is positively related to the ratio;
And/or the number of the groups of groups,
And if the deviation between the detected magnetic flux phase angle distribution data and the reference magnetic flux phase angle distribution data is larger than a fourth preset threshold value, and the difference value between the detected magnetic flux density of each sampling point and the reference magnetic flux density of the corresponding position is smaller than a fifth preset threshold value, judging that the fault degree of the transformer winding is mild.
Optionally, the sampling module is further configured to:
In the sampling area, a Hall effect sensor arranged at the position of the preset sampling point is utilized to collect sampling signals corresponding to the sampling point, and the current density corresponding to the sampling point is determined according to the magnetic field intensity in the sampling signals;
And calculating the magnetic flux density of the position of the sampling point according to the current density.
Optionally, the sampling module is further configured to:
Detecting electromagnetic field changes of the preset sampling point positions by using the Hall effect sensor, and determining the current electromagnetic field peak time according to the electromagnetic field changes;
And determining the magnetic flux density phase angle of the position where the sampling point is located according to the time difference between the current electromagnetic field peak time and the electromagnetic field peak time under the fault-free condition.
Optionally, the apparatus further comprises a conversion module for:
amplifying the sampling signal and converting the amplified sampling signal into a digital signal format.
According to another aspect of the present application, there is provided a transformer winding short-circuit fault detection platform for implementing the above transformer winding short-circuit fault detection method, the platform comprising:
The detection probe is provided with a linear array Hall effect sensor, and the linear array Hall effect sensor comprises a plurality of sensors which are respectively arranged at the positions of sampling points;
the signal amplifying circuit is used for amplifying the sampling signal output by the detection probe;
an a/D conversion circuit for converting the amplified sampling signal into a digital format;
the computer equipment is used for processing the sampling signals in the digital format to obtain a transformer winding short-circuit fault detection result;
The detection probe, the signal amplifying circuit, the A/D conversion circuit and the computer equipment are sequentially connected;
the platform is used for realizing the method for detecting the short-circuit faults of the transformer winding.
According to still another aspect of the present application, there is provided a medium having stored thereon a program or instructions which, when executed by a processor, implements the above-described transformer winding short-circuit fault detection method.
According to a further aspect of the present application, there is provided an apparatus comprising a storage medium storing a computer program and a processor implementing the above-mentioned transformer winding short-circuit fault detection method when executing the computer program.
By means of the technical scheme, the application provides a short circuit fault detection method by analyzing the space leakage magnetic field characteristics between the turns in the health state and the short circuit fault state, analyzing one or more of the current density, the magnetic flux density and the magnetic flux phase angle of each turn in the winding, and judging whether the turns are faulty or not by comparing the data with the data under the fault-free condition. By the design, specific values of the current do not need to be detected, so that the problem that the short-circuit fault position and the short-circuit fault degree cannot be accurately judged under the condition that the turn-to-turn short-circuit fault current is small in the existing detection method is solved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 shows a schematic flow chart of a method for detecting a short-circuit fault of a transformer winding according to an embodiment of the present application;
fig. 2 shows a schematic diagram of a physical model of a transformer core and windings according to an embodiment of the present application;
FIG. 3 shows a cross-sectional view of a physical model of a transformer core and windings provided by an embodiment of the present application;
FIG. 4 shows a sample point distribution diagram provided by an embodiment of the present application;
FIG. 5 shows a comparison of current density in a healthy state and a fault state provided by an embodiment of the present application;
FIG. 6 illustrates the distribution of magnetic flux density for different degrees of failure provided by an embodiment of the present application;
FIG. 7 shows a turn-to-turn connection diagram of a shorted fault winding provided by an embodiment of the application;
FIG. 8 (a) shows a distribution of magnetic flux density values at different short circuit fault levels provided by an embodiment of the present application;
FIG. 8 (b) shows a graph of the phase angle of the flux density at various short circuit fault levels provided by embodiments of the present application;
FIG. 9 is a schematic diagram showing the relationship between single turn wires and a spatial magnetic field provided by an embodiment of the present application;
FIG. 10 is a schematic diagram showing the relationship between a plurality of current turns and the magnetic flux density of a plurality of calculation zones provided by an embodiment of the present application;
FIG. 11 illustrates a graph of output voltage of a linear array Hall effect sensor provided by an embodiment of the present application;
FIG. 12 (a) is a schematic diagram showing a comparison of magnetic flux density values provided by an embodiment of the present application;
FIG. 12 (b) is a schematic diagram showing a comparison of the phase angles of magnetic flux densities provided by embodiments of the present application;
Fig. 13 shows a block diagram of a transformer winding short-circuit fault detection device according to an embodiment of the present application.
In the figure:
41 iron cores, 42 high-voltage windings, 43 low-voltage windings, 130 transformer winding short-circuit fault detection devices, 1301 sampling point setting modules, 1302 reference data acquisition modules, 1303 sampling modules and 1304 judgment modules.
Detailed Description
The application will be described in detail hereinafter with reference to the drawings in conjunction with embodiments. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
Currently, sensors are provided outside the transformer to measure low-amplitude, stray magnetic flux radiated from the end of the transformer, either electrical signals or internally and externally, to detect transformer faults. Frequency response detection is widely used for transformer fault diagnosis. For example, milad et al studied the effect of early insulation deterioration on the internal temperature of transformers using a finite element electromagnetic thermal fluid method and proposed an on-line predictive fault diagnosis method based on external sensors; ali et al combine time series analysis with Frequency Response Analysis (FRA), simulating and measuring the mapping of different fault types to frequency ranges. However, in the dry type transformer, since the inter-turn short fault current is small and the fault transient is complicated, it is difficult to determine a specific degree and position of the inter-turn short by the frequency response. In particular, in order to avoid serious faults and wide influence caused by the distortion of original fault signals caused by a transformer in an operating state, the method for detecting the turn-to-turn short circuit faults with high precision and high reliability is provided, and has important significance for ensuring the stable operation of the whole power transmission network.
The short-circuit fault judgment of the transformer is seemingly simple, but is difficult and heavy in the actual operation process. Through continuous summary analysis, repeated experiments and theoretical deductions of various traditional methods, the distribution of parameters such as current density and the like in the transformer is found to have a certain rule under the condition of no faults, based on the thinking, through a great deal of demonstration, the current density, the magnetic flux density and the magnetic flux phase angle are found to be also used for fault detection, the method for detecting the short-circuit faults of the transformer is provided originally, whether the short-circuit faults occur or not is judged through the deviation of the parameters and the condition of no faults, and the fault position and the fault degree are further determined. On the basis, considering that the short-circuit fault detection can comprehensively analyze different parameters and take the characteristics and the effect of each parameter into consideration, the application provides a method for comprehensively analyzing the corresponding judging results of each parameter to obtain the final short-circuit fault judging result so as to ensure that the state of the equipment can be comprehensively and accurately judged, avoid misjudgment caused by insufficient deviation between certain parameters and the fault-free condition, and avoid unilateralness of the detecting result caused by excessively biasing a certain parameter.
In this embodiment, a method for detecting a short-circuit fault of a transformer winding is provided, specifically, as shown in fig. 1, the method includes:
Step 101, setting a plurality of sampling points on the transformer winding based on preset sampling point positions in at least one sampling area, wherein the sampling area comprises at least one of the inner side of the inner winding, the outer side of the outer winding and the gap between the inner winding and the outer winding.
The method for detecting the short-circuit fault of the transformer winding is suitable for detecting the abnormality of the leakage magnetic field of the transformer winding, has weaker magnetic field strength and smaller fault current, and is used for setting a plurality of sampling points on the transformer winding and judging the short-circuit fault by utilizing the current density, the magnetic flux density and the magnetic flux phase angle at the sampling points.
Specifically, a physical model of the core and windings of the power transformer is shown in fig. 2. Taking a single-phase single-column winding as an example, two winding coils are stacked and nested inside and outside, and the winding coils are formed by winding parallel and braided copper wires/copper guide plates. Fig. 3 shows a cross-sectional view of the physical model, the small square parts with numerical designations in fig. 3 being the subject of investigation, these parts being combined together into a winding single turn model, the windings being numbered from top to bottom for clarity of description.
In the step, the installation position of the sampling device is flexible, the sampling area can be arranged on the inner side of the inner winding, the outer side of the outer winding or the gap between the inner winding and the outer winding, at least one area in the areas is selected to be provided with a sampling point, and a sampling device (such as a sensor) is arranged at the sampling point to obtain a sampling signal of the position of the sampling point, so that the sampling signal is analyzed to determine whether a short circuit fault occurs at the position of each sampling point. Fig. 4 shows a schematic diagram of a sample point distribution, in which 50 sample points are arranged and uniformly distributed in the gap between the inner winding and the outer winding. Specifically, the high voltage winding 42 and the low voltage winding 43 are wound in this order on the outer side of the core 41 of the transformer, and 50 sampling points are provided between the high voltage winding 42 and the low voltage winding 43.
Step 102, acquiring reference detection signal distribution data of preset sampling point positions in each sampling area under the condition of no faults, wherein reference detection signals corresponding to the reference detection signal distribution data comprise at least one of the following: current density, magnetic flux phase angle.
Step 103, based on the types of the reference detection signals, respectively acquiring detection signals corresponding to sampling points in each sampling area, and based on the detection signals, respectively determining detection signal distribution data of each detection signal corresponding to each sampling area.
In step 102-103, in the sampling area, using a sensor arranged at each preset sampling point position to collect detection signals at corresponding positions, such as current density, magnetic flux density and magnetic flux phase angle, so as to obtain detection signal distribution data; and acquiring reference detection signal distribution under the condition of no faults, and judging whether short circuit faults occur at the corresponding positions of the sampling points according to the comparison result of the reference detection signal distribution and the reference detection signal distribution.
Fig. 5 shows a comparison of current densities in a healthy state (left graph) and a fault state (right graph), as shown, when a short circuit fault occurs between turns, the current density J of the turns in the fault state is much higher than in the healthy state.
Fig. 6 is a graph showing the magnetic flux density of the target surface at different levels of failure under rated load conditions, with the outer layer failure coil as an observation target. As shown, the flux density distribution at the target surface under healthy conditions is symmetrical with the radial midline of the coil. The magnetic flux density in the middle of the coil is smaller than that at the two ends. When a short-circuit fault occurs, the magnetic flux density inside the coil changes, and becomes larger. When the degree of failure increases, the magnetic flux density in the coil is concentrated at the center of the coil. As the degree of failure increases, the asymmetry of the distribution of magnetic flux density around the coil increases.
In addition, when the coil is short-circuited, the magnetic flux phase angle of the coil is also changed, so that whether the winding is short-circuited can be judged according to the magnetic flux phase angle.
Step 104, comparing the detected signal distribution data of each detected signal corresponding to each sampling area with the reference detected signal distribution data, and identifying the short circuit fault of the transformer winding according to the comparison result.
In this step, in the sampling region, the detection signal distribution data and the reference detection signal distribution data are compared, and it is understood that since the reference detection signal distribution data is distribution data in the case of no failure, the larger the deviation between the detection signal distribution data and the reference detection signal distribution data is, the higher the probability of failure of the transformer winding is; conversely, the smaller the deviation between the detection signal distribution data and the reference detection signal distribution data, the lower the probability of the transformer winding failing.
In addition, some calculation assumptions are made based on the construction of the winding coil and the operating rules of the transformer before fault determination is made. The following assumptions are given:
1. the conductivity of the material is constant, ignoring the effect of temperature changes on conductivity; 2. neglecting skin effect and proximity effect of the winding; 3. neglecting the effects of higher harmonics and displacement currents; 4. the difference in resistance between turns is ignored.
The circuit model of the short-circuit fault winding is divided into A, B and C parts, and fig. 7 is a turn-to-turn connection diagram of the short-circuit fault winding. In fig. 7, the failure position is an elliptical dotted line position. R n,A is the n-th strand of the equivalent resistance of the A part. L n,A is the equivalent reactance Nth strand of the A part, including the self inductance and mutual inductance of the turn wire part.
The embodiment provides a short-circuit fault detection method by analyzing the space leakage magnetic field characteristics between turns in a healthy state and a short-circuit fault state, analyzing one or more of current density, magnetic flux density and magnetic flux phase angle of each turn in a winding, and judging whether the turn is faulty or not by comparing the data with data in a fault-free condition. By the design, specific values of the current do not need to be detected, so that the problem that the short-circuit fault position and the short-circuit fault degree cannot be accurately judged under the condition that the turn-to-turn short-circuit fault current is small in the existing detection method is solved.
Further, as a refinement and extension of the specific implementation of the above embodiment, for fully explaining the specific implementation process of the embodiment, another method for detecting a short-circuit fault of a transformer winding is provided, which further defines the content of "identifying a short-circuit fault of a transformer winding according to a comparison result", and includes the following steps:
Step 201-a, if the deviation between the detected current density distribution data and the reference current density distribution data is greater than a first preset threshold value, determining that the first recognition result is that a first short-circuit fault exists in the transformer winding, and the turn line at the position where the detected current density and the reference current density deviate is a short-circuit fault turn line.
And/or the number of the groups of groups,
Step 201-b, if the deviation between the detected magnetic flux density distribution data and the reference magnetic flux density distribution data is greater than a second preset threshold value, determining that a second recognition result is that a second short-circuit fault exists in the transformer winding, and the turn line where the detected magnetic flux density and the reference magnetic flux density deviate is a short-circuit fault turn line.
And/or the number of the groups of groups,
And step 201-c, if the deviation between the detected magnetic flux phase angle distribution data and the reference magnetic flux phase angle distribution data is larger than a third preset threshold value, determining that a third recognition result is that a third short-circuit fault exists in the transformer winding.
Step 202, determining a short-circuit fault recognition result corresponding to the transformer winding according to at least one of the first recognition result, the second recognition result and the third recognition result.
In this embodiment, the identification result of the short-circuit fault corresponding to the transformer winding is determined according to one or more of the current density, the magnetic flux density and the magnetic flux phase angle, wherein the magnetic flux phase angle is only used for judging whether the short-circuit fault occurs, and the current density and the magnetic flux density can also be used for determining the fault position, namely determining the turn line where the short-circuit fault occurs.
Specifically, considering the current density characteristic shown in fig. 5, the winding short-circuit fault can be determined according to the current density, and if the current density distribution deviates from the fault-free condition, the current density of a certain turn is higher than the current density in the normal state and the deviation is greater than a first preset threshold value, the turn can be considered to have a short-circuit fault.
In addition, considering the magnetic flux density characteristics shown in fig. 6, winding faults can be determined according to the magnetic flux density, and if the magnetic flux density of a position where a certain sampling point is located deviates from the magnetic flux density in the case of no faults and the deviation is larger than a second preset threshold value, the position is considered to have short-circuit faults; or if there is a difference between the distribution of the magnetic flux density of the winding and the trend of the distribution of the magnetic flux density in the case of no fault, it is considered that a short-circuit fault occurs at a position where the distribution trend is different.
If the magnetic flux phase angle at the position of a certain sampling point deviates from the magnetic flux phase angle when no fault exists and the deviation is larger than a third preset threshold value based on the characteristic that the magnetic flux phase angle changes when short circuit occurs, the position is considered to have short circuit fault; or if there is a difference between the flux phase angle distribution of the winding and the flux phase angle distribution trend in the case of no fault, it is considered that a short-circuit fault occurs at a position where the distribution trend is different.
In addition, the recognition result corresponding to each parameter can be comprehensively analyzed to obtain a final short-circuit fault recognition result, and each parameter is fully utilized in the detection process, so that the accuracy of short-circuit fault judgment is improved.
Further, as a refinement and extension of the specific implementation manner of the foregoing embodiment, in order to fully describe the specific implementation process of the embodiment, another method for detecting a short-circuit fault of a transformer winding is provided, where after the step of determining that a turn at a position of a short-circuit fault is a short-circuit fault turn, the method further includes determining a content of a short-circuit fault degree, and includes the following steps:
Step 301-a, determining the fault degree of the transformer winding according to the ratio of the number of short-circuit fault turns to the total number of turns on the transformer winding, wherein the fault degree of the transformer winding is positively related to the ratio.
In this step, it is understood that the more turns that fail, the more serious the failure, and thus the degree of failure can be judged according to the ratio between the number of failed turns and the total number of turns, the greater the ratio, the more serious the failure. In particular, the degree of failureCan be expressed in the following form:
Wherein, the method comprises the steps of, wherein, For the number of faulty turns of the winding,Is the total number of turns of the winding.
This step uses the ratio between the number of faulty turns in the winding and the total number of turns to evaluate the degree of winding faults. Generally, the lower the ratio, the lower the degree of failure, and the higher the ratio, the higher the degree of failure. By detecting the degree of winding failure, maintenance or detection requirements can be further predicted, for example, if the degree of failure exceeds a certain threshold, maintenance or service operations can be performed to prevent further damage to the failure; in addition, the fault is found out in time and processed in time when the fault degree is low, so that the maintenance cost can be reduced, and the service life of equipment can be prolonged.
And/or the number of the groups of groups,
Step 301-b, if the deviation between the detected magnetic flux phase angle distribution data and the reference magnetic flux phase angle distribution data is greater than a fourth preset threshold value, and the difference between the detected magnetic flux density distribution of each sampling point and the reference magnetic flux density of the corresponding position is less than a fifth preset threshold value, determining that the fault degree of the transformer winding is mild.
In this step, besides the proportion of the fault turn line, a fault degree judging mode is provided. Specifically, as shown by experimental analysis, when the degree of failure is low, the magnetic flux density value of the winding calculation region does not change much; the apparent change occurs only when the degree of failure exceeds a certain level (e.g., 15%), while the phase angle of the magnetic flux density changes significantly when the coil is at a light degree of failure. Based on this, the degree of failure can be determined from the magnetic flux phase angle and the magnetic flux density, and if the degree of abnormality of the magnetic flux phase angle is higher than that of the non-failure case, but the degree of abnormality of the magnetic flux density is lower than that of the non-failure case, the current degree of failure can be regarded as a mild failure.
Fig. 8 (a) shows a distribution of magnetic flux density values at different levels of short-circuit failure, and fig. 8 (b) shows a distribution of magnetic flux phase angles at different levels of short-circuit failure. In this experiment, the root mean square of the analog current of the turn was 100A, the current phase angle was 90 °, and the current frequency was 50Hz, resulting in the graph of the magnetic flux density value and the magnetic flux density phase angle shown in fig. 8. As can be seen from the figure, the short-circuit fault occurs near the 15 th sensor, and when the degree of the fault is lower than the magnetic flux density distribution in the case of no fault, the change in the magnetic flux density is not large, and as the degree of the fault is higher, the change in the magnetic flux density value is more remarkable. In contrast, even in the case of a slight fault, the phase angle of the magnetic flux density changes significantly.
Further, as a refinement and extension of the specific implementation manner of the foregoing embodiment, for fully explaining the specific implementation process of the embodiment, another method for detecting a short-circuit fault of a transformer winding is provided, where the method further defines the content of "obtaining detection signals corresponding to each sampling point in each sampling area, respectively", and includes the following steps:
step 401, collecting sampling signals corresponding to sampling points by using Hall effect sensors arranged at the positions of the preset sampling points in a sampling area, and determining current densities corresponding to the sampling points according to the magnetic field intensities in the sampling signals;
step 402, calculating the magnetic flux density of the position of the sampling point according to the current density.
In this embodiment, the current density and the magnetic flux density corresponding to the sampling point are obtained. Typically, the turn portion of the winding is a linear structure that readily determines the relationship between the turn current and the surrounding spatial magnetic field. Based on the above, a Hall effect sensor is used as a sampling tool, sampling is carried out at the position of a sampling point, a sampling signal is obtained, the sampling signal comprises magnetic field intensity, and the corresponding current density is estimated through detecting the obtained magnetic field intensity according to the linear relation between the current and the magnetic field intensity. The embodiment is provided with a plurality of sampling points, each sampling point detects the magnetic field intensity through the Hall effect sensor, the output of the sensors can be comprehensively analyzed, and the current density distribution corresponding to each turn line in the winding can be estimated more accurately.
After the current density is obtained, the magnetic flux density can be obtained by a calculation mode, and the specific principle and the calculation mode are as follows:
fig. 9 shows the relationship between single turn wire and space magnetic field, and a single-phase transformer with n layers of windings is obtained by kirchhoff's law, and the circuit equation expression in normal operation is:
(1)
Wherein U (t) is the external voltage at two ends of the transformer, I (t) is the current through the winding,R is the resistance of the winding,A is the mutual inductance coefficient between windings,
。
In a, L 1 is the inductance of the first layer winding, M 1,2 is the mutual inductance between the first layer and the second layer winding, and so on.
When the turn-to-turn short circuit occurs, firstly the resistance and self inductance of the barrier layer winding change, and according to the mutual inductance principle between coils, the mutual inductance between the fault layer winding and other windings changes, so that the current parameters of each winding change.
By calculating the area according to ampere circuit theoremIs used for the current of the wire windingWith magnetic fluxThe relationship between them is as follows:
(2)
Wherein, Is the magnetic permeability of the vacuum and is equal to the magnetic permeability of the vacuum,For the current in the current-carrying coil,For the length of the magnetic circuit,In order to calculate the area of the object,For calculating the area unit area.To calculate the areaIs arranged in the magnetic flux of the (c) a,To pass throughIs a magnetic flux of (c).
The strand current of the turnAnd calculate areaIs of the magnetic flux density of (2)The relationship of (2) may be expressed as follows:
(3)
Wherein, For current element and current conductorDistance between points. According to the superposition theorem, calculate the regionThe position of the partTotal flux density of the currentAnd is:
(4)
Wherein, 。Is the sequence number of the winding turn line, from 1 to n; Is the magnetic path length resulting from the mth chain current.
As shown in fig. 10, the calculated areas of the magnetic flux density correspond to each turn line one by one. The current of all windings is distributed in the winding space through linear transformation to generate a leakage magnetic field, the leakage magnetic field distribution of a certain phase winding is obtained through superposition of the current leakage magnetic fields of all windings, and a linear leakage magnetic field output x transformation matrix of the winding leakage magnetic field generated by superposition of the current of all windings can be established. For convenience of description, the number of areas and the number of turns are uniformly calculated. The relation between n current and the magnetic flux density of n calculation area is:
(5)
Wherein the method comprises the steps of The flux densities generated for all chain currents of the mth calculation region.Is the mth current.Is the firstThe first calculation regionThe coefficient between the individual link current and the magnetic flux field,Calculated according to formula (4).
According to formulas (1) - (5), when current flows through the winding turns, leakage flux density is generated around the winding, and when the winding has turn-to-turn short circuit fault, the current changes, so that the leakage flux density also changes. According to the analysis, the current density and the magnetic flux density are calculated according to the output data of the Hall effect sensor, and the state of the winding turn line can be determined by comparing the current density and the magnetic flux density of the winding. Furthermore, this embodiment derives a functional relationship between coil current on the coil and spatial magnetic field strength, explaining from physical properties why leakage detection winding turn-to-turn short detection is selected.
Further, as a refinement and extension of the specific implementation manner of the foregoing embodiment, for fully explaining the specific implementation process of the embodiment, another method for detecting a short-circuit fault of a transformer winding is provided, where the method further defines the content of "obtaining detection signals corresponding to each sampling point in each sampling area, respectively", and includes the following steps:
Step 501, detecting electromagnetic field changes at the position of a preset sampling point by using a Hall effect sensor, and determining the current electromagnetic field peak time according to the electromagnetic field changes;
Step 502, determining the magnetic flux density phase angle corresponding to the sampling point according to the time difference between the current electromagnetic field peak time and the electromagnetic field peak time under the fault-free condition.
In this embodiment, the phase analysis is performed based on the time series data collected from each sensor, and the magnetic flux density phase angle can be determined based on the time difference between the electromagnetic field peak time of different degrees of failure and the electromagnetic field peak time of no degree of failure. Specifically, the hall effect sensor is capable of outputting a magnetic field strength, and based on the magnetic field strength output thereby, a change in the magnetic field is obtained, and further, a digital signal processing technique such as a differential operation or a peak detection algorithm is used to identify the peak time of the magnetic field. Based on such principle, the peak time of the current electromagnetic field can be determined and compared with the peak time of the electromagnetic field under the condition of no fault, so as to obtain the time difference between the peak time and the peak time of the electromagnetic field, and finally obtain the magnetic flux density phase angle. If the phase angle of the magnetic flux density changes significantly, then a short circuit fault is considered to be currently occurring.
Further, as a refinement and extension of the foregoing embodiment, for fully explaining the implementation process of the embodiment, another method for detecting a short-circuit fault of a transformer winding is provided, where the method further includes the following steps after the step of "collecting a sample signal corresponding to a sample point", where the method further includes the content of "signal conversion", and includes the following steps:
step 601, amplifying the sampled signal and converting the amplified sampled signal into a digital signal format.
In this embodiment, the sampling signal output from the hall effect sensor is an analog voltage signal, which is amplified by the signal amplifying circuit and converted into a digital signal by the a/D conversion circuit. And (3) performing post-processing on the digital signal on a computer to obtain an experimental result of the magnetic flux density.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Further, as a specific implementation of the method for detecting a short-circuit fault of a transformer winding, an embodiment of the present application provides a platform for detecting a short-circuit fault of a transformer winding, where the platform includes:
the detection probe is provided with a linear array Hall effect sensor, and the linear array Hall effect sensor comprises a plurality of sensors which are respectively arranged at the positions of sampling points;
the signal amplifying circuit is used for amplifying the sampling signal output by the detection probe;
an a/D conversion circuit for converting the amplified sampling signal into a digital format;
the computer equipment is used for processing the sampling signals in the digital format to obtain a transformer winding short-circuit fault detection result;
The detection probe, the signal amplifying circuit, the A/D conversion circuit and the computer equipment are connected in sequence;
the transformer winding short-circuit fault detection platform is used for realizing the transformer winding short-circuit fault detection method.
In this embodiment, a high-precision, stable-performance hall effect sensor is employed as a detection probe for measuring the space leakage magnetic field, considering the use of a linear array hall effect sensor. Structural linear array Hall effect sensor comprisingAnd a plurality of sensors. The number of the sensor is the same as the number of the turns on one side of the winding, and the measuring area of the sensor is the same as the calculating area. Neglecting the effect of hall effect sensor control current toIs the firstThe output signals of the individual sensors. Output signalAnd magnetic flux densityThe relation of (2) is:
（6）
Wherein, Is the firstThe number of the output signals of the Hall effect sensors is the same as the number of turns.Is the firstSensitivity of the individual hall effect sensors.The control current of the Hall effect sensor is direct current. The sensitivity and control current are constant.Is the firstThe magnetic flux density of the region is calculated.
During the experiment, a linear array hall effect sensor was placed in the gap between the high and low voltage coils, and then current was applied to the high voltage coils. The output signal of the linear array hall effect sensor is an analog voltage. The analog voltage signal is amplified by the signal amplifying circuit and is converted into a digital signal by the a/D conversion circuit. The experimental result of the magnetic flux density is obtained by post-processing the digital signal on a computer.
The short-circuit fault detection method is the same as the short-circuit fault detection method of the transformer winding in the previous embodiment. Taking a coil with a failure degree of 5% as an example as an experimental object. The fault location is the junction of the 14 th turn and the 15 th turn of the outer coil. The root mean square of the experimental current of the turn is 100A. The current phase angle is 90 deg. and the current frequency is 50Hz. A single cycle of the linear array hall effect sensor output voltage is shown in fig. 11, where each curve is the output of one sensor. The collected data is processed to obtain a comparison of the magnetic flux density at the measured and simulated value calculation region, as shown in fig. 12, where fig. 12 (a) shows a comparison schematic of the measured and simulated values of the magnetic flux density and fig. 12 (b) shows a comparison schematic of the measured and simulated values of the magnetic flux phase angle. As can be seen from fig. 12 (a) and 12 (b), the trend of change of both results is the same. The average correlation error of the magnetic flux density values between the simulation and the measurement is less than 6.4%. The average correlation error of the magnetic flux density values was 3.72%, and the maximum absolute error of the magnetic flux density phase angles was 1.83 °. The experimental result verifies the rationality and feasibility of the test method. The cause of the error is a calculation error of the three-dimensional finite element method or an experimental error of the measurement data.
Through the experiment of the transformer winding short-circuit fault detection platform, the following conclusion is obtained:
1. By comparing the measured value with the simulation value, the average related error of the magnetic flux density value was found to be 6.4%, and the maximum absolute error of the magnetic flux density phase angle was found to be 4.97 °. The trend of the results obtained was substantially the same. Thus, the detection method presented herein is rational and provides a high accuracy in detecting the health of the windings.
2. Because the Hall effect sensor has high measurement accuracy, strong anti-interference capability and small volume size, the linear array Hall effect sensor can be used for a large power transformer to monitor the health condition of a winding.
3. The detection method is not only suitable for single-phase single-column windings, but also suitable for structures with various types and sizes. The simulated value of the coil magnetic flux density in the healthy state can be used as a standard.
4. The detection method is suitable for detecting windings with parallelism and compactness. The method can also provide a reference for toroidal transformer winding detection.
Further, as a specific implementation of the method for detecting a short-circuit fault of a transformer winding, an embodiment of the present application provides a device 130 for detecting a short-circuit fault of a transformer winding, as shown in fig. 13, where the device includes: a sampling point setting module 1301, a reference data acquiring module 1302, a sampling module 1303, and a judging module 1304, wherein:
A sampling point setting module 1301, configured to set a plurality of sampling points on the transformer winding based on a preset sampling point position in at least one sampling area, where the sampling area includes at least one of an inner side of the inner winding, an outer side of the outer winding, and a gap between the inner winding and the outer winding;
The reference data obtaining module 1302 is configured to obtain a reference detection signal distribution for a preset sampling point position in each sampling area under a fault-free condition, where a reference detection signal corresponding to the reference detection signal distribution data includes at least one of the following: current density, magnetic flux density, and magnetic flux phase angle;
The sampling module 1303 is configured to obtain detection signals corresponding to sampling points in each sampling area based on the types of the reference detection signals, and determine detection signal distribution data of each detection signal corresponding to each sampling area based on the detection signals;
The judging module 1304 is configured to compare the detected signal distribution data of each detected signal corresponding to each sampling area with the reference detected signal distribution data, and identify a short-circuit fault of the transformer winding according to the comparison result.
In a specific application scenario, optionally, the determining module 1304 is configured to:
if the deviation between the detected current density distribution data and the reference current density distribution data is larger than a first preset threshold value, determining that a first recognition result is that a first short-circuit fault exists in the transformer winding, and the turn line at the position where the detected current density and the reference current density deviate is a short-circuit fault turn line; and/or the number of the groups of groups,
If the deviation between the detected magnetic flux density distribution data and the reference magnetic flux density distribution data is larger than a second preset threshold value, determining that a second identification result is that a second short-circuit fault exists in the transformer winding, and the turn line at the position where the detected magnetic flux density and the reference magnetic flux density deviate is a short-circuit fault turn line; and/or the number of the groups of groups,
If the deviation between the detected magnetic flux phase angle distribution data and the reference magnetic flux phase angle distribution data is larger than a third preset threshold value, determining that a third identification result is that a third short circuit fault exists in the transformer winding;
and determining a short-circuit fault identification result corresponding to the transformer winding according to at least one of the first identification result, the second identification result and the third identification result.
In a specific application scenario, optionally, the determining module 1304 is further configured to:
Determining the fault degree of the transformer winding according to the ratio of the number of short-circuit fault turns to the total number of turns on the transformer winding, wherein the fault degree of the transformer winding is positively related to the ratio;
And/or the number of the groups of groups,
And if the deviation between the detected magnetic flux phase angle distribution data and the reference magnetic flux phase angle distribution data is larger than a fourth preset threshold value, and the difference value between the detected magnetic flux density of each sampling point and the reference magnetic flux density of the corresponding position is smaller than a fifth preset threshold value, judging that the fault degree of the transformer winding is mild.
In a specific application scenario, optionally, the sampling module 1303 is further configured to:
In the sampling area, a Hall effect sensor arranged at a preset sampling position is utilized to collect sampling signals corresponding to sampling points, and the current density corresponding to the sampling points is determined according to the magnetic field intensity in the sampling signals;
and calculating the magnetic flux density of the position of the sampling point according to the current density.
In a specific application scenario, optionally, the sampling module 1303 is further configured to:
Detecting electromagnetic field changes at the position of a preset sampling point by using a Hall effect sensor, and determining the current electromagnetic field peak value moment according to the electromagnetic field changes;
And determining the magnetic flux density phase angle of the position of the sampling point according to the time difference between the current electromagnetic field peak time and the electromagnetic field peak time under the fault-free condition.
In a specific application scenario, optionally, the transformer winding short-circuit fault detection device 130 further includes a conversion module, configured to:
amplifying the sampled signal and converting the amplified sampled signal into a digital signal format.
According to still another aspect of the present application, there is provided a medium having stored thereon a program or instructions which, when executed by a processor, implements the above-described transformer winding short-circuit fault detection method.
It should be noted that, other corresponding descriptions of each functional module related to the transformer winding short-circuit fault detection device provided by the embodiment of the present application may refer to corresponding descriptions in the above method, and are not repeated herein.
Based on the above method, correspondingly, the embodiment of the application also provides a storage medium, on which a computer program is stored, which when executed by a processor, implements the above method for detecting a short-circuit fault of a transformer winding.
Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.), and includes several instructions for causing an electronic device (may be a personal computer, a server, or a network device, etc.) to execute the method of each implementation scenario of the present application.
Based on the method shown in fig. 1 to 12 and the virtual device embodiment shown in fig. 13, in order to achieve the above object, the embodiment of the present application further provides a computer device, which may specifically be a personal computer, a server, or the like, where the electronic device includes a storage medium and a processor; a storage medium storing a computer program; a processor for executing a computer program to implement the transformer winding short-circuit fault detection method as described above and shown in fig. 1 to 12.
Optionally, the computer device may also include a user interface, a network interface, a camera, radio Frequency (RF) circuitry, sensors, audio circuitry, WI-FI modules, and the like. The user interface may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (e.g., bluetooth interface, WI-FI interface), etc.
It will be appreciated by those skilled in the art that the structure of the electronic device provided in this embodiment is not limited to the electronic device, and may include more or fewer components, or may be combined with certain components, or may be arranged with different components.
The storage medium may also include an operating system, a network communication module. An operating system is a program that manages and saves electronic device hardware and software resources, supporting the execution of information handling programs, as well as other software and/or programs. The network communication module is used for realizing communication among all the controls in the storage medium and communication with other hardware and software in the entity equipment.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented by means of software plus necessary general hardware platforms, or may be implemented by hardware.
Those skilled in the art will appreciate that the drawing is merely a schematic illustration of one preferred implementation scenario and that elements or processes in the drawing are not necessarily required to practice the application. Those skilled in the art will appreciate that elements of an apparatus in an implementation may be distributed throughout the apparatus in an implementation as described in the implementation, or that corresponding variations may be located in one or more apparatuses other than the present implementation. The units of the implementation scenario may be combined into one unit, or may be further split into a plurality of sub-units.
The above-mentioned inventive sequence numbers are merely for description and do not represent advantages or disadvantages of the implementation scenario. The foregoing disclosure is merely illustrative of some embodiments of the application, and the application is not limited thereto, as modifications may be made by those skilled in the art without departing from the scope of the application.
Claims (10)
1. A method for detecting a short-circuit fault in a transformer winding, the method comprising:
Setting a plurality of sampling points on a transformer winding based on preset sampling point positions in at least one sampling area, wherein the sampling area comprises at least one of an inner side of an inner winding, an outer side of an outer winding and a gap between the inner winding and the outer winding;
Acquiring reference detection signal distribution data aiming at the preset sampling point position in each sampling area under the condition of no faults, wherein reference detection signals corresponding to the reference detection signal distribution data comprise at least one of the following: current density, magnetic flux density, and magnetic flux phase angle;
Based on the types of the reference detection signals, respectively acquiring detection signals corresponding to sampling points in each sampling area, and respectively determining detection signal distribution data of each detection signal corresponding to each sampling area based on the detection signals;
And comparing the detection signal distribution data of each detection signal corresponding to each sampling area with the reference detection signal distribution data respectively, and identifying the short-circuit fault of the transformer winding according to the comparison result.
2. The method of claim 1, wherein the identifying a short circuit fault of the transformer winding based on the comparison result comprises:
If the deviation between the detected current density distribution data and the reference current density distribution data is larger than a first preset threshold value, determining that a first recognition result is that a first short-circuit fault exists in the transformer winding, and the turn line at the position where the detected current density and the reference current density deviate is a short-circuit fault turn line; and/or the number of the groups of groups,
If the deviation between the detected magnetic flux density distribution data and the reference magnetic flux density distribution data is larger than a second preset threshold value, determining that a second identification result is that a second short circuit fault exists in the transformer winding, and the turn line at the position where the detected magnetic flux density and the reference magnetic flux density deviate is the short circuit fault turn line; and/or the number of the groups of groups,
If the deviation between the detected magnetic flux phase angle distribution data and the reference magnetic flux phase angle distribution data is larger than a third preset threshold value, determining that a third identification result is that a third short-circuit fault exists in the transformer winding;
and determining a short-circuit fault identification result corresponding to the transformer winding according to at least one of the first identification result, the second identification result and the third identification result.
3. The method according to claim 2, wherein the method further comprises:
Determining the fault degree of the transformer winding according to the ratio of the number of the short-circuit fault turns to the total number of turns on the transformer winding, wherein the fault degree of the transformer winding is positively related to the ratio;
And/or the number of the groups of groups,
And if the deviation between the detected magnetic flux phase angle distribution data and the reference magnetic flux phase angle distribution data is larger than a fourth preset threshold value, and the difference value between the detected magnetic flux density of each sampling point and the reference magnetic flux density of the corresponding position is smaller than a fifth preset threshold value, judging that the fault degree of the transformer winding is mild.
4. The method of claim 1, wherein the separately obtaining the detection signals corresponding to the sampling points in each sampling area includes:
In the sampling area, a Hall effect sensor arranged at the position of the preset sampling point is utilized to collect sampling signals corresponding to the sampling point, and the current density corresponding to the sampling point is determined according to the magnetic field intensity in the sampling signals;
And calculating the magnetic flux density of the position of the sampling point according to the current density.
5. The method of claim 4, wherein the separately obtaining the detection signals corresponding to the sampling points in each sampling area includes:
Detecting electromagnetic field changes of the preset sampling point positions by using the Hall effect sensor, and determining the current electromagnetic field peak time according to the electromagnetic field changes;
and determining the magnetic flux density phase angle corresponding to the sampling point according to the time difference between the current electromagnetic field peak time and the electromagnetic field peak time under the fault-free condition.
6. The method of claim 4, wherein after the acquisition of the sampled signal corresponding to the sampling point, the method further comprises:
amplifying the sampling signal and converting the amplified sampling signal into a digital signal format.
7. A transformer winding short circuit fault detection device, the device comprising:
a sampling point setting module, configured to set a plurality of sampling points on a transformer winding based on preset sampling point positions in at least one sampling area, where the sampling area includes at least one of an inner side of an inner winding, an outer side of an outer winding, and a gap between the inner winding and the outer winding;
The reference data acquisition module is used for acquiring reference detection signal distribution of the preset sampling point positions in each sampling area under the condition of no faults, wherein the reference detection signals corresponding to the reference detection signal distribution data comprise at least one of the following: current density, magnetic flux density, and magnetic flux phase angle;
The sampling module is used for respectively acquiring detection signals corresponding to sampling points in each sampling area based on the types of the reference detection signals and respectively determining detection signal distribution data of each detection signal corresponding to each sampling area based on the detection signals;
And the judging module is used for respectively comparing the detection signal distribution data of each detection signal corresponding to each sampling area with the reference detection signal distribution data and identifying the short circuit fault of the transformer winding according to the comparison result.
8. A transformer winding short circuit fault detection platform, the platform comprising:
The detection probe is provided with a linear array Hall effect sensor, and the linear array Hall effect sensor comprises a plurality of sensors which are respectively arranged at the positions of sampling points;
the signal amplifying circuit is used for amplifying the sampling signal output by the detection probe;
an a/D conversion circuit for converting the amplified sampling signal into a digital format;
the computer equipment is used for processing the sampling signals in the digital format to obtain a transformer winding short-circuit fault detection result;
The detection probe, the signal amplifying circuit, the A/D conversion circuit and the computer equipment are sequentially connected;
the platform is for implementing the method of any one of claims 1 to 6.
9. A storage medium having stored thereon a program or instructions which, when executed by a processor, implement the method of any of claims 1 to 6.
10. An apparatus comprising a storage medium storing a computer program and a processor implementing the method of any one of claims 1 to 6 when the computer program is executed by the processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410693057.8A CN118275938A (en) | 2024-05-31 | 2024-05-31 | Transformer winding short-circuit fault detection method, device, platform, medium and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410693057.8A CN118275938A (en) | 2024-05-31 | 2024-05-31 | Transformer winding short-circuit fault detection method, device, platform, medium and equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118275938A true CN118275938A (en) | 2024-07-02 |
Family
ID=91634198
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410693057.8A Pending CN118275938A (en) | 2024-05-31 | 2024-05-31 | Transformer winding short-circuit fault detection method, device, platform, medium and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118275938A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6064172A (en) * | 1997-02-11 | 2000-05-16 | Power Superconductor Applications Corporation | Method and apparatus for detection, classification and reduction of internal electrical faults in alternating current propulsion machinery using synchronous detection scheme |
| CN105004962A (en) * | 2015-03-24 | 2015-10-28 | 华北电力大学（保定） | Improved method for detecting short trouble of exciting winding of turbonator |
| CN110221159A (en) * | 2019-07-18 | 2019-09-10 | 杭州电力设备制造有限公司 | A kind of fault detection method, system and the equipment of the transformer based on iron core leakage field |
| CN117169710A (en) * | 2023-08-25 | 2023-12-05 | 西安热工研究院有限公司 | Method and device for monitoring turn-to-turn short circuit fault of stator winding of wind driven generator |
-
2024
- 2024-05-31 CN CN202410693057.8A patent/CN118275938A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6064172A (en) * | 1997-02-11 | 2000-05-16 | Power Superconductor Applications Corporation | Method and apparatus for detection, classification and reduction of internal electrical faults in alternating current propulsion machinery using synchronous detection scheme |
| CN105004962A (en) * | 2015-03-24 | 2015-10-28 | 华北电力大学（保定） | Improved method for detecting short trouble of exciting winding of turbonator |
| CN110221159A (en) * | 2019-07-18 | 2019-09-10 | 杭州电力设备制造有限公司 | A kind of fault detection method, system and the equipment of the transformer based on iron core leakage field |
| CN117169710A (en) * | 2023-08-25 | 2023-12-05 | 西安热工研究院有限公司 | Method and device for monitoring turn-to-turn short circuit fault of stator winding of wind driven generator |
Non-Patent Citations (4)
| Title |
|---|
| DIANYANG LI ET AL.: "《Simulation Analysis of Power Transformer Internal Short Circuit for Magnetic Flux Leakage Distribution Feature Extraction》", 《2023 CAA SYMPOSIUM ON FAULT DETECTION, SUPERVISION AND SAFETY FOR TECHNICAL PROCESSES》, 3 November 2023 (2023-11-03) * |
| QIANG OU ET AL.: "《An Improved Radial Buckling Analysis Method and Test Investigation for Power Transformer Under Short Circuit Impact》", 《IEEE TRANSACTIONS ON POWER DELIVERY》, vol. 38, no. 4, 31 August 2023 (2023-08-31) * |
| 樊静;李俊卿;: "汽轮发电机转子绕组匝间短路时的电磁场分析", 电力科学与工程, no. 03, 28 March 2010 (2010-03-28) * |
| 郝艳 等: "《基于电压、电流不平衡度差值的干式变压器匝间短路故障识别方法》", 《电力***保护与控制》, vol. 51, no. 4, 16 February 2023 (2023-02-16), pages 12 - 21 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Abu-Siada et al. | A novel online technique to detect power transformer winding faults | |
| Nurmanova et al. | A new transformer FRA measurement technique to reach smart interpretation for inter-disk faults | |
| EP2466322B1 (en) | Method and apparatus for transformer diagnosis | |
| Bhide et al. | Analysis of winding inter-turn fault in transformer: A review and transformer models | |
| Masoum et al. | Online transformer internal fault detection based on instantaneous voltage and current measurements considering impact of harmonics | |
| Yousof et al. | Frequency response analysis to investigate deformation of transformer winding | |
| Saleh et al. | Proposed diagnostic methodology using the cross‐correlation coefficient factor technique for power transformer fault identification | |
| US20150204934A1 (en) | Method of determining a characteristic of a power transformer and a system therefor | |
| Zhao et al. | Detection of stator interturn short-circuit faults in inverter-fed induction motors by online common-mode impedance monitoring | |
| CN110546519B (en) | Current measuring method and current measuring device | |
| Zhao et al. | Experimental evaluation of detecting power transformer internal faults using FRA polar plot and texture analysis | |
| Samimi et al. | Detection of transformer mechanical deformations by comparing different FRA connections | |
| Banaszak et al. | Transformer frequency response analysis with the grouped indices method in end-to-end and capacitive inter-winding measurement configurations | |
| JP5456582B2 (en) | Transformer soundness diagnosis method, soundness diagnosis device, and soundness diagnosis program | |
| CN111273199A (en) | Intelligent detection method for transformer winding deformation based on sweep frequency impedance curve identification | |
| Bhowmick et al. | Online detection of an interturn winding fault in single-phase distribution transformers using a terminal measurement-based modeling technique | |
| Zhou et al. | Detection of transformer winding faults using FRA and image features | |
| Mahvi et al. | Analysis and interpretation of power auto-transformer winding axial displacement and radial deformation using frequency response analysis | |
| KR102260550B1 (en) | Facility health monitoring method by measuring the electric circuit constant inside the power facility in operation | |
| CN105067878B (en) | Transformer efficiency characteristic testing instrument for electrified | |
| CN114509649A (en) | Method and system for diagnosing turn-to-turn insulation defects of coil equipment | |
| Nirgude et al. | Frequency response analysis approach for condition monitoring of transformer | |
| CN118275938A (en) | Transformer winding short-circuit fault detection method, device, platform, medium and equipment | |
| Pourhossein et al. | Buckling severity diagnosis in power transformer windings using Euclidean Distance classifier | |
| Samimi et al. | Using the complex values of the frequency response to improve power transformer diagnostics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination |