CN117330137A - Intelligent identification and fault detection method and system for transformer inspection image - Google Patents
Intelligent identification and fault detection method and system for transformer inspection image Download PDFInfo
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Abstract
The invention relates to the technical field of transformers, and discloses a method and a system for intelligently identifying and detecting faults of a transformer inspection image.
Description
Technical Field
The invention relates to the technical field of transformers, in particular to a method and a system for intelligent identification and fault detection of a transformer inspection image.
Background
A transformer is an electrical device for changing an alternating voltage. It is possible to convert a voltage from one voltage level to another without changing the magnitude of the current (in an ideal case).
Transformers are commonly used in power transmission, electronics, and various applications to meet different voltage requirements.
The transformer generates heat during operation, which causes an increase in internal temperature.
When the temperature starts to drop after the transformer stops running, moisture in the surrounding air can condense into water in the cooling process so as to remain on the transformer, and the long-term accumulated condensed water easily causes problems of corrosion, insulation performance drop and the like of the transformer, so that the transformer needs to be monitored periodically.
Traditional carry out comdenstion water to the transformer and detect, generally adopt the mode that temperature detection humidification degree detected to go on, through carrying out a lot of to the temperature variation and the humidity variation of transformer and detect, through the size of its difference, judge whether its inside has the comdenstion water, but the mode external disturbance that this kind of combination of temperature humidification degree detected is great, because the transformer temperature variation rate is faster, produced temperature is higher, monitors from the change of temperature and humidity value alone, causes great detection misarea easily to influence maintenance work.
Disclosure of Invention
The invention provides an intelligent identification and fault detection method for a transformer inspection image, which has the beneficial effects that condensed water detection is carried out by combining a texture value, a humidity value and a sound wave value, and solves the problems that the external interference of the combined detection mode of temperature and humidity mentioned in the background art is large, the temperature change rate of a transformer is high, the generated temperature is high, and a large detection error area is easily caused by monitoring the change of the temperature and the humidity value.
The invention provides the following technical scheme: a method for intelligent identification and fault detection of a transformer inspection image comprises the following specific steps:
s1, determining the position of a target transformer, and deploying acquisition equipment around the target transformer by adopting a uniform interval distribution method; establishing a three-dimensional model, simulating the position of the transformer, and obviously marking the geographic coordinates of the transformer and the coordinates of the acquisition equipment in the three-dimensional model;
s2, acquiring initial monitoring data and secondary monitoring data of the acquisition equipment, respectively preprocessing the initial monitoring data and the secondary monitoring data, and respectively establishing a first data set and a second data set based on the processed initial monitoring data and the processed secondary monitoring data;
s3, establishing a digital twin model, and respectively analyzing a first data set and a second data set, wherein the first data set comprises a first texture valueFirst humidity value->First sound wave value +.>Said second data set comprising second texture values +.>Second humidity value->And second sound value->；
S4, comparing the first data set with the second data set through a digital twin model, and further obtaining texture difference coefficient respectivelyHumidity difference coefficient->Sound wave difference coefficient +.>；
S5, texture difference coefficient is obtainedHumidity difference coefficient->Sound wave difference coefficient +.>Input to digital twin model and obtaining fault resolution coefficient by calculation through model>Said failure resolution factor->The method is obtained through calculation according to the following formula:
wherein:for the texture difference coefficient, < >>Is the humidity difference coefficient>Is the difference coefficient of sound wave, ">And +.>Are all weight values, and +.> Is a correction constant, and->、/>And +.>The values of (2) are set by the adjustment of the client or generated by fitting an analytical function;
s6, calculating and obtaining a fault resolution coefficient according to the step S5The obtained failure resolution coefficient +.>And fault threshold->Comparing, if the fault resolution coefficient is->More than or equal to fault threshold->Representing a fault in the transformer, if the fault resolution factor +.>< failure threshold->Representing that the transformer has not failed.
As an alternative scheme of the intelligent identification and fault detection method for the transformer inspection image, the invention comprises the following steps: the collection devices are divided into three categories: the intelligent humidity sensor comprises a plurality of infrared cameras, a plurality of humidity sensors and a plurality of sound wave sensors, wherein various acquisition devices are not in the same circuit with a transformer;
the infrared camera is used for detecting the change condition of the texture value when the temperature of the outer shell of the transformer changes, wherein the detection of the outer shell of the transformer before the transformer is started is recorded as initial detection, and the detection of the outer shell of the transformer after the transformer is started is recorded as secondary detection;
the humidity sensor is used for detecting the humidity value change condition of the outer transformer shell, wherein the detection of the outer transformer shell before the transformer is started is recorded as initial detection, and the detection of the outer transformer shell after the transformer is started is recorded as secondary detection;
the sound wave sensor is used for detecting the sound wave value change condition of the outer transformer shell, wherein the detection of the outer transformer shell before the transformer is started is recorded as initial detection, and the detection of the outer transformer shell after the transformer is started is recorded as secondary detection.
As an alternative scheme of the intelligent identification and fault detection method for the transformer inspection image, the invention comprises the following steps: the infrared cameras are uniformly distributed around the transformer, so that the monitoring range of the cameras covers the shell of the transformer;
meanwhile, the parallel interval between the infrared cameras is less than or equal to 0.15 meter, and the vertical interval is less than or equal to 0.25 meter;
the plurality of infrared cameras continuously monitor the transformer for a long time, so as to respectively acquire initial monitoring data and secondary monitoring data of the temperature rise of the transformer shell, namely a first texture value under initial monitoringAnd a second texture value +.>。
As an alternative scheme of the intelligent identification and fault detection method for the transformer inspection image, the invention comprises the following steps: the texture difference coefficientBy adding the first texture data->And second texture data->The specific calculation formula is as follows:
wherein:for the first texture data->For the second texture data->Is a correction constant, and->Is set by the customer adjustment or is generated by an analytical function fit.
As an alternative scheme of the intelligent identification and fault detection method for the transformer inspection image, the invention comprises the following steps: the humidity sensors are uniformly arranged at the top of the transformer outer shell, so that the monitoring range of the humidity sensors covers the top of the transformer;
meanwhile, the horizontal distance between the humidity sensors is less than or equal to 0.1 meter, and the distance between the humidity sensors and the transformer is less than or equal to 0.15 meter;
the plurality of humidity sensors are used for continuously monitoring the transformer for a long time, so that the data of initial monitoring of the air humidity at the upper end of the outer shell of the transformer and the data of secondary monitoring of the transformer, namely a first humidity value under the initial monitoring, are respectively obtainedAnd a second humidity value +.>。
As an alternative scheme of the intelligent identification and fault detection method for the transformer inspection image, the invention comprises the following steps: the humidity difference coefficientThe method is obtained through calculation according to the following formula:
wherein:for the first humidity value, ">For the second humidity value, ">Is a correction constant, and->Is set by the customer adjustment or is generated by an analytical function fit.
As an alternative scheme of the intelligent identification and fault detection method for the transformer inspection image, the invention comprises the following steps: the sound wave sensors are symmetrically arranged at the top and the bottom of the transformer, so that the monitoring range of the sound wave sensors covers the transformer, and the sound change inside the transformer is monitored;
the plurality of acoustic wave sensors are used for continuously monitoring the transformer for a long time, so that data of initial monitoring of the acoustic wave change in the outer shell of the transformer and data of secondary monitoring of the transformer, namely a first acoustic wave value under the initial monitoring, are respectively obtainedAnd a second acoustic value under secondary monitoring +.>。
As an alternative scheme of the intelligent identification and fault detection method for the transformer inspection image, the invention comprises the following steps: the acoustic wave difference coefficientThe method is obtained through calculation according to the following formula:
wherein:for the first sonic value, +.>Is the second sound value, ++>Is a correction constant, and->Is set by the customer adjustment or is generated by an analytical function fit.
As an alternative scheme of the intelligent identification and fault detection method for the transformer inspection image, the invention comprises the following steps: resolving a fault into coefficientsAnd fault threshold->Comparing, and obtaining a comparison result:
when the failure resolution coefficientMore than or equal to fault threshold->When the condensate water in the transformer exceeds the safety range, the transformer is in a fault state and needs to be overhauled;
when the failure resolution coefficientFailure threshold +.>When the condensate water in the transformer does not exceed the safety range, the transformer is not required to be overhauled;
resolving a fault into coefficientsAnd fault threshold->Obtaining a comparison difference value by combining calculation>Performing secondary comparison on the comparison difference value and the fault threshold value to obtain a comparison result:
the condensate water of the transformer is represented as a first-stage fault state, shutdown drying treatment is set, the drying time is less than or equal to 20 minutes and less than or equal to 40 minutes, and the drying temperature is less than or equal to 80 degrees and less than or equal to 120 degrees;
representing that the condensed water of the transformer is in a secondary fault state, setting to carry out shutdown maintenance treatment, wherein the shutdown time is more than or equal to 30 minutes and less than or equal to 50 minutes so as to determine the condensed waterIf the water leakage phenomenon of the refrigerating system does not occur, drying is carried out for 30 minutes or less, the drying time is 50 minutes or less, and the drying temperature is 100 degrees or more and 120 degrees or less;
the method comprises the steps that the condensate water of a representative transformer is in a three-level fault state, disassembly and inspection are carried out, and professional overhaul equipment is used for detecting all unit parts in the transformer;
wherein the difference is comparedThe method is obtained through calculation according to the following formula:
wherein:for failure resolution factor, +.>Is a fault threshold.
The invention also provides the following technical scheme: the intelligent identification and fault detection system for the transformer inspection image comprises any one of the intelligent identification and fault detection methods for the transformer inspection image, and the intelligent identification and fault detection method comprises the following steps: the system comprises a monitoring module, a three-dimensional modeling module, a data processing module, a data analysis module, a data comparison module and an execution module:
the monitoring module monitors condensed water in the transformer through the infrared camera, the humidity sensor and the acoustic wave sensor under the monitoring module so as to acquire initial monitoring data and secondary monitoring data;
the three-dimensional modeling module is used for obviously marking the geographic coordinates of the transformer and the coordinates of the acquisition equipment;
the data processing module is used for preprocessing the acquired initial monitoring data and the secondary monitoring data and processing the processed initial monitoring data and the processed secondary monitoring dataMonitoring data, which is divided into a first data set and a second data set, wherein the first data set comprises a first texture valueFirst humidity value->First sound wave value +.>The second data set comprises a second texture value +.>Second humidity value->And second sound value->；
The data analysis module is used for carrying out combined calculation on the first data set and the second data set so as to obtain a fault resolution coefficient；
The data comparison module is used for comparing the acquired fault resolution coefficientsAnd fault threshold->Comparing, if the fault resolution coefficient is->More than or equal to fault threshold->Representing a fault in the transformer, if the fault resolution factor +.>< failure threshold->Representing that the transformer has not failed;
and the execution module is used for executing the corresponding overhaul flow according to the comparison result of the data comparison module.
The invention has the following beneficial effects:
1. the intelligent identification and fault detection method for the transformer inspection image realizes intelligent monitoring and fault detection of the state of the transformer, improves the reliability and safety of the operation of the transformer, is different from the traditional detection mode of temperature and humidity, adopts texture value change in the temperature rising process to replace the conventional temperature detection so as to improve the detection accuracy, and simultaneously adds sound wave detection to detect condensed water in the transformer in a deeper way, thereby detecting the inside of the transformer in a deep way while further eliminating external interference, and being beneficial to timely finding and processing potential faults.
2. According to the intelligent identification and fault detection method for the transformer inspection image, comprehensive monitoring and maintenance decision of transformer condensate water problems are realized through calculation of a fault resolution coefficient, comparison with a fault threshold value and further analysis of a poor comparison value. The method is favorable for timely finding and solving the cooling problem in the transformer, improves the usability and stability of the power system, and reduces the maintenance cost and the downtime.
Drawings
FIG. 1 is a schematic diagram of the method steps of the present invention.
FIG. 2 is a schematic flow chart of the system of the present invention.
Fig. 3 is a schematic diagram of the composition structure of the monitoring module of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1 to 3, a method for intelligently identifying and detecting faults of a transformer inspection image specifically includes the following steps:
s1, determining the position of a target transformer, and deploying acquisition equipment around the target transformer by adopting a uniform interval distribution method; establishing a three-dimensional model, simulating the position of the transformer, and obviously marking the geographic coordinates of the transformer and the coordinates of the acquisition equipment in the three-dimensional model;
s2, acquiring initial monitoring data and secondary monitoring data of the acquisition equipment, respectively preprocessing the initial monitoring data and the secondary monitoring data, and respectively establishing a first data set and a second data set based on the processed initial monitoring data and the processed secondary monitoring data;
s3, establishing a digital twin model, and respectively analyzing a first data set and a second data set, wherein the first data set comprises a first texture valueFirst humidity value->First sound wave value +.>Said second data set comprising second texture values +.>Second humidity value->And second sound value->；
S4, comparing the first data set with the second data set through a digital twin model, and further obtaining texture difference coefficient respectivelyHumidity difference coefficient->Sound wave difference coefficient +.>；
S5, texture difference coefficient is obtainedHumidity difference coefficient->Sound wave difference coefficient +.>Input to digital twin model and obtaining fault resolution coefficient by calculation through model>Said failure resolution factor->The method is obtained through calculation according to the following formula:
wherein:for the texture difference coefficient, < >>Is the humidity difference coefficient>Is the difference coefficient of sound wave, ">And +.>Are all weight values, and +.> Is a correction constant, and->、/>And +.>The values of (2) are set by the adjustment of the client or generated by fitting an analytical function;
s6, calculating and obtaining a fault resolution coefficient according to the step S5The obtained failure resolution coefficient +.>And fault threshold->Comparing, if the fault resolution coefficient is->More than or equal to fault threshold->Representing a fault in the transformer, if the fault resolution factor +.>< failure threshold->Representing that the transformer has not failed.
In this embodiment: the position of the transformer is simulated by establishing a three-dimensional model, acquisition equipment is deployed around the transformer, and the geographic coordinates of the transformer and the coordinates of the acquisition equipment are marked obviously, so that a visual monitoring environment is established, accurate positioning of the target transformer is facilitated, and a foundation is provided for subsequent monitoring and analysis.
By acquiring the primary monitoring data and the secondary monitoring data and preprocessing them, a first data set and a second data set are established. The data sets comprise texture value, humidity value, sound wave value and other information related to the state of the transformer, and powerful data support is provided for subsequent analysis and comparison.
The model is used to analyze the first data set and the second data set. The model can help us to understand the monitoring data deeply and recognize the correlation between the data, so that whether the transformer has condensed water or not can be found better.
And comparing the first data set with the second data set through the digital twin model, and calculating a texture difference coefficient, a humidity difference coefficient and an acoustic wave difference coefficient, wherein the difference coefficients reflect the change condition of the monitoring data and are helpful for judging whether condensed water exists in the transformer.
And calculating a fault resolution coefficient by using the calculated difference coefficient and a certain weight value and a correction constant. This coefficient is a key indicator, and by comparing with the fault threshold, it can be accurately determined whether the transformer has failed.
The method comprises the steps of accurately positioning a target transformer, effectively acquiring monitoring data, establishing a digital twin model for deep analysis, calculating a fault resolution coefficient and comparing the fault resolution coefficient with a fault threshold value, so that intelligent monitoring and fault detection of the state of the transformer are realized, the reliability and safety of the operation of the transformer are improved, meanwhile, the method is different from a traditional temperature and humidity detection mode, the conventional temperature detection is replaced by texture value change in the temperature rising process, the detection accuracy is improved, and meanwhile, sound wave detection is added to detect condensed water in the transformer in a deeper way, so that the internal of the transformer is detected in a depth way while external interference is further removed, and potential faults are found and processed timely.
Example 2
Referring to fig. 1 to 3, the collection devices are classified into three types: the intelligent humidity sensor comprises a plurality of infrared cameras, a plurality of humidity sensors and a plurality of sound wave sensors, wherein various acquisition devices are not in the same circuit with a transformer;
the infrared camera is used for detecting the change condition of the texture value when the temperature of the outer shell of the transformer changes, wherein the detection of the outer shell of the transformer before the transformer is started is recorded as initial detection, and the detection of the outer shell of the transformer after the transformer is started is recorded as secondary detection;
the humidity sensor is used for detecting the humidity value change condition of the outer transformer shell, wherein the detection of the outer transformer shell before the transformer is started is recorded as initial detection, and the detection of the outer transformer shell after the transformer is started is recorded as secondary detection;
the sound wave sensor is used for detecting the sound wave value change condition of the outer transformer shell, wherein the detection of the outer transformer shell before the transformer is started is recorded as initial detection, and the detection of the outer transformer shell after the transformer is started is recorded as secondary detection.
In this embodiment: the equipment adopting the infrared camera can monitor the temperature change of the outer shell of the transformer in real time, capture the data of the temperature rising texture change of the outer shell from the initial monitoring to the secondary monitoring process, and find out whether the temperature rising rate is different from other areas due to the existence of condensed water and water stains on the outer shell.
The humidity sensor is used for monitoring the change of the air humidity value in the working process of the transformer, so that whether condensate water exists in the transformer is judged through the change of the humidity value.
The sound wave sensor is used for monitoring the change of the noise value of the transformer in the working process, so that whether condensate water exists in the transformer is judged through the change of the noise value.
The state of the transformer can be comprehensively monitored by adopting a plurality of different types of acquisition equipment, so that the accuracy and the reliability of fault detection are improved. This helps to discover potential problems in time, prevents faults from occurring, and reduces the cost of maintenance and repair of the power equipment.
Example 3
Referring to fig. 1 to 3, the infrared cameras are uniformly distributed around the transformer, so that the monitoring range of the cameras covers the shell of the transformer;
meanwhile, the parallel interval between the infrared cameras is less than or equal to 0.15 meter, and the vertical interval is less than or equal to 0.25 meter;
the plurality of infrared cameras continuously monitor the transformer for a long time, so as to respectively acquire initial monitoring data and secondary monitoring data of the temperature rise of the transformer shell, namely a first texture value under initial monitoringAnd a second texture value +.>。
The texture difference coefficientBy adding the first texture data->And second texture data->The specific calculation formula is as follows:
wherein:for the first texture data->As a result of the second texture data being provided,/>is a correction constant, and->Is set by the customer adjustment or is generated by an analytical function fit.
In this embodiment: the cameras are evenly distributed around the transformer, ensuring that their monitoring range covers the entire transformer housing. Furthermore, the parallel and vertical spacing between cameras is limited to within 0.15 meters and 0.25 meters, which configuration helps to obtain comprehensive and accurate surface temperature data of the transformer housing. Meanwhile, the infrared camera is used for continuously monitoring the transformer for a long time, from initial monitoring to secondary monitoring, the heating data of the outer shell are recorded, and first texture values under the initial monitoring are respectively obtainedAnd second texture value under secondary monitoring +.>。
Texture difference coefficientBy adding the first texture data->And second texture data->Obtained by combination calculation, wherein the calculation formula comprises a correction constant +.>And->The values of (2) may be set by customer adjustment or generated by an analytical function fit. This flexibility allows the method to be tailored to the characteristics and requirements of different transformersAnd the method is adjusted, so that the applicability and the customizability of the method are improved.
Reasonably configured infrared cameras ensure accurate monitoring of the temperature change of the transformer housing and calculation of the texture difference coefficientImportant data for analyzing the temperature change is provided. The method is not only helpful for timely detecting the rationality of the change of the texture value when the temperature of the transformer rises, but also has the capability of customizing the client, so that the method is suitable for various transformers with different types, and the purposes of improving the monitoring precision and the fault detection efficiency of the transformer are achieved.
Example 4
Referring to fig. 1 to 3, a plurality of humidity sensors are uniformly arranged on the top of the transformer outer shell, so that the monitoring range of the humidity sensors covers the top of the transformer;
meanwhile, the horizontal distance between the humidity sensors is less than or equal to 0.1 meter, and the distance between the humidity sensors and the transformer is less than or equal to 0.15 meter;
the plurality of humidity sensors are used for continuously monitoring the transformer for a long time, so that the data of initial monitoring of the air humidity at the upper end of the outer shell of the transformer and the data of secondary monitoring of the transformer, namely a first humidity value under the initial monitoring, are respectively obtainedAnd a second humidity value +.>。
The humidity difference coefficient SDX is obtained through calculation according to the following formula:
wherein:for the first humidity value, ">For the second humidity value, ">Is a correction constant, and->Is set by the customer adjustment or is generated by an analytical function fit.
In this embodiment: the sensors are uniformly positioned on top of the transformer housing to ensure that the monitoring range covers the entire top surface of the transformer. Meanwhile, the horizontal spacing between the sensors is limited to be within 0.1 meter, and the spacing between the sensors and the transformer is also limited to be within 0.15 meter. This arrangement ensures accurate monitoring of the air humidity at the upper end of the transformer housing.
The humidity sensor is used for continuously monitoring the transformer for a long time, and records the data of the upper air humidity in the initial monitoring and the secondary monitoring stages respectively to obtain a first humidity value under the initial monitoringAnd a second humidity value under secondary monitoring +.>。
Coefficient of humidity differenceBy taking the first humidity value->And a second humidity value->Obtained by combination calculation, wherein the calculation formula comprises a correction constant +.>And->The values of (2) may be set by customer adjustment or generated by an analytical function fit. The flexibility enables the method to be adjusted according to the characteristics and requirements of different transformers, and improves the applicability and the customizability of the method.
The reasonably configured humidity sensor ensures accurate monitoring of the change of the air humidity at the upper end of the transformer, and calculates the humidity difference coefficientImportant data for analysis of humidity changes is provided. The method is not only helpful for timely detecting the existence of condensed water in the potential transformer, but also has the capability of customizing the potential transformer, so that the potential transformer is suitable for different types of transformers, and the purposes of improving the monitoring precision and the fault detection efficiency of the potential transformer are achieved. By these features, the method facilitates better maintenance and management of the transformer by power plant operators.
Example 5
Referring to fig. 1 to 3, a plurality of acoustic wave sensors are symmetrically arranged at the top and the bottom of the transformer, so that the monitoring range of the acoustic wave sensors covers the transformer, thereby monitoring the sound change inside the transformer;
the plurality of acoustic wave sensors are used for continuously monitoring the transformer for a long time, so that data of initial monitoring of the acoustic wave change in the outer shell of the transformer and data of secondary monitoring of the transformer, namely a first acoustic wave value under the initial monitoring, are respectively obtainedAnd a second acoustic value under secondary monitoring +.>。
The acoustic wave difference coefficientThe method is obtained through calculation according to the following formula:
wherein:for the first sonic value, +.>Is the second sound value, ++>Is a correction constant, and->Is set by the customer adjustment or is generated by an analytical function fit.
In this embodiment: the acoustic wave sensors are symmetrically arranged at the top and bottom of the transformer, ensuring that their monitoring range covers the whole transformer interior. This configuration allows the acoustic wave sensor to monitor the sound changes inside the transformer, providing important information on the internal operating conditions. And a part of acoustic wave sensors are used for continuously monitoring the transformer for a long time, data of acoustic wave changes in the outer shell of the transformer are recorded in the initial monitoring and the secondary monitoring stages respectively, and a first acoustic wave value SB1 under the initial monitoring and a second acoustic wave value SB2 under the secondary monitoring are obtained.
Acoustic wave difference coefficientBy taking the first sound wave value +.>And second sound value->Obtained by combination calculation, wherein the calculation formula comprises a correction constant +.>And->The values of (2) may be set by customer adjustment or generated by an analytical function fit. The flexibility enables the method to be adjusted according to the characteristics and requirements of different transformers, and improves the applicability and the customizability of the method.
The application of the acoustic wave sensor increases the monitoring capability of the acoustic wave sensor to the internal sound change of the transformer, and is helpful for timely identifying the interference of mechanical vibration or condensed water. By calculating the acoustic wave difference coefficient SBC, the method can provide key data for analyzing sound variations. The method is not only helpful for timely finding out internal mechanical faults or loosening problems, but also has the capability of customizing the transformer, so that the method is suitable for transformers of different types, and the purposes of improving the monitoring precision and fault detection efficiency of the transformer are achieved. By these features, the method facilitates better maintenance and management of the transformer by power plant operators.
Example 6
Referring to fig. 1 to 3, the fault resolution coefficients are shownAnd fault threshold->Comparing, and obtaining a comparison result:
when the failure resolution coefficientMore than or equal to fault threshold->When the condensate water in the transformer exceeds the safety range, the transformer is in a fault state and needs to be overhauled;
when the failure resolution coefficient< failure threshold->When the condensed water in the representative transformer does not exceed the safety rangeThe enclosure is not required to be overhauled;
resolving a fault into coefficientsAnd fault threshold->Obtaining a comparison difference value by combining calculation>Performing secondary comparison on the comparison difference value and the fault threshold value to obtain a comparison result:
the condensate water of the transformer is represented as a first-stage fault state, shutdown drying treatment is set, the drying time is less than or equal to 20 minutes and less than or equal to 40 minutes, and the drying temperature is less than or equal to 80 degrees and less than or equal to 120 degrees;
the method is characterized in that the transformer condensate water is in a secondary fault state, shutdown maintenance treatment is carried out for 30 minutes or less and the shutdown time is 50 minutes or less, so that whether the source of the condensate water and a refrigerating system have water leakage phenomenon is determined, if the water leakage phenomenon of the refrigerating system does not occur, drying treatment is carried out for 30 minutes or less and the drying time is 50 minutes or less, and the drying temperature is 100 degrees or less and 120 degrees or less;
the method comprises the steps that the condensate water of a representative transformer is in a three-level fault state, disassembly and inspection are carried out, and professional overhaul equipment is used for detecting all unit parts in the transformer;
wherein the difference is comparedThe method is obtained through calculation according to the following formula:
wherein:for failure resolution factor, +.>Is a fault threshold.
In this embodiment: by resolving the fault coefficientsAnd fault threshold->By means of the method, the state of the transformer can be accurately judged. When the failure resolution coefficient->Greater than or equal to the fault threshold->When the condensate water in the transformer exceeds the safety range, the transformer is in a fault state, and maintenance is needed. This helps to find potential cooling system problems in time, preventing further damage to the transformer.
When the failure resolution coefficientLess than or equal to the fault threshold->And when the condensate water in the transformer does not exceed the safety range, the transformer is not required to be overhauled. This result provides maintenance personnel with clear information about the state of the transformer, avoiding unnecessary interventions and outages.
By resolving the fault coefficientsAnd fault threshold->Is +.>Further calculations are made, which enables a more detailed assessment of the severity of the condensation problem. According to->The method may formulate different operating schemes.
Through calculation of the fault resolution coefficient, comparison with a fault threshold value and further analysis of a comparison poor value, comprehensive monitoring and maintenance decision of the transformer condensate water problem are realized. The method is favorable for timely finding and solving the cooling problem in the transformer, improves the usability and stability of the power system, and reduces the maintenance cost and the downtime.
The invention also provides the following technical scheme: the intelligent identification and fault detection system for the transformer inspection image comprises any one of the intelligent identification and fault detection methods for the transformer inspection image, and the intelligent identification and fault detection method comprises the following steps: the system comprises a monitoring module, a three-dimensional modeling module, a data processing module, a data analysis module, a data comparison module and an execution module:
the monitoring module monitors condensed water in the transformer through the infrared camera, the humidity sensor and the acoustic wave sensor under the monitoring module so as to acquire initial monitoring data and secondary monitoring data;
the three-dimensional modeling module is used for obviously marking the geographic coordinates of the transformer and the coordinates of the acquisition equipment;
the data processing module is used for preprocessing the acquired initial monitoring data and the secondary monitoring data, dividing the processed initial monitoring data and the processed secondary monitoring data into a first data set and a second data set, wherein the first data set comprises a first texture valueFirst humidity value->First sound wave value +.>The second data set comprises a second texture value +.>Second humidity value->And second sound value->；
The data analysis module is used for carrying out combined calculation on the first data set and the second data set so as to obtain a fault resolution coefficient；
The data comparison module is used for comparing the acquired fault resolution coefficientsAnd fault threshold->Comparing, if the fault resolution coefficient is->More than or equal to fault threshold->Representing a fault in the transformer, if the fault resolution factor +.>< failure threshold->Representing that the transformer has not failed;
and the execution module is used for executing the corresponding overhaul flow according to the comparison result of the data comparison module.
Examples:
first texture value:15.2, first humidity value->:45%, first sound wave value->：72dB；
Second texture value:15.1, second humidity value->:47% of the second sound value->：75dB；
Texture difference coefficientCorrection constant->0.02;
texture difference coefficient；
Coefficient of humidity differenceCorrection constant->0.01; />
Coefficient of humidity difference；
Acoustic wave difference coefficientCorrection constant->0.03;
acoustic wave difference coefficient；
Correction constant->0.05;
fault threshold0.02;
due to failure resolution coefficientGreater than the fault threshold->Therefore, the transformer is in fault and needs to be overhauled.
Comparing the difference
Comparing the difference
Due toSatisfy->Is represented by the standard of transformer condensate waterIn the second-stage fault state, the shutdown maintenance treatment is carried out, the shutdown time is less than or equal to 30 minutes and less than or equal to 50 minutes, so as to determine the source of condensed water and whether the water leakage phenomenon exists in the refrigerating system, and if the water leakage phenomenon does not occur in the refrigerating system, the drying treatment is carried out, the drying time is less than or equal to 30 minutes and less than or equal to 50 minutes, and the drying temperature is less than or equal to 100 degrees and less than or equal to 120 degrees.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that it will be apparent to those skilled in the art that several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the scope of the invention.
Claims (10)
1. A transformer inspection image intelligent identification and fault detection method is characterized in that: the method comprises the following specific steps:
s1, determining the position of a target transformer, and deploying acquisition equipment around the target transformer by adopting a uniform interval distribution method; establishing a three-dimensional model, simulating the position of the transformer, and obviously marking the geographic coordinates of the transformer and the coordinates of the acquisition equipment in the three-dimensional model;
s2, acquiring initial monitoring data and secondary monitoring data of the acquisition equipment, respectively preprocessing the initial monitoring data and the secondary monitoring data, and respectively establishing a first data set and a second data set based on the processed initial monitoring data and the processed secondary monitoring data;
S3、establishing a digital twin model for analyzing a first data set and a second data set, respectively, the first data set comprising a first texture valueFirst humidity value->First sound wave value +.>Said second data set comprising second texture values +.>Second humidity value->And second sound value->；
S4, comparing the first data set with the second data set through a digital twin model, and further obtaining texture difference coefficient respectivelyHumidity difference coefficient->Sound wave difference coefficient +.>；
S5, texture difference coefficient is obtainedHumidity difference coefficient->Sound wave difference coefficient +.>Input to digital twin model and obtaining fault resolution coefficient by calculation through model>Said failure resolution factor->The method is obtained through calculation according to the following formula:
wherein:for the texture difference coefficient, < >>Is the humidity difference coefficient>Is the difference coefficient of sound wave, ">AndAre all weight values, and +.> Is a correction constant, and->、/>And +.>The values of (2) are set by the adjustment of the client or generated by fitting an analytical function;
s6, calculating and obtaining a fault resolution coefficient according to the step S5The obtained failure resolution coefficient +.>And failure thresholdContrast, failure resolution factor->More than or equal to fault threshold->Representing the fault of the transformer and the fault resolution coefficient< failure threshold->Representing that the transformer has not failed.
2. The intelligent identification and fault detection method for the transformer inspection image according to claim 1, wherein the intelligent identification and fault detection method is characterized by comprising the following steps of: the collection devices are divided into three categories: the intelligent humidity sensor comprises a plurality of infrared cameras, a plurality of humidity sensors and a plurality of sound wave sensors, wherein various acquisition devices are not in the same circuit with a transformer;
the infrared camera is used for detecting the change condition of the texture value when the temperature of the outer shell of the transformer changes, wherein the detection of the outer shell of the transformer before the transformer is started is recorded as initial detection, and the detection of the outer shell of the transformer after the transformer is started is recorded as secondary detection;
the humidity sensor is used for detecting the humidity value change condition of the outer transformer shell, wherein the detection of the outer transformer shell before the transformer is started is recorded as initial detection, and the detection of the outer transformer shell after the transformer is started is recorded as secondary detection;
the sound wave sensor is used for detecting the sound wave value change condition of the outer transformer shell, wherein the detection of the outer transformer shell before the transformer is started is recorded as initial detection, and the detection of the outer transformer shell after the transformer is started is recorded as secondary detection.
3. The intelligent identification and fault detection method for the transformer inspection image according to claim 2, wherein the intelligent identification and fault detection method is characterized by comprising the following steps of: the infrared cameras are uniformly distributed around the transformer, so that the monitoring range of the cameras covers the shell of the transformer;
meanwhile, the parallel interval between the infrared cameras is less than or equal to 0.15 meter, and the vertical interval is less than or equal to 0.25 meter;
the plurality of infrared cameras continuously monitor the transformer for a long time, so as to respectively acquire initial monitoring data and secondary monitoring data of the temperature rise of the transformer shell, namely a first texture value under initial monitoringAnd a second texture value +.>。
4. The intelligent identification and fault detection method for the transformer inspection image according to claim 3, wherein the intelligent identification and fault detection method is characterized by comprising the following steps of: the texture difference coefficientBy adding the first texture data->And second texture data->The specific calculation formula is as follows:
wherein:for the first texture data->For the second texture data->Is a correction constant, and->Is set by the customer adjustment or is generated by an analytical function fit.
5. The intelligent identification and fault detection method for the transformer inspection image according to claim 4, wherein the intelligent identification and fault detection method is characterized by comprising the following steps of: the humidity sensors are uniformly arranged at the top of the transformer outer shell, so that the monitoring range of the humidity sensors covers the top of the transformer;
meanwhile, the horizontal distance between the humidity sensors is less than or equal to 0.1 meter, and the distance between the humidity sensors and the transformer is less than or equal to 0.15 meter;
the humidity sensors monitor the transformer continuously for a long time, so that the data of initial monitoring and the data of secondary monitoring of the transformer, namely the initial monitoring, of the air humidity at the upper end of the outer shell of the transformer are respectively obtainedFirst humidity value measuredAnd a second humidity value +.>。
6. The intelligent identification and fault detection method for the transformer inspection image according to claim 5, wherein the intelligent identification and fault detection method is characterized by comprising the following steps of: the humidity difference coefficientThe method is obtained through calculation according to the following formula:
wherein:for the first humidity value, ">For the second humidity value, ">Is a correction constant, and->Is set by the customer adjustment or is generated by an analytical function fit.
7. The intelligent identification and fault detection method for the transformer inspection image according to claim 6, wherein the intelligent identification and fault detection method is characterized by comprising the following steps of: the sound wave sensors are symmetrically arranged at the top and the bottom of the transformer, so that the monitoring range of the sound wave sensors covers the transformer, and the sound change inside the transformer is monitored;
the plurality of acoustic wave sensors are long-timeContinuously monitoring the transformer so as to respectively acquire data of the initial monitoring of the acoustic wave change in the outer shell of the transformer and data of the secondary monitoring of the transformer, namely a first acoustic wave value under the initial monitoringAnd a second acoustic value under secondary monitoring +.>。
8. The intelligent identification and fault detection method for the transformer inspection image according to claim 7, wherein the intelligent identification and fault detection method is characterized by comprising the following steps of: the acoustic wave difference coefficientThe method is obtained through calculation according to the following formula:
wherein:for the first sonic value, +.>Is the second sound value, ++>Is a correction constant, and->Is set by the customer adjustment or is generated by an analytical function fit.
9. The intelligent identification and fault detection method for the transformer inspection image according to claim 8, wherein the intelligent identification and fault detection method is characterized by comprising the following steps of: resolving a fault into coefficientsAnd fault threshold->Comparing, and obtaining a comparison result:
failure resolution coefficientMore than or equal to fault threshold->When the condensate water in the transformer exceeds the safety range, the transformer is in a fault state and needs to be overhauled;
failure resolution coefficient< failure threshold->When the condensate water in the transformer does not exceed the safety range, the transformer is not required to be overhauled;
resolving a fault into coefficientsAnd fault threshold->Obtaining a comparison difference value by combining calculation>Performing secondary comparison on the comparison difference value and the fault threshold value to obtain a comparison result:
the condensate water of the transformer is represented as a first-stage fault state, shutdown drying treatment is set, the drying time is less than or equal to 20 minutes and less than or equal to 40 minutes, and the drying temperature is less than or equal to 80 degrees and less than or equal to 120 degrees;
the method is characterized in that the transformer condensate water is in a secondary fault state, shutdown maintenance treatment is carried out for 30 minutes or less and the shutdown time is 50 minutes or less, so that whether the source of the condensate water and a refrigerating system have water leakage phenomenon is determined, if the water leakage phenomenon of the refrigerating system does not occur, drying treatment is carried out for 30 minutes or less and the drying time is 50 minutes or less, and the drying temperature is 100 degrees or less and 120 degrees or less;
the method comprises the steps that the condensate water of a representative transformer is in a three-level fault state, disassembly and inspection are carried out, and professional overhaul equipment is used for detecting all unit parts in the transformer;
wherein the difference is comparedThe method is obtained through calculation according to the following formula:
wherein:for failure resolution factor, +.>Is a fault threshold.
10. The intelligent identification and fault detection system for the transformer inspection image comprises the intelligent identification and fault detection method for the transformer inspection image, which is characterized in that: comprising the following steps: the system comprises a monitoring module, a three-dimensional modeling module, a data processing module, a data analysis module, a data comparison module and an execution module:
the monitoring module monitors condensed water in the transformer through the infrared camera, the humidity sensor and the acoustic wave sensor under the monitoring module so as to acquire initial monitoring data and secondary monitoring data;
the three-dimensional modeling module is used for obviously marking the geographic coordinates of the transformer and the coordinates of the acquisition equipment;
the data processing module is used for preprocessing the acquired initial monitoring data and the secondary monitoring data, dividing the processed initial monitoring data and the processed secondary monitoring data into a first data set and a second data set, wherein the first data set comprises a first texture valueFirst humidity value->First sound wave value +.>The second data set comprises a second texture value +.>Second humidity value->And second sound value->；
The data analysis module is used for carrying out combined calculation on the first data set and the second data set so as to obtain a fault resolution coefficient；
The data comparison module is used for comparing the acquired fault resolution coefficientsAnd fault threshold->Contrast, failure resolution factor->More than or equal to fault threshold->Representing the fault of the transformer, the fault resolution coefficient +.>< failure threshold->Representing that the transformer has not failed;
and the execution module is used for executing the corresponding overhaul flow according to the comparison result of the data comparison module.
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