CN112285494A - Power cable partial discharge mode recognition analysis system - Google Patents
Power cable partial discharge mode recognition analysis system Download PDFInfo
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- CN112285494A CN112285494A CN202010975613.2A CN202010975613A CN112285494A CN 112285494 A CN112285494 A CN 112285494A CN 202010975613 A CN202010975613 A CN 202010975613A CN 112285494 A CN112285494 A CN 112285494A
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- 238000000605 extraction Methods 0.000 claims abstract description 12
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- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1263—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
- G01R31/1272—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation of cable, line or wire insulation, e.g. using partial discharge measurements
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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Abstract
The invention provides a power cable partial discharge mode identification and analysis system which can accurately identify different discharge types. The system comprises: the preprocessing module is used for preprocessing the collected partial discharge signals of various discharge types; the characteristic extraction module is used for extracting the characteristics of the preprocessed partial discharge signals to form a characteristic library; and the pattern recognition module is used for inputting the characteristics in the characteristic library and the discharge types to which the characteristics belong into a support vector machine, calculating the optimal classification hyperplane of different discharge types so as to input the obtained partial discharge data to be classified into the support vector machine, and determining the hyperplane to which the partial discharge data to be classified belong through the support vector machine to obtain the discharge types of the partial discharge data to be classified. The invention relates to the technical field of high-voltage insulation detection and analysis.
Description
Technical Field
The invention relates to the technical field of high-voltage insulation detection and analysis, in particular to a power cable partial discharge mode identification and analysis system.
Background
With the development of enterprises to resource saving and environment friendliness, the national power grid advances to the construction of smart power grids, the safety of power grid operation becomes more and more important, and in order to more accurately judge the defect types of electrical equipment and prevent further expansion of faults, an intelligent identification system of the electrical equipment becomes an important research direction for the safe operation of the power grids.
The cable line is used as important electrical equipment in a power system, and an internal insulation fault causes a local electric field to be increased to cause partial discharge, so that accurate identification of the partial discharge of the cable is particularly important.
In the field of partial discharge mode identification and analysis, most identification systems are only suitable for fault analysis of a single partial discharge source, and the analysis result is fuzzy when multiple partial discharge source faults occur to cable equipment.
Disclosure of Invention
The embodiment of the invention provides a power cable partial discharge mode identification and analysis system, which can obviously improve the identification accuracy of different discharge types. The technical scheme is as follows:
the embodiment of the invention provides a power cable partial discharge mode identification and analysis system, which comprises:
the preprocessing module is used for preprocessing the collected partial discharge signals of various discharge types;
the characteristic extraction module is used for extracting the characteristics of the preprocessed partial discharge signals to form a characteristic library;
and the pattern recognition module is used for inputting the characteristics in the characteristic library and the discharge types to which the characteristics belong into a support vector machine, calculating the optimal classification hyperplane of different discharge types so as to input the obtained partial discharge data to be classified into the support vector machine, and determining the hyperplane to which the partial discharge data to be classified belong through the support vector machine to obtain the discharge types of the partial discharge data to be classified.
Further, the discharge types include: partial discharge corona faults, internal discharge faults, creeping discharge faults, and noise disturbances.
Further, the preprocessing module comprises: a signal acquisition unit;
the preprocessing module is connected with an external HFCT sensor, and the HFCT sensor is used for acquiring partial discharge signals and transmitting the acquired partial discharge signals to the signal acquisition unit;
the maximum sampling rate of the signal acquisition unit is more than 2 times of that of the HFCT sensor.
Further, the preprocessing module is specifically configured to perform filtering, denoising, and amplification processing on the acquired partial discharge signal.
Further, the feature extraction module is specifically configured to obtain a partial discharge spectrogram according to the preprocessed partial discharge signal, calculate a feature value of the spectrogram, perform principal component analysis on the feature value, and form a feature library by features obtained after the principal component analysis.
Further, the spectrogram comprises: discharge capacity, discharge phase, discharge frequency and phase-resolved partial discharge spectrogram.
Further, the characteristic values include: skewness, steepness, peak point number, average discharge amount, discharge factor, correlation coefficient, correction correlation coefficient, Weibull distribution shape parameter and Weibull distribution scale parameter of positive and negative half shafts of the spectrogram.
Further, the pattern recognition module is specifically configured to divide the feature library into a training set and a test set, input the features in the training set and the discharge types to which the features belong into a support vector machine to train the support vector machine, so that a weight normal vector w and an intercept b of the support vector machine are optimal, thereby obtaining optimal classification hyperplanes of different discharge types, so as to input the obtained partial discharge data to be classified into the support vector machine, and determine the hyperplane to which the partial discharge data to be classified belongs by using the support vector machine, thereby obtaining the discharge types of the partial discharge data to be classified.
Further, the trained support vector machine determines the discharge type of the partial discharge data to be classified by using a voting mechanism.
The technical scheme of the invention has the following beneficial effects:
in the scheme, the collected partial discharge signals of various discharge types are preprocessed through the preprocessing module; extracting the characteristics of the preprocessed partial discharge signals through a characteristic extraction module to form a characteristic library; inputting the characteristics in the characteristic library and the discharge types to which the characteristics belong into a support vector machine through a pattern recognition module to calculate the optimal classification hyperplane of different discharge types so as to input the obtained partial discharge data to be classified into the support vector machine, and determining the hyperplane to which the partial discharge data to be classified belong through the support vector machine to obtain the discharge types of the partial discharge data to be classified. Therefore, the support vector machine algorithm is adopted to carry out dichotomy mode separation on various fault signals, the mode identification is converted into a quadratic form to find the optimal solution problem, the optimal classification hyperplane of different discharge types can be effectively found, the non-point areas on two sides of the optimal classification hyperplane are maximized, and the identification accuracy of different discharge types is remarkably improved.
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Fig. 1 is a schematic structural diagram of a power cable partial discharge pattern recognition analysis system according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, a power cable partial discharge pattern recognition analysis system provided in an embodiment of the present invention includes:
the pretreatment module 11 is used for pretreating the collected partial discharge signals (namely, multiple fault signals) of multiple discharge types;
the feature extraction module 12 is configured to extract features of the preprocessed partial discharge signals to form a feature library;
and the pattern recognition module 13 is configured to input the features in the feature library and the discharge types to which the features belong into a support vector machine, calculate optimal classification hyperplanes of different discharge types, so as to input the obtained partial discharge data to be classified into the support vector machine, and determine the hyperplane to which the partial discharge data to be classified belongs by using the support vector machine, so as to obtain the discharge type of the partial discharge data to be classified.
According to the power cable partial discharge mode identification and analysis system, the collected partial discharge signals of various discharge types are preprocessed through the preprocessing module; extracting the characteristics of the preprocessed partial discharge signals through a characteristic extraction module to form a characteristic library; inputting the characteristics in the characteristic library and the discharge types to which the characteristics belong into a support vector machine through a pattern recognition module to calculate the optimal classification hyperplane of different discharge types so as to input the obtained partial discharge data to be classified into the support vector machine, and determining the hyperplane to which the partial discharge data to be classified belong through the support vector machine to obtain the discharge types of the partial discharge data to be classified. Therefore, the support vector machine algorithm is adopted to carry out dichotomy mode separation on various fault signals, the mode identification is converted into a quadratic form to find the optimal solution problem, the optimal classification hyperplane of different discharge types can be effectively found, the non-point areas on two sides of the optimal classification hyperplane are maximized, and the identification accuracy of different discharge types is remarkably improved.
In this embodiment, the transmission cable will cause partial discharge due to insulation defect, and the partial discharge signal is the most effective characteristic quantity for representing the insulation state. Therefore, the partial discharge signal of the cable is effectively analyzed, and the detected characteristic value is subjected to pattern recognition, so that early insulation hidden dangers can be found in time, the aging speed and the current state of the medium can be judged, and sudden accidents of the cable can be avoided.
In the embodiment, after the partial discharge is detected and analyzed, the accurate identification of different discharge types can be realized by adopting the support vector machine, and the cable fault type (namely, the discharge type) can be accurately identified, so that a maintainer can be helped to determine the fault reason, the power failure range can be accurately mastered, and the fault cable equipment can be quickly processed.
In an embodiment of the foregoing power cable partial discharge pattern recognition analysis system, further, the discharge types include: partial discharge corona faults, internal discharge faults, creeping discharge faults, and noise disturbances.
In an embodiment of the foregoing power cable partial discharge pattern recognition analysis system, further, the preprocessing module includes: a signal acquisition unit;
the preprocessing module is connected with an external HFCT sensor, and the HFCT sensor is used for acquiring partial discharge signals and transmitting the acquired partial discharge signals to the signal acquisition unit.
In this embodiment, the HFCT sensor is an external high-frequency current sensor, and it can be known from Nyquist sampling theorem that the maximum sampling rate of a signal acquisition unit in the preprocessing module is 2 times greater than that of the HFCT sensor, the maximum sampling rate of the signal acquisition unit can reach 200MS/s, the sampling time is 10us, the total sampling time and the total acquisition number of the acquired data can be fixed, and meanwhile, in order to obtain the discharge phase of the acquired partial discharge signal, a hardware synchronization device is added in the HFCT sensor, the hardware synchronization device is composed of a trigger and the like, when the partial discharge signal is found, the trigger is started, and corresponding hardware is made to record the phase information of the ac voltage at that time.
In this embodiment, when detecting partial discharge of a cable, the HFCT sensor needs to be sleeved on a ground wire of the cable, and a hardware synchronization device of the HFCT sensor is installed on the cable or other electrical devices, so as to achieve phase acquisition of a partial discharge signal.
In an embodiment of the foregoing power cable partial discharge pattern recognition analysis system, further, the preprocessing module is specifically configured to perform filtering, denoising, and amplification processing on the acquired partial discharge signal.
In this embodiment, since the sensitivity of the HFCT sensor is limited, the acquired partial discharge signal needs to be filtered, denoised and amplified by the preprocessing module, and then the preprocessed partial discharge signal forms a partial discharge map, so as to further form a feature library for pattern recognition of partial discharge.
In this embodiment, the preprocessing module can implement low-pass, band-pass, and high-pass filtering, and can select a special filtering effect for different types of HFCT sensor characteristics, and the signal amplification function can be adjusted by user-defined so that the processed signal can be better.
In a specific embodiment of the foregoing power cable partial discharge pattern recognition analysis system, further, the feature extraction module is specifically configured to obtain a partial discharge spectrogram according to the preprocessed partial discharge signal, calculate a feature value of the spectrogram, perform principal component analysis on the feature value, and form a feature library by features obtained after the principal component analysis.
In this embodiment, the feature extraction module may use the preprocessed Partial Discharge signal to draw a map of a corresponding Discharge amount, a Discharge Phase, a Discharge frequency, and a Phase Resolved Partial Discharge (PRPD), and further calculate a feature value of the map, where the map includes: the skewness (Sk), the abruptness (Ku), the number of peaks (Pe), the average discharge capacity, the discharge factor, the correlation coefficient, the correction correlation coefficient, the Weibull distribution shape parameter, the Weibull distribution scale parameter and the like of the positive and negative half shafts of the spectrogram, and the characteristic values are subjected to principal component analysis (namely, dimension reduction processing) to obtain the dimension-reduced characteristic to form a characteristic library.
In this embodiment, since the partial discharge signal is divided into a positive pulse and a negative pulse, it is necessary to divide the partial discharge signal into Sk +, Sk-, Ku +, Ku-, Pe +, Pe-, and the like in the calculation of the partial discharge characteristics.
In a specific embodiment of the power cable partial discharge pattern recognition analysis system, the pattern recognition module is specifically configured to divide a feature library into a training set and a test set, input features in the training set and discharge types to which the features belong into a support vector machine, and train the support vector machine, so that a weight normal vector w and an intercept b of the support vector machine are optimized, thereby obtaining optimal classification hyperplanes of different discharge types, so as to input the obtained partial discharge data to be classified into the support vector machine, and determine the hyperplane to which the partial discharge data to be classified belongs by using the support vector machine, thereby obtaining the discharge type of the partial discharge data to be classified.
In this embodiment, the principle of the support vector machine algorithm is as follows: and mapping the input feature vector to a high-dimensional feature vector space, constructing an optimal classification hyperplane in the feature space, and training every two partial discharge types by a binary classification method until constructing a hyperplane which is exactly positioned in the center of different types of partial discharge types.
In this embodiment, according to the statistical characteristics of partial discharge and in combination with the noise interference characteristics of actual detection, partial discharge signals of four discharge types, namely, a partial discharge corona fault, an internal discharge fault, a creeping discharge fault, and noise interference, need to be collected.
In the embodiment, after the collected partial discharge signals of the four discharge types, namely the partial discharge corona fault, the internal discharge fault, the creeping discharge fault and the noise interference, are processed by the preprocessing module and the characteristic extraction module, forming a feature library, dividing the feature library into a training set and a testing set, inputting the features in the training set and the discharge types of the features into a support vector machine to train the support vector machine, calculating the hyperplane, wherein, the corresponding characteristics of each discharge type form a group of signals, the four groups of signals are paired in pairs, so that the weight normal vector w and intercept b of the support vector machine are optimal, thereby obtaining 6 optimal classification hyperplanes so as to input the acquired partial discharge data to be classified into a support vector machine, and determining the hyperplane to which the partial discharge data to be classified belong by using a support vector machine to obtain the discharge type of the partial discharge data to be classified.
In this embodiment, when new partial discharge data (i.e. partial discharge data to be classified) is obtained, the new partial discharge data may be classified by using a trained support vector machine, specifically: after the partial discharge data to be classified are processed by utilizing the preprocessing module and the feature extraction module, the features of the partial discharge data are extracted and input into the support vector machine, and the support vector machine determines which type of partial discharge hyperplane the partial discharge data are in, so that the discharge type of the new partial discharge data is obtained.
In this embodiment, the trained support vector machine can determine the discharge type of the new partial discharge data by using a voting mechanism (vote-high win), the recognition accuracy can reach 92%, and the accuracy of the pattern recognition is improved.
It is noted that, herein, relational terms such as first and second, and the like may be 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.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (9)
1. A power cable partial discharge pattern recognition analysis system, comprising:
the preprocessing module is used for preprocessing the collected partial discharge signals of various discharge types;
the characteristic extraction module is used for extracting the characteristics of the preprocessed partial discharge signals to form a characteristic library;
and the pattern recognition module is used for inputting the characteristics in the characteristic library and the discharge types to which the characteristics belong into a support vector machine, calculating the optimal classification hyperplane of different discharge types so as to input the obtained partial discharge data to be classified into the support vector machine, and determining the hyperplane to which the partial discharge data to be classified belong through the support vector machine to obtain the discharge types of the partial discharge data to be classified.
2. A power cable partial discharge pattern recognition analysis system as claimed in claim 1, wherein the discharge type includes: partial discharge corona faults, internal discharge faults, creeping discharge faults, and noise disturbances.
3. The power cable partial discharge pattern recognition analysis system of claim 1, wherein the preprocessing module comprises: a signal acquisition unit;
the preprocessing module is connected with an external HFCT sensor, and the HFCT sensor is used for acquiring partial discharge signals and transmitting the acquired partial discharge signals to the signal acquisition unit;
the maximum sampling rate of the signal acquisition unit is more than 2 times of that of the HFCT sensor.
4. The power cable partial discharge pattern recognition analysis system of claim 1, wherein the preprocessing module is specifically configured to perform filtering, denoising, and amplification processing on the collected partial discharge signal.
5. The power cable partial discharge pattern recognition analysis system of claim 1, wherein the feature extraction module is specifically configured to obtain a partial discharge spectrogram according to the preprocessed partial discharge signal, calculate a feature value of the spectrogram, and perform principal component analysis on the feature value, where features obtained after the principal component analysis constitute a feature library.
6. The power cable partial discharge pattern recognition analysis system of claim 5, wherein the spectrogram comprises: discharge capacity, discharge phase, discharge frequency and phase-resolved partial discharge spectrogram.
7. A power cable partial discharge pattern recognition analysis system as claimed in claim 5, wherein the characteristic values comprise: skewness, steepness, peak point number, average discharge amount, discharge factor, correlation coefficient, correction correlation coefficient, Weibull distribution shape parameter and Weibull distribution scale parameter of positive and negative half shafts of the spectrogram.
8. The power cable partial discharge pattern recognition analysis system according to claim 1, wherein the pattern recognition module is specifically configured to divide a feature library into a training set and a test set, input features in the training set and discharge types to which the features belong into a support vector machine, and train the support vector machine, so that a weight normal vector w and an intercept b of the support vector machine are optimized, thereby obtaining optimal classification hyperplanes of different discharge types, so as to input the obtained partial discharge data to be classified into the support vector machine, and determine the hyperplane to which the partial discharge data to be classified belongs by using the support vector machine, thereby obtaining the discharge type of the partial discharge data to be classified.
9. The power cable partial discharge pattern recognition analysis system of claim 8, wherein the trained support vector machine utilizes a voting mechanism to determine the discharge type of the partial discharge data to be classified.
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