CN113866571A - Partial discharge source positioning method, device and equipment - Google Patents

Partial discharge source positioning method, device and equipment Download PDF

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CN113866571A
CN113866571A CN202110903978.9A CN202110903978A CN113866571A CN 113866571 A CN113866571 A CN 113866571A CN 202110903978 A CN202110903978 A CN 202110903978A CN 113866571 A CN113866571 A CN 113866571A
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spectrogram
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CN113866571B (en
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唐艳
关健杰
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Xiamen Oyic Robot Co ltd
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Abstract

The invention discloses a partial discharge source positioning method, which comprises the following steps: three voiceprint acquisition devices are distributed in equipment to be detected to acquire voiceprint signals in the equipment to be detected; converting the voiceprint signals acquired by the three voiceprint acquisition devices into spectrogram respectively, and synthesizing the three spectrogram to obtain a synthesized first spectrogram; processing the first spectrogram to obtain a first feature vector, and inputting the first feature vector into a CNN network for classification training to obtain a first network model; modifying the LOSS function of the first network model into HingeLoss, modifying the learning rate of a full connection layer into 0, modifying a softmax layer into an SVM, and inputting the first spectrogram into the SVM for classification training to obtain a second network model; and detecting the spectrogram to be detected according to the second network model, so as to determine the position of the partial discharge source of the device to be detected. The device can realize rapid and accurate positioning of the partial discharge source of the medium-high voltage equipment, and improves the detection efficiency.

Description

Partial discharge source positioning method, device and equipment
Technical Field
The invention relates to the technical field of monitoring of medium and high voltage equipment, in particular to a method, a device and equipment for positioning a partial discharge source.
Background
The development of electric power in China is changing day by day, and the characteristics of informatization, automation and intellectualization become more and more obvious. However, the failure of some equipment such as a transformer may cause a blackout in a large area, which results in a significant economic loss, and therefore it is necessary to monitor the operation state of the equipment such as the transformer. Partial discharge occurs in medium-high voltage equipment, mainly the discharge of the transformer, the mutual inductor and other high-voltage electrical equipment in the internal insulation occurs under the action of high voltage, and the discharge only exists in the local position of the insulation, and the whole insulation penetration breakdown or flashover cannot be formed immediately, so the partial discharge is called as partial discharge. The insulation fault condition in the transformer can be judged as soon as possible by quickly and accurately positioning the local discharge source, unnecessary maintenance is reduced, and the maintenance time and the power failure loss are reduced for necessary maintenance. Currently, a method for positioning by using an arrival time difference method and an RSSI fingerprint method is frequently used, a monitoring device based on the method has high requirements on sampling rate and synchronization accuracy or needs to establish a discharge fingerprint map in advance, and the method has high cost and low detection efficiency.
Disclosure of Invention
In view of this, the present invention provides a method, an apparatus, and a device for positioning an partial discharge source, which can realize fast and accurate positioning of the partial discharge source of a medium-high voltage device, thereby greatly improving detection efficiency and reducing cost.
In order to achieve the above object, the present invention provides a method for positioning a partial discharge source, including:
three voiceprint acquisition devices are distributed in equipment to be detected to acquire voiceprint signals in the equipment to be detected;
converting the voiceprint signals acquired by the three voiceprint acquisition devices into spectrogram respectively, and synthesizing the three spectrogram to obtain a synthesized first spectrogram;
processing the first spectrogram to obtain a first feature vector, and inputting the first feature vector into a CNN network for classification training to obtain a first network model;
modifying the LOSS function of the first network model into HingeLoss, modifying the learning rate of a full connection layer into 0, modifying a softmax layer into an SVM, and inputting the first spectrogram into the SVM for classification training to obtain a second network model;
and detecting the spectrogram to be detected according to the second network model, so as to determine the position of the partial discharge source of the device to be detected.
Preferably, before the step of collecting the voiceprint signals in the device to be detected by arranging three voiceprint collecting devices in the device to be detected, the method further comprises the following steps:
dividing and marking the detection space of the equipment to be detected according to a preset distribution range, and setting a partial discharge source in the divided detection space.
Preferably, the step of processing the first spectrogram to obtain a first feature vector includes:
and after carrying out convolution layer processing on the first spectrogram for N times, carrying out pooling layer processing to obtain a first feature vector, wherein the first feature vector comprises a feature vector containing partial discharge.
Preferably, the step of performing convolution layer processing on the first spectrogram N times includes:
processing the first spectrogram sequentially through a first convolution layer, a second convolution layer and a third convolution layer; the convolution kernel size of the first layer of convolution layer is 11 × 3, the step size is 3, and the number of convolution kernels is 9; the convolution kernel size of the second layer of convolution is 7 x 7, the step length is 4, and the number is 9; the convolution kernel size of the third convolution layer is 4 x 4, the step length is 4, and the number is 9.
Preferably, the size of the pooling layer is 3 x 3 with a step size of 2.
Preferably, after the inputting the first spectrogram into the SVM for classification training, the method further includes:
and optimizing the parameters of the SVM by using a genetic algorithm.
In order to achieve the above object, the present invention further provides a partial discharge source positioning apparatus, including:
the acquisition unit is used for acquiring voiceprint signals in the equipment to be detected by arranging three voiceprint acquisition devices in the equipment to be detected;
the synthesis unit is used for respectively converting the voiceprint signals acquired by the three voiceprint acquisition devices into spectrogram and synthesizing the three spectrogram to obtain a synthesized first spectrogram;
the first training unit is used for processing the first spectrogram to obtain a first feature vector, and inputting the first feature vector into a CNN (CNN network) for classification training to obtain a first network model;
the second training unit is used for modifying the LOSS function of the first network model into HingeLoss, modifying the learning rate of a full connection layer into 0, modifying a softmax layer into an SVM, and inputting the first spectrogram into the SVM for classification training to obtain a second network model;
and the positioning unit is used for detecting the spectrogram to be detected according to the second network model so as to determine the position of the partial discharge source of the device to be detected.
Preferably, the apparatus further comprises:
and the dividing unit is used for dividing and marking the detection space of the equipment to be detected according to a preset distribution range and setting a partial discharge source in the divided detection space.
To achieve the above object, the present invention further provides an partial discharge source positioning device, including a processor, a memory, and a computer program stored in the memory, where the computer program is executable by the processor to implement a partial discharge source positioning method as described in the above embodiments.
Has the advantages that:
according to the scheme, after voiceprint signals of the acquisition equipment of the voiceprint acquisition device are converted into a spectrogram, CNN network training is carried out to obtain a CNN network model and extract features, and classification is carried out by combining with an SVM (support vector machine), so that the localization of the partial discharge source is realized.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a partial discharge source positioning method according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a partial discharge source positioning method according to another embodiment of the present invention.
Fig. 3 is a schematic diagram of segmentation labeling for the detection space according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a network structure according to an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a partial discharge source positioning device according to an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of a partial discharge source positioning device according to another embodiment of the present invention.
Fig. 7 is a schematic structural diagram of a partial discharge source positioning device according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The present invention will be described in detail with reference to the following examples.
Fig. 1-2 are schematic flow diagrams of a partial discharge source positioning method according to an embodiment of the present invention.
In this embodiment, the method includes:
and S11, arranging three voiceprint acquisition devices in the equipment to be detected to acquire the voiceprint signals in the equipment to be detected.
In this embodiment, the voiceprint acquisition device is a sound sensor, and three sound sensors arranged in the device to be detected are used for acquiring voiceprint signals, wherein the voiceprint signals include voiceprint signals containing partial discharge and voiceprint signals not containing partial discharge, and are used for CNN network training in the following, so that sounds containing partial discharge and sounds not containing partial discharge are learned from data. Further, the three sound sensors can be installed inside a switch cabinet, a ring main unit and other equipment of the equipment, and can be specifically arranged according to the specific structure inside the equipment to be detected. Furthermore, the sound sensor is provided with a magnet which can be directly adsorbed inside the equipment to be detected.
Further, in another embodiment, before step S11, the method further includes:
and S10, dividing and marking the detection space of the equipment to be detected according to a preset distribution range, and setting a partial discharge source in the divided detection space.
In this embodiment, for the devices of different users and different requirements of the users, the devices may be spatially divided according to actual conditions, different spatial division modes are different, and the sizes of the corresponding detection regions are different, so that the devices may be flexibly set according to the requirements of the users without limitation. In a specific implementation, as shown in fig. 3, the device to be detected is divided into 27 blocks according to the detection space, and each divided position is labeled and distinguished. Where the partial discharge source may be represented in the form of a vector y, e.g., y ═ a1，a2，...，a27]，aiWhen (i ═ 1, 2,. 27) is 0, it means that there is no partial discharge, and when it is 1, it means that there is partial discharge. After the target detection space is divided, one or more partial discharge sources are arranged at 27 divided positions, sound is collected through a sound sensor, wherein a plurality of partial discharge sources can be freely combined, and after voiceprints are collected, voiceprint data are marked, for example, when voiceprint a has partial discharge at the positions of 3 rd and 17 th, y is [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, and the like]. The detection space is divided and marked, so that the specific position of the partial discharge source can be conveniently determined in the training stage, the subsequent data marking is convenient, and the training efficiency is improved.
And S12, converting the voiceprint signals acquired by the three voiceprint acquisition devices into spectrogram respectively, and synthesizing the three spectrogram to obtain a synthesized first spectrogram.
In this embodiment, fourier transform is performed on the obtained sound information, and then an RGB image having an image size of 512 × 174 is drawn by fourier transform according to an energy power spectrum. The first spectrogram is obtained by combining the three spectrograms subjected to the voiceprint signal processing in the step S11 in the longitudinal direction to fill the RGB images at 512 × 512.
And S13, processing the first spectrogram to obtain a first feature vector, and inputting the first feature vector to a CNN (CNN network) for classification training to obtain a first network model.
Wherein, the step of processing the first spectrogram to obtain a first feature vector comprises:
and after carrying out convolution layer processing on the first spectrogram for N times, carrying out pooling layer processing to obtain a first feature vector, wherein the first feature vector comprises a feature vector containing partial discharge.
The step of performing convolution layer processing on the first spectrogram for N times comprises the following steps:
processing the first spectrogram sequentially through a first convolution layer, a second convolution layer and a third convolution layer; the convolution kernel size of the first layer of convolution layer is 11 × 3, the step size is 3, and the number of convolution kernels is 9; the convolution kernel size of the second layer of convolution is 7 x 7, the step length is 4, and the number is 9; the convolution kernel size of the third convolution layer is 4 x 4, the step length is 4, and the number is 9. Wherein the size of the pooling layer is 3 x 3 with a step size of 2.
In this embodiment, as shown in fig. 4, the first spectrogram is subjected to convolution layer processing three times, and then subjected to flattening processing to obtain a feature vector containing partial discharge, and the feature vector is sent to a fully-connected neural network for classification training. The method specifically comprises the following steps:
s13-1, processing the first spectrogram through a first layer of convolution layer, wherein the convolution kernel size of the first layer is 11 × 3, the step size is 3, the number of the convolution kernels is 9, and 9 two-dimensional Feature maps 1 with 167 × 167 are output; then the Feature map1 is processed by a second layer of convolution layers, wherein the number of convolution kernels of the second layer is 7 × 7, the step length is 4, the number is 9, and 81 40 × 40 two-dimensional Feature maps 2 are output; and then the Feature map2 is processed by a third layer of convolution layer, wherein the third layer of convolution kernels has 4 × 4, step size of 4 and number of 9, and 729 Feature maps 3 with 9 × 9 are output.
And S13-2, processing the Feature map3 by a pooling layer, wherein the size is 3 × 3, the step size is 2, and 729 Feature maps 4 of 3 × 3 are output.
And S13-3, inputting Feature map4 into the fully-connected neural network to obtain a 27 x 1 vector as an output result.
And S13-4, outputting classification selection softmax, taking the cross entropy as a loss function, and using Adam as an optimizer to train and store the network. In specific implementation, the images can be effectively reduced by performing convolution processing for 3 times, and the network operation efficiency is improved. Because the number of convolutions is not as large as possible, the original features may be lost by the multiple convolutions, and the network performance is affected. Therefore, the adjustment can be carried out according to the actual situation.
S14, modifying the LOSS function of the first network model into HingeLoss, modifying the learning rate of a full connection layer into 0, modifying the softmax layer into an SVM, and inputting the first spectrogram into the SVM for classification training to obtain a second network model.
After the inputting the first spectrogram into the SVM for classification training, the method further comprises: and optimizing the parameters of the SVM by using a genetic algorithm.
In this embodiment, the final LOSS function is modified to hindloss according to the trained CNN network, the learning rate of the full connectivity layer is modified to 0, the softmax layer is modified to the SVM classifier, after the new network is obtained, the first spectrogram is input to the new network for classification training, a genetic algorithm is adopted for SVM parameter optimization aiming at SVM optimization to obtain an optimal solution, and the whole network training is completed. By adopting the genetic algorithm to carry out efficient optimization on the network parameters, the detection accuracy can be greatly improved.
And S15, detecting the spectrogram to be detected according to the second network model, thereby determining the position of the partial discharge source of the device to be detected.
In this embodiment, after sound is collected and processed in the device to be detected, the sound is input as a parameter and sent to the second network model, and finally, the specific position of the partial discharge source is determined according to the value of the output vector. The position of the partial discharge source can be rapidly calculated, and the positioning precision is greatly improved.
Fig. 5 to 6 are schematic structural diagrams of a partial discharge source positioning device according to an embodiment of the present invention.
In this embodiment, the apparatus 50 includes:
the acquisition unit 51 is used for acquiring voiceprint signals in equipment to be detected by arranging three voiceprint acquisition devices in the equipment to be detected;
the synthesis unit 52 is configured to convert the voiceprint signals acquired by the three voiceprint acquisition devices into spectrogram respectively, and synthesize the three spectrogram to obtain a synthesized first spectrogram;
the first training unit 53 is configured to process the first spectrogram to obtain a first feature vector, and input the first feature vector to a CNN network for classification training to obtain a first network model;
a second training unit 54, configured to modify the LOSS function of the first network model to hindloss, modify the learning rate of the full connectivity layer to 0, modify the softmax layer to an SVM, and input the first spectrogram into the SVM for classification training to obtain a second network model;
and the positioning unit 55 is configured to detect the spectrogram to be detected according to the second network model, so as to determine the position of the partial discharge source of the device to be detected.
Wherein the first training unit 53 is further configured to:
and after carrying out convolution layer processing on the first spectrogram for N times, carrying out pooling layer processing to obtain a first feature vector, wherein the first feature vector comprises a feature vector containing partial discharge.
Wherein the second training unit 54 is further configured to:
and optimizing the parameters of the SVM by using a genetic algorithm.
Further, in another embodiment, the apparatus 60 further includes:
and the dividing unit 61 is used for dividing and marking the detection space of the equipment to be detected according to a preset distribution range, and setting a partial discharge source in the divided detection space.
Each unit module of the apparatus 50/60 can respectively execute the corresponding steps in the above method embodiments, and therefore, the detailed description of each unit module is omitted here, please refer to the description of the corresponding steps above.
The embodiment of the present invention further provides an partial discharge source positioning device, which includes a processor, a memory, and a computer program stored in the memory, where the computer program can be executed by the processor to implement the partial discharge source positioning method according to the above embodiment.
As shown in fig. 7, the partial discharge source positioning device may include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the schematic diagram is merely an example of the partial discharge source locating device, and does not constitute a limitation of the partial discharge source locating device, and may include more or less components than those shown, or combine some components, or different components, for example, the partial discharge source locating device may further include an input-output device, a network access device, a bus, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc., and the control center of the partial discharge source localization apparatus connects the various parts of the entire partial discharge source localization apparatus by using various interfaces and lines.
The memory may be configured to store the computer program and/or the module, and the processor may implement various functions of the partial discharge source positioning apparatus by executing or executing the computer program and/or the module stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the cellular phone (such as voiceprint data, a phonebook, etc.), and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The unit integrated with the partial discharge source positioning device may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
The embodiments in the above embodiments can be further combined or replaced, and the embodiments are only used for describing the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention, and various changes and modifications made to the technical solution of the present invention by those skilled in the art without departing from the design idea of the present invention belong to the protection scope of the present invention.

Claims (9)

1. A method for positioning a partial discharge source, the method comprising:
three voiceprint acquisition devices are distributed in equipment to be detected to acquire voiceprint signals in the equipment to be detected;
converting the voiceprint signals acquired by the three voiceprint acquisition devices into spectrogram respectively, and synthesizing the three spectrogram to obtain a synthesized first spectrogram;
processing the first spectrogram to obtain a first feature vector, and inputting the first feature vector into a CNN network for classification training to obtain a first network model;
modifying the LOSS function of the first network model into HingeLoss, modifying the learning rate of a full connection layer into 0, modifying a softmax layer into an SVM, and inputting the first spectrogram into the SVM for classification training to obtain a second network model;
and detecting the spectrogram to be detected according to the second network model, so as to determine the position of the partial discharge source of the device to be detected.
2. The method for positioning the partial discharge source according to claim 1, wherein before the step of arranging three voiceprint acquisition devices in the device to be detected to acquire the voiceprint signals therein, the method further comprises:
dividing and marking the detection space of the equipment to be detected according to a preset distribution range, and setting a partial discharge source in the divided detection space.
3. The partial discharge source positioning method according to claim 1, wherein the step of processing the first spectrogram to obtain a first feature vector comprises:
and after carrying out convolution layer processing on the first spectrogram for N times, carrying out pooling layer processing to obtain a first feature vector, wherein the first feature vector comprises a feature vector containing partial discharge.
4. The method according to claim 3, wherein the step of performing convolution layer processing on the first spectrogram N times comprises:
processing the first spectrogram sequentially through a first convolution layer, a second convolution layer and a third convolution layer; the convolution kernel size of the first layer of convolution layer is 11 × 3, the step size is 3, and the number of convolution kernels is 9; the convolution kernel size of the second layer of convolution is 7 x 7, the step length is 4, and the number is 9; the convolution kernel size of the third convolution layer is 4 x 4, the step length is 4, and the number is 9.
5. A method according to claim 3, wherein the size of the pooling layer is 3 x 3 and the step size is 2.
6. The partial discharge source localization method according to claim 1, wherein after the inputting the first spectrogram into the SVM for classification training, the method further comprises:
and optimizing the parameters of the SVM by using a genetic algorithm.
7. An apparatus for locating a local discharge source, the apparatus comprising:
the acquisition unit is used for acquiring voiceprint signals in the equipment to be detected by arranging three voiceprint acquisition devices in the equipment to be detected;
the synthesis unit is used for respectively converting the voiceprint signals acquired by the three voiceprint acquisition devices into spectrogram and synthesizing the three spectrogram to obtain a synthesized first spectrogram;
the first training unit is used for processing the first spectrogram to obtain a first feature vector, and inputting the first feature vector into a CNN (CNN network) for classification training to obtain a first network model;
the second training unit is used for modifying the LOSS function of the first network model into HingeLoss, modifying the learning rate of a full connection layer into 0, modifying a softmax layer into an SVM, and inputting the first spectrogram into the SVM for classification training to obtain a second network model;
and the positioning unit is used for detecting the spectrogram to be detected according to the second network model so as to determine the position of the partial discharge source of the device to be detected.
8. The device according to claim 7, further comprising:
and the dividing unit is used for dividing and marking the detection space of the equipment to be detected according to a preset distribution range and setting a partial discharge source in the divided detection space.
9. An partial discharge source localization device, comprising a processor, a memory, and a computer program stored in the memory, the computer program being executable by the processor to implement a partial discharge source localization method according to any one of claims 1 to 6.
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