Partial discharge type identification method, system and device based on two-dimensional statistical spectrogram and storage medium

Abstract

The invention discloses a partial discharge type identification method and system based on a two-dimensional statistical spectrogram, equipment and a storage medium. The method comprises the following steps: acquiring the two-dimensional statistical spectrogram of partial discharge of power equipment; according to the power frequency correlation of the partial discharge signals in the two-dimensional statistical spectrogram, the partial discharge type with the maximum probability is judged; according to the amplitude fluctuation degree and change degree of the partial discharge signals in the two-dimensional statistical spectrogram, the partial discharge type with the maximum probability is judged; according to the symmetry of the positive and negative half-cycle discharge amplitudes of the partial discharge signal in the two-dimensional statistical spectrogram, judging the partial discharge type with the maximum probability; according to the difference of the positive and negative half-cycle discharge times of the partial discharge signal in the two-dimensional statistical spectrogram, judging the partial discharge type with the maximum probability; and taking the partial discharge type with the maximum comprehensive probability as an identification result. The partial discharge type can be identified in a simple process.

Description

Technical Field

[0001] The present invention belongs to the field of insulation status monitoring of power equipment, and relates to a method, system, equipment and storage medium for identifying partial discharge types based on a two-dimensional statistical spectrum. Background Art

[0002] Power equipment is an important part of the power system and the basis for ensuring power supply reliability. Statistics show that the deterioration of the insulation performance of power equipment is the main cause of its failure, which may cause large-scale power outages in the power grid in severe cases. Partial discharge is both a sign of insulation deterioration of power equipment and a key factor causing insulation deterioration. Partial discharge pattern recognition is the key to partial discharge signal analysis, and its purpose is to identify the type of insulation defects that induce partial discharge.

[0003] In recent years, many experts and scholars at home and abroad have carried out a lot of work on the identification of partial discharge patterns of power equipment. It mainly includes single pulse waveform mode and partial discharge phase distribution mode, among which the partial discharge phase distribution mode (PRPD) is the most widely used. This mode describes the relationship between the power frequency phase φ, discharge amount q and discharge number n corresponding to the partial discharge pulse. It often collects partial discharge pulses of multiple power frequency cycles to obtain the corresponding φ-qn three-dimensional statistical map, q max -φ two-dimensional statistical spectrum, q ave -φ two-dimensional statistical spectrum, n-φ two-dimensional statistical spectrum, and then extract characteristic parameters to identify different discharge types. max -φ two-dimensional statistical spectrum, q ave -φ two-dimensional statistical spectrum, n-φ two-dimensional statistical spectrum, generally extract the skewness S K , steepness K u , discharge factor Q, phase asymmetry ψ , phase correlation coefficient Cc, etc., and has achieved certain results. However, this recognition mode often requires the extraction of nearly 20 eigenvalues, the calculation process is relatively complicated, and a large amount of experimental data is required for training. Summary of the invention

[0004] The object of the present invention is to overcome the above-mentioned shortcomings of the prior art and to provide a method, system, device and storage medium for identifying partial discharge types based on a two-dimensional statistical spectrum, which can identify the partial discharge type in a simple process.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A method for identifying partial discharge types based on two-dimensional statistical spectrograms includes the following steps: S1, obtaining a two-dimensional statistical spectrum of partial discharge of power equipment; S2, judging the most likely partial discharge type based on the power frequency correlation of the partial discharge signal in the two-dimensional statistical spectrum; S3, judging the most likely partial discharge type according to the amplitude fluctuation degree and change degree of the partial discharge signal in the two-dimensional statistical spectrum; S4, judging the most likely partial discharge type according to the symmetry of the discharge amplitudes of the positive and negative half cycles of the partial discharge signal in the two-dimensional statistical spectrum; S5, judging the most likely partial discharge type according to the difference in the positive and negative half-cycle discharge times of the partial discharge signal in the two-dimensional statistical spectrum; S6, taking the partial discharge type with the largest comprehensive probability as the identification result S3, and judging the partial discharge type with the largest probability according to the amplitude fluctuation degree of the partial discharge signal in the two-dimensional statistical spectrum; S4, judging the most likely partial discharge type according to the amplitude variation of the partial discharge signal in the two-dimensional statistical spectrum; S5, judging the most likely partial discharge type according to the symmetry of the discharge amplitudes of the positive and negative half cycles of the partial discharge signal in the two-dimensional statistical spectrum; S6, judging the most likely partial discharge type according to the difference in the number of discharges in the positive and negative half cycles of the partial discharge signal in the two-dimensional statistical spectrum; S7, taking the partial discharge type with the largest comprehensive probability as the identification result.

[0006] Preferably, in S2, based on q max -φ Two-dimensional statistical spectrum calculation 50Hz correlation coefficient r 50 Correlation coefficient r with 100 Hz 100 , the correlation coefficient r 50 、r 100 Normalization is performed, if r 50 <0.05 and r 100 <0.05, indicating that the partial discharge signal has no power frequency correlation, and the probability of particle discharge is the highest; if r 50 ≥0.05 or r 100 ≥0.05, indicating that the partial discharge signal has power frequency correlation, and the probability of tip discharge, solid insulation discharge or suspension discharge is the highest. 50 >2*r 100 , indicating that the probability of tip discharge is the largest. If r 50 ≤2*r 100 , indicating that the probability of solid insulation discharge or suspended discharge is the highest.

[0007] Preferably, in S3, based on q max -φ two-dimensional statistical spectrum, calculate the ratio of the average value to the peak value of the positive and negative half-cycle signals respectively, and the ratio of the average value to the peak value of the positive half-cycle signal rave / max+ , the ratio of the average value to the peak value of the negative half-cycle signal r ave / max- Normalization is performed, if r ave / max+ ≥0.8 and r ave / max- ≥0.8, indicating that the probability of suspended discharge is the highest; if r ave / max+ <0.8 or r ave / max- <0.8, indicating that the probability of tip discharge or solid insulation discharge is the highest.

[0008] Furthermore, the peak value is the average of the first three maximum values.

[0009] Preferably, in S4, based on q max -φ two-dimensional statistical spectrum, sort the positive and negative half-cycle signals according to the amplitude size, and calculate the slope. + , the signal amplitude slope of the negative half cycle - Normalize the slope. + <0.1 and slope - <0.1, indicating that the signal amplitude changes little and the probability of suspended discharge is the highest; if slope + ≥0.1 or slope - ≥0.1, indicating that the signal amplitude changes greatly, and the probability of tip discharge or solid insulation discharge is the highest.

[0010] Preferably, in S5, based on q max -φ two-dimensional statistical spectrum, calculate the similarity s of positive and negative half-cycle spectra q , if s q >0.7, the probability of suspended discharge or solid insulation discharge is the highest; if s q ≤0.7, the probability of tip discharge is the highest.

[0011] Preferably, in S6, based on the n-φ two-dimensional statistical spectrum, the frequency ratio of the positive and negative half-cycle discharge signals is calculated, and the positive and negative half-cycle frequency ratio n is taken. + / - , the ratio of negative and positive half cycles n - / + The maximum value among them is taken as the discharge number ratio n r , and for n r Normalization is performed, if n r <0.2, the probability of tip discharge is the largest, if n r ≥0.2, the probability of suspended discharge or solid insulation discharge is the highest.

[0012] A partial discharge type identification system based on two-dimensional statistical spectrum, comprising: A two-dimensional statistical spectrum acquisition module is used to acquire a two-dimensional statistical spectrum of partial discharge of power equipment; A power frequency correlation judgment module is used to judge the most likely partial discharge type according to the power frequency correlation of the partial discharge signal in the two-dimensional statistical spectrum; An amplitude judgment module is used to judge the most likely type of partial discharge according to the amplitude fluctuation degree and variation degree of the partial discharge signal in the two-dimensional statistical spectrum. An amplitude fluctuation judgment module is used to judge the most likely type of partial discharge according to the amplitude fluctuation degree of the partial discharge signal in the two-dimensional statistical spectrum. An amplitude change judgment module is used to judge the most likely partial discharge type according to the amplitude change degree of the partial discharge signal in the two-dimensional statistical spectrum; A symmetry judgment module is used to judge the most likely partial discharge type according to the symmetry of the positive and negative half-cycle discharge amplitudes of the partial discharge signal in the two-dimensional statistical spectrum; A difference judgment module is used to judge the most likely partial discharge type according to the difference in the positive and negative half-cycle discharge times of the partial discharge signal in the two-dimensional statistical spectrum; The comprehensive judgment module is used to take the partial discharge type with the largest comprehensive probability as the identification result.

[0013] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the partial discharge type identification method based on a two-dimensional statistical spectrum are implemented.

[0014] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the partial discharge type identification method based on a two-dimensional statistical spectrum are implemented.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention is based on max -φ two-dimensional statistical spectrum to determine the power frequency correlation of partial discharge signals; based on q max -φ two-dimensional statistical spectrum, respectively judge the volatility of the positive and negative half-cycle signal amplitude; based on q max -φ two-dimensional statistical spectrum and n-φ two-dimensional statistical spectrum are used to judge the symmetry of the positive and negative half-cycles of the partial discharge signal; based on the above features, the partial discharge type is identified. This method uses the principle that different types of partial discharges correspond to different principles, and simplifies the feature quantities on this basis. It can greatly simplify the partial discharge feature quantities and does not require a large amount of test data training. It is simple, efficient and convenient, providing important support for the insulation status assessment of power equipment and has great practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1This is a flow chart of partial discharge type identification based on two-dimensional statistical spectrum of the present invention. DETAILED DESCRIPTION

[0017] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0018] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs. The terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or can communicate with each other; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. The term "and / or" used in this article includes any and all combinations of one or more related listed items. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0020] The disclosure below provides many different embodiments or examples to realize different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention. In addition, the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplicity and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed. In addition, the present invention provides various specific examples of processes and materials, but those of ordinary skill in the art can be aware of the application of other processes and / or the use of other materials.

[0021] like Figure 1 As shown in FIG. 1 , the method for identifying partial discharge types based on a two-dimensional statistical spectrum according to the present invention includes the following steps: (1) Use ultra-high frequency, high frequency and other partial discharge detection instruments to obtain a two-dimensional statistical spectrum of partial discharge of power equipment.

[0022] (2) Based on q max -φ two-dimensional statistical spectrum calculates the 50Hz correlation coefficient and 100Hz correlation coefficient, and determines the most likely partial discharge type based on the power frequency correlation of the partial discharge signal.

[0023] (3) Based on q max -φ two-dimensional statistical spectrum, calculate the ratio of the average value to the peak value of the positive and negative half-cycle signals respectively, and judge the most likely partial discharge type according to the amplitude fluctuation degree of the partial discharge signal.

[0024] (4) Based on q max -φ two-dimensional statistical spectrum, sort the positive and negative half-cycle signals according to the amplitude, and calculate the slope. According to the degree of change in the amplitude of the partial discharge signal, the most likely partial discharge type is determined.

[0025] (5) Based on q max -φ two-dimensional statistical spectrum, calculate the similarity of positive and negative half-cycle spectra, and judge the most likely partial discharge type based on the symmetry of the positive and negative half-cycle discharge amplitudes of the partial discharge signal.

[0026] (6) Based on the n-φ two-dimensional statistical spectrum, the ratio of the number of positive and negative half-cycle discharge signals is calculated. According to the difference in the number of positive and negative half-cycle discharges of the local discharge signal, the local discharge type with the highest probability is determined.

[0027] (7) Based on the above characteristics, the local discharge type with the highest comprehensive probability is taken as the identification result to achieve local discharge type identification.

[0028] Based on q max -φ Two-dimensional statistical spectrum calculation 50Hz correlation coefficient r 50Correlation coefficient r with 100 Hz 100 , determine the power frequency correlation of the partial discharge signal, the specific process is: in this embodiment, q max -φ 50Hz, 100Hz correlation coefficient r of the two-dimensional statistical spectrum 50 、r 100 Normalization is performed. Statistics show that if r 50 <0.05 and r 100 <0.05, indicating that the partial discharge signal has no power frequency correlation, and it is likely to be particle discharge. 50 ≥0.05 or r 100 ≥0.05, indicating that the partial discharge signal has power frequency correlation, which may be tip discharge, solid insulation discharge or suspension discharge. 50 >2*r 100 , indicating that the tip discharge is most likely, if r 50 ≤2*r 100 , indicating that it is most likely solid insulation discharge or suspended discharge.

[0029] Based on q max -φ two-dimensional statistical spectrum, respectively calculate the ratio of the average value and peak value of the positive and negative half-cycle signals, and judge the amplitude fluctuation degree of the partial discharge signal. The specific process is as follows: considering that the amplitude of the suspended discharge signal is basically stable, while the amplitude of the tip discharge and solid insulation discharge signals varies greatly, the discharge type can be judged by the amplitude fluctuation. In this embodiment, for q max -φThe ratio of the average value to the peak value of the positive half-cycle signal of the two-dimensional statistical spectrum ave / max+ , the ratio of the average value to the peak value of the negative half-cycle signal r ave / max- Normalization is performed. Statistics show that if r ave / max+ ≥0.8 and r ave / max- ≥0.8, indicating a high probability of suspended discharge; if r ave / max+ <0.8 or r ave / max- <0.8, indicating that it is most likely a tip discharge or solid insulation discharge. To avoid external interference, the peak value can be the average of the first three maximum values.

[0030] Based on q max -φ two-dimensional statistical spectrum, sort the positive and negative half-cycle signals according to the amplitude, and calculate the slope to judge the degree of change of the partial discharge signal amplitude. The specific process is: considering that the amplitude of the suspended discharge signal is basically stable, while the amplitude of the tip discharge and solid insulation discharge signals varies greatly, the discharge type can be judged by the amplitude fluctuation. In this embodiment, the slope of the signal amplitude of the positive half-cycle is + , the signal amplitude slope of the negative half cycle - Normalize it. Statistics show that if the slope +<0.1 and slope - <0.1, indicating that the signal amplitude changes little, and it is likely to be suspended discharge; if slope + ≥0.1 or slope - ≥0.1, indicating that the signal amplitude changes greatly, which is probably caused by tip discharge or solid insulation discharge.

[0031] Based on q max -φ two-dimensional statistical spectrum, calculate the similarity of positive and negative half-cycle spectrum, and judge the symmetry of positive and negative half-cycle discharge amplitude of partial discharge signal. The specific process is as follows: considering that the positive and negative half-cycle of suspended discharge and solid insulation discharge signals are symmetrical, while the positive and negative half-cycle of tip discharge are generally not symmetrical, through q max -φ Two-dimensional statistical spectrum positive and negative half-cycle spectrum similarity s q The discharge type can be determined. In this embodiment, if s q >0.7, it is probably suspended discharge or solid insulation discharge; if s q ≤0.7, most likely tip discharge.

[0032] Based on the n-φ two-dimensional statistical spectrum, the ratio of the number of positive and negative half-cycle discharge signals is calculated to determine the difference in the number of positive and negative half-cycle discharges of the local discharge signal. The specific process is as follows: considering that the number of positive and negative half-cycle discharges of suspended discharge and solid insulation discharge signals is similar, while the number of positive and negative half-cycle discharges of tip discharge is very different, the discharge type can be determined by the ratio of the number of positive and negative half-cycle discharge signals in the n-φ two-dimensional statistical spectrum. In this embodiment, for the convenience of analysis, the ratio of the number of positive and negative half-cycles is taken as n. + / - , the ratio of negative and positive half cycles n - / + The maximum value among them is taken as the discharge number ratio n r , and for n r Normalization is performed, if n r <0.2, it is probably the tip discharge, if n r ≥0.2, it is most likely suspended discharge or solid insulation discharge.

[0033] The following are device embodiments of the present invention, which can be used to perform method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0034] In another embodiment of the present invention, a local discharge type identification system based on a two-dimensional statistical spectrum is provided. The local discharge type identification system based on a two-dimensional statistical spectrum can be used to implement the above-mentioned local discharge type identification method based on a two-dimensional statistical spectrum. Specifically, the local discharge type identification system based on a two-dimensional statistical spectrum includes a two-dimensional statistical spectrum acquisition module, a power frequency correlation judgment module, an amplitude judgment module, an amplitude fluctuation judgment module, an amplitude change judgment module, a symmetry judgment module, a difference judgment module and a comprehensive judgment module.

[0035] Among them, the two-dimensional statistical spectrum acquisition module is used to obtain the two-dimensional statistical spectrum of partial discharge of power equipment.

[0036] The power frequency correlation judgment module is used to judge the most likely partial discharge type according to the power frequency correlation of the partial discharge signal in the two-dimensional statistical spectrum.

[0037] The amplitude judgment module is used to judge the most likely type of partial discharge according to the amplitude fluctuation degree and change degree of the partial discharge signal in the two-dimensional statistical spectrum. The amplitude fluctuation judgment module is used to judge the most likely type of partial discharge according to the amplitude fluctuation degree of the partial discharge signal in the two-dimensional statistical spectrum.

[0038] The amplitude change judgment module is used to judge the most likely partial discharge type according to the amplitude change degree of the partial discharge signal in the two-dimensional statistical spectrum.

[0039] The symmetry judgment module is used to judge the most likely partial discharge type according to the symmetry of the positive and negative half-cycle discharge amplitudes of the partial discharge signal in the two-dimensional statistical spectrum.

[0040] The difference judgment module is used to judge the most likely partial discharge type according to the difference in the positive and negative half-cycle discharge times of the partial discharge signal in the two-dimensional statistical spectrum.

[0041] The comprehensive judgment module is used to take the partial discharge type with the largest comprehensive probability as the identification result.

[0042] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs), or other processors. GateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the partial discharge type identification method based on the two-dimensional statistical spectrum, including: S1, obtaining a two-dimensional statistical spectrum of partial discharge of power equipment; S2, judging the most likely partial discharge type according to the power frequency correlation of the partial discharge signal in the two-dimensional statistical spectrum; S3, judging the most likely partial discharge type according to the amplitude fluctuation degree and change degree of the partial discharge signal in the two-dimensional statistical spectrum; S4, judging the most likely partial discharge type according to the positive and negative half-cycle discharge amplitudes of the partial discharge signal in the two-dimensional statistical spectrum. S3, according to the amplitude fluctuation degree of the local discharge signal in the two-dimensional statistical spectrum, the local discharge type with the highest probability is judged; S4, according to the amplitude change degree of the local discharge signal in the two-dimensional statistical spectrum, the local discharge type with the highest probability is judged; S5, according to the symmetry of the positive and negative half-cycle discharge amplitudes of the local discharge signal in the two-dimensional statistical spectrum, the local discharge type with the highest probability is judged; S6, according to the difference in the positive and negative half-cycle discharge times of the local discharge signal in the two-dimensional statistical spectrum, the local discharge type with the highest probability is judged; S7, the local discharge type with the highest comprehensive probability is taken as the recognition result.

[0043] In another embodiment, the present invention further provides a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0044] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the method for identifying the type of partial discharge based on a two-dimensional statistical spectrum in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and the following steps are executed: S1, obtaining a two-dimensional statistical spectrum of partial discharge of power equipment; S2, judging the type of partial discharge with the highest probability according to the power frequency correlation of the partial discharge signal in the two-dimensional statistical spectrum; S3, judging the type of partial discharge with the highest probability according to the amplitude fluctuation degree and the variation degree of the partial discharge signal in the two-dimensional statistical spectrum; S4, judging the type of partial discharge with the highest probability according to the symmetry of the discharge amplitude of the positive and negative half cycles of the partial discharge signal in the two-dimensional statistical spectrum; S5, judging the type of partial discharge with the highest probability according to the two-dimensional statistical spectrum. S3, according to the amplitude fluctuation degree of the local discharge signal in the two-dimensional statistical spectrum, judge the local discharge type with the highest probability; S4, according to the amplitude change degree of the local discharge signal in the two-dimensional statistical spectrum, judge the local discharge type with the highest probability; S5, according to the symmetry of the positive and negative half-cycle discharge amplitudes of the local discharge signal in the two-dimensional statistical spectrum, judge the local discharge type with the highest probability; S6, according to the difference of the positive and negative half-cycle discharge times of the local discharge signal in the two-dimensional statistical spectrum, judge the local discharge type with the highest probability; S7, take the local discharge type with the highest comprehensive probability as the recognition result.

[0045] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0046] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0047] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0048] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0049] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0050] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0051] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0052] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0053] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0054] It should be understood that the above description is for illustration and not for limitation. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of this patent should not be determined with reference to the above description, but rather with reference to the preceding claims and the full scope of equivalents to which such claims are entitled. For the purpose of comprehensiveness, all articles and references, including disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the preceding claims is not intended to be a waiver of such subject matter, nor should it be considered that the applicant has not considered such subject matter to be part of the disclosed inventive subject matter.

Claims

1. A method for identifying partial discharge types based on two-dimensional statistical spectrograms, characterized in that: The process includes: S1, obtaining a two-dimensional statistical spectrum of partial discharge of power equipment; S2, judging the most likely partial discharge type based on the power frequency correlation of the partial discharge signal in the two-dimensional statistical spectrum; S3, judging the most likely partial discharge type according to the amplitude fluctuation degree and the variation degree of the partial discharge signal in the two-dimensional statistical spectrum; judging the most likely partial discharge type according to the amplitude fluctuation degree of the partial discharge signal in the two-dimensional statistical spectrum; S4, judging the most likely partial discharge type according to the amplitude variation of the partial discharge signal in the two-dimensional statistical spectrum; S5S4, judging the most likely partial discharge type according to the symmetry of the discharge amplitudes of the positive and negative half cycles of the partial discharge signal in the two-dimensional statistical spectrum; S6S5, judging the most likely partial discharge type based on the difference in the number of discharges in the positive and negative half cycles of the partial discharge signal in the two-dimensional statistical spectrum; S7S6, the partial discharge type with the largest comprehensive probability is taken as the identification result.

2. The method for identifying partial discharge types based on two-dimensional statistical spectrogram according to claim 1, characterized in that: In S2, based on q max -φ Two-dimensional statistical spectrum calculation 50Hz correlation coefficient r 50 Correlation coefficient r with 100 Hz 100 , the correlation coefficient r 50 、r 100 Normalization is performed, if r 50 <0.05 and r 100 <0.05, indicating that the partial discharge signal has no power frequency correlation, and the probability of particle discharge is the highest; if r 50 ≥0.05 or r 100 ≥0.05, indicating that the partial discharge signal has power frequency correlation, and the probability of tip discharge, solid insulation discharge or suspension discharge is the highest. 50 >2*r 100 , indicating that the probability of tip discharge is the largest. If r 50 ≤2*r 100 , indicating that the probability of solid insulation discharge or suspended discharge is the highest.

3. The method for identifying partial discharge types based on two-dimensional statistical spectrogram according to claim 1, characterized in that: In S3, based on q max -φ two-dimensional statistical spectrum, calculate the ratio of the average value to the peak value of the positive and negative half-cycle signals respectively, and the ratio of the average value to the peak value of the positive half-cycle signal r ave / max+ , the ratio of the average value to the peak value of the negative half-cycle signal r ave / max- Normalization is performed, if r ave / max+ ≥0.8 and r ave / max- ≥0.8, indicating that the probability of suspended discharge is the highest; if r ave / max+ <0.8 or r ave / max- <0.8, indicating that the probability of tip discharge or solid insulation discharge is the highest.

4. The method for identifying partial discharge types based on two-dimensional statistical spectrogram according to claim 3, characterized in that: The peak value is the average of the first three maximum values.

5. The method for identifying partial discharge types based on two-dimensional statistical spectrogram according to claim 1, characterized in that: In S4, based on q max -φ two-dimensional statistical spectrum, sort the positive and negative half-cycle signals according to the amplitude size, and calculate the slope. + , the signal amplitude slope of the negative half cycle - Normalize the slope. + <0.1 and slope - <0.1, indicating that the signal amplitude changes little and the probability of suspended discharge is the highest; if slope + ≥0.1 or slope - ≥0.1, indicating that the signal amplitude changes greatly, and the probability of tip discharge or solid insulation discharge is the highest.

6. The method for identifying partial discharge types based on two-dimensional statistical spectrogram according to claim 1, characterized in that: In S5, based on q max -φ two-dimensional statistical spectrum, calculate the similarity s of positive and negative half-cycle spectra q , if s q >0.7, the probability of suspended discharge or solid insulation discharge is the highest; if s q ≤0.7, the probability of tip discharge is the highest.

7. The method for identifying partial discharge types based on two-dimensional statistical spectrogram according to claim 1, characterized in that: In S6, based on the n-φ two-dimensional statistical spectrum, the ratio of the number of positive and negative half-cycle discharge signals is calculated, and the positive and negative half-cycle ratio n is taken. + / - , the ratio of negative and positive half cycles n - / + The maximum value among them is taken as the discharge number ratio n r , and for n r Normalization is performed, if n r <0.2, the probability of tip discharge is the largest, if n r ≥0.2, the probability of suspended discharge or solid insulation discharge is the highest.

8. A partial discharge type identification system based on two-dimensional statistical spectrogram, characterized in that: include: A two-dimensional statistical spectrum acquisition module is used to acquire a two-dimensional statistical spectrum of partial discharge of power equipment; A power frequency correlation judgment module is used to judge the most likely partial discharge type according to the power frequency correlation of the partial discharge signal in the two-dimensional statistical spectrum; An amplitude fluctuation judgment module is used to judge the most likely partial discharge type according to the amplitude fluctuation degree and change degree of the partial discharge signal in the two-dimensional statistical spectrum. An amplitude change judgment module is used to judge the most likely partial discharge type according to the amplitude change degree of the partial discharge signal in the two-dimensional statistical spectrum; A symmetry judgment module is used to judge the most likely partial discharge type according to the symmetry of the positive and negative half-cycle discharge amplitudes of the partial discharge signal in the two-dimensional statistical spectrum; A difference judgment module is used to judge the most likely partial discharge type according to the difference in the positive and negative half-cycle discharge times of the partial discharge signal in the two-dimensional statistical spectrum; The comprehensive judgment module is used to take the partial discharge type with the largest comprehensive probability as the identification result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for identifying partial discharge types based on a two-dimensional statistical spectrum as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for identifying partial discharge types based on a two-dimensional statistical spectrum as claimed in any one of claims 1 to 7 are implemented.

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