Partial discharge frequency detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a partial discharge frequency detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring current partial discharge data and partial discharge signal sequence data of power equipment; based on the current partial discharge data, generating a plurality of partial discharge phase distribution spectrograms in sequence according to a preset interval time; based on the partial discharge signal sequence data, analyzing offset conditions of discharge sequences corresponding to the plurality of partial discharge phase distribution spectrograms, and determining a first partial discharge phase distribution spectrogram and a second partial discharge phase distribution spectrogram; and obtaining the partial discharge frequency of the power equipment based on the first partial discharge phase distribution spectrogram and the second partial discharge phase distribution spectrogram. According to the invention, the technical problem of large error of the detection result of the partial discharge frequency in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of power systems and equipment detection, and in particular to a method, device, electronic equipment and storage medium for detecting a partial discharge frequency. Background Art

[0002] In modern power systems, power equipment relies on the integrity of its insulation system to ensure safe and reliable operation. Due to the long-term operation of power equipment and the influence of environmental factors, the insulating materials in the insulation system may be damaged by aging, pollution and mechanical stress, resulting in insulation defects and subsequent partial discharge. Partial discharge will generate high-temperature and high-voltage arc phenomena through discharge channels such as gas foam, solid insulation defects or liquid contaminants, gradually damaging the insulation materials, thereby affecting the long-term operation safety of the equipment. Therefore, the detection and analysis of partial discharge is becoming increasingly important.

[0003] In the related art, the frequency detection of the collected field data is performed by detection software, which is easily affected by the field environment and causes data disorder, so that the error of partial discharge frequency detection using field data is large.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, electronic device and storage medium for detecting partial discharge frequency, so as to at least solve the technical problem that the detection result of partial discharge frequency in the related art has a large error.

[0006] According to one aspect of an embodiment of the present invention, a method for detecting a partial discharge frequency is provided, comprising: obtaining current partial discharge data and partial discharge signal sequence data of an electric device, wherein the partial discharge signal sequence data is obtained by performing a similarity analysis on historical partial discharge data, and the partial discharge signal sequence data comprises a phase reference line and an endpoint; based on the current partial discharge data, sequentially generating a plurality of partial discharge phase distribution spectra at preset intervals; based on the partial discharge signal sequence data, analyzing the offset of discharge sequences corresponding to the plurality of partial discharge phase distribution spectra, and determining a first partial discharge phase distribution spectrum and a second partial discharge phase distribution spectrum, wherein the second partial discharge phase distribution spectrum is obtained by offsetting the phase of the endpoint of the discharge sequence corresponding to the first partial discharge phase distribution spectrum based on a preset angle; based on the first partial discharge phase distribution spectrum and the second partial discharge phase distribution spectrum, obtaining the partial discharge frequency of the electric device.

[0007] Optionally, obtaining local discharge signal sequence data includes: obtaining initial historical local discharge data of the power equipment, wherein the initial local discharge data includes voltage, discharge amount and phase; based on a preset time, screening the initial historical local discharge data to obtain historical local discharge data; based on the historical local discharge data, generating multiple historical local discharge phase distribution spectra; performing similarity analysis on multiple historical local discharge phase distribution spectra to obtain local discharge signal sequence data.

[0008] Optionally, based on the historical local discharge data, multiple historical local discharge phase distribution spectra are generated, including: based on the historical local discharge data, multiple initial historical local discharge phase distribution spectra are generated; noise identification is performed on the multiple initial historical local discharge phase distribution spectra to determine the noise threshold; based on the noise threshold, the historical local discharge data is filtered to obtain filtered historical local discharge data; based on the filtered historical local discharge data, multiple historical local discharge phase distribution spectra are generated in sequence according to preset intervals.

[0009] Optionally, performing similarity analysis on multiple historical partial discharge phase distribution spectra to obtain partial discharge signal sequence data includes: performing similarity detection on multiple historical partial discharge phase distribution spectra to determine the partial discharge sequence; identifying the partial discharge sequence to determine the endpoint and phase reference line.

[0010] Optionally, based on the local discharge signal sequence data, the offset conditions of the discharge sequences corresponding to the multiple local discharge phase distribution spectra are analyzed to determine the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum, including: based on the local discharge signal sequence data, the offset conditions of the discharge sequences corresponding to the multiple local discharge phase distribution spectra are analyzed to determine the first local discharge phase distribution spectrum, wherein the phases of the endpoints of the discharge sequence corresponding to the first local discharge phase distribution spectrum are aligned with the phase reference line; after the phases of the endpoints of the discharge sequence corresponding to the first local discharge phase distribution spectrum are offset by a preset angle, the offset conditions of the discharge sequences corresponding to the multiple local discharge phase distribution spectra are analyzed to determine the second local discharge phase distribution spectrum, wherein the phases of the endpoints of the discharge sequence corresponding to the second local discharge phase distribution spectrum are aligned with the phase reference line.

[0011] Optionally, based on the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum, the local discharge frequency of the electrical equipment is obtained, including: determining a first time of the first local discharge phase distribution spectrum and a second time of the second local discharge phase distribution spectrum; based on the first time, the second time and a preset cycle time, calculating the local discharge frequency of the electrical equipment.

[0012] Optionally, the method further includes: generating a plurality of verified local discharge phase distribution spectra based on the local discharge frequency; determining the phases of the endpoints of the discharge sequence corresponding to two adjacent verified local discharge phase distribution spectra; calculating the phase offset rate through a preset offset algorithm; in response to the offset rate being less than or equal to a preset threshold, the local discharge frequency passes the verification.

[0013] According to another aspect of an embodiment of the present invention, there is also provided a detection device for partial discharge frequency, comprising: an acquisition module, for acquiring current partial discharge data and partial discharge signal sequence data of an electric device, wherein the partial discharge signal sequence data is obtained by performing similarity analysis on historical partial discharge data, and the partial discharge signal sequence data comprises a phase reference line and an endpoint; a generation module, for sequentially generating a plurality of partial discharge phase distribution spectra according to a preset interval time based on the current partial discharge data; an analysis module, for analyzing the offset of discharge sequences corresponding to the plurality of partial discharge phase distribution spectra based on the partial discharge signal sequence data, and determining a first partial discharge phase distribution spectrum and a second partial discharge phase distribution spectrum, wherein the second partial discharge phase distribution spectrum is obtained by offsetting the phase of the endpoint of the discharge sequence corresponding to the first partial discharge phase distribution spectrum based on a preset angle; and a calculation module, for obtaining the partial discharge frequency of the electric device based on the first partial discharge phase distribution spectrum and the second partial discharge phase distribution spectrum.

[0014] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method in each embodiment of the present invention is executed when the program is running.

[0015] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0016] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0017] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0018] According to another aspect of the embodiments of the present invention, a computer program is further provided. When the computer program is executed by a processor, the methods in the embodiments of the present invention are implemented.

[0019] In an embodiment of the present invention, current local discharge data and local discharge signal sequence data of the power equipment are obtained; based on the current local discharge data, multiple local discharge phase distribution spectra are sequentially generated according to preset intervals; based on the local discharge signal sequence data, the offset of the discharge sequence corresponding to the multiple local discharge phase distribution spectra is analyzed to determine the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum, wherein the second local discharge phase distribution spectrum is obtained by offsetting the phase of the end point of the discharge sequence corresponding to the first local discharge phase distribution spectrum based on a preset angle; based on the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum, the local discharge frequency of the power equipment is obtained. It is easy to notice that the method of calculating the local discharge frequency using the local discharge phase distribution spectrum is to identify the phase change of the local discharge by analyzing the offset of the discharge sequence corresponding to the multiple local discharge phase distribution spectra, so as to calculate the local discharge frequency using the phase offset, thereby achieving the purpose of accurately calculating the local discharge frequency, thereby achieving the technical effect of improving the accuracy of local discharge frequency detection, and thus solving the technical problem of large error in the detection result of the local discharge frequency in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0021] Figure 1 is a flow chart of a method for detecting partial discharge frequency according to an embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of an optional partial discharge phase distribution spectrum according to an embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of an optional partial discharge phase distribution spectrum according to an embodiment of the present invention;

[0024] Figure 4 is a schematic diagram of an optional partial discharge phase distribution spectrum according to an embodiment of the present invention;

[0025] Figure 5 is a schematic diagram of an optional partial discharge phase distribution spectrum according to an embodiment of the present invention;

[0026] Figure 6is a schematic diagram of an optional partial discharge phase distribution spectrum according to an embodiment of the present invention;

[0027] Figure 7 is a schematic diagram of an optional partial discharge phase distribution spectrum according to an embodiment of the present invention;

[0028] Figure 8 is a schematic diagram of a partial discharge frequency detection device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] According to an embodiment of the present invention, an embodiment of a method for detecting a partial discharge frequency is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] Figure 1 FIG. 1 is a flow chart of a method for detecting partial discharge frequency according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0033] Step S102, obtaining current partial discharge data and partial discharge signal sequence data of the power equipment.

[0034] The partial discharge signal sequence data is obtained by performing similarity analysis on historical partial discharge data, and the partial discharge signal sequence data includes a phase reference line and an endpoint.

[0035] The above-mentioned power equipment may refer to equipment used for power generation, transmission, distribution and consumption in the power system. The power equipment may be power generation equipment, power transmission equipment or power distribution equipment, and the content of the power equipment is not limited here.

[0036] In power equipment, such as transformers, power cables, or composite insulators, partial discharge phenomena can usually be detected. Partial discharge refers to the discharge caused by the electric field strength exceeding the breakdown field strength inside the solid medium or liquid medium of the insulating structure. In the case of partial discharge, a very small part of the inside of the insulating medium undergoes charge rearrangement or material decomposition, thereby generating a pulse current inside the medium or between the medium and other electrodes. Partial discharge can cause insulation aging, reduced electrical performance, increased temperature in local areas, and tiny heat sources can cause tiny carbonization of the insulating structure. For power equipment, the occurrence of partial discharge can easily cause the insulation performance of the power equipment to be reduced, and even cause power equipment failure, thereby affecting the normal operation of the power system. Partial discharge can be electrode discharge, suspended discharge, corona discharge, internal discharge of solid insulation, internal discharge of liquid insulation, liquid surface discharge, bubble discharge in liquid-immersed medium, solid insulation surface discharge, or impurity discharge, etc.

[0037] The above-mentioned current partial discharge data can be used to characterize the data of the partial discharge situation of the power equipment at the current moment. The current partial discharge data may include but is not limited to: voltage, discharge amount, current, discharge time and phase, etc. The content of the current partial discharge data is not limited here and can be determined as needed. By detecting the partial discharge data, it is easy to analyze the discharge frequency. The greater the partial discharge frequency, the smaller the partial discharge cycle, indicating that the more frequent the partial discharge, the greater the threat of the partial discharge phenomenon to the power equipment. Therefore, the current partial discharge data can be analyzed to detect the insulation defects of the power equipment early, prevent equipment failures, and avoid power system shutdowns caused by sudden equipment failures. It can also help evaluate the health of the insulation system, including the degree of degradation of the insulation material, the pollution situation, or whether there is mechanical stress damage. Analyzing the partial discharge data can also facilitate the determination of more effective maintenance and repair plans. In addition, long-term monitoring of partial discharge data can facilitate understanding the performance of the equipment under different operating conditions, such as how the behavior of partial discharge changes under the influence of high load, temperature changes or ambient humidity, thereby optimizing the design or operation strategy of the power equipment and improving the stability and reliability of the power system.

[0038] The above-mentioned partial discharge signal sequence data can be used to characterize the data with repetitiveness or regularity in the partial discharge of the power equipment. The partial discharge signal sequence data can be obtained by performing similarity analysis on the historical partial discharge data. The partial discharge signal sequence data can include but is not limited to the phase reference line and the endpoint. The content of the partial discharge signal sequence data is not limited here and can be determined as needed. The actual discharge defects in the power equipment can be found through the partial discharge signal sequence data. The partial discharge signal sequence data can provide necessary data for subsequent frequency calculation and offset analysis.

[0039] The endpoints may be the endpoints of a partial discharge signal sequence, such as a starting point or an ending point, so that the phase position of the discharge event can be determined, which is essential for determining the mode of partial discharge and the state of the power equipment. The partial discharge signal sequence may be a collection of discharge events recorded in chronological order over a period of time, and the discharge event may be a partial discharge, i.e., a discharge phenomenon in the insulation system of the power equipment when the electric field strength in a local area reaches a strength sufficient to cause discharge but insufficient to cause breakdown of the entire insulation system.

[0040] The role of the phase reference line is to provide a stable phase reference, which is convenient for comparing the phase position of partial discharge events in different phase distribution spectra. The phase value of the phase reference line can be 0°, 90°, 180° or 360°. There is no limit on the phase value of the phase reference line here, which can be determined as needed to ensure that the offset of the partial discharge event relative to the phase reference line is clearly visible. This also provides an important basis for frequency correction, discharge pattern recognition and trend analysis.

[0041] In an optional embodiment, the current partial discharge data can be obtained through a digital detection system. The digital detection system is connected to the power equipment. The digital detection system can be a partial discharge detector or a high-voltage partial discharge monitoring system. The digital detection system is not limited here and can be determined as needed. When the power equipment is running, the digital detection system can record partial discharge data and can be exported to a computer. In addition, the current partial discharge data can be further pre-processed by data processing software, and discharge events with similar characteristics can be identified by algorithms, and partial discharge signal sequence data can be recorded.

[0042] In another optional embodiment, the current partial discharge data can be obtained through wireless sensors. Wireless sensors are installed on power equipment, which can monitor the partial discharge phenomenon of power equipment in real time and transmit the obtained current partial discharge data to the cloud platform wirelessly. The cloud platform receives the raw data from the wireless sensors, uses big data and machine learning technology to perform data cleaning, feature extraction and pattern recognition, and automatically constructs partial discharge signal sequence data.

[0043] In another optional embodiment, similarity analysis can be performed on historical partial discharge data by artificial neural network to obtain partial discharge signal sequence data. The artificial neural network can be a convolutional neural network or a recurrent neural network, which is not limited here and can be determined as needed. The artificial neural network can identify partial discharge events with different characteristics, thereby constructing partial discharge signal sequence data.

[0044] Step S104: based on the current partial discharge data, a plurality of partial discharge phase distribution spectra are sequentially generated at preset intervals.

[0045] The above-mentioned partial discharge phase distribution spectrum (Phase Resolved Partial Discharge, referred to as PRPD spectrum) can be used to identify the distribution of partial discharge (PD) events in the phase of the power cycle. The partial discharge phase distribution spectrum can be used to analyze and visualize partial discharge events. Partial discharge usually occurs in the insulation system of power equipment. When the local electric field strength reaches a strength sufficient to cause discharge but not enough to cause the entire insulation system to break down, a partial discharge phenomenon will occur. The PRPD spectrum can record the frequency and intensity of these discharge events at different phase points of the power cycle, so as to facilitate the analysis of the characteristics of partial discharge. The PRPD spectrum is usually presented in the form of two-dimensional coordinates. The horizontal axis can represent the phase angle of the power cycle, and the horizontal axis generally ranges from 0° to 360°. The horizontal axis shows the phase position of the partial discharge event relative to the power frequency power supply voltage waveform. The vertical axis can represent the amount of discharge, which is usually measured in picocoulombs (pC). The discharge amount can represent the amount of energy released in each discharge event. The density of points can represent the number or probability of partial discharge events under a specific phase and discharge amount. The denser the points, the higher the frequency of partial discharge events in the area.

[0046] Since different types of partial discharges, such as corona discharge, suspended discharge, and surface discharge, show different phases and discharge distribution patterns in the PRPD spectrum, the types of insulation defects that may exist in power equipment can be identified by analyzing the PRPD spectrum. By monitoring the changes in partial discharge events over time, especially in continuous PRPD spectra, the trend of the state of the insulation system of power equipment can be evaluated, which is of great significance for predicting the health status and remaining service life of power equipment. In the case of frequency fluctuations in the power system, the distribution of discharge events in the PRPD spectrum will be affected by frequency changes. By analyzing the PRPD spectrum, the detection frequency can be corrected to ensure the accuracy of the measurement results.

[0047] In an optional embodiment, multiple partial discharge phase distribution spectra can be generated by software MATLAB. The start time can be selected, the interval time can be set, the PRPD spectrum duration can be set, and the noise threshold can be set. The current partial discharge data is read by software MATLAB, and multiple partial discharge phase distribution spectra starting from the preset start time and lasting for a preset duration are constructed through loop statements. Optionally, the preset start time can be 0s, 0.1s, etc., and the preset start time is not limited here and can be determined as needed. The preset duration can be 0.02s, 0.04s, etc., and the preset duration is not limited here and can be determined as needed.

[0048] In another optional embodiment, the current partial discharge data may be divided into multiple arrays according to preset time intervals, and then multiple partial discharge phase distribution spectra may be drawn manually based on the multiple arrays.

[0049] In another optional embodiment, a plurality of partial discharge phase distribution spectra can be generated according to the current partial discharge data at preset intervals through a deep learning model. The deep learning model can be a convolutional neural network, a recurrent neural network, or a long short-term memory network. The current partial discharge data and the preset interval can be input into the deep learning model, and the output of the deep learning model is a PRPD spectra corresponding to the preset interval, each spectra including the phase and discharge amount and a timestamp.

[0050] Step S106, based on the partial discharge signal sequence data, analyzing the offset of the discharge sequences corresponding to the plurality of partial discharge phase distribution spectra to determine a first partial discharge phase distribution spectrum and a second partial discharge phase distribution spectrum.

[0051] The second partial discharge phase distribution spectrum is obtained by shifting the phases of the end points of the discharge sequence corresponding to the first partial discharge phase distribution spectrum based on a preset angle.

[0052] The above-mentioned discharge sequence may refer to a series of local discharge events that occur continuously in phase order within a specific time window. These local discharge events occur at different phase points of the power cycle. In the PRPD spectrum, the discharge sequence may be a set of signal points within a specific phase range. The distribution and number of these signal points reflect the regularity and intensity of the local discharge event. Because different fault modes correspond to different discharge sequence characteristics. By analyzing the phase distribution and discharge amount of the discharge sequence in different PRPD spectra, different types of insulation defects or faults that may exist in the power equipment can be identified. The discharge sequence can also help evaluate the health status of the power equipment and the degree of deterioration of the fault. For example, if the intensity and frequency of the discharge sequence gradually increase over time, this may indicate that the insulation condition of the power equipment is deteriorating. The detection frequency can also be corrected by analyzing the phase offset of the discharge sequence in the PRPD spectrum to ensure the accuracy of the measurement results. In the PRPD spectrum, the discharge sequence is usually manifested as regular signal points, while the noise is manifested as randomly distributed points. By adjusting the noise threshold, the discharge sequence can be highlighted to improve the accuracy of the analysis.

[0053] The first partial discharge phase distribution spectrum diagram can be used to identify the distribution of partial discharge events in the phase of a power cycle. The first partial discharge phase distribution spectrum diagram can be used to analyze and visualize partial discharge events.

[0054] The second partial discharge phase distribution spectrum can be obtained by offsetting the phase of the end point of the discharge sequence corresponding to the first partial discharge phase distribution spectrum based on a preset angle. The second partial discharge phase distribution spectrum can be used to identify the distribution of partial discharge events in the power cycle phase. The second partial discharge phase distribution spectrum can be used to analyze and visualize partial discharge events. The preset angle can be 360°, which is not limited here and can be determined as needed.

[0055] In an optional embodiment, by analyzing the discharge sequences corresponding to a plurality of local discharge phase distribution spectra, the data corresponding to the discharge sequences are compared with the local discharge signal sequence data, thereby determining a first local discharge phase distribution spectrum and a second local discharge phase distribution spectrum. The data corresponding to the discharge sequence can be manually compared with the local discharge signal sequence data, or can be compared by hash value, or can also be compared by correlation analysis. When the data corresponding to the discharge sequence of the local discharge phase distribution spectrum is consistent with the local discharge signal sequence data, the local discharge phase distribution spectrum is determined to be the first local discharge phase distribution spectrum. Then, based on a preset angle, the phase of the endpoint of the discharge sequence corresponding to the first local discharge phase distribution spectrum is offset to determine the second local discharge phase distribution spectrum.

[0056] In another optional embodiment, the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum can be determined based on the spectrum analysis of the machine learning model. Features, such as the phase offset, offset rate, discharge amount, etc. of the discharge sequence, can be extracted from the PRPD spectrum as input to the machine learning model. The machine learning model can be a support vector machine, a random forest, a neural network, etc., and the machine learning model is not limited here and can be determined as needed. The machine learning model can identify the offset of the discharge sequence and predict the time required for the phase offset of the discharge sequence to a preset angle. The machine learning model can also determine the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum by predicting the time difference between the first PRPD spectrum and the second PRPD spectrum.

[0057] Step S108, obtaining the partial discharge frequency of the power equipment based on the first partial discharge phase distribution spectrum and the second partial discharge phase distribution spectrum.

[0058] The above-mentioned partial discharge frequency may refer to the frequency of repeated discharge events when partial discharge occurs in the insulation system of power equipment. The partial discharge frequency of power equipment is affected by many factors, including the balance of power generation and load, the dynamic response of the generator, the change of load, the frequency modulation control strategy, the grid structure and the equipment status. When the power system is operating normally, the partial discharge frequency remains stable, but when the grid is subject to a large disturbance, the active power imbalance of the system causes the active power flow distribution in the grid to change, causing the speed of the generator to change, thereby causing the fluctuation of the partial discharge frequency.

[0059] In an optional embodiment, a machine learning model can be used to identify the partial discharge frequency. The first PRPD spectrum and the second PRPD spectrum can be input into the machine learning model, so as to predict the partial discharge frequency using the machine learning model. The machine learning model can be a time series forest, a long short-term memory network, a convolutional neural network, etc. The machine learning model is not limited here and can be determined as needed.

[0060] In another optional embodiment, the timestamps of the first partial discharge phase distribution spectrum and the second partial discharge phase distribution spectrum may be recorded, and then the partial discharge frequency of the power equipment may be calculated using a frequency calculation formula.

[0061] In another optional embodiment, a statistical method or time series analysis technique, such as an autoregressive moving average model, can be used to analyze the periodic time series and identify the pattern and trend of frequency changes. Then, based on the periodic time series and the current phase offset, the partial discharge frequency of the power equipment is calculated in real time, which can quickly respond to frequency fluctuations and improve the accuracy of frequency measurement.

[0062] In an embodiment of the present invention, current local discharge data and local discharge signal sequence data of the power equipment are obtained; based on the current local discharge data, multiple local discharge phase distribution spectra are sequentially generated according to preset intervals; based on the local discharge signal sequence data, the offset of the discharge sequence corresponding to the multiple local discharge phase distribution spectra is analyzed to determine the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum, wherein the second local discharge phase distribution spectrum is obtained by offsetting the phase of the end point of the discharge sequence corresponding to the first local discharge phase distribution spectrum based on a preset angle; based on the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum, the local discharge frequency of the power equipment is obtained. It is easy to notice that the method of calculating the local discharge frequency using the local discharge phase distribution spectrum is to identify the phase change of the local discharge by analyzing the offset of the discharge sequence corresponding to the multiple local discharge phase distribution spectra, so as to calculate the local discharge frequency using the phase offset, thereby achieving the purpose of accurately calculating the local discharge frequency, thereby achieving the technical effect of improving the accuracy of local discharge frequency detection, and thus solving the technical problem of large error in the detection result of the local discharge frequency in the related art.

[0063] Optionally, obtaining local discharge signal sequence data includes: obtaining initial historical local discharge data of the power equipment, wherein the initial local discharge data includes voltage, discharge amount and phase; based on a preset time, screening the initial historical local discharge data to obtain historical local discharge data; based on the historical local discharge data, generating multiple historical local discharge phase distribution spectra; performing similarity analysis on multiple historical local discharge phase distribution spectra to obtain local discharge signal sequence data.

[0064] In an optional embodiment, the initial historical partial discharge data of the power equipment may be obtained through a digital detection system or a wireless sensor, etc. The initial partial discharge data may include but is not limited to voltage, discharge amount and phase, and the initial partial discharge data is not limited here and can be determined as needed.

[0065] Furthermore, the initial historical partial discharge data can be screened according to a preset start time, a preset interval time, a preset duration, etc., to obtain historical partial discharge data that can be used for analysis. By screening, invalid or abnormal data can be removed, which is suitable for preprocessing and quality control of partial discharge data of power equipment.

[0066] Then, multiple historical partial discharge phase distribution spectra can be generated through the software MATLAB. Alternatively, multiple historical partial discharge phase distribution spectra can be generated through a deep learning model. Thus, similarity analysis can be performed on multiple historical partial discharge phase distribution spectra to obtain partial discharge signal sequence data. Through similarity analysis, typical patterns of partial discharge can be identified, which is suitable for the identification and classification of partial discharge patterns of power equipment.

[0067] Optionally, based on the historical local discharge data, multiple historical local discharge phase distribution spectra are generated, including: based on the historical local discharge data, multiple initial historical local discharge phase distribution spectra are generated; noise identification is performed on the multiple initial historical local discharge phase distribution spectra to determine the noise threshold; based on the noise threshold, the historical local discharge data is filtered to obtain filtered historical local discharge data; based on the filtered historical local discharge data, multiple historical local discharge phase distribution spectra are generated in sequence according to preset intervals.

[0068] In an optional embodiment, multiple historical partial discharge phase distribution spectra can be generated by software MATLAB. Alternatively, multiple historical partial discharge phase distribution spectra can be generated by a deep learning model.

[0069] Furthermore, the operation and maintenance personnel can analyze multiple historical partial discharge phase distribution spectra to find partial discharge signals with discharge regularity and distinguish the partial discharge signals from gas partial discharges, and determine the noise threshold with the partial discharge signals. Alternatively, the noise detection algorithm can be used to identify the noise of multiple initial historical partial discharge phase distribution spectra to determine the noise threshold. Through noise identification, noise interference can be effectively removed, which is suitable for noise suppression and signal enhancement of partial discharge data of power equipment. Thus, the historical partial discharge data greater than the noise threshold can be screened out to generate multiple historical partial discharge phase distribution spectra. That is, multiple historical partial discharge phase distribution spectra can be generated by software MATLAB. Alternatively, the screened historical partial discharge data can be input into a deep learning model to generate multiple historical partial discharge phase distribution spectra by the deep learning model.

[0070] Optionally, performing similarity analysis on multiple historical partial discharge phase distribution spectra to obtain partial discharge signal sequence data includes: performing similarity detection on multiple historical partial discharge phase distribution spectra to determine the partial discharge sequence; identifying the partial discharge sequence to determine the endpoint and phase reference line.

[0071] In an optional embodiment, a dynamic time warping algorithm can be used to compare multiple historical partial discharge phase distribution spectra to determine the partial discharge sequence. The dynamic time warping algorithm can process the differences between the signals and identify the repeated partial discharge sequence through the similarity score. Alternatively, the similarity of different historical partial discharge phase distribution spectra can be compared through cosine similarity, Euclidean distance, etc., to determine the historical partial discharge phase distribution spectra with a similarity greater than a preset similarity, so as to obtain the partial discharge sequence corresponding to the historical partial discharge phase distribution spectra.

[0072] Furthermore, the endpoints can be detected by wavelet analysis. Alternatively, the operation and maintenance personnel can analyze the partial discharge sequence to determine the endpoints and phase reference lines. Alternatively, the endpoints and phase reference lines can be determined by a recognition algorithm. The partial discharge sequence can be input into the recognition algorithm, and the endpoints and phase reference lines can be identified by the recognition algorithm. The recognition algorithm can be a support vector machine, a decision tree, etc., and the recognition algorithm is not limited here and can be determined as needed.

[0073] This method can identify the partial discharge sequence and is suitable for the identification and classification of the partial discharge sequence of power equipment. By identifying the endpoints and phase reference lines, the phase of the partial discharge can be accurately aligned, which is suitable for the alignment and correction of the partial discharge phase of power equipment.

[0074] Optionally, based on the local discharge signal sequence data, the offset conditions of the discharge sequences corresponding to the multiple local discharge phase distribution spectra are analyzed to determine the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum, including: based on the local discharge signal sequence data, the offset conditions of the discharge sequences corresponding to the multiple local discharge phase distribution spectra are analyzed to determine the first local discharge phase distribution spectrum, wherein the phases of the endpoints of the discharge sequence corresponding to the first local discharge phase distribution spectrum are aligned with the phase reference line; after the phases of the endpoints of the discharge sequence corresponding to the first local discharge phase distribution spectrum are offset by a preset angle, the offset conditions of the discharge sequences corresponding to the multiple local discharge phase distribution spectra are analyzed to determine the second local discharge phase distribution spectrum, wherein the phases of the endpoints of the discharge sequence corresponding to the second local discharge phase distribution spectrum are aligned with the phase reference line.

[0075] In an optional embodiment, the first local discharge phase distribution spectrum can be determined by analyzing the discharge sequences corresponding to a plurality of local discharge phase distribution spectra and comparing the data corresponding to the discharge sequences with the local discharge signal sequence data. The data corresponding to the discharge sequence can be compared with the local discharge signal sequence data manually, or by hash value comparison, or by correlation analysis. When the phases of the endpoints of the discharge sequence of the local discharge phase distribution spectrum are aligned with the phase reference line, the local discharge phase distribution spectrum is determined to be the first local discharge phase distribution spectrum.

[0076] In another optional embodiment, the partial discharge signal sequence data and the plurality of partial discharge phase distribution spectra may also be input into a machine learning model. The first partial discharge phase distribution spectra are determined by machine learning model identification. The machine learning model may be a support vector machine, a random forest, a neural network, etc. The machine learning model is not limited here and may be determined as needed.

[0077] Furthermore, after the phase of the endpoint of the discharge sequence corresponding to the first local discharge phase distribution spectrum is offset by a preset angle, the discharge sequences corresponding to the multiple local discharge phase distribution spectra are analyzed, and the data corresponding to the discharge sequence is compared with the local discharge signal sequence data, so as to determine the second local discharge phase distribution spectrum. The data corresponding to the discharge sequence can be compared with the local discharge signal sequence data manually, or by hash value comparison, or by correlation analysis, to compare the data corresponding to the discharge sequence with the local discharge signal sequence data. When the phase of the endpoint of the discharge sequence of the local discharge phase distribution spectrum is aligned with the phase reference line, the local discharge phase distribution spectrum is determined to be the second local discharge phase distribution spectrum.

[0078] This method can accurately align the phase of partial discharge and improve the accuracy of partial discharge frequency detection. It is suitable for partial discharge phase alignment and frequency detection of power equipment. By shifting the phase and aligning it again, it can be used to calculate the stability of partial discharge frequency. It is suitable for verification and stability analysis of partial discharge frequency of power equipment.

[0079] Optionally, based on the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum, the local discharge frequency of the electrical equipment is obtained, including: determining a first time of the first local discharge phase distribution spectrum and a second time of the second local discharge phase distribution spectrum; based on the first time, the second time and a preset cycle time, calculating the local discharge frequency of the electrical equipment.

[0080] In an optional embodiment, the first time corresponding to the first PRPD spectrum and the second time corresponding to the second PRPD spectrum can be determined from the current partial discharge data through the spectrum title. Alternatively, the corresponding time can also be determined through the spectrum identifier. Then, the time difference or phase difference can be determined through the first time, the second time and the preset cycle time, so as to calculate the partial discharge frequency of the power equipment.

[0081] In another optional embodiment, the first time corresponding to the first PRPD spectrum may be extracted and the second time corresponding to the second PRPD spectrum may be determined through feature extraction.

[0082] When the discharge sequence endpoint corresponding to the first PRPD spectrum is shifted to the left to obtain the second PRPD spectrum, the partial discharge frequency can be calculated by the following formula:

[0083]

[0084] Where f is the partial discharge frequency, t 1 For the first time, t 2 is the second time, and n is the cycle.

[0085] When the discharge sequence endpoint corresponding to the first PRPD spectrum is shifted to the right to obtain the second PRPD spectrum, the partial discharge frequency can be calculated by the following formula:

[0086]

[0087] Where f is the partial discharge frequency, t 1 For the first time, t 2 is the second time, and n is the cycle.

[0088] This method can accurately measure the time interval of partial discharge, and is suitable for the measurement of the time interval and frequency calculation of partial discharge of power equipment. By calculating the frequency by the time interval and cycle time, a more accurate partial discharge frequency can be obtained, which is suitable for the precise calculation and data analysis of the partial discharge frequency of power equipment.

[0089] Optionally, the method further includes: generating a plurality of verified local discharge phase distribution spectra based on the local discharge frequency; determining the phases of the endpoints of the discharge sequence corresponding to two adjacent verified local discharge phase distribution spectra; calculating the phase offset rate through a preset offset algorithm; in response to the offset rate being less than or equal to a preset threshold, the local discharge frequency passes the verification.

[0090] In an optional embodiment, based on the partial discharge frequency, multiple time window continuous offset verification partial discharge phase distribution spectra can be generated using MATLAB, and the time window length can be set to the length of half a cycle or a cycle. Alternatively, multiple verification partial discharge phase distribution spectra can be generated based on the partial discharge frequency through a deep learning model.

[0091] Then, in each PRPD spectrum, identify and record the phase value of the endpoint of the discharge sequence. The offset rate can be calculated using the difference in the phase of the endpoints in adjacent spectra and the theoretical phase change of one cycle. Alternatively, the phase difference can be compared with the preset phase difference. The closer the two are, the closer the offset rate is to 0. When the calculated offset rates are less than or equal to the preset threshold, the calculated partial discharge frequency is accurate. The offset rate can be compared with the preset threshold by hash value, standard deviation, etc. This method can accurately calculate the phase offset and is suitable for the offset calculation and error analysis of the partial discharge phase of power equipment. The preset threshold can be 0.5%, 1%, etc. The preset threshold is not limited here and can be determined as needed.

[0092] This method can generate a spectrum for verification, improve the reliability and accuracy of partial discharge frequency detection, and is suitable for partial discharge frequency verification and fault warning of power equipment. By comparing the endpoint phase, the stability of the partial discharge frequency can be verified, which is suitable for partial discharge phase comparison and frequency verification of power equipment.

[0093] When the phase of the endpoint of the discharge sequence of the adjacent previous PRPD spectrum is >270° and the phase of the endpoint of the discharge sequence of the adjacent next PRPD spectrum is <90°, or the phase of the endpoint of the discharge sequence of the adjacent previous PRPD spectrum is <90° and the phase of the endpoint of the discharge sequence of the adjacent next PRPD spectrum is >270°, the offset rate is calculated by the following formula 1, otherwise, the offset rate is calculated by the following formula 2:

[0094]

[0095] Where r is the offset rate, is the phase of the discharge sequence endpoint of the adjacent previous PRPD spectrum, φ 2 is the phase of the discharge sequence endpoint of the adjacent next PRPD spectrum.

[0096] The technical solution proposed in this application is described below in combination with an optional application scenario. This application proposes a method for detecting partial discharge frequency.

[0097] The present application can use a digital system to measure the voltage, discharge amount and phase information of partial discharge on site. The phase of the partial discharge signal directly collected on site is distributed in the entire cycle, and the field data is disorganized. A phasor file can be generated based on the measured field data and a MATLAB compatible file can be exported. The exported file can be read using the software MATLAB to obtain a phase array, a discharge amount array and a discharge time array.

[0098] Furthermore, the starting time is selected as 0.1s, the interval time is set as 0s, the duration of the PRPD spectrum is set as 0.02s, and the noise threshold is set as 0pC. The software MATLAB is used to construct 9 consecutive PRPD spectra starting from 0.1s and lasting for 0.02s through a loop statement, as shown in the following figure: Figure 2 As shown, they are the PRPD spectrum of 0.1s-0.12s, the PRPD spectrum of 0.12s-0.14s, the PRPD spectrum of 0.14s-0.16s, the PRPD spectrum of 0.16s-0.18s, the PRPD spectrum of 0.18s-0.2s, the PRPD spectrum of 0.2s-0.22s, the PRPD spectrum of 0.22s-0.24s, the PRPD spectrum of 0.24s-0.26s, and the PRPD spectrum of 0.26s-0.28s, wherein the horizontal axis of the PRPD spectrum is the phase, and the vertical axis is the discharge amount.

[0099] Nine consecutive partial discharge PRPD spectra were analyzed to find the partial discharge signals in the PRPD spectra that were easy to distinguish from other partial discharges and had strong discharge regularity. Based on these partial discharge signals, the noise threshold was selected as 40pC to exclude other interfering discharge signals.

[0100] The 9 consecutive PRPD spectra constructed previously were redrawn using the MATLAB software according to the noise threshold of 40 pC, as shown in the figure. Figure 3 Analyze the similarity of each partial discharge PRPD spectrum, determine the similar partial discharge sequence in the PRPD spectrum, select the starting point or end point of the discharge sequence, and select a phase reference line. The phase of the reference line can be 0°, 90°, 180° or 360°. For example, the starting point of the discharge sequence that can be selected is Figure 3 The PRPD spectrum from 0.1s to 0.12s is marked by circles and vertical lines, and the phase of the reference line is selected as 90°.

[0101] The software MATLAB is used to construct a continuous partial discharge PRPD spectrum starting from 0s and lasting for 0.02s through a loop statement until a PRPD spectrum with the starting point of the discharge sequence aligned with the selected phase reference line is found, such as Figure 4As shown, the PRPD spectrum from 0.68s to 0.7s is selected, the starting point of the discharge sequence is marked by a circle and a vertical line, and the time for recording this PRPD spectrum is 0.68s.

[0102] Analyze multiple PRPD spectra and find the PRPD spectra whose starting or ending points of the selected discharge sequence are offset by 360° and then aligned with the phase reference line, such as Figure 5 As shown, the starting point of the selected discharge sequence is marked by a circle and a vertical line, which is Figure 5 The PRPD spectrum from 35.54s to 35.56s is recorded at 35.54s.

[0103] Subtract the time corresponding to the two selected PRPD spectra to obtain the time elapsed when the starting point of the discharge sequence is offset by 360°. In this embodiment, the duration of this time period is 34.86s. This duration is divided by 0.02s to obtain the number of cycles in this time period with 50Hz as the standard frequency, that is, 1743. This number of cycles minus 1 is the number of cycles corresponding to the real frequency, that is, 1742. The number of cycles corresponding to the real frequency, 1742, is divided by the duration of 34.86s in this time period to obtain the real frequency of 49.97Hz.

[0104] Use MATLAB software to construct 9 standard cycles with a start time of 20s and an interval of 50 cycles through loop statements, such as Figure 6 In addition, MATLAB software was used to construct 9 PRPD spectra with a start time of 20 seconds and an interval of 50 real cycles through loop statements, as shown in Figure 7 The phase of the starting point of the discharge sequence of each PRPD spectrum is recorded, and the phase deviation rate is calculated. The deviation rate after frequency correction is less than 1%, so the real frequency is obtained.

[0105] According to an embodiment of the present invention, an embodiment of a partial discharge frequency detection device is provided. It should be noted that the device can be used to execute the above-mentioned partial discharge frequency detection method. The specific implementation scheme and application scenario of this embodiment are the same as those of the above-mentioned embodiment and will not be repeated here.

[0106] Figure 8 is a schematic diagram of a partial discharge frequency detection device according to an embodiment of the present application, such as Figure 8 As shown, the device includes the following:

[0107] An acquisition module 80 is used to acquire current local discharge data and local discharge signal sequence data of the power equipment, wherein the local discharge signal sequence data is obtained by performing similarity analysis on historical local discharge data, and the local discharge signal sequence data includes a phase reference line and an endpoint; a generation module 82 is used to sequentially generate a plurality of local discharge phase distribution spectra according to a preset interval time based on the current local discharge data; an analysis module 84 is used to analyze the offset of the discharge sequences corresponding to the plurality of local discharge phase distribution spectra based on the local discharge signal sequence data, and determine a first local discharge phase distribution spectrum and a second local discharge phase distribution spectrum, wherein the second local discharge phase distribution spectrum is obtained by offsetting the phase of the endpoint of the discharge sequence corresponding to the first local discharge phase distribution spectrum based on a preset angle; a calculation module 88 is used to obtain the local discharge frequency of the power equipment based on the first local discharge phase distribution spectrum and the second local discharge phase distribution spectrum.

[0108] Optionally, the acquisition module is also used to obtain initial historical partial discharge data of the power equipment, wherein the initial partial discharge data includes voltage, discharge amount and phase; based on a preset time, the initial historical partial discharge data is screened to obtain historical partial discharge data; based on the historical partial discharge data, multiple historical partial discharge phase distribution spectra are generated; and similarity analysis is performed on multiple historical partial discharge phase distribution spectra to obtain partial discharge signal sequence data.

[0109] Optionally, the generation module is also used to generate multiple initial historical local discharge phase distribution spectra based on the historical local discharge data; perform noise identification on the multiple initial historical local discharge phase distribution spectra to determine the noise threshold; based on the noise threshold, filter the historical local discharge data to obtain filtered historical local discharge data; based on the filtered historical local discharge data, generate multiple historical local discharge phase distribution spectra in sequence according to preset intervals.

[0110] Optionally, the generating module is further used to perform similarity detection on a plurality of historical partial discharge phase distribution spectra to determine a partial discharge sequence; and to identify the partial discharge sequence to determine endpoints and phase reference lines.

[0111] Optionally, the analysis module is further used to analyze the offset of discharge sequences corresponding to multiple local discharge phase distribution spectra based on the local discharge signal sequence data, and determine a first local discharge phase distribution spectrum, wherein the phase of the endpoint of the discharge sequence corresponding to the first local discharge phase distribution spectrum is aligned with the phase reference line; after the phase of the endpoint of the discharge sequence corresponding to the first local discharge phase distribution spectrum is offset by a preset angle, the offset of the discharge sequence corresponding to the multiple local discharge phase distribution spectra is analyzed to determine a second local discharge phase distribution spectrum, wherein the phase of the endpoint of the discharge sequence corresponding to the second local discharge phase distribution spectrum is aligned with the phase reference line.

[0112] Optionally, the calculation module is further used to determine a first time of the first local discharge phase distribution spectrum and a second time of the second local discharge phase distribution spectrum; and calculate the local discharge frequency of the power equipment based on the first time, the second time and a preset cycle time.

[0113] Optionally, the device further includes a verification module. The verification module is used to generate multiple verified local discharge phase distribution spectra based on the local discharge frequency; determine the phases of the endpoints of the discharge sequence corresponding to two adjacent verified local discharge phase distribution spectra; calculate the phase offset rate through a preset offset algorithm; in response to the offset rate being less than or equal to a preset threshold, the local discharge frequency passes the verification.

[0114] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention when running.

[0115] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0116] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0117] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0118] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0119] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0120] 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.

[0121] 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.

[0122] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0123] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0124] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for detecting partial discharge frequency, characterized in that: include: Acquire current partial discharge data and partial discharge signal sequence data of the power equipment, wherein the partial discharge signal sequence data is obtained by performing similarity analysis on historical partial discharge data, and the partial discharge signal sequence data includes a phase reference line and an endpoint; Based on the current partial discharge data, sequentially generating a plurality of partial discharge phase distribution spectra at preset intervals; Based on the partial discharge signal sequence data, the offset of the discharge sequence corresponding to the multiple partial discharge phase distribution spectra is analyzed to determine a first partial discharge phase distribution spectrum and a second partial discharge phase distribution spectrum, wherein the second partial discharge phase distribution spectrum is obtained by offsetting the phase of the endpoint of the discharge sequence corresponding to the first partial discharge phase distribution spectrum based on a preset angle; Based on the first partial discharge phase distribution spectrum and the second partial discharge phase distribution spectrum, a partial discharge frequency of the electric power equipment is obtained.

2. The method according to claim 1, characterized in that Acquiring the partial discharge signal sequence data, comprising: Acquiring initial historical partial discharge data of the electric power equipment, wherein the initial partial discharge data includes voltage, discharge amount and phase; Based on a preset time, the initial historical partial discharge data is screened to obtain the historical partial discharge data; Based on the historical partial discharge data, generating a plurality of historical partial discharge phase distribution spectra; A similarity analysis is performed on the multiple historical partial discharge phase distribution spectra to obtain the partial discharge signal sequence data.

3. The method according to claim 2, characterized in that Based on the historical partial discharge data, a plurality of historical partial discharge phase distribution spectra are generated, including: Based on the historical partial discharge data, generating a plurality of initial historical partial discharge phase distribution spectra; Performing noise identification on the multiple initial historical partial discharge phase distribution spectra to determine a noise threshold; Based on the noise threshold, the historical partial discharge data is screened to obtain screened historical partial discharge data; Based on the filtered historical partial discharge data, the plurality of historical partial discharge phase distribution spectra are sequentially generated at preset intervals.

4. The method according to claim 2, characterized in that: Performing similarity analysis on the multiple historical partial discharge phase distribution spectra to obtain the partial discharge signal sequence data, including: Performing similarity detection on the multiple historical partial discharge phase distribution spectra to determine a partial discharge sequence; The partial discharge sequence is identified, and the endpoint and the phase reference line are determined.

5. The method according to claim 1, characterized in that: Based on the partial discharge signal sequence data, analyzing the offset of the discharge sequences corresponding to the multiple partial discharge phase distribution spectra to determine a first partial discharge phase distribution spectrum and a second partial discharge phase distribution spectrum, including: Based on the partial discharge signal sequence data, analyzing the offset of the discharge sequences corresponding to the multiple partial discharge phase distribution spectra to determine the first partial discharge phase distribution spectrum, wherein the phases of the endpoints of the discharge sequence corresponding to the first partial discharge phase distribution spectrum are aligned with the phase reference line; After the phase of the endpoint of the discharge sequence corresponding to the first local discharge phase distribution spectrum is shifted by a preset angle, the shift conditions of the discharge sequences corresponding to the multiple local discharge phase distribution spectra are analyzed to determine the second local discharge phase distribution spectrum, wherein the phase of the endpoint of the discharge sequence corresponding to the second local discharge phase distribution spectrum is aligned with the phase reference line.

6. The method according to claim 1, characterized in that Obtaining a partial discharge frequency of the power equipment based on the first partial discharge phase distribution spectrum and the second partial discharge phase distribution spectrum includes: Determine a first time of the first partial discharge phase distribution spectrum and a second time of the second partial discharge phase distribution spectrum; The partial discharge frequency of the electric equipment is calculated based on the first time, the second time and a preset cycle time.

7. The method according to any one of claims 1 to 6, characterized in that Also includes: Based on the partial discharge frequency, generating a plurality of partial discharge phase distribution spectra for verification; Determine the phases of the endpoints of the discharge sequences corresponding to two adjacent verified partial discharge phase distribution spectra; Calculating the phase shift rate by using a preset shift algorithm; In response to the offset rate being less than or equal to a preset threshold, the partial discharge frequency passes verification.

8. A partial discharge frequency detection device, characterized in that: include: An acquisition module, used for acquiring current partial discharge data and partial discharge signal sequence data of the power equipment, wherein the partial discharge signal sequence data is obtained by performing similarity analysis on historical partial discharge data, and the partial discharge signal sequence data includes a phase reference line and an endpoint; A generating module, configured to sequentially generate a plurality of partial discharge phase distribution spectra at preset intervals based on the current partial discharge data; an analysis module, configured to analyze the offset of the discharge sequences corresponding to the multiple partial discharge phase distribution spectra based on the partial discharge signal sequence data, and determine a first partial discharge phase distribution spectrum and a second partial discharge phase distribution spectrum, wherein the second partial discharge phase distribution spectrum is obtained by offsetting the phases of the endpoints of the discharge sequence corresponding to the first partial discharge phase distribution spectrum based on a preset angle; A calculation module is used to obtain the partial discharge frequency of the power equipment based on the first partial discharge phase distribution spectrum and the second partial discharge phase distribution spectrum.

9. An electronic device, characterized in that: include: A memory storing an executable program; A processor, configured to run the program, wherein the program executes the method according to any one of claims 1 to 6 when running.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

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