Partial discharge detection method based on phase and frequency synchronization

Abstract

The invention discloses a partial discharge detection method based on phase and frequency synchronization, and the method comprises the following steps: obtaining an acoustic signal in the operation of electrical equipment and an externally applied voltage waveform of the electrical equipment, generating an acoustic image spectrum, and adjusting the frequency of the externally applied voltage waveform to enable the frequency to be consistent with the reference frequency in the acoustic image spectrum; the externally applied voltage waveforms are matched and fused with the sound image atlas to obtain an integrated signal atlas; intercepting a complete cycle wave from a signal spectrum corresponding to each discharge source in the optimized integrated signal spectrum, and analyzing complete cycle data to obtain a PRPD spectrum; and comparing data information contained in the PRPD atlas with different types of insulation defects in a database, and determining which type of insulation defect the partial discharge belongs to and recording. According to the partial discharge detection method based on phase and frequency synchronization, the acoustic imaging and phase synchronization technology is combined, accurate partial discharge detection is achieved, the acoustic signals and the electrical signals can be synchronized, and the detection precision and reliability are greatly improved.

Description

Technical Field

[0001] The invention belongs to the technical field of power equipment fault detection and relates to a partial discharge detection method based on phase and frequency synchronization. Background Art

[0002] Partial discharge (PD) in power equipment is a key factor contributing to insulation material aging and equipment failure. Therefore, accurately detecting and locating PD sources is crucial for equipment condition monitoring and maintenance. Currently, acoustic imaging technology has been widely used to locate PD sources, capable of capturing acoustic signals in equipment in real time and visualizing the spatial distribution of PD sources. However, existing acoustic imaging and PRPD (Phase-Resolved Partial Discharge) spectrum analysis techniques do not consider the phase and frequency synchronization between acoustic signals and applied voltage signals, making data fusion difficult. Furthermore, PRPD spectrum mapping, which maps the amplitude of the PD signal to the phase information of the applied voltage, lacks a synchronization mechanism, making it difficult to provide sufficiently accurate information when combining electrical and acoustic signals.

[0003] In summary, there is a problem of lack of synchronization mechanism between phase and frequency between existing acoustic imaging technology and PRPD spectrum analysis technology, which leads to low accuracy and reliability of partial discharge detection. Summary of the Invention

[0004] The purpose of the present invention is to provide a partial discharge detection method based on phase and frequency synchronization, which solves the problem of lack of phase and frequency synchronization mechanism between acoustic imaging technology and PRPD spectrum analysis technology in the prior art, which further leads to low accuracy and reliability of partial discharge detection.

[0005] The technical solution adopted by the present invention is a partial discharge detection method based on phase and frequency synchronization, comprising the following steps: Step 1: Start the acoustic imaging device and perform a self-test. Step 2: Acquire the acoustic signal of the power equipment in operation and the external voltage waveform of the electrical equipment, generate an acoustic image spectrum based on the acoustic signal, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the acoustic image spectrum; Step 3: Match and fuse the external voltage waveform with the sound and image spectrum to obtain an integrated signal spectrum, and optimize the integrated signal spectrum; Step 4: Select the zero-crossing point as the initial phase point from the signal spectrum corresponding to each discharge source in the optimized integrated signal spectrum, intercept the complete power frequency cycle data respectively, and analyze the complete cycle data to obtain n groups of PRPD spectra; Step 5: Compare the data information contained in the n groups of PRPD maps with different types of insulation defects in the database to determine which type of insulation defect each partial discharge belongs to and record it.

[0006] The present invention is also characterized in that: The device self-test includes the function test of the microphone array and the function test of the signal processing module.

[0007] Step 2 includes the following steps: Step 2.1, using a microphone array to obtain acoustic signals from the power equipment during operation and to collect the voltage waveform applied to the electrical equipment; Step 2.2: Analyze the abnormal discharge points in the acoustic signal to determine n discharge sources, record the sound intensity characteristic information of each discharge source, and extract the corresponding phase and frequency information to obtain an acoustic image spectrum; Step 2.3: Extract the phase and frequency information of the external voltage waveform of the electrical equipment, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the audio-visual spectrum.

[0008] The microphone array is arranged according to the structure of the device to be tested and the location of the discharge source; Acoustic signals include the spatial distribution and intensity characteristic information of the sound source; The voltage waveform applied to the electrical equipment is collected using a voltage sensor.

[0009] The acoustic image spectrum is obtained by calculating the time difference of multiple microphone signals using beamforming technology or least squares method; the acoustic image spectrum includes the sound source intensity, phase and frequency information, and spatial distribution position of n discharge sources.

[0010] Step three includes the following steps: Step 3.1, collecting the delay information of the acoustic image spectrum and the adjusted external voltage waveform, and after calculation, performing a delay operation to make the timing of the two consistent, that is, the two are at the same phase reference point; Step 3.2, matching the acoustic image spectrum of each discharge source with the adjusted external voltage waveform so that the acoustic signal of each discharge source and the corresponding adjusted external voltage waveform are synchronized within the same phase period; Step 3.3: Process the acoustic signal of each discharge source and the applied voltage waveform using signal fusion technology to obtain an integrated signal spectrum; Step 3.4: Remove noise or interference signals from the integrated signal spectrum, and use filtering and signal enhancement techniques to improve the clarity and recognizability of the integrated signal spectrum.

[0011] The integrated signal spectrum shows the combined abnormal sound intensity distribution of electrical and acoustic signals at different discharge source locations.

[0012] Step 4 includes the following steps: Step 4.1. Select a zero-crossing point from the PRPD spectrum corresponding to each discharge source in the optimized integrated signal spectrum as the starting position. The position of the zero-crossing point is aligned with the phase of the voltage waveform and serves as the starting point for intercepting the complete cycle. Step 4.2, intercept the integrated signal spectrum from each zero-crossing point to obtain m complete power frequency cycle data; Step 4.3: By analyzing each set of intercepted complete cycle data, n sets of PRPD spectra containing partial discharge sound intensity information are generated.

[0013] Step five includes the following steps: Step 5.1, extracting the spectrum feature information from the PRPD spectrum respectively; the feature information includes the peak value, frequency component, and waveform shape of the discharge intensity; Step 5.2: Compare the extracted graph feature information with the pre-built defect database; Step 5.3: Determine the type of partial discharge based on the comparison results. If the extracted spectral feature information matches multiple defect types in the defect database, perform a comprehensive judgment based on the discharge source location and sound intensity information, select the most similar defect type, and finally record the detection results.

[0014] The beneficial effects of the present invention are: combining acoustic imaging and phase synchronization technology to achieve accurate partial discharge detection. The present invention can synchronize acoustic signals and electrical signals, greatly improving detection accuracy and reliability; at the same time, a database is used to compare different types of defect patterns to achieve efficient and automated defect type identification; the detection process is non-contact and highly safe, and is suitable for various types of power equipment, especially key equipment such as transformers, and can effectively improve the safety and reliability of equipment maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the partial discharge detection method based on phase and frequency synchronization of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] Partial discharge detection methods based on phase and frequency synchronization, such as Figure 1 As shown, the following steps are included: Step 1: Start the acoustic imaging device and perform a self-test. The device self-test includes the function test of the microphone array and the function test of the signal processing module; Step 2: Acquire the acoustic signal of the power equipment in operation and the external voltage waveform of the electrical equipment, generate an acoustic image spectrum based on the acoustic signal, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the acoustic image spectrum; Step 2.1, using a microphone array to obtain acoustic signals from the power equipment during operation and to collect the voltage waveform applied to the electrical equipment; The microphone array is arranged according to the structure of the device to be tested and the location of the discharge source. The acoustic signal includes the spatial distribution and intensity characteristics of the sound source. The voltage waveform applied to the electrical equipment is collected using a voltage sensor. Step 2.2: Analyze the abnormal discharge points in the acoustic signal to determine n discharge sources, record the sound intensity characteristic information of each discharge source, and extract the corresponding phase and frequency information to obtain an acoustic image spectrum; The acoustic image spectrum is obtained by using beamforming technology or least squares method through the time difference calculation of multiple microphone signals; the acoustic image spectrum includes the sound source intensity, phase and frequency information, and spatial distribution position of n discharge sources; Step 2.3, extracting the phase and frequency information of the voltage waveform applied to the electrical equipment, and adjusting the frequency of the voltage waveform applied to keep it consistent with the reference frequency in the audio-visual spectrum; Step 3: Match and fuse the external voltage waveform with the sound and image spectrum to obtain an integrated signal spectrum, and optimize the integrated signal spectrum; Step 3.1, collecting the delay information of the acoustic image spectrum and the adjusted external voltage waveform, and after calculation, performing a delay operation to make the timing of the two consistent, that is, the two are at the same phase reference point; Step 3.2, matching the acoustic image spectrum of each discharge source with the adjusted external voltage waveform so that the acoustic signal of each discharge source and the corresponding adjusted external voltage waveform are synchronized within the same phase period; Step 3.3: Process the acoustic signal of each discharge source and the applied voltage waveform using signal fusion technology to obtain an integrated signal spectrum; The integrated signal spectrum shows the comprehensive abnormal sound intensity distribution of electrical and acoustic signals at different discharge source locations; Step 3.4: Remove noise or interference signals from the integrated signal spectrum and use filtering and signal enhancement techniques to improve the clarity and recognizability of the integrated signal spectrum; Step 4: Select the zero-crossing point as the initial phase point from the signal spectrum corresponding to each discharge source in the optimized integrated signal spectrum, intercept the complete power frequency cycle data respectively, and analyze the complete cycle data to obtain n groups of PRPD spectra; Step 4.1. Select a zero-crossing point from the PRPD spectrum corresponding to each discharge source in the optimized integrated signal spectrum as the starting position. The position of the zero-crossing point is aligned with the phase of the voltage waveform and serves as the starting point for intercepting the complete cycle. Step 4.2, intercept the integrated signal spectrum from each zero-crossing point to obtain m complete power frequency cycle data; Step 4.3, by analyzing each set of intercepted complete cycle data, generate n sets of PRPD spectra containing partial discharge sound intensity information; Step 5: Compare the data information contained in the n groups of PRPD maps with different types of insulation defects in the database to determine which type of insulation defect each partial discharge belongs to and record it; Step 5.1, extracting the spectral feature information from the PRPD spectrum respectively; Characteristic information includes the peak value, frequency component, and waveform shape of the discharge intensity; Step 5.2: Compare the extracted graph feature information with the pre-built defect database; Step 5.3: Determine the type of partial discharge based on the comparison results. If the extracted spectral feature information matches multiple defect types in the defect database, perform a comprehensive judgment based on the discharge source location and sound intensity information, select the most similar defect type, and finally record the detection results.

[0018] Before performing partial discharge detection, the present invention first starts the acoustic imaging instrument, performs a self-test on the equipment to ensure that the acoustic imaging equipment is operating normally, and performs a functional test on the microphone array and signal processing module to ensure that they can accurately capture the acoustic signals of the transformer during operation. During the operation of the transformer, the present invention uses a microphone array arranged outside the transformer to obtain acoustic signals; the microphone array is reasonably arranged according to the structural characteristics of the transformer and the location of the discharge source to ensure that it can cover the location where partial discharge may occur; the microphone array receives the acoustic signals generated by the partial discharge source and uses a signal processing algorithm to accurately locate the locations of several partial discharge sources; for each discharge source, its spatial position, signal strength, frequency, phase and other information are recorded. The external voltage waveform of the transformer is collected by a high-precision voltage sensor, and then the phase information and frequency information in the voltage waveform are extracted by a signal processing method, and the frequency of the collected voltage signal is adjusted to make it consistent with the reference frequency in the acoustic image spectrum. Then, a delay operation is performed to ensure that the timing of the two is consistent, and the phase synchronization of the electrical signal and the acoustic signal is ensured.

[0019] The present invention integrates the adjusted applied voltage waveform with collected acoustic signature information. First, the acoustic signal delay and applied voltage delay information are collected. By adjusting the delays, the acoustic and electrical signals are aligned, maintaining synchronization within the same phase cycle and avoiding errors caused by phase asynchrony. The integrated signal spectrum is then optimized to ensure that the resulting spectrum accurately reflects the discharge intensity, phase variations, and frequency characteristics of different discharge sources.

[0020] The present invention selects the zero-crossing point of each discharge source from the integrated signal spectrum as the initial phase point. Starting from the initial phase point, m complete power frequency cycle data are intercepted; the data within each cycle represents the change in the acoustic field intensity generated by the local discharge source under different voltage phases. Using the intercepted power frequency cycle data, n groups of PRPD spectra are generated, with each group of PRPD spectra corresponding to a discharge source. The horizontal axis of the PRPD spectra represents the voltage phase, and the vertical axis represents the local discharge acoustic field intensity. The PRPD spectra display the intensity distribution of local discharges under different phases, which can reflect the discharge activity of the discharge source in the electrical signal cycle. In the present invention, the data information contained in the n groups of PRPD spectra is compared with a pre-built defect database. The database contains a variety of typical insulation defect types, such as corona discharge, suspended discharge, and insulation-related discharge, and provides corresponding PRPD spectrum features for each defect. By comparing the peak value, frequency component, waveform morphology and other characteristics of the sound intensity of the PRPD spectra, the system can automatically identify the specific discharge type present in the transformer and record information such as the type, discharge source location, intensity and phase of each local discharge into the system.

[0021] Example 1 This embodiment proposes a partial discharge detection method based on phase and frequency synchronization, including the following steps: Step 1: Start the acoustic imaging device and perform a self-test. Step 2: Acquire the acoustic signal of the power equipment in operation and the external voltage waveform of the electrical equipment, generate an acoustic image spectrum based on the acoustic signal, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the acoustic image spectrum; Step 3: Match and fuse the external voltage waveform with the sound and image spectrum to obtain an integrated signal spectrum, and optimize the integrated signal spectrum; Step 4: Select the zero-crossing point as the initial phase point from the signal spectrum corresponding to each discharge source in the optimized integrated signal spectrum, intercept the complete power frequency cycle data respectively, and analyze the complete cycle data to obtain two sets of PRPD spectra; Step 5: Compare the data information contained in the two sets of PRPD maps with different types of insulation defects in the database to determine which type of insulation defect each partial discharge belongs to and record it.

[0022] Example 2 This embodiment proposes a partial discharge detection method based on phase and frequency synchronization, including the following steps: Step 1: Start the acoustic imaging device and perform a self-test. The device self-test includes the function test of the microphone array and the function test of the signal processing module.

[0023] Step 2: Acquire the acoustic signal of the power equipment in operation and the external voltage waveform of the electrical equipment, generate an acoustic image spectrum based on the acoustic signal, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the acoustic image spectrum; Step 2.1, using a microphone array to obtain acoustic signals from the power equipment during operation and to collect the voltage waveform applied to the electrical equipment; Step 2.2: Analyze the abnormal discharge points in the acoustic signal to determine the three discharge sources, record the sound intensity characteristics of the discharge sources, and extract the corresponding phase and frequency information to obtain an acoustic image spectrum; Step 2.3, extracting the phase and frequency information of the voltage waveform applied to the electrical equipment, and adjusting the frequency of the voltage waveform applied to keep it consistent with the reference frequency in the audio-visual spectrum; Step 3: Match and fuse the external voltage waveform with the sound and image spectrum to obtain an integrated signal spectrum, and optimize the integrated signal spectrum; Step 4: Select the zero-crossing point as the initial phase point from the signal spectrum corresponding to each discharge source in the optimized integrated signal spectrum, intercept the complete power frequency cycle data respectively, and analyze the complete cycle data to obtain three sets of PRPD spectra; Step 5: Compare the data information contained in the three sets of PRPD maps with different types of insulation defects in the database to determine which type of insulation defect each partial discharge belongs to and record it.

[0024] Example 3 This embodiment proposes a partial discharge detection method based on phase and frequency synchronization, including the following steps: Step 1: Start the acoustic imaging device and perform a self-test. The device self-test includes the function test of the microphone array and the function test of the signal processing module.

[0025] Step 2: Acquire the acoustic signal of the power equipment in operation and the external voltage waveform of the electrical equipment, generate an acoustic image spectrum based on the acoustic signal, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the acoustic image spectrum; Step 2.1, using a microphone array to obtain acoustic signals from the power equipment during operation and to collect the voltage waveform applied to the electrical equipment; The microphone array is arranged according to the structure of the device to be tested and the location of the discharge source. The acoustic signal includes the spatial distribution and intensity characteristics of the sound source. The voltage waveform applied to the electrical equipment is collected using a voltage sensor. Step 2.2: Analyze the abnormal discharge points in the acoustic signal to determine the three discharge sources, record the sound intensity characteristics of the discharge sources, and extract the corresponding phase and frequency information to obtain an acoustic image spectrum; The acoustic image spectrum is obtained by using beamforming technology or least squares method through the time difference calculation of multiple microphone signals; the acoustic image spectrum includes the sound source intensity, phase and frequency information, and spatial distribution position of the three discharge sources; Step 2.3, extracting the phase and frequency information of the voltage waveform applied to the electrical equipment, and adjusting the frequency of the voltage waveform applied to keep it consistent with the reference frequency in the audio-visual spectrum; Step 3: Match and fuse the external voltage waveform with the sound and image spectrum to obtain an integrated signal spectrum, and optimize the integrated signal spectrum; Step 4: Select the zero-crossing point as the initial phase point from the signal spectrum corresponding to each discharge source in the optimized integrated signal spectrum, intercept the complete power frequency cycle data respectively, and analyze the complete cycle data to obtain three sets of PRPD spectra; Step 5: Compare the data information contained in the three sets of PRPD maps with different types of insulation defects in the database to determine which type of insulation defect each partial discharge belongs to and record it.

[0026] Example 4 This embodiment proposes a partial discharge detection method based on phase and frequency synchronization, including the following steps: Step 1: Start the acoustic imaging device and perform a self-test. Step 2: Acquire the acoustic signal of the power equipment in operation and the external voltage waveform of the electrical equipment, generate an acoustic image spectrum based on the acoustic signal, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the acoustic image spectrum; Step 3: Match and fuse the external voltage waveform with the sound and image spectrum to obtain an integrated signal spectrum, and optimize the integrated signal spectrum; Step 3.1, collecting the delay information of the acoustic image spectrum and the adjusted external voltage waveform, and after calculation, performing a delay operation to make the timing of the two consistent, that is, the two are at the same phase reference point; Step 3.2, matching the acoustic image spectrum of each discharge source with the adjusted external voltage waveform so that the acoustic signal of each discharge source and the corresponding adjusted external voltage waveform are synchronized within the same phase period; Step 3.3: Process the acoustic signal of each discharge source and the applied voltage waveform using signal fusion technology to obtain an integrated signal spectrum; The integrated signal spectrum shows the combined abnormal sound intensity distribution of electrical and acoustic signals at different discharge source locations.

[0027] Step 3.4: Remove noise or interference signals from the integrated signal spectrum and use filtering and signal enhancement techniques to improve the clarity and recognizability of the integrated signal spectrum; Step 4: Select the zero-crossing point as the initial phase point from the signal spectrum corresponding to each discharge source in the optimized integrated signal spectrum, intercept the complete power frequency cycle data respectively, and analyze the complete cycle data to obtain four sets of PRPD spectra; Step 5: Compare the data information contained in the four sets of PRPD maps with different types of insulation defects in the database to determine which type of insulation defect each partial discharge belongs to and record it.

[0028] Example 5 This embodiment proposes a partial discharge detection method based on phase and frequency synchronization, including the following steps: Step 1: Start the acoustic imaging device and perform a self-test. Step 2: Acquire the acoustic signal of the power equipment in operation and the external voltage waveform of the electrical equipment, generate an acoustic image spectrum based on the acoustic signal, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the acoustic image spectrum; Step 3: Match and fuse the external voltage waveform with the sound and image spectrum to obtain an integrated signal spectrum, and optimize the integrated signal spectrum; Step 4: Select the zero-crossing point as the initial phase point from the signal spectrum corresponding to each discharge source in the optimized integrated signal spectrum, intercept the complete power frequency cycle data respectively, and analyze the complete cycle data to obtain five groups of PRPD spectra; Step 4.1. Select a zero-crossing point from the PRPD spectrum corresponding to each discharge source in the optimized integrated signal spectrum as the starting position. The position of the zero-crossing point is aligned with the phase of the voltage waveform and serves as the starting point for intercepting the complete cycle. Step 4.2: intercept the integrated signal spectrum from each zero-crossing point to obtain six complete power frequency cycle data; Step 4.3: By analyzing each set of intercepted complete cycle data, five sets of PRPD spectra containing partial discharge sound intensity information are generated.

[0029] Step 5: Compare the data information contained in the five sets of PRPD maps with different types of insulation defects in the database to determine which type of insulation defect each partial discharge belongs to and record it.

[0030] Example 6 This embodiment proposes a partial discharge detection method based on phase and frequency synchronization, including the following steps: Step 1: Start the acoustic imaging device and perform a self-test. Step 2: Acquire the acoustic signal of the power equipment in operation and the external voltage waveform of the electrical equipment, generate an acoustic image spectrum based on the acoustic signal, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the acoustic image spectrum; Step 3: Match and fuse the external voltage waveform with the sound and image spectrum to obtain an integrated signal spectrum, and optimize the integrated signal spectrum; Step 4: Select the zero-crossing point as the initial phase point from the signal spectrum corresponding to each discharge source in the optimized integrated signal spectrum, intercept the complete power frequency cycle data respectively, and analyze the complete cycle data to obtain ten sets of PRPD spectra; Step 5: Compare the data information contained in the ten sets of PRPD maps with different types of insulation defects in the database to determine which type of insulation defect each partial discharge belongs to and record it; Step 5.1, extracting the spectral feature information from the PRPD spectrum respectively; The characteristic information includes the peak value, frequency component, and waveform shape of the discharge intensity; Step 5.2: Compare the extracted graph feature information with the pre-built defect database; Step 5.3: Determine the type of partial discharge based on the comparison results. If the extracted spectral feature information matches multiple defect types in the defect database, perform a comprehensive judgment based on the discharge source location and sound intensity information, select the most similar defect type, and finally record the detection results.

[0031] The partial discharge detection method based on phase and frequency synchronization overcomes the disconnection problem between acoustic signals and externally applied voltage signals in existing partial discharge detection by combining phase synchronization technology. By utilizing precisely synchronized acoustic imaging and PRPD spectra, efficient and accurate partial discharge detection and type identification can be achieved. This method can be widely used in the maintenance and fault diagnosis of power transformers, improving the reliability and safety of power equipment.

Claims

1. A partial discharge detection method based on phase and frequency synchronization, characterized in that: The following steps are involved: Step 1: Start the acoustic imaging device and perform a self-test. Step 2: Acquire the acoustic signal of the power equipment in operation and the external voltage waveform of the electrical equipment, generate an acoustic image spectrum based on the acoustic signal, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the acoustic image spectrum; Step 3: Match and fuse the external voltage waveform with the sound and image spectrum to obtain an integrated signal spectrum, and optimize the integrated signal spectrum; Step 4: Select the zero-crossing point as the initial phase point from the signal spectrum corresponding to each discharge source in the optimized integrated signal spectrum, intercept the complete power frequency cycle data respectively, and analyze the complete cycle data to obtain n groups of PRPD spectra; Step 5: Compare the data information contained in the n groups of PRPD maps with different types of insulation defects in the database to determine which type of insulation defect each partial discharge belongs to and record it.

2. The partial discharge detection method based on phase and frequency synchronization according to claim 1, characterized in that: The device self-test includes a function test of the microphone array and a function test of the signal processing module.

3. The partial discharge detection method based on phase and frequency synchronization according to claim 1, characterized in that: The second step comprises the following steps: Step 2.1, using a microphone array to obtain acoustic signals from the power equipment during operation and to collect the voltage waveform applied to the electrical equipment; Step 2.2: Analyze the abnormal discharge points in the acoustic signal to determine n discharge sources, record the sound intensity characteristic information of each discharge source, and extract the corresponding phase and frequency information to obtain an acoustic image spectrum; Step 2.3: Extract the phase and frequency information of the external voltage waveform of the electrical equipment, and adjust the frequency of the external voltage waveform to keep it consistent with the reference frequency in the audio-visual spectrum.

4. The partial discharge detection method based on phase and frequency synchronization according to claim 3, characterized in that: The microphone array is arranged according to the structure of the device to be detected and the location of the discharge source; The acoustic signal includes spatial distribution and intensity characteristic information of the sound source; The voltage waveform applied externally to the electrical equipment is acquired using a voltage sensor.

5. The partial discharge detection method based on phase and frequency synchronization according to claim 3, characterized in that: The acoustic image spectrum is obtained by using beamforming technology or least squares method through time difference calculation of multiple microphone signals; the acoustic image spectrum includes sound source intensity, phase and frequency information, and spatial distribution position of n discharge sources.

6. The partial discharge detection method based on phase and frequency synchronization according to claim 1, characterized in that: The step three comprises the following steps: Step 3.1, collecting the delay information of the acoustic image spectrum and the adjusted external voltage waveform, and after calculation, performing a delay operation to make the timing of the two consistent, that is, the two are at the same phase reference point; Step 3.2, matching the acoustic image spectrum of each discharge source with the adjusted external voltage waveform so that the acoustic signal of each discharge source and the corresponding adjusted external voltage waveform are synchronized within the same phase period; Step 3.3: Process the acoustic signal of each discharge source and the applied voltage waveform using signal fusion technology to obtain an integrated signal spectrum; Step 3.4: Remove noise or interference signals from the integrated signal spectrum, and use filtering and signal enhancement techniques to improve the clarity and recognizability of the integrated signal spectrum.

7. The partial discharge detection method based on phase and frequency synchronization according to claim 6, characterized in that: The integrated signal map shows the comprehensive abnormal sound intensity distribution of the electrical signal and the acoustic signal at different discharge source locations.

8. The partial discharge detection method based on phase and frequency synchronization according to claim 1, characterized in that: The step 4 includes the following steps: Step 4.

1. Select a zero-crossing point from the PRPD spectrum corresponding to each discharge source in the optimized integrated signal spectrum as the starting position. The position of the zero-crossing point is aligned with the phase of the voltage waveform and serves as the starting point for intercepting the complete cycle. Step 4.2, intercept the integrated signal spectrum from each zero-crossing point to obtain m complete power frequency cycle data; Step 4.3: By analyzing each set of intercepted complete cycle data, n sets of PRPD spectra containing partial discharge sound intensity information are generated.

9. The partial discharge detection method based on phase and frequency synchronization according to claim 1, characterized in that: The step five comprises the following steps: Step 5.1, extracting the spectral feature information from the PRPD spectrum respectively; The characteristic information includes the peak value, frequency component, and waveform shape of the discharge intensity; Step 5.2: Compare the extracted graph feature information with the pre-built defect database; Step 5.3: Determine the type of partial discharge based on the comparison results. If the extracted spectral feature information matches multiple defect types in the defect database, perform a comprehensive judgment based on the discharge source location and sound intensity information, select the most similar defect type, and finally record the detection results.

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