Partial discharge signal diagnosis method and equipment based on variable frequency division and narrowband phase fusion analysis

Abstract

The invention discloses a partial discharge signal diagnosis method and equipment based on variable frequency division and narrowband phase fusion analysis, which are used for diagnosing partial discharge signals in a power system. The method comprises the following steps: receiving a broadband electromagnetic wave signal on a broadband frequency band containing ultrahigh frequency, carrying out frequency division on the broadband electromagnetic wave signal to obtain a plurality of narrowband electromagnetic wave signals of different frequency bands, and carrying out frequency reduction on each narrowband high-frequency electromagnetic wave signal after frequency division to obtain a low-frequency electromagnetic wave signal corresponding to each frequency division frequency band; and performing partial discharge power frequency phase analysis on each low-frequency electromagnetic wave signal, and judging whether a noise signal exists in the low-frequency electromagnetic wave signal of each sub-frequency band according to a phase analysis result of each narrow-band low-frequency electromagnetic wave signal. And finally, processing the narrowband low-frequency electromagnetic wave signal which is judged to have no noise signal, so as to judge the related condition of the partial discharge signal in the broadband electromagnetic wave signal. The method has the advantages of low cost, low noise influence, high signal detection sensitivity and high reliability.

Description

Technical Field

[0001] The present invention belongs to the technical field of fault detection in power systems, and particularly relates to a diagnostic technique for partial discharge signals in power systems, especially a method for diagnosing partial discharge signals based on variable frequency division and narrowband phase fusion analysis and corresponding equipment. Background Art

[0002] As a core means for evaluating the insulation status of power equipment, partial discharge detection technology plays a crucial role in preventing equipment failures and extending service life. Partial discharge (PD, hereinafter referred to as partial discharge) is an early sign of insulation deterioration, and its cumulative effect can cause insulation breakdown, threatening the stable operation of the power system.

[0003] Existing partial discharge detection technologies include ultra-high frequency detection method (UHF), ultrasonic detection method (AE), infrared thermal imaging method, etc. The ultra-high frequency detection method (UHF) uses the 300 MHz - 3 GHz frequency band to collect electromagnetic wave signals, can detect weak discharges at the 10 pC level, and is suitable for on-line monitoring of enclosed equipment such as GIS and transformers. However, due to the shielding effect of the metal structure on electromagnetic waves, it has insufficient sensitivity to non-discharge defects (such as mechanical looseness). Moreover, when using traditional high-speed ADCs to collect raw data, problems such as high ADC cost, high data storage requirements, high data transmission costs, and high data calculation costs will be faced. For the process of collecting broadband UHF signals, a large amount of noise signals will be included, seriously affecting the measurement accuracy.

[0004] The ultrasonic detection method receives 20 - 200 kHz acoustic wave signals through piezoelectric sensors, has outstanding anti-electromagnetic interference characteristics, and is especially suitable for physical positioning of open equipment such as switch cabinets. However, it is sensitive to the attenuation of the acoustic wave propagation medium (such as oil-immersed environment) and is easily interfered by mechanical vibration noise. The infrared thermal imaging method identifies abnormal discharge areas through the temperature field distribution, has the advantage of non-contact detection, but is significantly affected by the equipment heat dissipation conditions and has insufficient quantitative analysis ability.

[0005] Multi-sensor fusion has become the mainstream direction for improving detection reliability. For example, the UHF-AE joint detection system can simultaneously obtain electromagnetic wave and acoustic wave signals, improving the defect identification accuracy of GIS equipment. Intelligent data processing technologies (such as improved EMD noise reduction algorithm, BP neural network positioning model) have significantly improved the signal analysis ability under complex working conditions. Summary of the Invention

[0006] (I) Technical Problems to be Solved Existing partial discharge ultra-high frequency signal detection will collect broadband signals and directly use the broadband signals for partial discharge analysis. The broadband signals will contain a large number of noise signals in different frequency bands, thus seriously affecting the monitoring sensitivity and accuracy of partial discharge signals.

[0007] The main technical problem to be solved by the present invention is the problems of high cost, insufficient detection sensitivity, and susceptibility to environmental noise signals when detecting partial discharges with ultra-high frequency electromagnetic signals.

[0008] (II) Technical Solution To solve the above technical problems, a first aspect of the present invention proposes a partial discharge signal diagnosis method for diagnosing partial discharge signals in a power system. The method includes the following steps: receiving a broadband electromagnetic wave signal in a broadband frequency band including ultra-high frequency; dividing the broadband electromagnetic wave signal to generate multiple narrowband high-frequency electromagnetic wave signals in different frequency bands; down-converting each of the divided narrowband high-frequency electromagnetic wave signals to generate multiple narrowband low-frequency electromagnetic wave signals; performing phase analysis on each of the narrowband low-frequency electromagnetic wave signals, and judging whether there are noise signals and partial discharge signals in each of the narrowband low-frequency electromagnetic wave signals according to the phase analysis results of each of the narrowband low-frequency electromagnetic wave signals; processing each of the narrowband low-frequency electromagnetic wave signals determined to have no noise signals to judge whether there is a partial discharge signal in the broadband electromagnetic wave signal.

[0009] According to a preferred embodiment of the present invention, the phase analysis refers to performing a correlation analysis on the phase of each narrowband low-frequency electromagnetic wave signal and the power frequency signal of the power system.

[0010] According to a preferred embodiment of the present invention, the step of judging whether there are noise signals and partial discharge signals in each of the narrowband low-frequency electromagnetic wave signals includes: analyzing the characteristics of the power frequency phase amplitude spectrum of each narrowband low-frequency electromagnetic wave signal, and determining whether there are noise signals and partial discharge signals according to the distribution of each phase amplitude spectrum characteristic in each narrowband low-frequency electromagnetic wave signal.

[0011] According to a preferred embodiment of the present invention, the step of determining whether there are noise signals and partial discharge signals according to the distribution of each phase amplitude spectrum characteristic in each narrowband low-frequency electromagnetic wave signal includes: analyzing the common phase amplitude spectrum characteristics and partial discharge power frequency phase amplitude spectrum characteristics in each narrowband low-frequency electromagnetic wave signal; when the amplitude of a certain narrowband low-frequency electromagnetic wave signal is greater than a first threshold, but the narrowband low-frequency electromagnetic wave signal does not have obvious partial discharge power frequency phase amplitude spectrum characteristics, it is determined that the narrowband low-frequency electromagnetic wave signal contains a noise signal; when the amplitude of a certain narrowband low-frequency electromagnetic wave signal is less than a second threshold, it is determined that the narrowband low-frequency electromagnetic wave signal has no noise signal and partial discharge signal, and the second threshold is less than the first threshold; when the amplitude of a certain narrowband low-frequency electromagnetic wave signal is greater than the first threshold, and the narrowband low-frequency electromagnetic wave signal has obvious partial discharge power frequency phase amplitude spectrum characteristics, it is determined that the narrowband low-frequency electromagnetic wave signal contains a partial discharge signal; when multiple narrowband low-frequency electromagnetic wave signals have obvious partial discharge power frequency phase amplitude spectrum characteristics, and the partial discharge power frequency phase amplitude spectrum characteristics are consistent, it is determined that there is a partial discharge signal in the broadband electromagnetic wave signal.

[0012] According to a preferred embodiment of the present invention, the step of determining whether there is a noise signal and a partial discharge signal according to the distribution of the characteristics of each phase amplitude spectrum in each narrowband low-frequency electromagnetic wave signal includes: establishing a noise feature judgment model based on machine learning, and training the noise feature judgment model with a training data set, where the training data set includes noise power frequency phase amplitude spectrum feature data; inputting the power frequency phase amplitude spectrum feature data in each narrowband low-frequency electromagnetic wave signal into the noise feature judgment model, calculating the probability that each narrowband low-frequency electromagnetic wave signal contains the noise power frequency phase amplitude spectrum feature, and determining whether there is a noise signal in each narrowband low-frequency electromagnetic wave signal according to this probability.

[0013] According to a preferred embodiment of the present invention, the step of processing each narrowband low-frequency electromagnetic wave signal determined to have no noise signal to determine whether there is a partial discharge signal in the broadband electromagnetic wave signal includes: establishing a partial discharge feature judgment model based on machine learning, and training the partial discharge feature judgment model with a training data set, where the training data set includes partial discharge power frequency phase amplitude spectrum feature data; inputting the power frequency phase amplitude spectrum feature data of each narrowband low-frequency electromagnetic wave signal determined to have no noise signal into the partial discharge feature judgment model, calculating the probability that each narrowband low-frequency electromagnetic wave signal contains the partial discharge power frequency phase amplitude spectrum feature, and determining whether there is a partial discharge signal in each narrowband low-frequency electromagnetic wave signal according to this probability.

[0014] According to a preferred embodiment of the present invention, the partial discharge power frequency phase amplitude spectrum feature includes a partial discharge type feature, and the partial discharge feature judgment model is further used to calculate the probability of the partial discharge power frequency phase amplitude spectrum feature of the partial discharge signals of each partial discharge type contained in each narrowband low-frequency electromagnetic wave signal.

[0015] A second aspect of the present invention provides a partial discharge signal diagnosis device for diagnosing partial discharge signals in a power system, including: a signal acquisition unit for receiving a broadband electromagnetic wave signal on a broadband frequency band including ultra-high frequency; a frequency division unit for frequency-dividing the broadband electromagnetic wave signal to obtain a plurality of narrowband high-frequency electromagnetic wave signals with different frequency bands; a frequency conversion unit for down-converting each of the frequency-divided narrowband high-frequency electromagnetic wave signals to generate a plurality of narrowband low-frequency electromagnetic wave signals; a noise analysis unit for respectively performing power frequency phase analysis on each of the frequency-divided and down-converted narrowband low-frequency electromagnetic wave signals, and determining whether there is a noise signal and a partial discharge signal in each of the narrowband low-frequency electromagnetic wave signals according to the phase analysis results of each narrowband low-frequency electromagnetic wave signal; a partial discharge diagnosis unit for processing each narrowband low-frequency electromagnetic wave signal with the amplitude of the noise signal less than a predetermined value according to the data analysis result of the noise analysis unit to determine whether there is a partial discharge signal in the broadband electromagnetic wave signal.

[0016] According to a preferred embodiment of the present invention, the noise analysis unit is used to perform a correlation analysis of the phase between each narrowband low-frequency electromagnetic wave signal after frequency division and frequency reduction and the power frequency signal of the power system.

[0017] According to a preferred embodiment of the present invention, the noise analysis unit is used to analyze the phase amplitude spectrum characteristics of each narrowband low-frequency electromagnetic wave signal based on the phase of the power frequency signal, and determine whether there are noise signals and partial discharge signals according to the distribution of each phase amplitude spectrum characteristic in each narrowband low-frequency electromagnetic wave signal.

[0018] According to a preferred embodiment of the present invention, the noise analysis unit is used for the common phase amplitude spectrum characteristics and partial discharge power frequency phase amplitude spectrum characteristics in each narrowband low-frequency electromagnetic wave signal, and: when the amplitude of a certain narrowband low-frequency electromagnetic wave signal is greater than the first threshold, but this narrowband low-frequency electromagnetic wave signal does not have obvious partial discharge power frequency phase amplitude spectrum characteristics, it is determined that this narrowband low-frequency electromagnetic wave signal contains a noise signal; when the amplitude of a certain narrowband low-frequency electromagnetic wave signal is less than the second threshold, it is determined that this narrowband low-frequency electromagnetic wave signal does not have noise signals and partial discharge signals, and the second threshold is less than the first threshold; when the amplitude of a certain narrowband low-frequency electromagnetic wave signal is greater than the first threshold, and this narrowband low-frequency electromagnetic wave signal has obvious partial discharge power frequency phase amplitude spectrum characteristics, it is determined that this narrowband low-frequency electromagnetic wave signal contains a partial discharge signal; when multiple narrowband low-frequency electromagnetic wave signals have obvious partial discharge power frequency phase amplitude spectrum characteristics, and the partial discharge power frequency phase amplitude spectrum characteristics are consistent, it is determined that there is a partial discharge signal in the broadband electromagnetic wave signal.

[0019] According to a preferred embodiment of the present invention, the noise analysis unit is used to: establish a noise feature judgment model based on machine learning, and train this noise feature judgment model with a training data set, and the training data set includes noise power frequency phase amplitude spectrum characteristic data; input the phase amplitude spectrum characteristic data in each narrowband low-frequency electromagnetic wave signal into the noise feature judgment model, calculate the probability that each narrowband low-frequency electromagnetic wave signal contains noise power frequency phase amplitude spectrum characteristics, and determine whether there are noise signals in each narrowband low-frequency electromagnetic wave signal according to this probability.

[0020] According to a preferred embodiment of the present invention, the partial discharge diagnosis unit is used to: establish a partial discharge feature judgment model based on machine learning, and train this partial discharge feature judgment model with a training data set, and the training data set includes partial discharge power frequency phase amplitude spectrum characteristic data; input the power frequency phase amplitude spectrum characteristic data of each narrowband low-frequency electromagnetic wave signal determined to have no noise signals into the partial discharge feature judgment model, calculate the probability that each narrowband low-frequency electromagnetic wave signal contains partial discharge power frequency phase amplitude spectrum characteristics, and determine whether there are partial discharge signals in each narrowband low-frequency electromagnetic wave signal according to this probability.

[0021] According to a preferred embodiment of the present invention, the partial discharge power frequency phase amplitude spectrum features include partial discharge type features, and the partial discharge feature judgment model is further used to calculate the probabilities of the partial discharge power frequency phase amplitude spectrum features of each partial discharge type contained in each narrowband low-frequency electromagnetic wave signal.

[0022] (III) Beneficial effects The partial discharge signal diagnosis method and device based on variable frequency division and narrowband phase fusion analysis proposed by the present invention have the advantages of low cost, little influence by noise, high detection sensitivity, and strong reliability. Brief description of the drawings

[0023] Figure 1 is the overall flowchart of the partial discharge signal diagnosis method based on variable frequency division and narrowband phase fusion analysis of the present invention.

[0024] Figure 2 is the step schematic diagram of the partial discharge signal diagnosis process based on variable frequency division and narrowband phase fusion analysis of an embodiment of the present invention.

[0025] Figures 3 to 12 is the PRPD spectrum of the phase analysis of the low-frequency electromagnetic wave signal after frequency division and variable frequency of another embodiment of the present invention.

[0026] Figure 13 is the functional unit structure diagram of the corresponding diagnosis device embodiment based on the above partial discharge signal diagnosis method of the present invention. Specific embodiments

[0027] The partial discharge signal is a periodic but non-stationary pulse signal, characterized by high signal amplitude and sparse occurrence, while the background noise is usually complex but uniformly distributed. Since the partial discharge signal is a broadband signal and has a strong phase correlation with the 50Hz power frequency signal, in the phase amplitude spectrum of each narrowband frequency division signal of the partial discharge signal, there is a strong correlation with the power frequency signal. However, the energy in different frequency bands is different, so the corresponding energy values will be different, but the phase distribution is relatively unified.

[0028] In the present invention, the performance of the signal in the phase amplitude spectrum is called "phase amplitude spectrum feature", and the common performance of different signals in the phase amplitude spectrum is called "common phase amplitude spectrum feature". Thus, the performance of the partial discharge signal in the phase amplitude spectrum is called "partial discharge phase amplitude spectrum feature", and the performance of the noise signal in the phase amplitude spectrum is called "noise phase amplitude spectrum feature". In particular, the "power frequency phase amplitude spectrum feature" refers to the phase amplitude spectrum feature of different signals based on the power frequency signal. Thus, in the present invention, the phase amplitude spectrum features of the partial discharge signal and the noise signal based on the power frequency signal phase are respectively called "partial discharge power frequency phase amplitude spectrum feature" and "noise power frequency phase amplitude spectrum feature".

[0029] The PRPD (Phase Resolved Partial Discharge) pattern is a typical phase-amplitude pattern feature of partial discharge signals. The following description of the embodiments of the present invention will be given by taking PRPD as an example.

[0030] Based on this, the present invention proposes a method for diagnosing partial discharge signals, which can effectively eliminate the influence of noise signals and quickly and accurately diagnose partial discharge signals in a power system. This method first uses a broadband electromagnetic wave sensing device to receive broadband electromagnetic wave signals in a broadband frequency band, and then performs subsequent operations. The broadband frequency band refers to a frequency band including ultra-high frequency (UHF), that is, usually a frequency band from 300 MHz to 3 GHz.

[0031] Figure 1 It is the overall flowchart of the partial discharge signal diagnosis method based on variable frequency division and narrowband phase fusion analysis of the present invention.

[0032] As Figure 1 shown, considering that using a traditional high-speed ADC to collect the original broadband electromagnetic wave signal will face problems such as high ADC cost, high data storage requirements, high data transmission cost, and high data calculation cost, the present invention also proposes to convert the ultra-high frequency signal into a relatively low-frequency signal through step S1 "frequency division" and step S2 "frequency conversion" for the convenience of data collection and analysis. For example, the frequency band from 1400 MHz to 1500 MHz after frequency division is converted into 0 MHz to 100 MHz through "frequency conversion". That is to say, the present invention performs frequency division and frequency down-conversion on the received broadband electromagnetic wave signal. For the convenience of description, the narrowband electromagnetic wave signals generated after frequency division and frequency down-conversion are collectively referred to as "narrowband low-frequency electromagnetic wave signals" in this article. The present invention does not limit the specific frequency band of the narrowband low-frequency electromagnetic wave signal, but preferably it is from 0 MHz to 100 MHz.

[0033] Moreover, it is worth noting that the present invention takes into account the prominent role of the PRPD pattern feature based on the power frequency phase in the judgment of partial discharge and noise signals, so frequency division is performed before reducing the frequency to low-frequency electromagnetic wave signals, thereby generating multiple narrowband low-frequency electromagnetic wave signals with different frequency bands. Generally speaking, the number of frequency bands should be at least 4, preferably 5 to 16. The more the number of narrowband frequency bands after frequency division, the higher the computational complexity of subsequent processing, but the higher its accuracy.

[0034] Different from the prior art, the present invention proposes to perform phase analysis on each narrowband low-frequency electromagnetic wave signal after frequency division and frequency reduction, and determine whether there is a noise signal in each narrowband low-frequency electromagnetic wave signal according to the phase analysis results of different narrowband low-frequency electromagnetic wave signals (step S3). Here, the phase analysis preferably performs a correlation analysis on the phase of each narrowband low-frequency electromagnetic wave signal and the power frequency signal to generate a PRPD map based on the power frequency phase. More notably, different from the PRPD map of a single frequency band in the prior art, the present invention analyzes the characteristics of the PRPD map based on the power frequency phase of each narrowband low-frequency electromagnetic wave signal after frequency division and frequency reduction, and determines whether there is a noise signal according to the distribution of the characteristics of each PRPD map based on the power frequency phase in each narrowband low-frequency electromagnetic wave signal.

[0035] As a preferred embodiment of the present invention, when determining whether there is a noise signal, the common PRPD map characteristics based on the power frequency phase in each narrowband low-frequency electromagnetic wave signal can be analyzed first, and when it is determined that the narrowband low-frequency electromagnetic wave signal does not contain the common PRPD map characteristics based on the power frequency phase, or contains the PRPD map characteristics of other signals, it is determined that there is a noise signal in the frequency band of the narrowband low-frequency electromagnetic wave signal.

[0036] For example, when the amplitude of a certain narrowband low-frequency electromagnetic wave signal is greater than the first threshold, but the narrowband low-frequency electromagnetic wave signal does not have obvious PRPD map characteristics, it is determined that the narrowband low-frequency electromagnetic wave signal contains a noise signal. When the amplitude of a certain narrowband low-frequency electromagnetic wave signal is less than the second threshold (less than the first threshold), it is determined that the narrowband low-frequency electromagnetic wave signal does not have a noise signal and a partial discharge signal; when the amplitude of a certain narrowband low-frequency electromagnetic wave signal is greater than the first threshold, and the narrowband low-frequency electromagnetic wave signal has obvious PRPD map characteristics based on the power frequency, it is determined that the narrowband low-frequency electromagnetic wave signal contains a partial discharge signal; when multiple narrowband low-frequency electromagnetic wave signals have obvious PRPD map characteristics based on the power frequency, and the PRPD map characteristics based on the power frequency are consistent, it is determined that there is a partial discharge signal in the broadband electromagnetic wave signal.

[0037] As another more preferred embodiment, a machine learning method is adopted. This method needs to establish a noise feature judgment model and train the noise feature judgment model with a training data set. Thus, after the model training is completed, the PRPD map feature data in each narrowband low-frequency electromagnetic wave signal is input into the noise feature judgment model, and the model can calculate the probability that each narrowband low-frequency electromagnetic wave signal contains PRPD map characteristics related to the noise signal. Subsequently, the user can determine whether there is a noise signal according to this probability and certain regulations.

[0038] The training dataset usually contains a large amount of training data. The training dataset in the present invention should include PRPD pattern feature data related to noise signals. It should be noted that the present invention is not limited to PRPD pattern data, and any phase amplitude pattern that can reflect the phase related to the power frequency signal can achieve a similar effect.

[0039] By the above means, the present invention can finally judge the narrowband low-frequency electromagnetic wave signals with less noise signals as having no noise signals, and process the narrowband low-frequency electromagnetic wave signals judged as having no noise signals, so that the collected partial discharge signals contain very little noise signals, thereby diagnosing the received broadband electromagnetic wave signals to determine whether there is a partial discharge signal in the originally received broadband electromagnetic wave signals, or even determining the type of partial discharge signal. When diagnosing the partial discharge signal, the present invention preferably also uses a machine learning method to establish a partial discharge feature judgment model, calculate the probability of containing partial discharge features, or the probability of specific types of partial discharge features, in each narrowband low-frequency electromagnetic wave signal judged as having no noise signals (which may actually have less noise signals), and based on this, determine whether there is a partial discharge signal, or even the type of partial discharge signal.

[0040] The types of partial discharge include, for example, internal discharge, surface discharge, floating discharge, etc. Internal discharge occurs inside the solid insulation, such as at small air bubbles or impurities in the transformer winding insulation or cable insulation. Surface discharge occurs on the surface of the solid insulation, and local air or surface contamination between the electrodes will trigger the discharge. Floating discharge refers to the introduction of irregular discharge when the conductor insulation layer is not grounded or has a floating potential. Of course, the partial discharge signals can also be classified according to other classification rules, and they all fall within the protection scope of the present invention.

[0041] Similarly, the present invention needs to use a training dataset to train the partial discharge feature judgment model. The training dataset should include a large amount of feature data related to partial discharge signals of each narrowband low-frequency electromagnetic wave signal. Partial discharge features include time domain features, frequency domain features, time-frequency domain features, etc. Time domain features include signal amplitude, pulse width, repetition frequency (partial discharge frequency), time interval statistical features, etc. Frequency domain features include signal power spectral density, energy distribution in specific frequency bands, etc. Time-frequency domain features are, for example, time-frequency analysis methods based on wavelet transform, short-time Fourier transform (STFT) to extract the time-frequency distribution characteristics of the signal, and multi-band narrowband time domain partial discharge statistical features supported by the broadband power spectrum features of broadband frequency domain signals.

[0042] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further elaborates on the present invention in detail with reference to specific embodiments and the accompanying drawings.

[0043] Figure 2It is a schematic diagram of the steps of the partial discharge signal diagnosis process based on variable frequency division and narrowband phase fusion analysis in an embodiment of the present invention. As Figure 2 shown, in this embodiment, it is assumed that the broadband signal received by the broadband electromagnetic wave sensor is 300Mhz~2000Mhz, that is, the ultra-high frequency electromagnetic wave signal, which contains partial discharge signals and environmental interference signals. Then, frequency division and frequency conversion processing are performed on this signal. When performing frequency division, the 300~2000MHz broadband signal is divided into multiple narrowband high-frequency bands, such as being divided into 6 bands: 300~500MHz, 500~800 MHz, 800~1200 MHz, 1200~1500 MHz, 1500~1800 MHz, and 1800~2000 MHz. The signals of each narrowband high-frequency band after frequency division are then "frequency-converted" into low-frequency signals, which all become low-frequency signals of 0~100MHZ in this embodiment for subsequent processing. The narrowband low-frequency signals after frequency division and frequency conversion are respectively called narrowband A, narrowband B, narrowband C, narrowband D, narrowband E, and narrowband F in Figure 2 Then, the phase correlation analysis of each narrowband with the power frequency signal is carried out, and it is found that the correlation between narrowband C and narrowband E and the partial discharge signal is relatively low, so it is judged that there are relatively large noise signals in narrowband C and narrowband E. On the other hand, since it is assumed that there are also partial discharge signals in the broadband signal, this step may also analyze that the signals of narrowband A, narrowband B, narrowband D, and narrowband F have a strong correlation with the partial discharge signal.

[0044] After obtaining the above analysis results, narrowband partial discharge monitoring narrowband frequency selection is performed, that is, only the signals of narrowband A, narrowband B, narrowband D, and narrowband F, which are judged to have relatively few noise signals, are used for partial discharge signal diagnosis.

[0045] It should be noted that in other embodiments, the range of the frequency band, the number of frequency bands, and the interval of the frequency band can all be adjusted according to the actual power system.

[0046]

[0047] Figures 3 to 12 It is a PRPD pattern of phase analysis of the narrowband low-frequency electromagnetic wave signal after frequency division and frequency conversion in another embodiment of the present invention.

[0048] Figure 2 In this embodiment, compared with Figure 2The illustrated embodiment is similar, but when performing frequency division, a broadband signal in the range of 300 - 2000 MHz is divided into 8 high-frequency bands, namely 300 - 500 MHz, 500 - 700 MHz, 700 - 900 MHz, 900 - 1100 MHz, 1100 - 1300 MHz, 1300 - 1500 MHz, 1500 - 1800 MHz, and 1800 - 2000 MHz. Then, this embodiment performs phase analysis on each narrowband low-frequency electromagnetic wave signal, that is, analyzes by plotting a PRPD pattern based on the power frequency phase.

[0049] For comparison, Figure 3 shows the result of correlation analysis of the collected broadband electromagnetic wave signal in the range of 300 MHz - 2000 MHz with the power frequency signal (50 Hz) in the prior art. As Figure 3 shown, the collected broadband signal contains both partial discharge signals and interference signals. In the figure, a point represents the distribution of the signal intensity measured at a time point on the power frequency signal phase axis, where red represents the partial discharge signal, and yellow and green represent noise signals. Here, the intensity of the noise signal represented by the yellow dot is comparable to that of the partial discharge signal, which will seriously interfere with the distribution of the partial discharge signal in the figure and subsequent diagnostic analysis. Therefore, the existing conventional method is prone to misjudgment.

[0050] Figures 4 to 11 are the PRPD patterns of the low-frequency electromagnetic wave signals after frequency down-conversion of the original electromagnetic wave signals in the frequency bands of 300 - 500 MHz, 500 - 700 MHz, 700 - 900 MHz, 900 - 1100 MHz, 1100 - 1300 MHz, 1300 - 1500 MHz, 1500 - 1800 MHz, and 1800 - 2000 MHz respectively.

[0051] From the multiple frequency division patterns, it can be seen that since partial discharge is a broadband signal, partial discharge has a strong correlation with the power frequency signal (50 Hz sine signal) in each frequency division pattern. However, the energy in different frequency bands is different, so the corresponding energy amplitudes will be different, but the phase distribution is relatively unified. Figure 6 、 Figure 8 、 Figure 9 The PRPD data in the frequency bands of 700 - 900 MHz, 1100 - 1300 MHz, and 1300 - 1500 MHz shown do not show consistent correlations in other frequency division intervals. In this way, it can be judged that there are relatively large noise signals in this signal frequency band, so this frequency band should be avoided for measurement.

[0052] Figure 12 is the PRPD pattern after removing the frequency bands interfered by noise signals. It can be clearly determined from the figure that there are partial discharge signals.

[0053] Figure 13 This is the structural diagram of the functional units of the corresponding diagnostic device embodiment of the partial discharge signal diagnosis method of the present invention. As Figure 13 shown, the device includes a signal acquisition unit, a frequency division unit, a frequency conversion unit, a noise analysis unit, and a partial discharge diagnosis unit.

[0054] The signal acquisition unit is, for example, a broadband electromagnetic wave sensing device, which is used to receive broadband electromagnetic wave signals in a broadband frequency band including ultra-high frequency. For the acquired broadband electromagnetic wave signals, this unit can also perform preprocessing such as filtering and gain control.

[0055] As Figure 13 shown, the frequency division unit and the frequency conversion unit can work independently or be combined into a frequency division and frequency conversion unit 1. As mentioned above, the frequency division unit divides the broadband electromagnetic wave signals to generate multiple narrowband high-frequency electromagnetic wave signals in different frequency bands, and the frequency conversion unit down-converts the narrowband high-frequency electromagnetic wave signals in each frequency band after frequency division to generate multiple narrowband low-frequency electromagnetic wave signals.

[0056] The noise analysis unit and the partial discharge diagnosis unit can be implemented by devices with data processing capabilities respectively, such as MCU, CPU, DSP, or FPGA, etc., or can be implemented by a single device with digital signal processing capabilities, forming the main control unit 1. The noise analysis unit performs phase analysis on each narrowband low-frequency electromagnetic wave signal respectively, and judges whether there is a noise signal in each narrowband low-frequency electromagnetic wave signal according to the phase analysis results of each narrowband low-frequency electromagnetic wave signal, while the partial discharge diagnosis unit performs fusion processing on multiple low-frequency electromagnetic wave signals with less noise signals to judge whether there is a partial discharge signal in the broadband electromagnetic wave signals. In addition, the device may further include a frequency selection unit to select narrowband low-frequency electromagnetic wave signals with less noise signals for partial discharge diagnosis processing. The frequency selection unit can be controlled by the main control unit 2.

[0057] As Figure 13 shown, as a more preferred implementation manner, the diagnostic device of this embodiment further includes a signal generation unit. The signal generation unit is used to generate a simulated UHF high-frequency electromagnetic wave signal, and this simulated UHF high-frequency electromagnetic wave signal is used to perform active calibration on the diagnostic device.

[0058] For the steps performed by each functional unit, they have been described in detail in the previous embodiment of the partial discharge signal diagnosis method, and will not be elaborated here. In addition, other devices required to implement the diagnostic device, such as memories, power supplies, switches, circuits and circuit boards, enclosures, etc., can be designed and installed by those skilled in the art according to the actual situation, so they will not be described here either.

[0059] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A partial discharge signal diagnosis method based on variable frequency division and narrowband phase fusion analysis, which is used to diagnose partial discharge signals in a power system, is characterized in that The method includes the following steps: Receiving a broadband electromagnetic wave signal on a broadband frequency band including ultra-high frequency; Dividing the broadband electromagnetic wave signal to generate a plurality of narrowband high-frequency electromagnetic wave signals with different frequency bands; Down-converting each of the divided narrowband high-frequency electromagnetic wave signals to generate a plurality of narrowband low-frequency electromagnetic wave signals; Performing phase analysis on each of the narrowband low-frequency electromagnetic wave signals respectively, and judging whether there are noise signals and partial discharge signals in the narrowband low-frequency electromagnetic wave signals according to the phase analysis results of the narrowband low-frequency electromagnetic wave signals; Processing the narrowband low-frequency electromagnetic wave signals judged to have no noise signals to judge whether there is a partial discharge signal in the broadband electromagnetic wave signal.

2. The partial discharge signal diagnosis method based on variable frequency division and narrowband phase fusion analysis according to claim 1, characterized in that: The phase analysis refers to performing a correlation analysis on the phase between each narrowband low-frequency electromagnetic wave signal and the power frequency signal of the power system.

3. The partial discharge signal diagnosis method based on variable frequency division and narrowband phase fusion analysis according to claim 2, wherein The steps of judging whether there are noise signals and partial discharge signals in the narrowband low-frequency electromagnetic wave signals include: Analyzing the characteristics of the power frequency phase amplitude spectrum of each narrowband low-frequency electromagnetic wave signal, and determining whether there are noise signals and partial discharge signals according to the distribution of each phase amplitude spectrum characteristic in each narrowband low-frequency electromagnetic wave signal.

4. The partial discharge signal diagnosis method based on variable frequency division and narrowband phase fusion analysis according to claim 3, wherein The steps of determining whether there are noise signals and partial discharge signals according to the distribution of each phase amplitude spectrum characteristic in each narrowband low-frequency electromagnetic wave signal include: Analyzing the common phase amplitude spectrum characteristics and partial discharge power frequency phase amplitude spectrum characteristics in each narrowband low-frequency electromagnetic wave signal; When the amplitude of a certain narrowband low-frequency electromagnetic wave signal is greater than the first threshold, but the narrowband low-frequency electromagnetic wave signal does not have obvious partial discharge power frequency phase amplitude spectrum characteristics, it is determined that the narrowband low-frequency electromagnetic wave signal contains a noise signal; when the amplitude of a certain narrowband low-frequency electromagnetic wave signal is less than the second threshold, it is determined that the narrowband low-frequency electromagnetic wave signal has no noise signal and partial discharge signal, and the second threshold is less than the first threshold; when the amplitude of a certain narrowband low-frequency electromagnetic wave signal is greater than the first threshold, and the narrowband low-frequency electromagnetic wave signal has obvious partial discharge power frequency phase amplitude spectrum characteristics, it is determined that the narrowband low-frequency electromagnetic wave signal contains a partial discharge signal; When a plurality of narrowband low-frequency electromagnetic wave signals have obvious partial discharge power frequency phase amplitude spectrum characteristics, and the partial discharge power frequency phase amplitude spectrum characteristics are consistent, it is determined that there is a partial discharge signal in the broadband electromagnetic wave signal.

5. The partial discharge signal diagnosis method based on variable frequency division and narrowband phase fusion analysis according to claim 3, characterized in that The steps of determining whether there are noise signals and partial discharge signals according to the distribution of each phase amplitude spectrum characteristic in each narrowband low-frequency electromagnetic wave signal include: Establishing a noise characteristic judgment model based on machine learning, and training the noise characteristic judgment model with a training data set, the training data set including noise power frequency phase amplitude spectrum characteristic data; Inputting the power frequency phase amplitude spectrum characteristic data in each narrowband low-frequency electromagnetic wave signal into the noise characteristic judgment model, respectively calculating the probability that each narrowband low-frequency electromagnetic wave signal contains noise power frequency phase amplitude spectrum characteristics, and determining whether there is a noise signal in each narrowband low-frequency electromagnetic wave signal according to the probability.

6. The partial discharge signal diagnosis method based on variable frequency division and narrow-band phase fusion analysis according to claim 1, wherein the step of processing each narrow-band low-frequency electromagnetic wave signal determined to have no noise signal to determine whether there is a partial discharge signal in the broadband electromagnetic wave signal includes: establishing a partial discharge feature judgment model based on machine learning and training the partial discharge feature judgment model with a training data set, where the training data set includes partial discharge power frequency phase amplitude spectrum feature data; inputting the power frequency phase amplitude spectrum feature data of each narrow-band low-frequency electromagnetic wave signal determined to have no noise signal into the partial discharge feature judgment model, calculating the probability that each narrow-band low-frequency electromagnetic wave signal contains the partial discharge power frequency phase amplitude spectrum feature, and determining whether there is a partial discharge signal in each narrow-band low-frequency electromagnetic wave signal according to this probability.

7. The partial discharge signal diagnosis method based on variable frequency division and narrowband phase fusion analysis according to claim 6, characterized in that The partial discharge power frequency phase amplitude spectrum feature includes a partial discharge type feature, and the partial discharge feature judgment model is also used to calculate the probability of the partial discharge power frequency phase amplitude spectrum feature of the partial discharge signals of each partial discharge type contained in each narrow-band low-frequency electromagnetic wave signal.

8. A partial discharge signal diagnosis device based on variable frequency division and narrowband phase fusion analysis, used for diagnosing partial discharge signals in a power system, characterized in that, including: a signal acquisition unit for receiving broadband electromagnetic wave signals in a wide frequency band including ultra-high frequency; a frequency division unit for dividing the broadband electromagnetic wave signals to obtain a plurality of narrow-band high-frequency electromagnetic wave signals in different frequency bands; a frequency conversion unit for down-converting each narrow-band high-frequency electromagnetic wave signal after frequency division to generate a plurality of narrow-band low-frequency electromagnetic wave signals; a noise analysis unit for respectively performing power frequency phase analysis on each narrow-band low-frequency electromagnetic wave signal after frequency division and down-conversion, and judging whether there is a noise signal and a partial discharge signal in each narrow-band low-frequency electromagnetic wave signal according to the phase analysis results of each narrow-band low-frequency electromagnetic wave signal; a partial discharge diagnosis unit for processing each narrow-band low-frequency electromagnetic wave signal with an amplitude value of the noise signal less than a predetermined value according to the data analysis result of the noise analysis unit to judge whether there is a partial discharge signal in the broadband electromagnetic wave signal.

9. The partial discharge signal diagnosis device based on variable frequency division and narrowband phase fusion analysis according to claim 8, characterized in that: The noise analysis unit is used to perform a correlation analysis on the phase of each narrow-band low-frequency electromagnetic wave signal after frequency division and down-conversion with the power frequency signal of the power system.

10. The partial discharge signal diagnosis device based on variable frequency division and narrowband phase fusion analysis according to claim 9, characterized in that, The noise analysis unit is used to analyze the phase amplitude spectrum feature of each narrow-band low-frequency electromagnetic wave signal based on the phase of the power frequency signal, and determine whether there is a noise signal and a partial discharge signal according to the distribution of each phase amplitude spectrum feature in each narrow-band low-frequency electromagnetic wave signal.

11. The partial discharge signal diagnosis device based on variable frequency division and narrow-band phase fusion analysis according to claim 10, wherein the common phase amplitude spectrum feature and the partial discharge power frequency phase amplitude spectrum feature in each narrow-band low-frequency electromagnetic wave signal used by the noise analysis unit, and: When the amplitude of a narrowband low-frequency electromagnetic wave signal is greater than the first threshold, but the narrowband low-frequency electromagnetic wave signal does not have obvious partial discharge power frequency phase amplitude spectrum characteristics, it is determined that the narrowband low-frequency electromagnetic wave signal contains a noise signal; when the amplitude of a narrowband low-frequency electromagnetic wave signal is less than the second threshold, it is determined that the narrowband low-frequency electromagnetic wave signal does not have a noise signal and a partial discharge signal, and the second threshold is less than the first threshold; when the amplitude of a narrowband low-frequency electromagnetic wave signal is greater than the first threshold, and the narrowband low-frequency electromagnetic wave signal has obvious partial discharge power frequency phase amplitude spectrum characteristics, it is determined that the narrowband low-frequency electromagnetic wave signal contains a partial discharge signal; When multiple narrowband low-frequency electromagnetic wave signals have obvious partial discharge power frequency phase amplitude spectrum characteristics, and the partial discharge power frequency phase amplitude spectrum characteristics are consistent, it is determined that the broadband electromagnetic wave signal has a partial discharge signal.

12. The partial discharge signal diagnosis device based on variable frequency division and narrowband phase fusion analysis according to claim 10, characterized in that The noise analysis unit is used for: Establishing a noise feature judgment model based on machine learning, and training the noise feature judgment model with a training data set, where the training data set includes noise power frequency phase amplitude spectrum feature data; Inputting the phase amplitude spectrum feature data of each narrowband low-frequency electromagnetic wave signal into the noise feature judgment model, calculating the probability that each narrowband low-frequency electromagnetic wave signal contains noise power frequency phase amplitude spectrum characteristics, and determining whether there is a noise signal in each narrowband low-frequency electromagnetic wave signal according to the probability.

13. The partial discharge signal diagnosis device based on variable frequency division and narrowband phase fusion analysis according to claim 8, characterized in that The partial discharge diagnosis unit is used for: Establishing a partial discharge feature judgment model based on machine learning, and training the partial discharge feature judgment model with a training data set, where the training data set includes partial discharge power frequency phase amplitude spectrum feature data; Inputting the power frequency phase amplitude spectrum feature data of each narrowband low-frequency electromagnetic wave signal determined to have no noise signal into the partial discharge feature judgment model, calculating the probability that each narrowband low-frequency electromagnetic wave signal contains partial discharge power frequency phase amplitude spectrum characteristics, and determining whether there is a partial discharge signal in each narrowband low-frequency electromagnetic wave signal according to the probability.

14. The partial discharge signal diagnosis device based on variable frequency division and narrowband phase fusion analysis according to claim 13, characterized in that, The partial discharge power frequency phase amplitude spectrum characteristics include partial discharge type characteristics, and the partial discharge feature judgment model is also used for calculating the probability of the partial discharge power frequency phase amplitude spectrum characteristics of the partial discharge signals of each partial discharge type contained in each narrowband low-frequency electromagnetic wave signal.

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