Transformer partial discharge signal separation method and system

Abstract

The invention belongs to the technical field of electrical variable measurement processing, and particularly relates to a transformer partial discharge signal separation method and system, and the method comprises the steps: carrying out the synchronous sampling of a plurality of measurement points of a to-be-measured transformer at different moments, and obtaining a plurality of aliasing test signals; decomposing each aliasing test signal to obtain a plurality of independent source signals; calculating rising fluctuation parameters of each independent source signal at different moments, and determining a target aliasing test signal with a partial discharge signal; determining a feature vector of each independent source signal in the target aliasing test signals, and performing clustering processing on all the independent source signals in the target aliasing test signals to obtain a plurality of clusters; determining a density characteristic value of each cluster, and determining a target cluster corresponding to the internal partial discharge signal; and separating internal partial discharge signals in the target aliasing test signals according to the target cluster. According to the scheme provided by the invention, accurate separation of internal partial discharge signals is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of electric variable measurement and processing, and in particular relates to a transformer partial discharge signal separation method and system. Background Art

[0002] In the construction of substations, transformers are indispensable and important hub equipment, which play the role of voltage conversion and energy transmission. Their insulation performance directly affects the safe operation of the power system. During long-term operation, transformers will be affected by various factors, causing equipment failure or even damage, causing local discharge inside or on the surface of the insulating material, which is called partial discharge. It usually manifests as weak electromagnetic waves, sound waves or light signals. By conducting partial discharge tests on transformers, insulation defects inside the transformer can be effectively detected, transformer failure problems can be discovered in time, and effective maintenance measures can be implemented, thereby extending the service life of the equipment and ensuring the safe and stable operation of the power system.

[0003] When conducting partial discharge tests on transformers, due to the influence of external pulse interference on site such as the test power supply, leads and discharge of other equipment around the laboratory, the measurement results will show the aliasing of interference pulses, discharge pulses of external equipment of the transformer and discharge pulses inside the transformer. In the traditional separation scheme, only the distribution state of outliers after clustering is used to judge the abnormal discharge state and thus achieve the separation of partial discharge signals. However, in the traditional separation scheme, the outlier power supply state formed by external partial discharge cannot be distinguished from internal partial discharge, resulting in inaccurate separation results of partial discharge signals, making it difficult to achieve effective separation of internal partial discharge signals.

[0004] Therefore, the traditional separation scheme has the technical problem of being unable to distinguish between external partial discharge signals and internal partial discharge signals, resulting in low accuracy and reliability of the separation results. Summary of the invention

[0005] In order to solve the technical problem that the traditional separation scheme cannot distinguish between external partial discharge signals and internal partial discharge signals, resulting in low accuracy and reliability of separation results, the purpose of the present invention is to provide a transformer partial discharge signal separation method and system, and the technical scheme adopted is as follows: In one aspect, the present invention provides a method for separating partial discharge signals of a transformer, the method comprising: Performing synchronous sampling on multiple measurement points of the transformer to be tested at different times to obtain multiple aliasing test signals; Decomposing each aliasing test signal to obtain multiple independent source signals in each aliasing test signal; Calculate the rising fluctuation parameters of each independent source signal at different times, and determine the target aliasing test signal with partial discharge signal according to the rising fluctuation parameters of each independent source signal at different times in each aliasing test signal; Determine a characteristic vector of each independent source signal in the target aliasing test signal, and perform clustering processing on all independent source signals in the target aliasing test signal according to the characteristic vector to obtain a plurality of cluster clusters; Determine a density characteristic value of each cluster, and determine a target cluster corresponding to the internal partial discharge signal based on the density characteristic value; Based on the target cluster, the internal partial discharge signal in the target aliasing test signal is separated.

[0006] According to a transformer partial discharge signal separation method provided by the present invention, the rising fluctuation parameters of each independent source signal at different times are calculated, including: Obtaining the first amplitude rise time of each independent source signal at different times and the second amplitude rise time of each independent source signal in the aliasing test signal where each independent source signal is located at different times; Subtract the first amplitude rise time from each second amplitude rise time and calculate the absolute value, so as to obtain a plurality of rise time absolute difference values; The absolute differences of multiple rising time lengths are averaged to calculate the rising fluctuation parameters of each independent source signal at different times.

[0007] According to a transformer partial discharge signal separation method provided by the present invention, a target aliasing test signal containing a partial discharge signal is determined according to rising fluctuation parameters of each independent source signal at different times in each aliasing test signal, including: Compare the rising fluctuation parameters of each independent source signal in each aliasing test signal at different times with the preset fluctuation threshold to obtain a comparison result; The aliasing test signal whose comparison result satisfies a preset judgment condition is determined as a target aliasing test signal with a partial discharge signal; wherein the preset judgment condition is that there is at least one comparison result that satisfies the rising fluctuation parameter of the independent source signal being above a preset fluctuation threshold.

[0008] According to a transformer partial discharge signal separation method provided by the present invention, determining a characteristic vector of each independent source signal in the target aliasing test signal includes: Determine the energy concentration parameters of each independent source signal at different times; According to the rising fluctuation parameter of each independent source signal at different times and the energy concentration parameter of each independent source signal at different times, the attenuation index of each independent source signal at different times is calculated; The rising fluctuation parameters of each independent source signal at different times are averaged to obtain the rising fluctuation parameter mean of each independent source signal; The energy concentration parameters of each independent source signal at different times are averaged to obtain the energy concentration parameter mean of each independent source signal; The attenuation index of each independent source signal at different times is averaged to obtain the mean attenuation index of each independent source signal; A feature vector of each independent source signal in the target aliasing test signal is established according to a rising fluctuation parameter mean, an energy concentration parameter mean and an attenuation index mean of each independent source signal.

[0009] According to a transformer partial discharge signal separation method provided by the present invention, a target cluster corresponding to the internal partial discharge signal is determined according to the density characteristic value, including: The density feature value of each cluster is compared with the preset density feature threshold to obtain the comparison result; The cluster whose density characteristic value is higher than the preset density characteristic threshold value in the comparison result is determined as the target cluster corresponding to the internal partial discharge signal.

[0010] According to a transformer partial discharge signal separation method provided by the present invention, after separating the internal partial discharge signal in the target aliasing test signal according to the target clustering cluster, the method further includes: Determine the source location of the internal partial discharge signal; The separated internal partial discharge signal is marked with a region according to the signal source position.

[0011] According to a transformer partial discharge signal separation method provided by the present invention, determining the signal source position of the internal partial discharge signal includes: Determine the position coordinates of each measuring point, the signal detection value at the current moment, the initial value of the partial discharge signal, and the signal attenuation index at the current moment; The distance weight of each measuring point relative to the signal source position is calculated based on the signal detection value at the current moment, the initial value of the partial discharge signal and the signal attenuation index at the current moment; Establishing a plurality of distance weight ratio equations according to the distance weight and the position coordinates of each measuring point; The signal source position of the internal partial discharge signal is calculated by jointly solving the multiple distance weight ratio equations.

[0012] According to a transformer partial discharge signal separation method provided by the present invention, after the separated internal partial discharge signal is region-marked according to the signal source position, the method further comprises: Obtaining signal parameters of the separated internal partial discharge signal and operating parameters of the transformer to be tested; According to the signal parameters and the operating parameters, a discharge diagnosis is performed on the signal source position to obtain a discharge diagnosis result.

[0013] According to a transformer partial discharge signal separation method provided by the present invention, after obtaining the discharge diagnosis result, the method further includes: A discharge analysis report and maintenance suggestion information are generated based on the discharge diagnosis result and the signal source location.

[0014] On the other hand, the present invention also provides a transformer partial discharge signal separation system, the system comprising: A sampling module is used to synchronously sample multiple measurement points of the transformer to be tested at different times to obtain multiple aliasing test signals; A decomposition module, used for decomposing each aliasing test signal to obtain multiple independent source signals in each aliasing test signal; A calculation module, used for calculating the rising fluctuation parameters of each independent source signal at different times, and determining the target aliasing test signal with partial discharge signal according to the rising fluctuation parameters of each independent source signal at different times in each aliasing test signal; A clustering module, used to determine a characteristic vector of each independent source signal in the target aliasing test signal, and perform clustering processing on all independent source signals in the target aliasing test signal according to the characteristic vector to obtain a plurality of cluster clusters; A processing module, used to determine the density characteristic value of each cluster, and determine the target cluster corresponding to the internal partial discharge signal according to the density characteristic value; A separation module is used to separate the internal partial discharge signal in the target aliasing test signal according to the target cluster.

[0015] The present invention has the following beneficial effects: By decomposing each aliasing test signal, multiple independent source signals in each aliasing test signal are obtained, and then the rising fluctuation parameters of each independent source signal at different times are calculated. According to the rising fluctuation parameters of each independent source signal in each aliasing test signal at different times, the target aliasing test signal with partial discharge signal is determined, and then the characteristic vector of each independent source signal in the target aliasing test signal is determined, and according to the characteristic vector, all independent source signals in the target aliasing test signal are clustered to obtain multiple cluster clusters, and then the density characteristic value of each cluster cluster is determined, and according to the density characteristic value, the target cluster cluster corresponding to the internal partial discharge signal is determined, and finally, according to the target cluster cluster, the internal partial discharge signal in the target aliasing test signal is separated. Because the target aliasing test signal with partial discharge signal is located according to the rising fluctuation parameter before the clustering operation, and then clustering is performed according to the characteristic vector of each independent source signal, and finally the target clustering cluster corresponding to the internal partial discharge signal is determined according to the density eigenvalue, the external partial discharge signal and the internal partial discharge signal can be effectively distinguished in the clustering link, and the target clustering cluster where the internal partial discharge signal is located can be accurately located, thereby realizing the accurate separation of the internal partial discharge signal in the target aliasing test signal, and effectively improving the accuracy and reliability of the separation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A method flow chart of a transformer partial discharge signal separation method provided by one embodiment of the present invention; Figure 2 It is a schematic diagram of the location distribution of each measurement point; Figure 3 It is the spectrum image of the theoretical partial discharge signal; Figure 4 It is a spectrum image of the aliasing test signal of multi-source pulse aliasing actually detected; Figure 5 A system structure diagram of a transformer partial discharge signal separation system provided by one embodiment of the present invention; Reference numerals: 201, capacitor voltage divider grounding wire; 202, low voltage winding grounding wire; 203, high voltage winding neutral point grounding wire; 204, high voltage bushing end screen grounding wire; 310, sampling module; 320, decomposition module; 330, calculation module; 340, clustering module; 350, processing module; 360, separation module. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a transformer partial discharge signal separation method and system proposed by the present invention, its specific implementation, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] During the partial discharge test of the transformer, there are three main types of pulse sources: external intrusion pulses, external partial discharge pulses, and internal partial discharge pulses. That is to say, during the partial discharge test of the transformer, there will be aliasing of external intrusion noise signals, external partial discharge signals, and internal partial discharge signals, among which the external intrusion noise signal usually refers to the noise signal caused by interference from the test power supply, leads, and other equipment around the laboratory, the external partial discharge signal usually refers to the partial discharge signal caused by the discharge of the external equipment of the transformer, and the internal partial discharge signal usually refers to the partial discharge signal caused by the internal discharge of the transformer. Since the main purpose of this embodiment is to separate the partial discharge signal from the aliased test signal for subsequent fault diagnosis, the partial discharge signal mentioned in this embodiment mainly refers to the internal partial discharge signal.

[0021] Due to multi-source pulse aliasing, the traditional separation scheme separates the PD signals by directly using the distribution to judge the discharge state. However, in the separation process, since some internal PD signals and external PD signals have similar information in the time domain and frequency domain, they will be marked as outlier noise points during the clustering process and are difficult to effectively distinguish. Therefore, the traditional separation scheme has the problem of low accuracy and reliability of the separation results.

[0022] Based on this, the present invention provides a solution to the above problem. Considering the differences between internal and external partial discharge signals during transmission, the present invention continuously monitors the propagation characteristics from the generation position of different pulse signals to the measurement point, analyzes the clustered outlier signals, and further determines the area where partial discharge occurs by analyzing the attenuation change of the influence of the internal partial discharge signal on different measurement points. The detailed scheme of a transformer partial discharge signal separation method and system provided by the present invention is specifically described below in conjunction with the accompanying drawings.

[0023] See also Figure 1 , which shows a method flow chart of a transformer partial discharge signal separation method provided by an embodiment of the present invention, combined with the attached Figure 1 As shown, the transformer partial discharge signal separation method specifically includes: Step 110: synchronously sampling multiple measurement points of the transformer to be tested at different times to obtain multiple aliasing test signals.

[0024] like Figure 2 As shown, the transformer to be tested is arranged and wired, wherein A, B, and C represent the line terminals of the high-voltage winding, and a, b, and c represent the line terminals of the low-voltage winding. In this embodiment, the capacitor voltage divider grounding wire 201, the low-voltage winding grounding wire 202, the high-voltage winding neutral point grounding wire 203, and the high-voltage bushing end screen grounding wire 204 are selected as measurement points respectively to ensure that multiple measurement points can cover the entire transformer area. A high-frequency current sensor is arranged at each measurement point, and a multi-segment synchronous measurement technology is used to synchronously collect high-frequency pulse signals at the four measurement points, and the collection time of each high-frequency pulse signal is recorded.

[0025] In practical applications, it is necessary to filter the high-frequency pulse signals recorded at the four measurement points at the same time, remove the background noise interference in the high-frequency pulse signals, and perform time stamp alignment and normalization on each high-frequency pulse signal to ensure that the high-frequency pulse signals at the same time between different measurement points are comparable. After filtering, time stamp alignment and normalization, the high-frequency pulse signals recorded at the four measurement points at the same time can be combined into an aliasing test signal. The aliasing test signal can be specifically expressed as , where t represents the acquisition time, m represents the number of measurement points, represents the aliasing test signal obtained at time t, Represents the high-frequency pulse signal corresponding to the first measurement point at time t, Represents the high-frequency pulse signal corresponding to the second measurement point at time t, It represents the high-frequency pulse signal corresponding to the mth measurement point at time t. In practical applications, 1000 aliased test signals measured under different test voltages can be selected for partial discharge signal separation and analysis.

[0026] Step 120: Decompose each aliasing test signal to obtain multiple independent source signals in each aliasing test signal.

[0027] Figure 3 and Figure 4 The spectrum image of the theoretical partial discharge signal and the spectrum image of the aliasing test signal of multi-source pulse aliasing actually detected are shown as examples, wherein: Figure 3 and Figure 4 In the spectrum image shown, the horizontal axis is the sampling time, the unit is μs; the vertical axis is the signal voltage, the unit is mV. In practical applications, the collected aliasing test signals need to be separated to obtain the time-frequency performance characteristics of each separated signal. Since the internal partial discharge signal is usually directly emitted from a specific point or defect point inside the transformer, the signal transmission distance is short, and the sensor receives the signal in a short time, resulting in a faster rise in signal amplitude and a short rise time; while the external partial discharge signal will experience attenuation and interference of several components on the transformer during propagation, and the time it takes for the signal to reach the sensor will be extended. Therefore, this embodiment analyzes the time domain distribution characteristics of the signal by combining the time domain characteristics of the signal at different times at a measurement point.

[0028] Since the collected aliasing test signal contains data of multiple source components, and the data of each source component are independent of each other, the independent component analysis method can be applied to decompose the collected aliasing test signal. Specifically, the independent component analysis method can be used to decompose a set of aliasing test signals to obtain several separated signals, and each separated signal is standardized to obtain an independent source signal after the standardized processing.

[0029] Step 130: Calculate the rising fluctuation parameters of each independent source signal at different times, and determine the target aliasing test signal with the partial discharge signal according to the rising fluctuation parameters of each independent source signal in each aliasing test signal at different times.

[0030] It can be understood that since the main purpose of signal separation is to separate the internal partial discharge signal and then provide data support for the partial discharge status diagnosis of the transformer to be tested, the partial discharge signal mentioned in this embodiment mainly refers to the internal partial discharge signal generated inside the transformer.

[0031] Step 140: Determine the characteristic vector of each independent source signal in the target aliasing test signal, and perform clustering processing on all independent source signals in the target aliasing test signal according to the characteristic vector to obtain a plurality of clusters.

[0032] It can be understood that the characteristic vector of each independent source signal in this embodiment includes the rising fluctuation parameter, energy concentration parameter mean and attenuation index mean of each independent source signal, which can comprehensively characterize the propagation characteristics of each independent source signal from the generation location to the measurement point.

[0033] Step 150: Determine the density characteristic value of each cluster, and determine the target cluster corresponding to the internal partial discharge signal according to the density characteristic value.

[0034] It can be understood that since the internal partial discharge signal is concentrated in a smaller internal space of the transformer and has relatively consistent frequency and amplitude characteristics, the clustering result shows a high-density characteristic. Therefore, this embodiment can determine the target clustering cluster corresponding to the internal partial discharge signal through the density eigenvalue.

[0035] Step 160: Separate the internal partial discharge signal in the target aliasing test signal according to the target cluster.

[0036] The solution provided in this embodiment has made improvements to the clustering link. By continuously monitoring the propagation characteristics from the generation position of different pulse signals to the measurement point, the clustered outlier signals are analyzed, so as to accurately locate the target clustering cluster corresponding to the internal partial discharge signal, and then the internal partial discharge signal in the target aliasing test signal can be accurately separated based on the target clustering cluster, thereby improving the accuracy and reliability of the separation result.

[0037] In one embodiment, calculating the rising fluctuation parameter of each independent source signal at different times specifically includes: The first step is to obtain the first amplitude rise time of each independent source signal at different times and the second amplitude rise time of each independent source signal in the aliasing test signal where each independent source signal is located at different times.

[0038] In the second step, the first amplitude rise time is subtracted from each second amplitude rise time and the absolute value is calculated to obtain multiple absolute differences of the rise time.

[0039] The third step is to find the average of the absolute differences of multiple rising time lengths and calculate the rising fluctuation parameters of each independent source signal at different times.

[0040] It can be understood that the amplitude of the independent source signal can represent the signal strength of the signal source at the current moment. Since partial discharge is a process of releasing energy, the amplitude of the internal partial discharge signal will increase rapidly in a short time. The amplitude rise time of the current independent source signal is calculated, for example , It represents the duration of the process from the amplitude reaching 10% to the amplitude reaching 90% in the jth independent source signal, reflecting the speed of signal change. It represents the time taken for the amplitude of the j-th independent source signal to reach 90%, It represents the time taken for the amplitude of the jth independent source signal to reach 10%.

[0041] Internal partial discharge signals usually show large transient fluctuations and instability, and will increase rapidly in a short period of time, while external partial discharge signals last for a long time and have a slow signal change cycle. Therefore, there is often a large fluctuation between the rise time of the internal partial discharge signal and the external partial discharge signal. For the aliasing test signal at the same time, by calculating the volatility of the rise time of the amplitude of different independent source signals, the change speed of the independent source signal at different measurement points can be identified, and the rise fluctuation parameters of the current independent source signal can be analyzed.

[0042] In this embodiment, the rising fluctuation parameter of the jth independent source signal at time t can be expressed as follows: (1) in, represents the rising fluctuation parameter of the jth independent source signal at time t, It represents the duration of the first amplitude rise of the jth independent source signal at time t. It represents the second amplitude rise time of the rth independent source signal in the aliasing test signal where the jth independent source signal is located at time t, n Indicates the number of independent source signals in the aliasing test signal where the jth independent source signal is located.

[0043] It can be understood that the first amplitude rise time and the second amplitude rise time both refer to the time when the signal amplitude changes from 10% to 90%. The larger the absolute difference in the rise time, the faster the change speed of the current independent source signal. Since the relative change frequency of the internal partial discharge signal is relatively fast, when The larger the value of , the greater the possibility that the aliasing test signal contains an internal partial discharge signal.

[0044] In one embodiment, determining a target aliasing test signal having a partial discharge signal according to rising fluctuation parameters of each independent source signal at different times in each aliasing test signal specifically includes: Firstly, the rising fluctuation parameters of each independent source signal in each aliasing test signal at different times are compared with the preset fluctuation threshold to obtain a comparison result.

[0045] Then, the aliasing test signal whose comparison result meets the preset judgment condition is determined as the target aliasing test signal with a partial discharge signal; wherein the preset judgment condition is that there is at least one comparison result that satisfies the rising fluctuation parameter of the independent source signal above the preset fluctuation threshold.

[0046] In this embodiment, the preset fluctuation threshold can be taken as 0.5. When a rising fluctuation parameter of several independent source signals obtained after decomposition of a certain aliasing test signal is greater than or equal to the preset fluctuation threshold, it indicates that an internal partial discharge signal exists in the aliasing signal.

[0047] In one embodiment, determining the characteristic vector of each independent source signal in the target aliasing test signal specifically includes: The first step is to determine the energy concentration parameters of each independent source signal at different times.

[0048] Since different independent source signals present different signal characteristics in the frequency domain, spectrum characteristic analysis can be performed based on the frequency domain information of several independent source signals.

[0049] Specifically, the independent source signal is subjected to fast Fourier transform to obtain the spectrum data of the independent source signal. The specific formula is as follows: (2) in, represents the vibration energy of the i-th vibration frequency in the j-th independent source signal at time t; Represents the spectrum of the i-th vibration frequency in the j-th independent source signal at time t.

[0050] Since the source of external partial discharge signals is complex, the frequency components of the signals are wide and may contain both high-frequency and low-frequency information; while internal partial discharge signals come from partial discharges caused by equipment discharge defects, and the frequency components are related to the discharge type and medium, and are usually concentrated in a specific frequency band. Therefore, by performing spectrum feature analysis based on the frequency domain information of independent source signals, the energy concentration parameters of each independent source signal at different times can be calculated.

[0051] In this embodiment, the energy concentration parameter of the j-th independent source signal at time t can be expressed as follows: (3) in, represents the energy concentration parameter of the jth independent source signal at time t; nj represents the number of different vibration frequencies in the jth independent source signal; represents the vibration energy of the i-th vibration frequency in the j-th independent source signal at time t; It represents the vibration energy of the ith vibration frequency in the sth independent source signal except the jth independent source signal at the current measurement point at time t; Indicates the contribution of each vibration frequency in the jth independent source signal to the current independent source signal at time t; represents the energy and value of all vibration frequencies in the jth independent source signal at time t, It represents the energy and value of all vibration frequencies in all independent source signals at the current measurement point at time t. [ ] represents the normalization function, exp ( ) represents an exponential function with the natural constant e as the base.

[0052] In the second step, the attenuation index of each independent source signal at different times is calculated based on the rising fluctuation parameter of each independent source signal at different times and the energy concentration parameter of each independent source signal at different times.

[0053] It can be understood that the attenuation characteristics of independent source signals from different sources vary significantly in the signal level during the transmission process. When the discharge source is located inside the transformer (such as inside the winding, near the core, or in the oil-paper insulation part), the signal energy is significantly weakened, and the internal partial discharge signal will be attenuated and distorted to a large extent during the transmission process; while the external partial discharge signal is transmitted through external connections or air, the high-frequency components contained in it are less attenuated, and the signal waveform remains relatively complete. Therefore, the attenuation characteristics of the signal can be obtained by analyzing the law of signal changes. Specifically, the attenuation index of the jth independent source signal at time t can be expressed as follows: (4) in, represents the attenuation index of the jth independent source signal at time t; represents the rising fluctuation parameter of the jth independent source signal at time t; Indicated in The energy concentration parameter of the jth independent source signal detected at the moment; Indicated in The energy concentration parameter of the jth independent source signal detected at the moment; Indicates the distance between the reference time and the corresponding time The preset reference time is within five seconds after the target time. It represents the difference in the frequency domain characteristic factor of the j-th independent source signal before and after the reference time, reflecting the degree of change of the signal frequency domain characteristics over time; Indicates the attenuation of the signal as time changes; The signal attenuation becomes more and more obvious as the value of the current independent source signal continues to increase, indicating that the current independent source signal is more likely to be an internal partial discharge signal.

[0054] The third step is to average the rising fluctuation parameters of each independent source signal at different times to obtain the mean rising fluctuation parameter of each independent source signal.

[0055] The fourth step is to calculate the average of the energy concentration parameters of each independent source signal at different times to obtain the mean value of the energy concentration parameter of each independent source signal.

[0056] The fifth step is to average the attenuation index of each independent source signal at different times to obtain the mean attenuation index of each independent source signal.

[0057] In the sixth step, a characteristic vector of each independent source signal in the target aliasing test signal is established based on the rising fluctuation parameter mean, energy concentration parameter mean and attenuation index mean of each independent source signal.

[0058] Taking the jth independent source signal as an example, the feature vector can be constructed ,in, represents the energy concentration parameter mean of the jth independent source signal, represents the mean value of the rising fluctuation parameter of the jth independent source signal, Represents the decaying exponential mean of the j-th independent source signal.

[0059] It is understandable that the internal partial discharge signal has a higher pulse frequency and significant amplitude variation, and the variation period is relatively stable; while the external pulse noise has an irregular shape, a random variation period and spectrum distribution, and a uniform energy distribution. When the time domain and frequency domain information are used to directly cluster and analyze several independent source signals, since some internal partial discharge signals and external partial discharge signals have similar information in the time domain and frequency domain, they will be marked as outlier noise points during the clustering process and are difficult to effectively distinguish. However, the information variation stability of the internal partial discharge signal and the external partial discharge signal is different. Therefore, this embodiment combines the signal variation law of the independent source signal to construct a feature vector, and then performs weighted processing on the outlier noise points.

[0060] In practical applications, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm can be used to classify the feature vectors of independent source signals to obtain several clusters, each of which represents a class of pulse signals with similar characteristics.

[0061] In one embodiment, determining the target cluster corresponding to the internal partial discharge signal according to the density characteristic value specifically includes: First, the density feature value of each cluster is compared with the preset density feature threshold to obtain the comparison result.

[0062] Then, the cluster whose density characteristic value is higher than the preset density characteristic threshold value in the comparison result is determined as the target cluster corresponding to the internal partial discharge signal.

[0063] It is understandable that since the internal partial discharge signal is concentrated in a smaller space inside the transformer and the frequency and amplitude characteristics are relatively consistent, the clustering results of the DBSCAN algorithm show high-density areas. Therefore, the density characteristics of the cluster clusters can be calculated. , and set the preset density feature threshold for screening. In practical applications, the preset density feature threshold can be set to T=0.75. , it can be determined that the cluster is a target cluster with internal partial discharge signals.

[0064] In one embodiment, after separating the internal partial discharge signal in the target aliasing test signal according to the target cluster, the transformer partial discharge signal separation method may further include: First, determine the source location of the internal PD signal.

[0065] In a specific implementation, determining the signal source position of the internal partial discharge signal specifically includes: The first step is to determine the position coordinates of each measurement point, the signal detection value at the current moment, the initial value of the partial discharge signal, and the signal attenuation index at the current moment.

[0066] It is understandable that the intensity and characteristics of the internal partial discharge signal are closely related to the location of the signal source. The internal partial discharge signal will be affected by the medium when propagating in the transformer to be tested, and there is a large attenuation characteristic in the transmission process; while the external partial discharge signal is a pulse signal caused by the external line or air. The collection time of the sensor at the measurement point in the partial discharge test is different, the propagation path of the signal is different, and the receiving effect of the sensor will also be different. Therefore, the signal amplitude ratio information in different clusters can be used to narrow the positioning range of the signal source and realize the precise positioning of the signal source of the partial discharge in the transformer to be tested.

[0067] In this embodiment, the position coordinates of m measuring points can be determined in the two-dimensional plane image of the transformer to be measured, that is, , ,… .

[0068] In the second step, the distance weight of each measurement point relative to the signal source position is calculated based on the signal detection value at the current moment, the initial value of the partial discharge signal, and the signal attenuation index at the current moment.

[0069] In this embodiment, the signal amplitude ratio information of the signal attenuation characteristics can be used to infer the distance weight from the signal source to each measurement point, and the triangulation method can be used to further determine the signal source position. Specifically, the distance weight of the mth measurement point relative to the signal source position can be expressed as follows: (5) in, represents the distance weight of the mth measurement point relative to the signal source position, Indicates the initial value of the partial discharge signal, Represents the signal detection value of the mth measurement point at the current moment, Indicates the signal attenuation index of the mth measurement point at the current moment.

[0070] The third step is to establish multiple distance weight ratio equations based on the distance weight and the location coordinates of each measurement point.

[0071] The fourth step is to jointly solve multiple distance weight ratio equations to calculate the signal source position of the internal partial discharge signal.

[0072] In this embodiment, multiple distance weight ratio equations can be expressed as follows: (6) (7) (8) in, , , ,… Indicates the position coordinates of each measurement point, Indicates the signal source location, , , , Represents the distance weight of each measurement point relative to the signal source position.

[0073] By solving the above equations jointly, the signal source position of the internal partial discharge signal can be obtained: .

[0074] Then, the separated internal PD signal is marked in regions according to the location of the signal source.

[0075] In the area marking stage, the signal source location of the internal partial discharge signal can be Mark on the two-dimensional plane image of the transformer to be tested.

[0076] In one embodiment, after the separated internal partial discharge signal is marked with regions according to the signal source position, the transformer partial discharge signal separation method may further include: Firstly, the signal parameters of the separated internal partial discharge signal and the operating parameters of the transformer to be tested are obtained.

[0077] In this embodiment, the signal parameters specifically include parameters that can characterize signal properties, such as signal amplitude and signal frequency, and the operating parameters of the transformer to be tested specifically include parameters that affect partial discharge, such as the operating voltage and load size of the transformer to be tested.

[0078] Then, according to the signal parameters and the operating parameters, the discharge diagnosis is performed on the signal source position to obtain the discharge diagnosis result.

[0079] In practical applications, the discharge degree can be determined based on the signal parameters of the internal partial discharge signal, and the operating status of the transformer under test can be determined based on the operating parameters of the transformer under test. The discharge severity can be comprehensively evaluated through the discharge degree and the operating status to obtain the discharge diagnosis result.

[0080] For example, the proportion of high-frequency components can be determined according to the signal frequency in the signal parameters of the internal partial discharge signal, and the proportion of high-frequency components can be normalized to serve as the discharge degree.

[0081] In some embodiments, multiple discharge severity levels can be pre-classified according to the discharge degree and the operating state. For example, if the discharge degree is in the first interval and the operating state is normal, the discharge severity level is low, and the safety hazard is small. If the discharge degree is in the second interval and the operating state is abnormal, the discharge severity level is medium, and the safety hazard is general. If the discharge degree is in the third interval and the operating state is abnormal, the discharge severity level is high, and the safety hazard is large.

[0082] Subsequently, the discharge degree and operating status are compared with the reference intervals at each level to determine the discharge severity level of the transformer to be tested, and the determined discharge severity level is used as the discharge diagnosis result.

[0083] In one embodiment, after obtaining the discharge diagnosis result, the transformer partial discharge signal separation method may further include: Generate a discharge analysis report and maintenance recommendation information based on the discharge diagnosis results and signal source location.

[0084] In this embodiment, the discharge diagnosis results and the signal source location can be summarized, and a discharge analysis report can be generated in the form of a table. At the same time, corresponding maintenance suggestion information can be generated according to the discharge severity level in the discharge diagnosis results. For example, when the discharge severity level is high, the corresponding maintenance suggestion information is shutdown diagnosis, and when the discharge severity level is low or medium, the corresponding maintenance suggestion information is continuous monitoring. Subsequently, the discharge analysis report and maintenance suggestion information can be sent to the terminal of the supervisor, so that the supervisor can timely understand the discharge status of the transformer to be tested, and can perform maintenance or monitoring in time in combination with the maintenance suggestion information.

[0085] Based on the same inventive concept, the present invention also protects a transformer partial discharge signal separation system. The transformer partial discharge signal separation system provided by the present invention is described below. The transformer partial discharge signal separation system described below and the transformer partial discharge signal separation method described above can be referenced to each other.

[0086] like Figure 5 As shown, an embodiment of the present invention further provides a transformer partial discharge signal separation system, which specifically includes: The sampling module 310 is used to synchronously sample multiple measurement points of the transformer to be tested at different times to obtain multiple aliasing test signals.

[0087] The decomposition module 320 is used to decompose each aliasing test signal to obtain multiple independent source signals in each aliasing test signal.

[0088] The calculation module 330 is used to calculate the rising fluctuation parameter of each independent source signal, and determine the target aliasing test signal with the partial discharge signal according to the rising fluctuation parameter of each independent source signal in each aliasing test signal.

[0089] The clustering module 340 is used to determine the characteristic vector of each independent source signal in the target aliasing test signal, and perform clustering processing on all independent source signals in the target aliasing test signal according to the characteristic vector to obtain multiple clusters.

[0090] The processing module 350 is used to determine the density characteristic value of each cluster, and determine the target cluster corresponding to the internal partial discharge signal according to the density characteristic value.

[0091] The separation module 360 ​​is used to separate the internal partial discharge signal in the target aliasing test signal according to the target cluster.

[0092] It can be seen that the transformer partial discharge signal separation system provided in the embodiment of the present invention can combine the signal attenuation characteristics of multiple measurement points, effectively distinguish internal partial discharge signals from external partial discharge signals, and accurately locate the signal source position of the internal partial discharge signal through the analysis of the propagation path, which can provide more accurate information for subsequent fault diagnosis.

[0093] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0094] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A transformer partial discharge signal separation method, characterized in that: The method comprises: Performing synchronous sampling on multiple measurement points of the transformer to be tested at different times to obtain multiple aliasing test signals; Decomposing each aliasing test signal to obtain multiple independent source signals in each aliasing test signal; Calculate the rising fluctuation parameters of each independent source signal at different times, and determine the target aliasing test signal with partial discharge signal according to the rising fluctuation parameters of each independent source signal at different times in each aliasing test signal; Determine a characteristic vector of each independent source signal in the target aliasing test signal, and perform clustering processing on all independent source signals in the target aliasing test signal according to the characteristic vector to obtain a plurality of cluster clusters; Determine a density characteristic value of each cluster, and determine a target cluster corresponding to the internal partial discharge signal based on the density characteristic value; Based on the target cluster, the internal partial discharge signal in the target aliasing test signal is separated.

2. A transformer partial discharge signal separation method according to claim 1, characterized in that: Calculate the rising fluctuation parameters of each independent source signal at different times, including: Obtaining the first amplitude rise time of each independent source signal at different times and the second amplitude rise time of each independent source signal in the aliasing test signal where each independent source signal is located at different times; Subtract the first amplitude rise time from each second amplitude rise time and calculate the absolute value, so as to obtain a plurality of rise time absolute difference values; The absolute differences of multiple rising time lengths are averaged to calculate the rising fluctuation parameters of each independent source signal at different times.

3. A transformer partial discharge signal separation method according to claim 1, characterized in that: According to the rising fluctuation parameters of each independent source signal at different times in each aliasing test signal, the target aliasing test signal with partial discharge signal is determined, including: Compare the rising fluctuation parameters of each independent source signal in each aliasing test signal at different times with the preset fluctuation threshold to obtain a comparison result; The aliasing test signal whose comparison result satisfies a preset judgment condition is determined as a target aliasing test signal with a partial discharge signal; wherein the preset judgment condition is that there is at least one comparison result that satisfies the rising fluctuation parameter of the independent source signal being above a preset fluctuation threshold.

4. A transformer partial discharge signal separation method according to claim 1, characterized in that: Determining a characteristic vector of each independent source signal in the target aliasing test signal includes: Determine the energy concentration parameters of each independent source signal at different times; According to the rising fluctuation parameter of each independent source signal at different times and the energy concentration parameter of each independent source signal at different times, the attenuation index of each independent source signal at different times is calculated; The rising fluctuation parameters of each independent source signal at different times are averaged to obtain the rising fluctuation parameter mean of each independent source signal; The energy concentration parameters of each independent source signal at different times are averaged to obtain the energy concentration parameter mean of each independent source signal; The attenuation index of each independent source signal at different times is averaged to obtain the mean attenuation index of each independent source signal; A feature vector of each independent source signal in the target aliasing test signal is established according to a rising fluctuation parameter mean, an energy concentration parameter mean and an attenuation index mean of each independent source signal.

5. A transformer partial discharge signal separation method according to claim 1, characterized in that: Determining a target cluster corresponding to the internal partial discharge signal according to the density characteristic value includes: The density feature value of each cluster is compared with the preset density feature threshold to obtain the comparison result; The cluster whose density characteristic value is higher than the preset density characteristic threshold value in the comparison result is determined as the target cluster corresponding to the internal partial discharge signal.

6. A transformer partial discharge signal separation method according to claim 1, characterized in that: After separating the internal partial discharge signal in the target aliasing test signal according to the target cluster, the method further includes: Determine the source location of the internal partial discharge signal; The separated internal partial discharge signal is marked with a region according to the signal source position.

7. A transformer partial discharge signal separation method according to claim 6, characterized in that: Locate the source of the internal PD signal, including: Determine the position coordinates of each measuring point, the signal detection value at the current moment, the initial value of the partial discharge signal, and the signal attenuation index at the current moment; The distance weight of each measuring point relative to the signal source position is calculated based on the signal detection value at the current moment, the initial value of the partial discharge signal and the signal attenuation index at the current moment; Establishing a plurality of distance weight ratio equations according to the distance weight and the position coordinates of each measuring point; The signal source position of the internal partial discharge signal is calculated by jointly solving the multiple distance weight ratio equations.

8. A transformer partial discharge signal separation method according to claim 6, characterized in that: After marking the separated internal partial discharge signal according to the signal source position, the method further includes: Obtaining signal parameters of the separated internal partial discharge signal and operating parameters of the transformer to be tested; According to the signal parameters and the operating parameters, a discharge diagnosis is performed on the signal source position to obtain a discharge diagnosis result.

9. A transformer partial discharge signal separation method according to claim 8, characterized in that: After obtaining the discharge diagnosis result, the method further includes: A discharge analysis report and maintenance suggestion information are generated based on the discharge diagnosis result and the signal source location.

10. A transformer partial discharge signal separation system, characterized in that: The system comprises: A sampling module is used to synchronously sample multiple measurement points of the transformer to be tested at different times to obtain multiple aliasing test signals; A decomposition module, used for decomposing each aliasing test signal to obtain multiple independent source signals in each aliasing test signal; A calculation module, used for calculating the rising fluctuation parameters of each independent source signal at different times, and determining the target aliasing test signal with partial discharge signal according to the rising fluctuation parameters of each independent source signal at different times in each aliasing test signal; A clustering module, used to determine a characteristic vector of each independent source signal in the target aliasing test signal, and perform clustering processing on all independent source signals in the target aliasing test signal according to the characteristic vector to obtain a plurality of cluster clusters; A processing module, used to determine the density characteristic value of each cluster, and determine the target cluster corresponding to the internal partial discharge signal according to the density characteristic value; A separation module is used to separate the internal partial discharge signal in the target aliasing test signal according to the target cluster.

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