GIS ultra-high frequency partial discharge signal fault diagnosis and state evaluation method

Abstract

A GIS ultra-high frequency partial discharge signal fault diagnosis and state evaluation method, characterized in that it comprises the following steps: step S1. Based on the physical model of GIS partial discharge typical defects, discharge test is carried out by using a specific test circuit, and the ultra-high frequency partial discharge signals of different defects in multiple power frequency cycles are collected and grouped, each group containing a predetermined number of power frequency cycle signals; step S2. Calculate the partial discharge statistical characteristic quantity and normalize it to form a data sample corresponding to each group of partial discharge signals; step S3. The data sample is regarded as the observation value in clustering, different total cluster number k is selected, K-means clustering analysis is carried out for each k value, and the within-cluster distance sum of squares (WCSS) under each clustering result is calculated; step S4. Draw the WCSS-k relationship curve, and preliminarily determine the best cluster number by using the elbow rule; step S5. Calculate the absolute value of Pearson correlation coefficient between WCSS and k under different k values, when |p| starts to rise close to 1, determine the best cluster number kopt as the final clustering output. The best cluster number kopt determined by the method can effectively distinguish and classify GIS partial discharge ultra-high frequency signals, and then identify different types of discharge signals, providing strong support for effective diagnosis of GIS partial discharge defects.

Description

Technical Field

[0001] The present invention belongs to the field of ultra-high frequency partial discharge signal type diagnosis for gas-insulated metal-enclosed switchgear (GIS), and relates to a fault diagnosis and status assessment method for GIS ultra-high frequency partial discharge signals. The method can use a theoretical calculation method when classifying different GIS ultra-high frequency partial discharge signals instead of selecting the number of clustering categories based on human experience, thereby achieving better classification effect, facilitating the distinction between interference and useful signals, greatly improving anti-interference capability, providing strong support for equipment status maintenance, improving equipment reliability, and facilitating the safe and stable operation of the power system. Background Art

[0002] Due to its numerous advantages, gas-insulated metal-enclosed switchgear (GIS) has become an indispensable piece of power transmission and transformation equipment in key locations such as urban load center substations. However, GIS is prone to insulation defects during actual operation, leading to gradually deteriorating partial discharge (PD) processes that can ultimately cause insulation breakdown, discharge, and accidents. By monitoring and diagnosing PD in operating equipment during this process, and promptly identifying and addressing the defect type, serious accidents can be effectively avoided, ensuring the safe and stable operation of the power system.

[0003] The ultra-high frequency method has high detection sensitivity and a wide coverage range, making it suitable for various partial discharge detection scenarios. It is also commonly used to detect partial discharge signals from insulation defects in GIS. Ultra-high frequency partial discharge signals can be used to identify different types of discharge defects. Regarding partial discharge signal identification, although current machine learning algorithms have a high degree of accuracy in identifying standard defects in laboratory environments, most of these algorithms rely on laboratory data for training, and their performance in field applications has not yet been verified. The complexity of the field environment, including external electromagnetic interference and discharge defect types that differ from laboratory conditions, poses challenges to accurate identification. If the total number of categories of all discharge signals can be effectively determined and label-free differentiation and classification can be achieved, the accuracy of identifying defect types in partial discharge signals will be significantly improved.

[0004] Clustering technology is one of the most widely used classification methods. It can distinguish and classify samples based on their characteristics and differences, even without knowing their labels. K-means clustering is an unsupervised machine learning algorithm that automatically divides multiple spatial samples into k clusters. The classification principle is that objects of the same type have high similarity, while objects of different types have low similarity.

[0005] However, there are few studies focusing on the selection of the number of clusters k in K-means clustering. The selection of the number of clusters k has a great impact on the clustering effect. If the k value is not selected appropriately, the clustering effect is often not ideal, and the classification results of the data samples may differ greatly from the actual results. In practical applications, the number of discharge types contained in the UHF discharge data samples on site is often unknown, which means that for different data samples, it is necessary to determine the optimal number of clusters k before clustering. opt It is necessary. But at present opt The selection of the value is often based on experience, and there is no way to determine the optimal clustering number k through theoretical calculation. opt In many cases, the data samples of different defects to be classified and identified are often composed of multiple (greater than 3) partial discharge feature quantities, and the sample points may be relatively scattered. It is difficult to manually determine the total number of categories and thus determine k by drawing a sample distribution diagram. opt Although the elbow rule provides a method to determine the optimal number of clusters in k-means clustering, it lacks theoretical support and is difficult to determine the optimal number of clusters k when the inflection point is not prominent. opt . Summary of the Invention

[0006] In order to address the shortcomings of the current existing technology, the present invention proposes a K-means clustering method for GIS ultra-high frequency partial discharge signals based on the elbow rule and the Pearson correlation coefficient to determine the optimal cluster number, in order to achieve the distinction and classification of GIS partial discharge ultra-high frequency signals and identify different types of signals, which is more conducive to the subsequent effective diagnosis of GIS partial discharge defects.

[0007] In order to achieve the above-mentioned object, the present invention adopts the following technical solutions:

[0008] A method for fault diagnosis and status assessment of GIS ultra-high frequency partial discharge signals is characterized in that it includes the following steps:

[0009] Step S1. Based on the physical model of typical GIS partial discharge defects, a discharge test is performed using a specific test circuit. Ultra-high frequency partial discharge signals of multiple power frequency cycles of different defects are collected and grouped, with each group containing a predetermined number of power frequency cycle signals.

[0010] Step S2. Calculating the statistical characteristic quantities of partial discharges and normalizing them to form a data sample corresponding to each group of partial discharge signals;

[0011] Step S3: Treat the data samples as observations in the cluster, select different total cluster numbers k, perform K-means cluster analysis for each k value, and calculate the within-cluster distance sum of squares (WCSS) under each clustering result:

[0012] Step S4. Draw the WCSS-k relationship curve and use the elbow rule to preliminarily determine the optimal number of clusters;

[0013] Step S5. Calculate the absolute value of the Pearson correlation coefficient between WCSS and k under different k values. When |ρ| begins to rise close to 1, determine the optimal number of clusters kopt as the final clustering output.

[0014] Preferably, in step S1, the typical GIS partial discharge defects include corona discharge, surface discharge and internal discharge.

[0015] Preferably, the ultra-high frequency partial discharge signal collected in step S1 is pre-processed to ensure that the signal quality meets the requirements of subsequent analysis.

[0016] Preferably, in step S2, the statistical characteristic quantities of partial discharge include discharge amplitude, discharge times, discharge phase distribution, discharge energy, discharge rise time and discharge duration.

[0017] Preferably, the calculation of the intra-cluster distance sum of squares (WCSS) under each clustering result in step S3 specifically includes:

[0018] When the number of clusters k is 1, the sum of squares of the within-cluster distances (WCSS) is calculated;

[0019] When k is greater than or equal to 2 and less than or equal to a preset positive integer n, a specific clustering method is used to update the cluster center until the maximum number of iterations is reached or the WCSS change in two consecutive iterations is less than a predetermined threshold.

[0020] Preferably, the specific clustering method specifically includes:

[0021] When k is greater than or equal to 2 and less than or equal to n, the clustering method is as follows:

[0022] 1) The first k observations are assigned as k cluster centers respectively.

[0023] 2) Traverse each remaining observation to see if it can replace the current cluster center. The judgment principle is: if the distance between the observation point and its nearest cluster center is greater than the distance between the two nearest cluster centers (cluster A and cluster B), then the observation point will replace the cluster center of cluster A or B, depending on which one is closer to the observation point; otherwise, if the distance between the observation point and the second nearest cluster center is greater than the closest distance between the nearest cluster center and the other cluster centers, then the observation point will replace the nearest cluster center.

[0024] 3) Assign the observation point to the nearest cluster; each observation point is assigned to the cluster with the nearest distance. If the i-th observation point is assigned to the k-th cluster, then the distance d between the observation point and the k-th cluster is ik Will be corrected to And the distance d between the observation point and other clusters ij Corrected to

[0025]

[0026] Among them, n k and n j are the number of observation points in the kth cluster and the jth cluster, respectively, d ik is the distance between the i-th observation point and cluster k, d ij is the distance between the i-th observation point and cluster j. If the distance between the corrected observation point and the l-th cluster is the smallest and l≠k, then the observation point will be assigned to the l-th cluster instead of the k-th cluster. Then, each cluster center will be updated to the average value of the observation points in each cluster;

[0027] 4) Using the updated cluster centers, repeat the above process (3), assign each observation point, and update the cluster centers. The iteration stops when the maximum number of iterations is reached or the WCSS change in two consecutive iterations is less than a certain threshold. The cluster centers updated in the last iteration are called the final cluster centers.

[0028] Preferably, the elbow rule refers to observing the WCSS-k curve and identifying the point where the slope of the curve changes significantly as the "elbow", and the k value corresponding to the point is the preliminary estimated optimal number of clusters.

[0029] Compared with the existing technology, the present invention has the following beneficial effects:

[0030] This paper proposes a K-means clustering method for GIS ultra-high frequency partial discharge (UHF) signals. Combining the elbow rule and the Pearson correlation coefficient, this method uses theoretical calculations to determine the optimal number of clusters (kopt). When classifying UHF partial discharge (UHF) data samples from different defects, this optimal number of clusters (kopt) can be determined in advance through theoretical calculations. This enables more precise and reliable K-means clustering, improving the accuracy of defect identification. This method not only helps accurately determine the fault type of GIS equipment but also facilitates equipment operation, maintenance, and fault diagnosis, thereby improving equipment reliability and ensuring the safety of personnel in production and life.

[0031] The optimal clustering number kopt determined by this method can effectively distinguish and classify GIS partial discharge UHF signals, and then identify different types of discharge signals, providing strong support for the effective diagnosis of GIS partial discharge defects. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Test circuit diagram

[0033] Figure 2 WCSS-k curve when k is 1 to 12

[0034] Figure 3 When q is 1 to 11, the absolute value of the Pearson correlation coefficient between sequence k and sequence WCSS |ρ| DETAILED DESCRIPTION

[0035] The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments, but this should not limit the scope of protection of the present invention.

[0036] In this embodiment, a K-means clustering method for GIS ultra-high frequency partial discharge signals is provided for determining the optimal number of clusters based on the elbow rule and the Pearson correlation coefficient, comprising the following steps:

[0037] Step 1: First, based on the physical model of typical GIS partial discharge defects, use Figure 1 The test circuit shown here conducts a discharge test and collects UHF analog signals: a fabricated defect model is placed within the GIS test tank, and voltage is applied using a step-by-step voltage ramp until the defect produces a sustained, stable discharge without causing overcurrent in the test transformer. At this voltage, UHF detection is used to continuously collect UHF signals over multiple power frequency cycles. For each typical defect, 3000 power frequency cycles of UHF partial discharge data are collected and divided into 60 groups, each containing signals from 50 power frequency cycles.

[0038] Step 2: Calculate the local discharge statistical characteristic quantity with clear physical meaning, which can characterize the actual discharge characteristics and is intuitive and clear based on the discharge data. There are no special requirements for the local discharge statistical characteristic quantity required to be calculated in the present invention, including but not limited to: pulse basic waveform characteristic quantity (such as pulse rise speed, pulse fall speed, etc.), pulse equivalent time-frequency characteristic quantity (such as pulse time domain equivalent time length, frequency domain equivalent bandwidth, etc.), local discharge statistical characteristic quantity based on amplitude-phase time series data (such as A max : The maximum amplitude of each group of partial discharge signals, reflecting the maximum discharge intensity of the defect; A ave : The average amplitude of each group of partial discharge signals, reflecting the overall discharge intensity; A total : The sum of the discharge amplitudes of each group of partial discharge signals, which characterizes the cumulative discharge intensity of the defect over a period of time and includes information characteristics such as the discharge repetition rate; N: The total number of discharges of each group of partial discharge signals, which can reflect the repetition rate of discharge within a certain period of time; r A: The ratio of the sum of the discharge signal intensity amplitudes in the positive half-cycle of each group of partial discharge signals to the sum of the discharge signal intensity amplitudes in the negative half-axis, reflecting the shape characteristics of the defect. Because the discharge strengths of the positive and negative half-cycles of defects with different shapes are different in one cycle, it can characterize the shape characteristics of the positive and negative half-cycles in the partial discharge PRPS spectrum; std ave : The average value of the standard deviation of the discharge amplitude in each cycle of each set of partial discharge signals, which reflects the volatility of the discharge amplitude and can reflect the shape characteristics of the amplitude change in the PRPS spectrum. The characteristic quantity corresponding to each set of partial discharge signals is regarded as a data sample, and 60 data samples can be obtained.

[0039] In order to eliminate the influence of different units between different feature quantities, the statistical feature quantities are normalized to the interval [0,1] before using K-means clustering. The normalization method is to perform the normalization of each feature quantity on all data of the feature quantity of this type. operation, where x i is the i-th feature quantity, x min is the minimum value of the feature quantity of this type among all samples, x max It is the maximum value of the feature value of this type among all samples.

[0040] Step 3: Treat the above 60 data samples as observations in the cluster:

[0041] When the number of clusters k is 1, all data samples belong to the same category. The average value of all sample observations is used as the cluster center and the within-cluster distance sum (WCSS) is calculated. WCSS is the sum of the distances between the cluster center of each cluster in the sample space and the sample points within the cluster. The calculation method is:

[0042]

[0043] In the formula, m is the total number of all sample points, n represents the number of features (i.e., dimension) of each sample point, and X j is the jth sample point, μ j is the sample point X j The cluster center of the cluster, x ji Represents the sample point X j The eigenvalue of the i-th dimension, μ ji Represents the cluster center μ j The eigenvalue of the i-th dimension.

[0044] In this method, the jth sample in each cluster is related to the cluster center μ of the cluster to which the sample belongs. j The distance between them is Euclidean distance. The calculation method is:

[0045]

[0046] Where, d(X j ,μ j ) represents the jth sample X j and the cluster center μ j The Euclidean distance between j represents the jth sample point in the cluster, μ j Represents X j The centroid of the cluster, x ji Represents the sample point X j The eigenvalue of the i-th dimension, n represents the number of features (i.e., dimension) of each sample point, μ ji Represents the cluster center μ j The eigenvalue of the i-th dimension.

[0047] Define n as a positive integer much larger than the actual total number of clusters. When k is greater than or equal to 2 and less than or equal to n, the clustering method is as follows:

[0048] 1) The first k observations are assigned as k cluster centers respectively.

[0049] 2) Traverse each remaining observation to see if it can replace the current cluster center. The judgment principle is: if the distance between the observation point and its nearest cluster center is greater than the distance between the two nearest cluster centers (cluster A and cluster B), then the observation point will replace the cluster center of cluster A or B, depending on which one is closer to the observation point; otherwise, if the distance between the observation point and the second nearest cluster center is greater than the closest distance between the nearest cluster center and the other cluster centers, then the observation point will replace the nearest cluster center.

[0050] 3) Assign the observation point to the nearest cluster. Each observation point is assigned to the cluster with the nearest distance. If the i-th observation point is assigned to the k-th cluster, then the distance d between the observation point and the k-th cluster is ik Will be corrected to And the distance d between the observation point and other clusters ij Corrected to

[0051]

[0052] Among them, n k and n j are the number of observation points in the kth cluster and the jth cluster, respectively, d ik is the distance between the i-th observation point and cluster k, d ijis the distance between the i-th observation point and cluster j. If the distance between the corrected observation point and the l-th cluster is the smallest and l≠k, then the observation point will be assigned to the l-th cluster instead of the k-th cluster. Then, each cluster center will be updated to the average value of the observation points in each cluster.

[0053] 4) Using the updated cluster centers, repeat the above process (3), assign each observation point, and update the cluster centers. The iteration stops when the maximum number of iterations is reached (the default maximum number of iterations in this method is 10, which can be modified as needed) or when the change in WCSS between two consecutive iterations is less than a certain threshold (which can be modified according to the data). The cluster centers updated in the last iteration are called the final cluster centers.

[0054] In the above clustering process, the centroid of each cluster of sample points is selected as the cluster center of each cluster, and the value of each dimension of the centroid is the average value of each cluster of data points in that dimension.

[0055] Step 4: Plot the WCSS results calculated for each k in a two-dimensional coordinate system with the number of clusters k on the horizontal axis and the sum of squared distances within the clusters WCSS on the vertical axis. Table 1 shows an implementation example with 3 defect categories and n = 12. It gives the results of the sum of squared distances within the clusters WCSS after clustering with k = 1, 2, 3, 4, ..., 12, and also plots the sum of squared distances within the clusters WCSS-number of clusters k curve, as shown in Table 1. Figure 2 shown.

[0056] Table 1 shows the sum of squares of intra-cluster distances WCSS for different values ​​of k.

[0057]

[0058]

[0059] from Figure 2 It can be seen that k = 3 is an inflection point, which corresponds to the actual number of clusters in this experiment, that is, the optimal number of clusters k that needs to be determined opt .because Figure 2 The middle curve is shaped like a human arm, and the optimal number of clusters k opt The corresponding inflection point is like the elbow of the arm, so the optimal number of clusters k is found in this way opt This method is also called the "elbow rule".

[0060] In order to more accurately judge and identify the "elbow" of the curve, that is, to find the optimal number of clusters k opt ,This paper adopts a mathematical calculation method.

[0061] In a similar Figure 2In the WCSS-k curve, it is generally believed that when WCSS begins to decrease linearly, the k value corresponding to this point is the optimal cluster number k. opt Therefore, when q=2, 3, 4, ..., n-1, the sequence WCSS={WCSS q ,WCSS q+1 ,……,WCSS n The Pearson correlation coefficient ρ between} and the sequence k = {q,q+1,…,n}. The Pearson correlation coefficient ρ is a commonly used linear correlation coefficient that can reflect the degree of linear correlation between two variables. It is defined as the quotient of the covariance and standard deviation between the two variables and is calculated as follows:

[0062]

[0063] In the formula, X and Y are variables, cov(X,Y) is the covariance of X and Y, σ X , σ Y are the standard deviations of X and Y, n is the total number of values ​​contained in variables X and Y, respectively, i is the i-th value of variable X, Y i 、Y j are the i-th and j-th values ​​of variable Y, respectively. are the mean values ​​of variables X and Y respectively.

[0064] The Pearson correlation coefficient ρ ranges from -1 to 1. When ρ < 0, variables X and Y are negatively correlated; when ρ > 0, variables X and Y are positively correlated; and when ρ = 0, there is no linear correlation between variables X and Y. The closer the absolute value of ρ |ρ| is to 1, the stronger the linear correlation between variables X and Y. Table 2 serves as a standard for evaluating the degree of correlation between variables.

[0065] Table 2 Absolute value of Pearson correlation coefficient |ρ| and correlation degree between variables

[0066]

[0067]

[0068] Therefore, when q=2, 3, 4, ..., n-1, the sequence k={q, q+1, ..., n} is calculated respectively, and the sequence WCSS={WCSS q ,WCSS q+1 ,……,WCSS n The absolute value of the Pearson correlation coefficient |ρ| between} and sequence k.

[0069] When q starts to take a certain value q optWhen |ρ| rises to a value close to 1, it can be considered that WCSS changes from k=q opt It starts to decrease linearly, and the corresponding k value is the optimal cluster number k opt , that is, k opt =q opt .

[0070] An implementation example with n=12 is given below, as shown in Table 3 and Figure 3 shown.

[0071] Table 3 Absolute values ​​of Pearson correlation coefficient when q is 1 to 11 |ρ|

[0072]

[0073] When q increases from 2 to 3, the absolute value of the Pearson correlation coefficient |ρ| increases rapidly from 0.518818881 to 0.908874263, which is close to 1. This method considers that when |ρ| starts to rise to ≥0.8, the two variables can be considered to be highly linearly correlated, that is, at this time, the sequence k={q,q+1,……,n-1} and the sequence WCSS={WCSS q ,WCSS q+1 ,……,WCSS n}(q=2, 3, 4, ..., n-1) are highly linearly correlated, so it can be considered that q opt =3, which means that the WCSS-k curve is close to linear when k is {3, 4, ..., n}, and WCSS begins to decrease linearly. The optimal cluster number k opt =q opt =3, which is consistent with Figure 2 The results of the preliminary judgment based on the inflection point are consistent. So far, by using a more rigorous mathematical method, by calculating the absolute value of the Pearson correlation coefficient |ρ| from a small value to the point close to 1, the corresponding q opt , the optimal number of clusters k is determined opt .

[0074] Finally, output k = k opt The clustering results when the number of clusters k is 3 are given in Table 4 below.

[0075] Table 4 Clustering results when the number of clusters k is 3

[0076]

[0077] As can be seen from the table, when the number of clusters is 3, the data of each defect type can be accurately classified, the clustering effect is good, and the accuracy rate reaches 100%, that is, reasonable and accurate K-means clustering of discharge samples with different defects is achieved. This shows that by finding the optimal k value through this method, a better K-means clustering effect can be achieved.

[0078] The present invention proposes a K-means clustering method for GIS ultra-high frequency partial discharge signals based on the elbow rule and the Pearson correlation coefficient to determine the optimal number of clusters. Assuming that the total number of clusters is less than n, the absolute value of the Pearson correlation coefficient |ρ| between the cluster number k sequence {q, q+1, ..., n} and the intra-cluster distance square sum WCSS sequence corresponding to different q (q is an integer greater than 1) is calculated. When |ρ| increases to >0.8, the corresponding k value can be used as the reasonable value for setting the number of cluster categories k. opt The theoretical calculated value of the value. This method can reasonably select the optimal clustering number k when classifying the partial discharge statistical feature data samples of different defects. opt , thereby achieving relatively accurate K-means clustering. Based on the clustering results, pattern recognition algorithms such as support vector machines and artificial neural networks can be used to more accurately identify and diagnose GIS partial discharge defect types. This method can also accurately classify partial discharge samples with different characteristics before identification, thereby distinguishing between noise and defect discharge signals. This can greatly improve anti-interference capabilities, provide strong support for equipment condition-based maintenance, enhance equipment reliability, and promote the safe and stable operation of the power system.

[0079] In summary, this technical solution successfully solves the problem of determining the optimal number of clusters in the cluster analysis of GIS ultra-high frequency partial discharge signals by combining the elbow rule and the Pearson correlation coefficient, improves the accuracy and reliability of cluster analysis, and provides strong technical support for fault diagnosis and status assessment of GIS equipment.

Claims

1. A method for fault diagnosis and status assessment of GIS ultra-high frequency partial discharge signals, characterized in that: The following steps are involved: Step S1. Based on the physical model of typical GIS partial discharge defects, a discharge test is performed using a specific test circuit. Ultra-high frequency partial discharge signals of multiple power frequency cycles of different defects are collected and grouped, with each group containing a predetermined number of power frequency cycle signals. Step S2. Calculating the statistical characteristic quantities of partial discharges and normalizing them to form a data sample corresponding to each group of partial discharge signals; Step S3: Treat the data samples as observations in the cluster, select different total cluster numbers k, perform K-means cluster analysis for each k value, and calculate the intra-cluster distance sum of squares WCSS under each clustering result: Step S4. Draw the WCSS-k relationship curve and use the elbow rule to preliminarily determine the optimal number of clusters; Step S5. Calculate the absolute value of the Pearson correlation coefficient between WCSS and k for different k values. When |ρ| begins to rise close to 1, determine the optimal number of clusters kopt as the final clustering output. The calculation of the intra-cluster distance sum of squares WCSS under each clustering result in step S3 specifically includes: When the number of clusters k is 1, the sum of squared distances within the cluster, WCSS, is calculated; When k is greater than or equal to 2 and less than or equal to a preset positive integer n, a specific clustering method is used to update the cluster center until the maximum number of iterations is reached or the WCSS change in two consecutive iterations is less than a predetermined threshold; The specific clustering method specifically includes: When k is greater than or equal to 2 and less than or equal to n, the clustering method is as follows: 1) The first k observations are assigned as k cluster centers respectively; 2) Traverse each remaining observation to see if it can replace the current cluster center. The judgment principle is: if the distance between the observation point and its nearest cluster center is greater than the distance between the two nearest cluster centers, the nearest cluster centers include cluster A and cluster B, then the observation point will replace the cluster center of cluster A or cluster B, depending on which one is closer to the observation point; otherwise, if the distance between the observation point and the second nearest cluster center is greater than the closest distance between the nearest cluster center and the other cluster centers, then the observation point will replace the nearest cluster center; 3) Assign the observation point to the nearest cluster; each observation point is assigned to the cluster with the nearest distance. If the i-th observation point is assigned to the k-th cluster, then the distance d between the observation point and the k-th cluster is ik Will be corrected to And the distance d between the observation point and other clusters ij Corrected to Among them, n k and n j are the number of observation points in the kth cluster and the jth cluster, respectively, d ik is the distance between the i-th observation point and cluster k, d ij is the distance between the i-th observation point and cluster j. If the distance between the corrected observation point and the r-th cluster is the smallest, and r≠k, then the observation point will be assigned to the r-th cluster instead of the k-th cluster. Then, each cluster center will be updated to the average value of the observation points in each cluster. 4) Using the updated cluster centers, repeat the above process 3), assign each observation point, and update the cluster center. The iteration stops when the maximum number of iterations is reached or the change in WCSS between two consecutive iterations is less than a certain threshold. The cluster center updated in the last iteration is called the final cluster center.

2. The method for fault diagnosis and status assessment of GIS ultra-high frequency partial discharge signals according to claim 1, characterized in that: The typical defects of GIS partial discharge in step S1 include corona discharge, surface discharge and internal discharge.

3. The fault diagnosis and status assessment method of GIS ultra-high frequency partial discharge signal according to claim 1 is characterized in that: The ultra-high frequency partial discharge signal collected in step S1 is pre-processed to ensure that the signal quality meets the requirements of subsequent analysis.

4. The method for fault diagnosis and status assessment of GIS ultra-high frequency partial discharge signals according to claim 1, characterized in that: In step S2, the statistical characteristics of partial discharge include discharge amplitude, discharge times, discharge phase distribution, discharge energy, discharge rise time and discharge duration.

5. The method for fault diagnosis and status assessment of GIS ultra-high frequency partial discharge signals according to claim 1, characterized in that: The elbow rule is to observe the WCSS-k curve and identify the point where the slope of the curve changes significantly as the "elbow". The k value corresponding to this point is the preliminary estimated optimal number of clusters.

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