DC GIL insulation defect severity assessment method and device

Abstract

The invention relates to the technical field of electrical equipment, in particular to a direct-current GIL insulation defect severity assessment method and device, and the method comprises the steps: collecting a high-voltage direct-current gas-insulated metal-enclosed transmission line GIL partial discharge pulse current signal, extracting a feature value according to the GIL partial discharge pulse current signal, and calculating the severity of a direct-current GIL insulation defect severity; and stage division of a fault evolution process is carried out according to a fuzzy C-means clustering algorithm so as to generate a DC GIL insulation defect severity degree evaluation result. Therefore, the problems of false alarm, missing alarm and the like of partial discharge detection caused by influence of various factors in related technologies are solved.

Description

Technical Field

[0001] The present application relates to the technical field of electrical equipment, and in particular to a method and device for evaluating the severity of insulation defects of a DC GIL. Background Art

[0002] The root cause of GIL (Gas Insulated Metal-enclosed Transmission Line) operation failures is insulation failure. This is because in the actual operation of GIL, the electric field distribution is distorted and PD (Partial Discharge) is induced due to the insulator manufacturing process, microscopic protrusions on the electrode surface, loose components or poor contact during transportation and installation, and dirty insulator surface caused by long-term operation, which leads to insulation degradation and eventual insulation failure.

[0003] In the related art, the study of PD in GIL showed that due to SF 6 The insulation strength is high, the rise time of the discharge pulse is short, and the wave head rise time is less than 1ns, which can stimulate electromagnetic wave signals up to several GHz and generate pulse current in the grounding wire. PD will also cause local pressure changes in the GIL, generate ultrasonic waves, and propagate to the metal shell. At the same time, it will also cause SF 6 Gas decomposition, accompanied by luminescence. The severity of GIL insulation defects is usually assessed based on these chemical and physical phenomena associated with PD.

[0004] However, in the related technologies, the waveform characteristics, spectrum characteristics, amplitude characteristics, etc. of partial discharge signals are not only related to the severity of defects, but also affected by operating conditions, environmental factors, acquisition equipment performance and other factors. In addition, the correlation between partial discharge signals and the health status of equipment is unclear, which makes it difficult to extract effective partial discharge signal characteristics to truly characterize the equipment status, and it is difficult to assess the severity of defects. The use of neural networks, machine learning and other methods requires a large amount of data training, which is difficult to implement and needs to be improved urgently. Summary of the invention

[0005] The present application provides a method and device for evaluating the severity of insulation defects of a DC GIL, so as to solve the problems of false alarms and missed alarms in partial discharge detection due to environmental influences in the related art.

[0006] The first aspect of the present application provides a method for assessing the severity of a DC GIL insulation defect, comprising the following steps: collecting a local discharge pulse current signal of a high-voltage DC gas-insulated metal-enclosed transmission line GIL; extracting characteristic values ​​based on the GIL local discharge pulse current signal; and dividing the fault evolution process into stages based on a fuzzy C-means clustering algorithm to generate a DC GIL insulation defect severity assessment result.

[0007] Through the above technical solution, the embodiment of the present application can collect the GIL local discharge pulse current signal and extract characteristic values, and use the rich information contained in the local discharge phenomenon to quantitatively describe the insulation state from multiple dimensions. These characteristic values ​​provide a comprehensive and detailed data basis for subsequent accurate evaluation. Secondly, the fuzzy C-means clustering algorithm is used to divide the stages of the fault evolution process, which fully considers the fuzziness of the boundaries of each stage of PD development. Compared with the traditional non-fuzzy method, it can more accurately reflect the actual situation and make the evaluation results more in line with the actual process of the development of GIL insulation defects, thereby effectively avoiding misjudgment caused by inaccurate boundary division, helping to timely discover insulation defects and grasp their severity, and providing reliable technical support for ensuring the safe and stable operation of high-voltage DC GIL, which is of great significance to the safe and stable operation of power grids.

[0008] Optionally, in one embodiment of the present application, the extracting characteristic values ​​according to the GIL local discharge pulse current signal includes: based on the GIL local discharge pulse current signal, obtaining at least one of a frequency density histogram of apparent discharge quantity, a trend diagram of average discharge power changing with relative time, a frequency density histogram of first-order difference of apparent discharge quantity, a frequency density histogram of natural logarithm of discharge time interval, and a two-dimensional relationship diagram of cumulative product of local discharge signal and apparent discharge quantity; and extracting at least one corresponding local discharge characteristic quantity according to the at least one.

[0009] Through the above technical scheme, the embodiment of the present application can obtain at least one of the frequency density histogram of the apparent discharge amount and other multiple charts to mine the key information in the partial discharge pulse current signal from different angles. At least one corresponding partial discharge feature quantity is extracted from these charts respectively to achieve multi-dimensional and multi-level feature extraction. The frequency density histogram of the apparent discharge amount is helpful to analyze the distribution law of the discharge amount, the trend chart of the average discharge power changing with relative time can reveal the dynamic change characteristics of the discharge power, the frequency density histogram of the first-order difference of the apparent discharge amount can reflect the trend characteristics of the discharge amount change, the frequency density histogram of the natural logarithm of the discharge time interval is helpful to grasp the characteristics of the discharge time interval, and the two-dimensional relationship diagram of the cumulative product of the partial discharge signal and the apparent discharge amount can further explore the relationship between the two. The extraction of multiple feature quantities provides a rich and accurate data basis for subsequent fault analysis and insulation defect assessment, so as to understand the insulation state of the GIL more comprehensively and deeply, and improve the accuracy and reliability of insulation defect diagnosis and assessment.

[0010] Optionally, in one embodiment of the present application, the calculation formula of the average discharge power changing with relative time trend graph is:

[0011]

[0012] Where DP(t) is the average discharge power at the relative time t (t∈(0,T]), U is the DC voltage applied to the two ends of the sample when the sample point is obtained, t is the relative time, q r is the apparent discharge capacity of the rth discharge, PDRT r for q r The corresponding relative time.

[0013] Through the above technical scheme, the embodiment of the present application can accurately depict the dynamic change trend of the average discharge power with relative time based on key physical quantities, which is helpful to intuitively observe and analyze the changes in the discharge power at different time points within the entire sample duration, and provides an accurate data basis for the subsequent extraction of characteristic parameters such as skewness and kurtosis from the trend chart, thereby enabling a deeper understanding of the power characteristics in the partial discharge process and its relationship with time, providing a strong quantitative basis for evaluating the severity of DC GIL insulation defects from the power and time dimensions, and helping to improve the accuracy and reliability of the entire insulation defect assessment method.

[0014] Optionally, in one embodiment of the present application, the calculation formula of the partial discharge signal cumulative product is:

[0015]

[0016] Where CP(q) is the cumulative product of partial discharge signal, n(qr ) is the apparent discharge equal to q r All discharge times, f PD (q r ) is the apparent discharge equal to q r All discharge repetition rates, T is the duration of the sample point, q is the discharge amount, and Q is the set of all discharge signal discharge amounts Q = {qr|r = 1, 2, …, N}.

[0017] Through the above technical solution, the embodiment of the present application can accurately calculate the cumulative product based on the partial discharge pulse current signal, and further extract characteristic parameters such as expectation, standard deviation, skewness and kurtosis through its two-dimensional relationship diagram with the apparent discharge amount. This calculation method helps to deeply explore the intrinsic characteristics of the partial discharge signal from the perspective of the cumulative product, and provides more dimensional and more refined characteristic information for the comprehensive assessment of the severity of DC GIL insulation defects, enhances the accuracy and comprehensiveness of insulation status assessment, and thus better guarantees the safe and stable operation of high-voltage DC gas-insulated metal-enclosed transmission lines.

[0018] Optionally, in one embodiment of the present application, the stage division of the fault evolution process is performed according to the fuzzy C-means clustering algorithm to generate a DC GIL insulation defect severity assessment result, including: randomly initializing the membership of each sample to each category; calculating each cluster center, and updating the membership of each category to calculate the objective function; iteratively updating until the reduction ratio of the objective function is lower than a preset minimum threshold, or reaches a preset maximum number of iterations, to obtain the membership of each sample belonging to different categories, so as to determine the DC GIL insulation defect severity assessment result.

[0019] Through the above technical scheme, the embodiment of the present application can provide a starting basis for subsequent calculations by randomly initializing the sample membership, and can effectively explore different clustering possibilities. The cluster center is calculated and the membership is updated to calculate the objective function. This process enables the algorithm to dynamically adjust the cluster center and the category attribution of the sample according to the data characteristics, and better adapt to complex data distribution. The iterative update mechanism, until the objective function is reduced by a ratio below the minimum threshold or the maximum number of iterations is reached, ensures the convergence and stability of the algorithm, prevents premature stopping and obtaining inaccurate results, and avoids excessive iterations causing waste of resources. Finally, the membership of each sample belonging to different categories is obtained, which can accurately reflect the fuzzy attribution relationship between samples in different categories, thereby accurately determining the DC GIL insulation defect severity assessment results, and improving the scientificity, objectivity and reliability of the assessment.

[0020] The second aspect of the present application provides a device for assessing the severity of a DC GIL insulation defect, including: an acquisition module for acquiring a local discharge pulse current signal of a high-voltage DC gas-insulated metal-enclosed transmission line GIL; an extraction module for extracting characteristic values ​​based on the GIL local discharge pulse current signal; and an assessment module for dividing the fault evolution process into stages according to a fuzzy C-means clustering algorithm to generate a DC GIL insulation defect severity assessment result.

[0021] Through the above technical solution, the embodiment of the present application can collect the GIL local discharge pulse current signal and extract characteristic values, and use the rich information contained in the local discharge phenomenon to quantitatively describe the insulation state from multiple dimensions. These characteristic values ​​provide a comprehensive and detailed data basis for subsequent accurate evaluation. Secondly, the fuzzy C-means clustering algorithm is used to divide the stages of the fault evolution process, which fully considers the fuzziness of the boundaries of each stage of PD development. Compared with the traditional non-fuzzy method, it can more accurately reflect the actual situation and make the evaluation results more in line with the actual process of the development of GIL insulation defects, thereby effectively avoiding misjudgment caused by inaccurate boundary division, helping to timely discover insulation defects and grasp their severity, and providing reliable technical support for ensuring the safe and stable operation of high-voltage DC GIL, which is of great significance to the safe and stable operation of power grids.

[0022] Optionally, in one embodiment of the present application, the extraction module includes: an acquisition unit, which is used to acquire at least one of a frequency density histogram of apparent discharge amount, a trend diagram of average discharge power changing with relative time, a frequency density histogram of first-order difference of apparent discharge amount, a frequency density histogram of natural logarithm of discharge time interval, and a two-dimensional relationship diagram of cumulative product of partial discharge signal and apparent discharge amount based on the GIL partial discharge pulse current signal; and an extraction unit, which is used to extract at least one corresponding partial discharge characteristic quantity according to the at least one.

[0023] Through the above technical scheme, the embodiment of the present application can obtain at least one of the frequency density histogram of the apparent discharge amount and other multiple charts to mine the key information in the partial discharge pulse current signal from different angles. At least one corresponding partial discharge feature quantity is extracted from these charts respectively to achieve multi-dimensional and multi-level feature extraction. The frequency density histogram of the apparent discharge amount is helpful to analyze the distribution law of the discharge amount, the trend chart of the average discharge power changing with relative time can reveal the dynamic change characteristics of the discharge power, the frequency density histogram of the first-order difference of the apparent discharge amount can reflect the trend characteristics of the discharge amount change, the frequency density histogram of the natural logarithm of the discharge time interval is helpful to grasp the characteristics of the discharge time interval, and the two-dimensional relationship diagram of the cumulative product of the partial discharge signal and the apparent discharge amount can further explore the relationship between the two. The extraction of multiple feature quantities provides a rich and accurate data basis for subsequent fault analysis and insulation defect assessment, so as to understand the insulation state of the GIL more comprehensively and deeply, and improve the accuracy and reliability of insulation defect diagnosis and assessment.

[0024] Optionally, in one embodiment of the present application, the calculation formula of the average discharge power changing with relative time trend graph is:

[0025]

[0026] Where DP(t) is the average discharge power at the relative time t (t∈(0,T]), U is the DC voltage applied to the two ends of the sample when the sample point is obtained, t is the relative time, q r is the apparent discharge capacity of the rth discharge, PDRT r for q r The corresponding relative time.

[0027] Through the above technical scheme, the embodiment of the present application can accurately depict the dynamic change trend of the average discharge power with relative time based on key physical quantities, which is helpful to intuitively observe and analyze the changes in the discharge power at different time points within the entire sample duration, and provides an accurate data basis for the subsequent extraction of characteristic parameters such as skewness and kurtosis from the trend chart, thereby enabling a deeper understanding of the power characteristics in the partial discharge process and its relationship with time, providing a strong quantitative basis for evaluating the severity of DC GIL insulation defects from the power and time dimensions, and helping to improve the accuracy and reliability of the entire insulation defect assessment method.

[0028] Optionally, in one embodiment of the present application, the calculation formula of the partial discharge signal cumulative product is:

[0029]

[0030] Where CP(q) is the cumulative product of partial discharge signal, n(qr ) is the apparent discharge equal to q r All discharge times, f PD (q r ) is the apparent discharge equal to q r All discharge repetition rates, T is the duration of the sample point, q is the discharge amount, and Q is the set of all discharge signal discharge amounts Q = {qr|r = 1, 2, …, N}.

[0031] Through the above technical solution, the embodiment of the present application can accurately calculate the cumulative product based on the partial discharge pulse current signal, and further extract characteristic parameters such as expectation, standard deviation, skewness and kurtosis through its two-dimensional relationship diagram with the apparent discharge amount. This calculation method helps to deeply explore the intrinsic characteristics of the partial discharge signal from the perspective of the cumulative product, and provides more dimensional and more refined characteristic information for the comprehensive assessment of the severity of DC GIL insulation defects, enhances the accuracy and comprehensiveness of insulation status assessment, and thus better guarantees the safe and stable operation of high-voltage DC gas-insulated metal-enclosed transmission lines.

[0032] Optionally, in one embodiment of the present application, the evaluation module includes: an initialization unit, used to randomly initialize the membership of each sample to each category; a calculation unit, used to calculate each cluster center and update the membership of each category to calculate the objective function; an evaluation unit, used to iteratively update until the reduction ratio of the objective function is lower than a preset minimum threshold, or reaches a preset maximum number of iterations, to obtain the membership of each sample belonging to different categories, so as to determine the evaluation result of the severity of the DC GIL insulation defect.

[0033] Through the above technical scheme, the embodiment of the present application can provide a starting basis for subsequent calculations by randomly initializing the sample membership, and can effectively explore different clustering possibilities. The cluster center is calculated and the membership is updated to calculate the objective function. This process enables the algorithm to dynamically adjust the cluster center and the category attribution of the sample according to the data characteristics, and better adapt to complex data distribution. The iterative update mechanism, until the objective function is reduced by a ratio below the minimum threshold or the maximum number of iterations is reached, ensures the convergence and stability of the algorithm, prevents premature stopping and obtaining inaccurate results, and avoids excessive iterations causing waste of resources. Finally, the membership of each sample belonging to different categories is obtained, which can accurately reflect the fuzzy attribution relationship between samples in different categories, thereby accurately determining the DC GIL insulation defect severity assessment results, and improving the scientificity, objectivity and reliability of the assessment.

[0034] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the DC GIL insulation defect severity assessment method as described in the above embodiment.

[0035] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for assessing the severity of DC GIL insulation defects.

[0036] A fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the above DC GIL insulation defect severity assessment method.

[0037] The embodiment of the present application can mine key information from multiple dimensions and extract multiple types of characteristic values ​​by collecting GIL local discharge pulse current signals and a series of processing, including apparent discharge quantity related charts, average discharge power trend charts, discharge time interval related charts, and local discharge signal cumulative product related charts, etc., to provide a rich and accurate data basis for insulation defect assessment, realize multi-dimensional and multi-level feature extraction, and deeply understand the insulation state of GIL, and improve the accuracy and reliability of diagnostic assessment. The average discharge power trend and cumulative product are calculated based on a specific formula, providing quantitative basis and feature mining from the perspective of power and time dimensions and cumulative product. Using the fuzzy C-means clustering algorithm, random initialization, dynamic adjustment, and iterative update operations ensure that the algorithm converges stably, accurately determines the sample category membership, fully considers the ambiguity of the development of local discharge, makes the evaluation results fit the real process, and avoids misjudgment, and ultimately provides reliable, scientific and objective technical support for ensuring the safe and stable operation of high-voltage DC GIL and the safe and stable operation of the power grid.

[0038] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0040] Figure 1 A flowchart of a method for evaluating the severity of DC GIL insulation defects provided according to an embodiment of the present application;

[0041] Figure 2 It is a structural schematic diagram of a DC GIL insulation defect severity assessment device provided according to an embodiment of the present application;

[0042] Figure 3 The figure is a structural example diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0043] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0044] The following describes the DC GIL insulation defect severity assessment method and device of the embodiment of the present application with reference to the accompanying drawings. In view of the problems of false alarms and missed alarms in partial discharge detection caused by various factors in the related technologies mentioned in the above background technology, the present application provides a DC GIL insulation defect severity assessment method. In this method, the GIL partial discharge pulse current signal can be collected and the characteristic value can be extracted, and the rich information contained in the partial discharge phenomenon can be used to quantitatively describe the insulation state from multiple dimensions. These characteristic values ​​provide a comprehensive and detailed data basis for subsequent accurate assessment. Secondly, the fuzzy C-means clustering algorithm is used to divide the stages of the fault evolution process, which fully considers the fuzziness of the boundaries of each stage of PD development. Compared with the traditional non-fuzzy method, it can more accurately reflect the actual situation and make the assessment results more in line with the real process of GIL insulation defect development, thereby effectively avoiding the misjudgment caused by inaccurate boundary division, helping to timely discover insulation defects and grasp their severity, and providing reliable technical support for ensuring the safe and stable operation of high-voltage DC GIL, which is of great significance to the safe and stable operation of the power grid. Therefore, the problems of false alarms and missed alarms in partial discharge detection caused by various factors in the related technology are solved.

[0045] Specifically, Figure 1 A flowchart of a method for evaluating the severity of DC GIL insulation defects provided in an embodiment of the present application.

[0046] like Figure 1 As shown, the DC GIL insulation defect severity assessment method includes the following steps:

[0047] In step S101, a local discharge pulse current signal of a high voltage direct current gas insulated metal enclosed transmission line GIL is collected.

[0048] It is understandable that the collection of local discharge pulse current signals can usually be carried out through methods including but not limited to ultrasonic methods, that is, local discharge is accompanied by the generation of current pulses, which causes the air around the local discharge point to expand and contract to form ultrasonic waves, which can be detected by an external ultrasonic sensor; ultra-high frequency method, local discharge will generate nanosecond current pulses, and then generate ultra-high frequency electromagnetic waves of 300mhz to 3ghz, and the electromagnetic wave signals are collected by sensors and processed to obtain local discharge characteristic quantities.

[0049] Specifically, the partial discharge pulse current signal sequence is represented by Q, Q = {q r |r=1,2,…,N}. That is, q r represents the apparent discharge amount of the rth discharge; the discharge time sequence is represented by PDT, PDT = {PDT r |r=1,2,…,N}, that is, PDT r represents the discharge time of the rth discharge, PDRT r =PDT r -PDT 1 for q r For the rth discharge, the forward discharge time interval is Δt pre =PDT r -PDT r-1 ; Its backward discharge interval is Δt suc =PDT r+1 -PDT r ; The first-order difference of its apparent discharge capacity is Δq = q r -q r-1 , the first-order difference of the discharge time interval is Δ(Δt) = PDT r -2PDT r-1 +PDT r-2 In addition, T is the duration of the sample point, n(q r ) and f PD (q r ) respectively represent the apparent discharge equal to q r DP(t) is the average discharge power at the relative time t(t∈(0,T]), CP is the accumulated product of partial discharge signal; U is the DC voltage applied to both ends of the sample when the sample point is obtained.

[0050] The pulse current signal collected by the embodiment of the present application contains rich insulation status information. Its amplitude, pulse width, rise time and other characteristics are closely related to the type and severity of insulation defects, and can provide a direct basis for subsequent accurate evaluation.

[0051] In step S102, a characteristic value is extracted according to the GIL partial discharge pulse current signal.

[0052] It can be understood that by extracting characteristic values ​​from the GIL partial discharge pulse current signal, the seemingly complex and abstract pulse current signal can be converted into a specific and quantifiable data indicator, so that these characteristic values ​​can be used with the help of relevant evaluation algorithms (such as but not limited to fuzzy C-means clustering algorithm, etc.) to perform subsequent fault evolution process stage division and accurate evaluation of the severity of insulation defects.

[0053] Optionally, in one embodiment of the present application, the extracting characteristic values ​​according to the GIL local discharge pulse current signal includes: based on the GIL local discharge pulse current signal, obtaining at least one of a frequency density histogram of apparent discharge quantity, a trend diagram of average discharge power changing with relative time, a frequency density histogram of first-order difference of apparent discharge quantity, a frequency density histogram of natural logarithm of discharge time interval, and a two-dimensional relationship diagram of cumulative product of local discharge signal and apparent discharge quantity; and extracting at least one corresponding local discharge characteristic quantity according to the at least one.

[0054] Specifically, the 28 eigenvalues ​​extracted in this application are as follows:

[0055] 1) H n (q) is the frequency density histogram of the apparent discharge amount q, and the PD feature extracted from this figure is the expected m 1 , standard deviation m 2 , Skewness, Kurtosis, Weibull distribution fitting parameters α and β, and optimal smoothing parameter h best , a total of 8 characteristic parameters.

[0056] 2) H t (DP) is the trend diagram of the average discharge power changing with the relative time t, that is, DP(t).

[0057] The calculation formula of DP(t) is:

[0058]

[0059] Where DP(t) is the average discharge power at the relative time t (t∈(0,T]), U is the DC voltage applied to the two ends of the sample when the sample point is obtained, t is the relative time, and q r is the apparent discharge capacity of the rth discharge, PDRT r Q r The corresponding relative moment. The extracted PD feature quantities are skewness and kurtosis, a total of 2 feature parameters.

[0060] 3) H n(Δq) is the frequency density histogram of the first-order difference Δq of the apparent discharge amount. The PD feature extracted from this figure is the expected m 1 , standard deviation m 2 , Skewness, Kurtosis, Peaks, and optimal smoothing parameter h best , a total of 6 characteristic parameters.

[0061] 4) H n (ln(Δt)) is the frequency density histogram of the natural logarithm of the discharge time interval ln(Δt) (since the distribution range of Δt values ​​may be very large, its natural logarithm is taken here). The PD feature quantity extracted from this figure is the expected m 1 , standard deviation m 2 , Skewness, Kurtosis, Peaks, and optimal smoothing parameter h best In addition, the probability distribution function of Δt (which must be non-negative) can be fitted using the Weibull distribution to obtain fitting parameters α and β, a total of 8 characteristic parameters.

[0062] 5) H q (CP) is a two-dimensional relationship diagram between the PD cumulative product CP and the apparent discharge amount q, where the calculation formula of CP is:

[0063]

[0064] Where CP(q) is the cumulative product of partial discharge signal, n(q r ) is the apparent discharge equal to q r All discharge times, f PD (q r ) is the apparent discharge equal to q r All discharge repetition rates, T is the duration of the sample point, q is the discharge amount, Q is the set of all discharge signal discharge amounts Q = {qr|r = 1, 2, ..., N}. The extracted PD feature is the expected m 1 , standard deviation m 2 , Skewness and Kurtosis, a total of 4 characteristic parameters.

[0065] The embodiments of the present application can obtain characteristic values ​​from charts of various dimensions, such as histograms related to apparent discharge, trend charts of average discharge power, etc., which can fully mine the information in the local discharge pulse current signal and make the evaluation more comprehensive. Secondly, the characteristic quantities extracted for different charts are rich and varied, covering various statistical parameters such as expectation, standard deviation, skewness, kurtosis, and Weibull distribution fitting parameters, etc. These characteristic quantities characterize the local discharge characteristics from different angles and improve the accuracy of insulation defect judgment. Furthermore, specific calculation formulas such as the formula for the cumulative product of average discharge power and local discharge signal are constructed based on key physical quantities, which provides an accurate basis for feature extraction, and by calculating the average discharge power at relative moments, the dynamic changes of discharge power can be deeply analyzed, which helps the evaluation from the power and time dimensions. In addition, extracting up to 28 characteristic values ​​can reflect the local discharge situation in a more nuanced way.

[0066] In step S103, the fault evolution process is divided into stages according to the fuzzy C-means clustering algorithm to generate a DC GIL insulation defect severity assessment result.

[0067] It can be understood that FCM (Fuzzy C-Means Clustering Algorithm) is based on fuzzy set theory to deal with clustering problems. It can obtain the membership degree of each sample belonging to different categories and can more objectively reflect the boundary fuzziness between various stages of PD development.

[0068] In the actual implementation process, based on the 28 feature quantities extracted in step S102, the fuzzy C-means clustering algorithm is used to divide the stages of the fault evolution process, thereby achieving severity assessment.

[0069] Specifically, let x i (i=1,2,…,M) is the sample to be clustered, CN is the set number of clusters, m j (j=1,2,…,CN) is the cluster center of the jth category and μ j (x)(j=1,2,…,CN) is the membership function of sample x to the jth category. The ultimate goal of FCM is to solve the membership of each category for all samples, that is, μ j (x)(i=1,2,…,M;j=1,2,…,CN), the formula is as follows:

[0070]

[0071] subject to the constraints:

[0072]

[0073] Among them, is the fuzzy allocation matrix index that controls the fuzzy overlap.

[0074] In some embodiments, the calculation steps of FCM include: randomly initializing the membership of each sample to each category; calculating each cluster center and updating the membership of each category to calculate the objective function; iteratively updating until the reduction ratio of the objective function is lower than a preset minimum threshold, or reaches a preset maximum number of iterations, to obtain the membership of each sample belonging to different categories, so as to determine the DC GIL insulation defect severity assessment result.

[0075] Specifically, the specific algorithm flow of FCM is as follows:

[0076] 1) Randomly initialize the membership of each sample to each category, that is, μ j (x i )(i=1,2,…,M; j=1,2,…,CN).

[0077] 2) Calculate each cluster center m j (j=1,2,…,CN), the formula is as follows:

[0078]

[0079] 3) μ j (x i ) is updated, the formula is as follows:

[0080]

[0081] 4) According to the existing m j (j=1,2,…,CN) and μ j (x i ) Calculate the objective function J of FCM FCM , the formula is as follows:

[0082]

[0083] 5) Repeatedly calculate and update each cluster center to calculate the objective function of FCM until the objective function of FCM is reduced by the minimum threshold set by the ratio after two iterations or reaches the specified maximum number of iterations.

[0084] The membership of each sample to different categories is used to determine the evaluation result of the severity of the DC GIL insulation defect. For example, if a sample has a high membership to the category representing severe insulation defects, then it can be judged that the GIL part corresponding to this sample has a relatively serious insulation defect; conversely, if the membership to the category representing minor insulation defects or normal status is high, it means that the insulation condition is relatively good. In this way, the severity of the DC GIL insulation defect is evaluated by using the membership information of the samples in the stage division of the fault evolution process.

[0085] The embodiment of the present application can process clustering problems based on fuzzy set theory, can accurately reflect the boundary fuzziness of each stage of PD development, conform to the uncertainty of the actual insulation defect development process, and make the evaluation results more in line with the actual situation. Secondly, by processing 28 feature quantities, full use is made of multi-dimensional feature information, and the data value is comprehensively and deeply mined to improve the accuracy and reliability of the evaluation. Furthermore, its calculation steps are rigorous and orderly, starting from randomly initializing the membership, gradually calculating the cluster center, updating the membership and calculating the objective function, and continuously optimizing the results during the iteration process, which not only avoids premature convergence leading to result deviation, but also prevents excessive iteration from causing waste of resources, and ensures stable convergence of the algorithm. Finally, the clear formula expression is easy to understand and implement, and has a strong scientificity and objectivity in determining the evaluation results of the severity of DC GIL insulation defects, and provides a solid technical support and effective decision-making basis for ensuring the safe and stable operation of high-voltage DC GIL.

[0086] According to the DC GIL insulation defect severity assessment method proposed in the embodiment of the present application, the insulation state can be quantitatively described from multiple dimensions by collecting the GIL local discharge pulse current signal and extracting characteristic values, using the rich information contained in the local discharge phenomenon, and these characteristic values ​​provide a comprehensive and detailed data basis for subsequent accurate assessment. Secondly, the fuzzy C-means clustering algorithm is used to divide the stages of the fault evolution process, which fully considers the fuzziness of the boundaries of each stage of PD development. Compared with the traditional non-fuzzy method, it can more accurately reflect the actual situation and make the assessment results more in line with the actual process of GIL insulation defect development, thereby effectively avoiding misjudgment caused by inaccurate boundary division, helping to timely discover insulation defects and grasp their severity, and providing reliable technical support for ensuring the safe and stable operation of high-voltage DC GIL, which is of great significance to the safe and stable operation of power grids.

[0087] Next, a device for assessing the severity of insulation defects of a DC GIL proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0088] Figure 2 4 is a block diagram of a device for assessing the severity of insulation defects of a DC GIL according to an embodiment of the present application.

[0089] like Figure 2 As shown, the DC GIL insulation defect severity assessment device 10 includes: a collection module 100 , an extraction module 200 and an assessment module 300 .

[0090] Specifically, the acquisition module 100 is used to acquire the local discharge pulse current signal of the high-voltage direct current gas-insulated metal-enclosed transmission line GIL.

[0091] The extraction module 200 is used to extract characteristic values ​​according to the GIL partial discharge pulse current signal.

[0092] The evaluation module 300 is used to divide the stages of the fault evolution process according to the fuzzy C-means clustering algorithm to generate a DC GIL insulation defect severity evaluation result.

[0093] Optionally, in one embodiment of the present application, the extraction module 200 includes: an acquisition unit and an extraction unit.

[0094] Among them, the acquisition unit is used to obtain at least one of the frequency density histogram of apparent discharge amount, the trend diagram of average discharge power changing with relative time, the frequency density histogram of first-order difference of apparent discharge amount, the frequency density histogram of natural logarithm of discharge time interval, and the two-dimensional relationship diagram of cumulative product of partial discharge signal and apparent discharge amount based on the GIL partial discharge pulse current signal.

[0095] The extraction unit is used to extract at least one corresponding local discharge characteristic quantity according to the at least one.

[0096] Optionally, in one embodiment of the present application, the calculation formula of the average discharge power changing with relative time trend graph is:

[0097]

[0098] Where DP(t) is the average discharge power at the relative time t (t∈(0,T]), U is the DC voltage applied to the two ends of the sample when the sample point is obtained, t is the relative time, q r is the apparent discharge capacity of the rth discharge, PDRT r Q r The corresponding relative time.

[0099] Optionally, in one embodiment of the present application, the calculation formula of the partial discharge signal cumulative product is:

[0100]

[0101] Where CP(q) is the cumulative product of partial discharge signal, n(q r ) is the apparent discharge equal to q rAll discharge times, f PD (q r ) is the apparent discharge equal to q r All discharge repetition rates, T is the duration of the sample point, q is the discharge amount, and Q is the set of all discharge signal discharge amounts Q = {qr|r = 1, 2, …, N}.

[0102] Optionally, in one embodiment of the present application, the evaluation module 300 includes: an initialization unit, a calculation unit and an evaluation unit.

[0103] The initialization unit is used to randomly initialize the membership of each sample to each category.

[0104] The calculation unit is used to calculate each cluster center and update the membership degree of each category to calculate the objective function.

[0105] The evaluation unit is used for iterative updating until the reduction ratio of the objective function is lower than a preset minimum threshold value or reaches a preset maximum number of iterations, and obtains the membership degree of each sample belonging to different categories to determine the evaluation result of the severity of the DC GIL insulation defect.

[0106] It should be noted that the above explanation of the embodiment of the DC GIL insulation defect severity assessment method is also applicable to the DC GIL insulation defect severity assessment device of this embodiment, and will not be repeated here.

[0107] According to the DC GIL insulation defect severity assessment device proposed in the embodiment of the present application, the insulation state can be quantitatively described from multiple dimensions by collecting the GIL local discharge pulse current signal and extracting characteristic values, using the rich information contained in the local discharge phenomenon, and these characteristic values ​​provide a comprehensive and detailed data basis for subsequent accurate assessment. Secondly, the fuzzy C-means clustering algorithm is used to divide the stages of the fault evolution process, which fully considers the fuzziness of the boundaries of each stage of PD development. Compared with the traditional non-fuzzy method, it can more accurately reflect the actual situation and make the assessment results more in line with the actual process of GIL insulation defect development, thereby effectively avoiding misjudgment caused by inaccurate boundary division, helping to timely discover insulation defects and grasp their severity, and providing reliable technical support for ensuring the safe and stable operation of high-voltage DC GIL, which is of great significance to the safe and stable operation of the power grid.

[0108] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0109] A memory 301 , a processor 302 , and a computer program stored in the memory 301 and executable on the processor 302 .

[0110] When the processor 302 executes the program, the DC GIL insulation defect severity assessment method provided in the above embodiment is implemented.

[0111] Furthermore, the electronic device further comprises:

[0112] The communication interface 303 is used for communication between the memory 301 and the processor 302 .

[0113] The memory 301 is used to store computer programs that can be run on the processor 302 .

[0114] The memory 301 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0115] If the memory 301, the processor 302 and the communication interface 303 are implemented independently, the communication interface 303, the memory 301 and the processor 302 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0116] Optionally, in a specific implementation, if the memory 301, the processor 302 and the communication interface 303 are integrated on a chip, the memory 301, the processor 302 and the communication interface 303 can communicate with each other through an internal interface.

[0117] The processor 302 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0118] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for assessing the severity of DC GIL insulation defects.

[0119] An embodiment of the present application also provides a computer program product, including a computer program, which is executed to implement the above DC GIL insulation defect severity assessment method.

[0120] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0121] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0122] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0124] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0125] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0126] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0127] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for evaluating the severity of DC GIL insulation defects, characterized in that: The following steps are involved: Collect the local discharge pulse current signal of high voltage DC gas insulated metal enclosed transmission line GIL; Extracting characteristic values ​​according to the GIL partial discharge pulse current signal; The fault evolution process is divided into stages according to the fuzzy C-means clustering algorithm to generate the DC GIL insulation defect severity assessment results.

2. The method according to claim 1, characterized in that The extracting characteristic values ​​according to the GIL partial discharge pulse current signal comprises: Based on the GIL partial discharge pulse current signal, obtaining at least one of a frequency density histogram of apparent discharge, a trend diagram of average discharge power changing with relative time, a frequency density histogram of first-order difference of apparent discharge, a frequency density histogram of natural logarithm of discharge time interval, and a two-dimensional relationship diagram of cumulative product of partial discharge signal and apparent discharge; At least one corresponding partial discharge characteristic quantity is extracted according to the at least one.

3. The method according to claim 2, characterized in that The calculation formula of the average discharge power versus relative time trend diagram is: Where DP(t) is the average discharge power at the relative time t (t∈(0,T]), U is the DC voltage applied to the two ends of the sample when the sample point is obtained, t is the relative time, q r is the apparent discharge capacity of the rth discharge, PDRT r for q r The corresponding relative time.

4. The method according to claim 2, characterized in that: The calculation formula of the partial discharge signal cumulative product is: Where CP(q) is the cumulative product of partial discharge signal, n(q r ) is the apparent discharge equal to q r All discharge times, f PD (q r ) is the apparent discharge equal to q r All discharge repetition rates, T is the duration of the sample point, q is the discharge amount, and Q is the set of all discharge signal discharge amounts Q = {qr|r = 1, 2, …, N}.

5. The method according to claim 1, characterized in that: The stage division of the fault evolution process according to the fuzzy C-means clustering algorithm to generate the DC GIL insulation defect severity assessment result includes: Randomly initialize the membership of each sample to each category; Calculating each cluster center and updating the membership of each category to calculate the objective function; Iterative update is performed until the reduction ratio of the objective function is lower than a preset minimum threshold value, or a preset maximum number of iterations is reached, and the membership degree of each sample belonging to different categories is obtained to determine the evaluation result of the severity of the DC GIL insulation defect.

6. A DC GIL insulation defect severity assessment device, characterized in that: include: An acquisition module is used to acquire the local discharge pulse current signal of the high-voltage DC gas-insulated metal-enclosed transmission line GIL; An extraction module, used for extracting characteristic values ​​according to the GIL partial discharge pulse current signal; The evaluation module is used to divide the stages of the fault evolution process according to the fuzzy C-means clustering algorithm to generate the DC GIL insulation defect severity evaluation result.

7. The device according to claim 6, characterized in that The extraction module comprises: an acquisition unit, for acquiring at least one of a frequency density histogram of apparent discharge amount, a trend diagram of average discharge power changing with relative time, a frequency density histogram of first-order difference of apparent discharge amount, a frequency density histogram of natural logarithm of discharge time interval, and a two-dimensional relationship diagram of cumulative product of partial discharge signal and apparent discharge amount based on the GIL partial discharge pulse current signal; The extraction unit is used to extract at least one corresponding local discharge characteristic quantity according to the at least one.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for assessing the severity of insulation defects of a DC GIL according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the DC GIL insulation defect severity assessment method as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the method for evaluating the severity of insulation defects of a DC GIL according to any one of claims 1 to 5.

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