A method for judging faults in the grounding grid of a hydropower station based on the temporal and spatial distribution of surface potential

Abstract

The present invention discloses a method for judging the fault of a hydropower station grounding grid in combination with the spatiotemporal distribution of the surface potential, and relates to the technical field of grounding grid fault detection, including obtaining the on-site surface potential value data of the grounding grid of a historical target hydropower station, constructing a surface potential data set, and introducing edge data to normalize the surface potential data set; using a B-type correlation model and a T-type correlation model to analyze the surface potential data set from the perspective of time characteristics and spatial characteristics, respectively, to determine the proximity and similarity of the surface potential at each characteristic point; and judging the fault of the hydropower station grounding grid based on the analysis results of the two correlation models. The present invention comprehensively analyzes the spatiotemporal distribution characteristics of the surface potential, and can more accurately diagnose the fault type, fault degree and fault location of the grounding grid, avoiding the blindness of traditional large-scale excavation and maintenance, with small workload and high efficiency, which is conducive to timely discovery and elimination of hidden dangers of the grounding grid and ensuring the safe operation of the power system.

Description

Technical Field

[0001] The invention relates to the technical field of grounding grid fault detection, in particular to a method for determining grounding grid faults in a hydropower station in combination with the temporal and spatial distribution of surface potential. Background Art

[0002] The grounding grid is an important guarantee for substations to protect the safety of power systems. Its main function is to provide a channel for the fault current to disperse. The grounding performance of a grounding grid is directly related to the personal safety of the staff in the power plant and the safe operation of various electrical equipment. my country's power system mostly uses galvanized flat steel, round steel and other grounding grid conductors as grounding grid materials, but steel materials have the characteristics of low conductivity and poor corrosion resistance. In particular, conductor metals are more prone to corrosion when working in humid or saline-alkali soil for a long time. With the intensification of the corrosion of the grounding grid conductor, the grounding resistance of the grounding grid continues to increase. When lightning current or fault current enters the ground, it will cause the grounding grid conductor to break due to heat or large current electromotive force. The corrosion and fracture of the grounding grid will not only lead to poor grounding effect, poor lightning protection and electric shock protection, but also damage the power system equipment, which may cause power system failures and bring economic losses to substations and society.

[0003] The traditional method of fault diagnosis of grounding grid is to conduct large-scale spot checks on the grounding grid. Although this method is simple, it is blind, labor-intensive, and slow to troubleshoot. In order to diagnose the fault of the grounding grid without excavation, the existing methods of fault diagnosis of grounding grid mainly include on-resistance method, electrochemical detection method, magnetic induction intensity method, and surface potential method. The surface potential method is also one of the hot spots in the current research on grounding grid corrosion diagnosis. For an actual substation grounding grid, after the design and construction are completed, the topological structure diagram and spatial position of the grounding grid are already determined. When the fault current flows into the grounding grid, a unique potential distribution will be generated in the spatial field around the entire grounding grid. When the grounding grid is corroded, broken, or other faults occur, causing the field source to change, the electric field and potential distribution in its spatial domain will also change with the change of the field source. Therefore, the change in potential distribution in the spatial field around the grounding grid can reflect the corresponding change in the field source, that is, the grounding grid. This idea can be used to guide the fault identification of the grounding grid. Although existing studies have given the calculation or measurement results of the grounding grid's surface potential and magnetic induction intensity, and have also located or diagnosed grounding grid faults to a certain extent, they are mainly based on simulation calculations. The grounding grid model used is significantly different from the shape and fault characteristics of the actual grounding grid, and the accuracy of the relevant results needs to be considered. Summary of the invention

[0004] In view of the problems existing in the existing method for determining faults in the grounding grid of a hydropower station in combination with the temporal and spatial distribution of the surface potential, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that there are large differences between the shape and fault characteristics of the grounding grid model and the actual grounding grid, and the grounding grid model lacks accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a method for judging a grounding grid fault in a hydropower station in combination with the spatiotemporal distribution of surface potential, comprising: obtaining on-site surface potential value data of the grounding grid of a historical target hydropower station, constructing a surface potential data set, and introducing edge data to normalize the surface potential data set; using a B-type correlation model to analyze the surface potential data set from a temporal feature perspective, and determining the proximity and similarity of the surface potential at each feature point; using a T-type correlation model to analyze the surface potential data set from a spatial feature perspective, and determining the similarity of surface potential change trends on different observation lines; and judging a grounding grid fault in a hydropower station based on the analysis results of the B-type correlation model and the T-type correlation model.

[0008] As a preferred solution of the method for determining a grounding grid fault in a hydropower station in combination with the spatiotemporal distribution of the surface potential described in the present invention, the construction of the surface potential data set includes: during the operation period of the hydropower station, measuring the surface potential at fixed intervals to obtain long-term continuous monitoring data; injecting an AC excitation current into the grounding grid through an accessible node using an AC signal generator, and measuring the surface potential above the grounding conductor using an industrial frequency parameter tester and a detection coil to obtain measurement data; arranging multiple measurement points at characteristic points along different observation lines around the grounding grid to obtain surface potential values ​​at different locations; constructing an original data set X{0} measured in the initial stage, and each subsequent measurement data set is represented as X{k}.

[0009] As a preferred solution of the method for judging the fault of the grounding grid of a hydropower station combined with the spatiotemporal distribution of the surface potential of the present invention, the normalization process is to correct the original surface potential value U' by introducing a correction coefficient α related to the edge distance to obtain a normalized potential value U, which is expressed as:

[0010]

[0011] Among them, α is the correction coefficient, and the calculation formula of α is expressed as:

[0012]

[0013] Among them, r 0 is the radius of the grounding grid, d is the distance from the measuring point to the edge of the grounding grid, and d 0is the empirical discrete length; perform local area correction on the data, divide the entire observation area into several small areas, calculate a correction coefficient in each area as a benchmark for the measuring points in each small area, and perform micro correction on each measuring point in the area:

[0014] U = U * (1+Δ)

[0015] Among them, Δ is a function of the distance between the measuring point and the center of the area. The closer the distance, the smaller Δ.

[0016] As a preferred solution of the method for judging the fault of the hydropower station grounding grid in combination with the spatiotemporal distribution of the surface potential of the present invention, the type B correlation model analyzes the change of the surface potential of each observation line over time. l The surface potential array collected is represented by X{k}, k=1,2,.., l It is converted into a two-dimensional matrix y consisting of time and measurement point m*n, which is used as the input parameter of the B-type correlation model to analyze the correlation rb of the surface potential data under different time series, which is expressed as:

[0017] ,

[0018] in, , , Discrete functions and The displacement difference, first-order slope difference, and second-order slope difference;

[0019] ,

[0020] ,

[0021] ,

[0022] in, and k is an alphabetical index, k is the time index, It is the index of the measurement point. When the fault current flows into the grounding grid, a unique potential distribution is generated in the spatial field around the entire grounding grid. When the grounding grid is corroded or broken, causing the field source to change, the electric field and potential distribution in the spatial domain will also change with the change of the field source. Based on the comparison of the results of the correlation analysis of each measurement data and the difference between each measurement and the initial measurement value, it is determined whether a fault has occurred.

[0023] As a preferred embodiment of the method for judging faults in the grounding grid of a hydropower station by combining the spatio-temporal distribution of surface potential according to the present invention, the following is provided: Based on the B-type correlation degree model, thresholds are set for the correlation degree rb, including a first threshold Q1, a second threshold Q2, a third threshold Q3, and a fourth threshold Q4, and Q1 > Q2 > Q3 > Q4; if Q1 < rb < 1, it indicates that the grounding grid is in good condition and in normal use; if Q2 < rb < Q1, there are defects in the grounding grid, and daily inspections need to be strengthened to find and eliminate potential hazards in a timely manner; if Q3 < rb < Q2, there are faults in local areas of the grounding grid, and on-site inspections need to be carried out immediately to determine the location of the fault area and implement targeted repairs or partial replacements for the fault area; if Q4 < rb < Q3, the overall condition of the grounding grid is poor and there are serious defects, and various electrical parameters need to be复测, the root cause of the fault needs to be comprehensively investigated, and an emergency temporary repair or overall renovation plan needs to be formulated; if 0 < rb < Q4, the entire grounding grid has seriously failed and cannot operate normally, and the equipment operation needs to be stopped immediately, a grounding grid reconstruction plan needs to be formulated, and at the same time, the cause of the fault needs to be thoroughly investigated.

[0024] As a preferred embodiment of the method for judging faults in the grounding grid of a hydropower station by combining the spatio-temporal distribution of surface potential according to the present invention, the following is provided: The T-type correlation degree model analyzes the surface potential data set from the perspective of spatial characteristics to determine the similarity of the change trends of surface potential on different observation lines. For the latest collected surface potential data X{l}, within the range of [m, n], the increments of the reference sequence and the comparison sequence are respectively:

[0025] ,

[0026] ,

[0027] ,

[0028] Among them, represents the original data of the surface potential, represents the newly measured data of the surface potential, and D represents the length between two measurement points k and k - 1; the calculation of the T-type correlation coefficient is expressed as:

[0029] ,

[0030] ,

[0031] Among them, and are respectively the arrays of the original data and the newly measured data of the surface potential, represents the original surface potential data of the kth measurement point and the (k - 1)th measurement point at time t, represents the newly measured surface potential data of the kth measurement point and the (k - 1)th measurement point at time t, It represents the difference between the data of the k-th measurement point and the (k - 1)-th measurement point.

[0032] As a preferred embodiment of the method for judging the fault of the grounding grid of a hydropower station by combining the spatio-temporal distribution of the surface potential, the method includes: setting thresholds for the correlation degree rt based on the T-type correlation degree modulus, including a first threshold Q5, a second threshold Q6, a third threshold Q7, and a fourth threshold Q8, and Q5 > Q6 > Q7 > Q8; if Q5 < rt < 1, it indicates that there is a deviation between the potential curve of this line and the reference curve, and it is necessary to pay close attention and promptly check and eliminate potential hazards; if Q7 < rt < Q5, it indicates that there is already a deviation between the potential curve of this line and the reference curve and there is a fault, and it is necessary to conduct in-depth detection on this line to determine the fault location and implement targeted repairs; if Q8 < rt < Q7, it indicates that the potential curve of this line is significantly different from the normal state and there are serious potential fault hazards, and it is necessary to conduct a comprehensive inspection along this line and prepare to replace some grounding conductors or the entire line; if 0 < rt < Q8, it indicates that the observed line has completely lost its normal potential characteristics and the grounding conductor or the line itself has failed, and it is necessary to demolish and reconstruct the entire line; further, combining the B-type correlation degree model and the T-type correlation degree model for explanation, if rb > Q1 and at least m% or more of the rt > Q5, it indicates that the overall condition of the grounding grid is good and there are no obvious faults in the line; if Q3 < rb ≤ Q2 and p% - q% of the rt < Q6, it indicates that there are defects in the grounding grid, and it is necessary to focus on checking the lines with rt values lower than Q6 to find out the reasons for the defects; if rb ≤ Q3 and k% of the rt < Q6, it indicates that the overall grounding grid has serious faults, and it is necessary to temporarily stop the operation of the equipment, comprehensively check the root causes of the faults, implement emergency repairs or formulate a plan for overall reconstruction of the grounding grid.

[0033] In a second aspect, an embodiment of the present invention provides a system for judging the fault of the grounding grid of a hydropower station by combining the spatio-temporal distribution of the surface potential, which includes: a construction module for obtaining the data of the surface potential values at the grounding grid site of a historical target hydropower station, constructing a surface potential data set, and introducing marginal data to normalize the surface potential data set; an observation module for analyzing the surface potential data set from different angles using the B-type correlation degree model and the T-type correlation degree model respectively to determine the similarity and resemblance of the surface potential at each characteristic point; and a judgment module for judging the fault of the grounding grid of the hydropower station according to the analysis results of the B-type correlation degree model and the T-type correlation degree model.

[0034] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where: when the processor executes the computer program, any step of the method for judging the fault of the grounding grid of a hydropower station by combining the spatio-temporal distribution of the surface potential described above is implemented.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for determining a grounding grid fault in a hydropower station combined with the spatiotemporal distribution of the surface potential is implemented.

[0036] The beneficial effect of the present invention is that the present invention obtains real data on site through long-term continuous monitoring, comprehensively reflects the potential fault conditions of the entire grounding grid, and the data is more accurate and reliable. The edge correction coefficient and local area correction are introduced to eliminate edge effects and local non-uniformity, and improve the accuracy of data processing. The B-type correlation model is used to analyze the surface potential data from the perspective of time characteristics, and the difference between each measurement and the initial value can be accurately compared to determine whether a fault has occurred. The T-type correlation model is used to analyze the surface potential changes of different observation lines from the perspective of spatial characteristics to determine the location of the fault point. Further combining the B-type and T-type correlation models, comprehensively analyzing the spatiotemporal distribution characteristics of the surface potential, can more accurately diagnose the fault type, fault degree and fault location of the grounding grid. It avoids the blindness of traditional large-scale excavation and maintenance, has a small workload and high efficiency, and is conducive to timely discovery and elimination of hidden dangers in the grounding grid to ensure the safe operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0038] Figure 1 is a flow chart of the present invention;

[0039] Figure 2 This is a schematic diagram of measurement data for the fault judgment method of the hydropower station grounding grid. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0043] Example 1

[0044] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for determining a grounding grid fault in a hydropower station in combination with the temporal and spatial distribution of the surface potential, comprising:

[0045] S1: Acquire the on-site surface potential value data of the grounding grid of the historical target hydropower station, construct a surface potential dataset, and introduce edge data to normalize the surface potential dataset.

[0046] During the operation of the hydropower station, the surface potential is measured at regular intervals to obtain long-term continuous monitoring data;

[0047] Use an AC signal generator to inject AC excitation current into the grounding grid through an accessible node, use a power frequency parameter tester and a detection coil to measure the surface potential above the grounding conductor to obtain relatively accurate data;

[0048] Along different observation lines around the grounding grid, multiple measurement points are arranged at characteristic points to obtain the surface potential values ​​at different locations, which fully reflects the potential fault conditions of the entire grounding grid;

[0049] The original data set X{0} measured in the initial construction period is represented as a two-dimensional array consisting of the surface potentials (i, j) of m measuring points on n lines, i=1,2,..,m; j=1,2,...,n. The data set of each subsequent measurement can be represented as X{k}, k=1,2,...,l-1.

[0050] The normalization process is to correct the original surface potential value U' by introducing a correction coefficient α related to the edge distance to obtain the normalized potential value U, which is expressed as:

[0051] ,

[0052] Among them, α is a value between 0 and 1, which is related to the distance d from the measuring point to the edge of the grounding grid. The closer the distance, the smaller α is. The calculation formula of α is expressed as:

[0053] ,

[0054] Among them, r 0 is the radius of the grounding grid, d is the distance from the measuring point to the edge of the grounding grid, and d 0 is the empirical discrete length;

[0055] Furthermore, the data is corrected in a local area. The entire observation area is divided into several small areas. For each measuring point in each small area, a correction coefficient is calculated as a benchmark, and each measuring point in the area is corrected slightly:

[0056] U = U * (1+Δ)

[0057] Among them, Δ is a function based on the distance between the measuring point and the center of the area. The closer the distance, the smaller Δ.

[0058] The entire observation area is divided into several small areas, and a unified benchmark correction is performed on the measuring points in each area. However, in fact, even in the same small area, the degree of influence of the edge effect will vary due to the different distances between the measuring point and the center of the area. It is necessary to further fine-tune each measuring point based on the benchmark correction.

[0059] Among them, Δ is a function of the distance d from the measuring point to the center of the area. The smaller the distance d, the smaller Δ and the smaller the correction amount. The function is expressed as:

[0060] ,

[0061] Where d is the distance from the measuring point to the center of the area, d 0 is the effective radius of the region, which can be taken as the average radial size of the region, k is a constant that controls the magnitude of Δ, and n is an exponent greater than 1 that controls the sensitivity of Δ to d.

[0062] This function ensures that when d=0, that is, the measuring point is at the center of the region, Δ=0, and no micro-correction is required. When d is close to d0, Δ approaches k. At this time, the measuring point is close to the edge of the region and the maximum micro-correction is required. The larger the value of n, the more sensitive Δ is to the change of d, which can better reflect the spatial distribution characteristics of the edge effect.

[0063] By properly selecting k and n, a reasonable Δ(d) function can be constructed to implement fine micro-correction for each measuring point:

[0064] U 修正后 = U 基准修正 * (1 + Δ),

[0065] Not only was the benchmark correction implemented for the entire area, but corresponding fine-tuning corrections were also applied to the special location of each measuring point, thereby better eliminating the influence of edge effects and obtaining more accurate surface potential distribution data.

[0066] S2: Using a B-type correlation model to analyze the surface potential data set from a time feature perspective, determine the proximity and similarity of the surface potential at each feature point.

[0067] The B-type correlation model is used to analyze the surface potential data set from the perspective of time characteristics to determine the proximity and similarity of the surface potential at each characteristic point over the years. Over time, the surface potential measured at each point in the grounding grid may change slightly.

[0068] The B-type correlation model analyzes the change of surface potential over time on each observation line. The B-type correlation model simultaneously examines the proximity and similarity between the comparison sequence and the reference sequence, comprehensively considering the overall displacement difference, the overall first-order slope difference and the overall second-order slope difference. l The surface potential array collected is represented by X{k}, k=1,2,.., l It is converted into a two-dimensional matrix y consisting of time and measurement point m*n, which is used as the input parameter of the B-type correlation model to analyze the correlation rb of the surface potential data under different time series, which is expressed as:

[0069] ,

[0070] in, , , Discrete functions and The displacement difference, first-order slope difference, and second-order slope difference;

[0071] ,

[0072] ,

[0073] ,

[0074] in, and k is an alphabetical index, k is the time index, The index of the measurement point.

[0075] When a fault current flows into the grounding grid, a unique potential distribution will be generated in the spatial field around the entire grounding grid. When the grounding grid undergoes faults such as corrosion and fracture, resulting in a change in the field source, the electric field and potential distribution within its spatial domain will also change with the change of the field source. Then, based on the comparison of the results of the correlation analysis of each measurement data, the difference between each measurement and the initial measurement value can be accurately compared to determine whether a fault has occurred.

[0076] For the grounding grid of a hydropower station, after the design and construction are completed, the topological structure diagram and spatial position of the grounding grid are already determined. When a fault current flows into this grounding grid, a unique potential distribution will be generated in the spatial field around the entire grounding grid. When the grounding grid undergoes faults such as corrosion and fracture, resulting in a change in the field source, the electric field and potential distribution within its spatial domain will also change with the change of the field source. Therefore, according to the comparison of the results of the correlation analysis of each measurement data in the present invention, the difference between each measurement and the initial measurement value can be accurately compared to determine whether a fault has occurred. And based on the data of the correlation degree rb calculated every year, the specific time and trend of the fault occurrence can be analyzed.

[0077] Set a threshold for the correlation degree rb:

[0078] If 0.9 < rb < 1, it indicates that the grounding grid is in good condition and in normal use;

[0079] If 0.7 < rb < 0.9, the grounding grid may have minor defects, and it is necessary to strengthen daily inspections to find and eliminate potential hazards in a timely manner;

[0080] If 0.4 < rb < 0.7, there are certain degrees of faults in local areas of the grounding grid, and it is necessary to immediately carry out on-site inspections to determine the location of the fault area and implement targeted repairs or local replacements for the fault area;

[0081] If 0.2 < rb < 0.4, the overall condition of the grounding grid is not good, and there are serious defects in a large area. It is necessary to复测各项电气参数,全面排查故障根源并制定应急临时维修或整体翻修计划;

[0082] If 0 < rb < 0.2, the entire grounding grid has seriously failed and cannot operate normally. It is necessary to immediately stop the operation of the equipment, formulate a grounding grid reconstruction plan, and at the same time thoroughly investigate the cause of the fault.

[0083] S3: Analyze the surface potential data set from the perspective of spatial characteristics by using the T-type correlation model to determine the similarity of the change trends of the surface potential on different observation lines.

[0084] It should be noted that there is an unclear expression "复测各项电气参数,全面排查故障根源并制定应急临时维修或整体翻修计划" in the original text. It may need to be further clarified for a more accurate translation. The above translation is based on the existing text as much as possible.The T - type correlation model analyzes the surface potential data set from the perspective of spatial characteristics to determine the similarity of the change trends of surface potentials on different observation lines. For the newly collected surface potential data X{l}, considering the data within the range [m, n], for two sequence curves, let The increments of the reference sequence and the comparison sequence are respectively:

[0085] ,

[0086] ,

[0087] ,

[0088] where represents the original data of the surface potential, represents the newly measured data of the surface potential, represents the length between two measuring points k and k - 1;

[0089] The calculation of the T - type correlation coefficient is expressed as:

[0090] ,

[0091] ,

[0092] where and are respectively the arrays of the original data and the newly measured data of the surface potential, represents the original surface potential data of the k - th measuring point and the (k - 1)-th measuring point at time t, represents the newly measured surface potential data of the k - th measuring point and the (k - 1)-th measuring point at time t, represents the difference between the data of the k - th measuring point and the (k - 1)-th measuring point.

[0093] For each potential curve, calculate the sum of the T - type correlation degrees between this curve and other curves to determine the location of the fault point. For the surface potential curves on each measurement line, if the T - type correlation degree between this curve and other curves is small, it indicates that the similarity between this curve and other curves is low and a fault may occur.

[0094] Set a threshold for the correlation degree rt:

[0095] If 0.9 < rt < 1, it indicates that there is a certain deviation between the potential curve of this line and the reference curve, but the overall deviation is not large, and there may be minor defects. It is necessary to pay close attention and check and eliminate potential hazards in a timely manner;

[0096] If 0.7 < rt < 0.9, it indicates that there is a large deviation between the potential curve of this line and the reference curve, and there is probably a certain degree of fault. It is necessary to conduct in-depth detection on this line to determine the fault location and implement targeted repairs;

[0097] If 0.5 < rt < 0.7, it indicates that there is a large deviation between the potential curve of this line and the reference curve, and there is likely a certain degree of fault. It is necessary to conduct in-depth detection on this line to determine the fault location and implement targeted repairs;

[0098] If 0.3 < rt < 0.5, it indicates that the potential curve of this line is very different from the normal state, and there are serious potential fault hazards. It is necessary to conduct a comprehensive inspection along this line and prepare to replace some grounding conductors or the entire line;

[0099] If 0 < rt < 0.3, it indicates that the observed line has completely lost its normal potential characteristics, and the grounding conductor or the line itself is basically ineffective. It is necessary to demolish and reconstruct the entire line.

[0100] S4: Judge the faults of the grounding grid of the hydropower station based on the analysis results of the B-type correlation model and the T-type correlation model.

[0101] Combining the B-type correlation model and the T-type correlation model,

[0102] If rb > 0.9 and at least more than 90% of rt > 0.9, it indicates that the overall condition of the grounding grid is good, and there are no obvious faults in most lines.

[0103] If 0.5 < rb ≤ 0.7 and 15% - 30% of rt < 0.9, it indicates that the overall grounding grid is still acceptable, but there are already some defects in some lines. It is necessary to focus on checking the lines with rt values less than 0.7 to investigate the causes of the defects;

[0104] If 0.5 < rb ≤ 0.7, but 70% of 0.7 < rt < 0.9 exists, it indicates that a certain degree of fault has occurred in the overall grounding grid, but the situation of most lines is still acceptable. It is necessary to immediately conduct an overall detection, focus on checking the abnormal lines with rt < 0.7, and implement local repairs on the severely defective lines with rt < 0.5;

[0105] If 0.4 < rb ≤ 0.7 and 30% - 50% of rt < 0.5, it indicates that there are widespread defects in the grounding grid, and a considerable part of the lines have serious faults. It is necessary to start from the severely defective lines with rt < 0.3, formulate a replacement or full-line maintenance plan, and conduct a thorough overhaul of the overall grounding grid;

[0106] If rb≤0.4, and 70% of rt<0.7, it indicates that the grounding network as a whole has experienced serious faults and most of the lines have obvious defects. It is necessary to temporarily stop the equipment operation, comprehensively investigate the root cause of the fault, implement emergency repairs or formulate an overall reconstruction plan for the grounding network.

[0107] In summary, the present invention organically combines the overall grounding grid status assessment and the local line detail analysis, establishes a scientific and reasonable hierarchical diagnosis standard, can efficiently and accurately determine the fault level, defect range and specific location of the grounding grid, and gives corresponding maintenance decision suggestions. It effectively integrates theoretical models and engineering practices, has clear diagnostic ideas and strong operability, can make full use of limited monitoring data, and realizes a comprehensive and in-depth intelligent assessment of fault conditions, thereby guiding the implementation of accurate and efficient maintenance and ensuring the long-term safe and reliable operation of the grounding grid. It has significant practical value and broad application prospects.

[0108] Example 2

[0109] Reference Figure 1 Based on the first embodiment, this embodiment further provides a hydropower station grounding grid fault judgment system combining the temporal and spatial distribution of surface potential, including:

[0110] A construction module is used to obtain the on-site surface potential value data of the grounding grid of the historical target hydropower station, construct a surface potential data set, and introduce edge data to normalize the surface potential data set;

[0111] The observation module uses the B-type correlation model and the T-type correlation model to analyze the surface potential data set from different angles to determine the proximity and similarity of the surface potential at each feature point;

[0112] The judgment module is used to judge the grounding grid fault of the hydropower station according to the analysis results of the B-type correlation model and the T-type correlation model.

[0113] This embodiment also provides a computer device, which is suitable for the method of determining the fault of a grounding grid of a hydropower station in combination with the temporal and spatial distribution of the surface potential, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method of determining the fault of a grounding grid of a hydropower station in combination with the temporal and spatial distribution of the surface potential as proposed in the above embodiment.

[0114] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0115] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for determining a fault in a grounding grid of a hydropower station in combination with the spatiotemporal distribution of the surface potential as proposed in the above embodiment is implemented.

[0116] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0117] Example 3

[0118] On the basis of the previous two embodiments, this embodiment provides a method for determining a grounding grid fault in a hydropower station in combination with the temporal and spatial distribution of the surface potential. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0119] A 500 kV substation grounding grid has 48 observation lines. The monitoring data of the past 10 years show that it has defects to varying degrees. The traditional method and the dual correlation model method of the present invention are used to judge the fault of the grounding grid, and the results are compared with the actual defect situation.

[0120] The test results of the present invention are:

[0121] Type B model analysis: rb = 0.63, judging as "the overall grounding grid has experienced a certain degree of fault and needs to be checked immediately"

[0122] T-type model analysis: The three lines with the lowest rt values ​​are: Line 7 (rt=0.38), Line 22 (rt=0.41), and Line 39 (rt=0.47). It is judged that "these lines have serious defects and need to be repaired as a priority". The rt of the remaining lines is generally in the range of 0.6-0.8, and the status is acceptable.

[0123] According to the diagnosis results, lines 7, 22 and 39 were tested in particular, and it was found that line 7 had 8 serious corrosions and 3 fractures, line 22 had a 5-meter continuous grounding body fracture, and line 39 had multiple joints with poor contact.

[0124] The results are shown in Table 1:

[0125] Table 1 Comparison of grounding grid fault judgment methods

[0126] Judgment method Traditional methods Double correlation model method of the present invention Overall Assessment Simply assessed as "unqualified" Type B model: rb=0.63, judged as "some overall faults exist, need to be detected" Defect Identification Failure to identify defect scope and location The T-type model indicates that the five lines with the lowest rt are the key defective lines: Line 7 (rt=0.38), Line 22 (rt=0.41), Line 39 (rt=0.47) Targeted testing Unable to conduct targeted testing Focusing on testing lines 7, 22, and 39, we found that: Line 7: 8 corrosions and 3 breaks; Line 22: 5-meter continuous breaks; Line 39: multiple poor contacts Maintenance Decision Unable to provide targeted maintenance suggestions Carry out local emergency maintenance and repair key defective lines Repair effect No way to evaluate Re-measured rb=0.78, rt is the lowest 0.71, judging "the overall situation has returned to normal, and the defective line is in good condition"

[0127] From the above table, we can see that the present invention can quickly locate the location of the serious defect area and guide the implementation of targeted local repairs. After the repair, the overall condition of the grounding grid is effectively improved. In contrast, the traditional single parameter method can only passively discover "unqualified" but cannot actively identify the defect location and take targeted measures. The diagnostic method of the present invention combines the advantages of different levels of correlation models, can comprehensively analyze the overall and local conditions of the grounding grid, escort efficient operation and maintenance, and has great practical value and application prospects.

[0128] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for determining faults in a hydropower station grounding grid in combination with the temporal and spatial distribution of surface potential, characterized in that: Including, Obtain the on-site surface potential value data of the grounding grid of the historical target hydropower station, construct a surface potential data set, and introduce edge data to normalize the surface potential data set; Use the B-type correlation model to analyze the surface potential data set from the perspective of time characteristics, and determine the proximity and similarity of the surface potential at each characteristic point; Use the T-type correlation model to analyze the surface potential data set from the perspective of spatial characteristics, and determine the similarity of the change trends of the surface potential on different observation lines; Judge the grounding grid fault of the hydropower station based on the analysis results of the B-type correlation model and the T-type correlation model; The normalization process is to correct the original surface potential value U' by introducing a correction coefficient α related to the edge distance to obtain the normalized potential value U, which is expressed as: ， Where α is the correction coefficient, and the calculation formula of α is expressed as: ， Where r0 is the radius of the grounding grid, d is the distance from the measurement point to the edge of the grounding grid, and d0 is the empirical discrete length; Perform local area correction on the data. Divide the entire observation area into several small areas. For each measurement point in each small area, calculate a regional correction coefficient as a reference, and perform micro-correction on each measurement point in the area: U = U * (1+Δ) Where Δ is a function of the distance between the measurement point and the center of the area. The closer the distance, the smaller Δ; The type B correlation model analyzes the change of surface potential over time on each observation line. l The surface potential array collected is represented by X{k}, k=1,2,.., l It is converted into a two-dimensional matrix y consisting of time and measurement point m*n, which is used as the input parameter of the B-type correlation model to analyze the correlation rb of the surface potential data under different time series, which is expressed as: ， in, , , Discrete functions and The displacement difference, first-order slope difference, and second-order slope difference; ， ， ， in, and k is an alphabetical index, k is the time index, is the index of the measurement point; When a fault current flows into the grounding grid, a unique potential distribution is generated in the spatial field around the entire grounding grid. When the grounding grid fails due to corrosion, fracture, etc., resulting in a change in the field source, the electric field and potential distribution in its spatial domain will also change with the change of the field source. Then, compare the results of the correlation analysis of each measurement data, and compare the difference between each measurement and the initial measurement value to determine whether a fault has occurred; Based on the B-type correlation model, set thresholds for the correlation degree rb, including the first threshold Q1, the second threshold Q2, the third threshold Q3, and the fourth threshold Q4, and Q1>Q2>Q3>Q4; If Q1<rb<1, it indicates that the grounding grid is in good condition and in normal use; If Q2<rb<Q1, there are defects in the grounding grid, and daily inspections need to be strengthened to find and eliminate potential hazards in a timely manner; If Q3<rb<Q2, there is a fault in a local area of the grounding grid, and on-site detection needs to be carried out immediately to determine the location of the fault area, and targeted maintenance or local replacement should be implemented for the fault area; If Q4<rb<Q3, the entire grounding grid has serious defects, and various electrical parameters need to be复测, and the root cause of the fault should be comprehensively investigated and an emergency temporary repair or overall renovation plan should be formulated; If 0<rb<Q4, the entire grounding grid has seriously failed and cannot operate normally. It is necessary to immediately stop the operation of the equipment, formulate a grounding grid reconstruction plan, and at the same time thoroughly investigate the cause of the fault; The T-type correlation model analyzes the surface potential data set from the perspective of spatial characteristics to determine the similarity of the change trends of the surface potential on different observation lines. For the latest collected surface potential data X{l}, within the range of [m, n], the increments of the reference sequence and the comparison sequence are respectively: ， ， ， in, Represents the raw data of the ground surface potential, represents the new measured data of the ground surface potential, and D represents the length between two measuring points k and k-1; The calculation of the T-type correlation coefficient is expressed as: ， ， in, and are arrays of original data and newly measured data of ground surface potential, represents the original surface potential data of the kth measuring point and k-1 measuring point at time t, represents the new geodetic surface potential data of the kth measuring point and k-1 measuring points at time t, Indicates the difference between the kth measuring point and the k-1th measuring point data; Based on the T-type correlation modulus, set thresholds for the correlation degree rt, including a first threshold Q5, a second threshold Q6, a third threshold Q7, and a fourth threshold Q8, and Q5 > Q6 > Q7 > Q8; If Q5 < rt < 1, it indicates that there is a deviation between the potential curve of the observation line and the reference curve, and it is necessary to pay close attention and promptly check and eliminate potential hazards; If Q7 < rt < Q5, it indicates that there is already a deviation between the potential curve of the observation line and the reference curve, and there is a fault. It is necessary to conduct in-depth detection on this line to determine the fault location and implement targeted repairs; If Q8 < rt < Q7, it indicates that the potential curve of this line is significantly different from the normal state, and there are serious potential fault hazards. It is necessary to conduct a comprehensive inspection along this line and prepare to replace some grounding electrodes or the entire line; If 0 < rt < Q8, it indicates that the observation line has completely lost its normal potential characteristics, and the grounding electrode or the line itself has failed, and it is necessary to implement the demolition and reconstruction of the entire line; Furthermore, the B-type correlation model and the T-type correlation model are combined for explanation. If rb > Q1 and at least more than m% of rt > Q5, it indicates that there are no obvious faults in the overall line of the grounding grid; If Q3 < rb ≤ Q2, and p% - q% of rt < Q6, it indicates that there are defects in the grounding grid, and it is necessary to focus on checking the lines with rt values lower than Q6 to investigate the causes of the defects; If rb ≤ Q3, and k% of rt < Q6, it indicates that serious faults have occurred in the overall grounding grid, and it is necessary to temporarily stop the operation of the equipment, comprehensively investigate the root causes of the faults, implement emergency repairs or formulate an overall reconstruction plan for the grounding grid.

2. The method for determining faults in the grounding grid of a hydropower station in combination with the temporal and spatial distribution of the surface potential according to claim 1, characterized in that: The construction of the surface potential data set includes: During the operation period of the hydropower station, measure the surface potential at fixed time intervals to obtain long-term continuous monitoring data; Use an AC signal generator to inject an AC excitation current into the grounding grid through an accessible node, and use a power frequency parameter tester and a detection coil to measure the surface potential above the grounding conductor to obtain measurement data; Along different observation lines around the grounding grid, arrange multiple measurement points at characteristic points to obtain surface potential values at different positions; The original data set X{0} measured at the initial stage of construction, and the data set of each subsequent measurement is represented as X{k}.

3. A hydropower station grounding grid fault judgment system combined with the spatiotemporal distribution of surface potential, based on the hydropower station grounding grid fault judgment method combined with the spatiotemporal distribution of surface potential according to claim 1 or 2, characterized in that: Including, A construction module for obtaining the on-site surface potential value data of the grounding grid of the historical target hydropower station, constructing a surface potential data set, and introducing edge data to normalize the surface potential data set; An observation module that uses the B-type correlation model and the T-type correlation model to analyze the surface potential data set from different perspectives to determine the similarity and resemblance of the surface potential at each characteristic point; A judgment module for judging the faults of the hydropower station grounding grid according to the analysis results of the B-type correlation model and the T-type correlation model.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, it implements the steps of the method for judging faults in the hydropower station grounding grid combined with the spatio-temporal distribution of the surface potential as described in claim 1 or 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for judging faults in the hydropower station grounding grid combined with the spatio-temporal distribution of the surface potential as described in claim 1 or 2.

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