Power system line fault location method and system

Abstract

This invention relates to the field of power line fault location technology, specifically to a method and system for power system line fault location. The method includes the following steps: S1, acquiring power frequency electrical quantity data collected by measuring devices at each node in the power system line; S2, calculating the data probability distribution divergence T between adjacent nodes based on the collected power frequency electrical quantity data, wherein the data probability distribution divergence is obtained by fusing the variance, standard deviation, and logarithmic operation of the electrical quantity data of the two nodes; S3, calculating the data probability distribution divergence difference between two adjacent monitoring intervals formed by three adjacent nodes; S4, introducing a flag function S to analyze the data probability distribution divergence difference and setting a location time threshold to determine whether a fault exists; S5, determining the specific location of the fault on the main line or branch line based on the value relationship of the flag function S; thereby improving the reliability and adaptability of power system fault location.

Description

Technical Field

[0001] This invention relates to the field of power line fault location technology, specifically to a method and system for locating power system line faults. Background Technology

[0002] Power system line faults, especially weak faults in single-phase ground faults (high-resistance faults and early cable faults), exhibit weak time-frequency domain characteristics and very high transition resistance, even exceeding 20 kiloohms, resulting in fault currents less than 10% of the load current. Currently, most protection devices cannot effectively locate these types of faults, making them difficult to locate and isolate, thus leading to more serious two-phase or three-phase short-circuit faults. Existing line fault location methods suffer from the following main problems:

[0003] 1. Most existing methods rely on obvious sudden changes in electrical quantities such as voltage and current as the basis for fault judgment. For high-resistance faults or early cable faults with weak characteristics that are difficult to identify, they cannot be effectively captured and located. This leads to such faults remaining dormant for a long time and easily developing into more serious two-phase or three-phase short-circuit faults, causing large-scale power outages.

[0004] 2. To improve the sensitivity to faults, existing technologies often achieve detection by lowering the activation threshold of equipment or algorithms. However, this approach can lead to frequent activation of equipment due to non-fault signals such as line noise and environmental interference, which significantly increases the maintenance cost of the device and reduces the accuracy of fault identification. In addition, such methods are completely unable to locate faults with a transition impedance of more than 5,000 ohms, making it difficult to cover the complex fault scenarios of actual power lines.

[0005] 3. With a large number of distributed power sources and energy storage devices connected to the power system, their normal switching process will generate time-frequency domain transient processes similar to minor faults. This will significantly reduce the reliability of fault detection methods based solely on the time / frequency characteristics of electrical quantities, increase the false alarm rate significantly, and seriously affect the accuracy of fault location.

[0006] 4. Existing similar technologies mostly rely on zero-sequence quantities for fault location. However, in high-noise interference scenarios in power systems, zero-sequence quantity signals are easily contaminated, leading to a significant reduction in location accuracy or even location failure.

[0007] 5. For power lines with branch lines, existing technologies lack an effective fault section identification mechanism, making it difficult to determine whether the fault occurs in the main line or the branch line, resulting in low fault isolation efficiency and prolonged power outage time. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a method and system for locating faults in power system lines.

[0009] This invention is achieved through the following technical solution: a method for locating faults in power system lines, comprising the following steps:

[0010] S1. Obtain power frequency electrical quantity data collected by measuring equipment at each node in the power system line;

[0011] S2. Calculate the data probability distribution divergence T between adjacent nodes based on the collected power frequency electrical quantity data. The data probability distribution divergence is obtained by fusing the variance, standard deviation and logarithmic operation of the electrical quantity data of the two nodes.

[0012] S3. For two adjacent monitoring intervals formed by three adjacent nodes, calculate the divergence difference of the data probability distribution between these two adjacent monitoring intervals. ;

[0013] S4. Introduce a flag function S to measure the divergence of the probability distribution of the data. Analyze the data and set a positioning time threshold to determine if a fault exists;

[0014] S5. Based on the value relationship of the flag function S, determine the specific location of the fault on the main line or branch line.

[0015] In S2, for any two adjacent nodes on the power line, denoted as node a and node b, the power frequency electrical quantity arrays measured by the measuring devices at nodes a and b are respectively x a and x b It is represented as follows:

[0016] ;

[0017] ;

[0018] in, For array x a The i-th data, For array x b The j-th data point in the array, where n is the array length;

[0019] Divergence of the probability distributions of the data corresponding to node a and node b Represented as:

[0020] ;

[0021] in, , x a and x b variance , and Standard deviation and log are respectively. n It is of order n.

[0022] In S3, a, b, and k are adjacent nodes on the power line equipped with measuring devices, and nodes a, b, and k are arranged sequentially. The two monitoring intervals they form are ab and bk. This represents the divergence of the probability distribution of the data within the monitoring interval ab. Let bk represent the divergence of the probability distribution of the data in monitoring interval bk, then the difference in divergence of the probability distributions of the data in two adjacent monitoring intervals. The calculation formula is:

[0023] .

[0024] The flag functions corresponding to nodes a, b, and k in S4 Represented as:

[0025] .

[0026] The criterion for determining whether a fault exists in S4 is: if the data probability distribution of all monitoring intervals has poor divergence. The values ​​are all outside the normal fluctuation range. If the duration exceeds the positioning time threshold, a fault is determined to exist; otherwise, the system operates normally.

[0027] Normal fluctuation range Among them Standard deviation of measurement data under normal conditions and mean The composition, and its calculation formula is:

[0028] .

[0029] The positioning time threshold is 0.16s.

[0030] Based on the volatility of power system measurement data T is definitely not 0, and the value of the flag function S is only -1 or 1. Therefore, the rule for determining the specific location of the main line fault in S5 is as follows:

[0031] If the results of the flag function S of two adjacent monitoring intervals are inconsistent, the fault is located in the segment between the common node of these two monitoring intervals.

[0032] If the results of the flag function S of two adjacent monitoring intervals are the same and both are -1, then the fault is located at the beginning of the main line; if both are 1, then the fault is located at the end of the main line.

[0033] The specific location determination rule for the branch line fault in S5 is as follows: when the identified fault section includes a branch line and the branch line is equipped with a measuring device, the number of times the measuring points in the fault section participate in the calculation is counted; if the number of times they participate in the calculation exceeds 2 and the results of the monitoring interval flag function associated with the main line and the branch line are inconsistent, then the fault is located on the main line; otherwise, the fault is located on the branch line.

[0034] A power system line fault location system, applied to the aforementioned power system line fault location method, includes a data acquisition module, a data processing module, and a fault location module;

[0035] The data acquisition module is used to execute S1 to collect power frequency electrical quantity data of each node through the measuring equipment;

[0036] The data processing module is used to execute S2-S4 to calculate the data probability distribution divergence T and the data probability distribution divergence difference. Calculate and perform flag function analysis;

[0037] The fault location module is used to execute S5 to determine the fault location and to distinguish between faults on the main line and branch lines.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] This application overcomes the bottleneck of weak fault location, balances sensitivity and anti-interference, adapts to distributed power source access scenarios, accurately distinguishes between trunk line and branch line faults, promotes low-cost deployment based on existing equipment, and adapts to complex operating environments. It solves the pain points of existing technologies in fault capture, anti-interference, and scenario adaptation, and significantly improves the reliability, adaptability, and economy of power system fault location.

[0040] This application constructs a highly sensitive fault feature identification mechanism by calculating the divergence and divergence difference of the data probability distribution. It can capture the subtle features of weak faults without relying on obvious changes in electrical quantities, effectively locate faults, solve the problem of failure of traditional technology in locating weak faults, and significantly improve the fault early warning and isolation capabilities of power systems.

[0041] This application effectively filters out non-fault interference signals such as distributed power source switching and line noise by matching the flag function with the normal fluctuation range and combining the positioning time threshold (adapting to the transient process characteristics of normal operation of the power system), avoiding equipment erroneous start-up. While ensuring high sensitivity to faults, it significantly improves anti-interference capability and ensures the accuracy and reliability of fault location results.

[0042] This application does not require knowledge of the types, locations, or quantities of distributed power sources connected to the system, nor does it require enumeration and modeling of fault scenarios. It is applicable to complex power systems with multiple points of access for different distributed power sources. Its core logic for analyzing faults through data probability distribution characteristics is unaffected by transient signals from distributed power source switching, thus solving the problem of fault location caused by the access of new energy sources.

[0043] This application, by setting a threshold for the number of times measurement points participate in calculations and a consistency judgment rule for the associated monitoring interval flag function, can accurately distinguish whether a fault occurs in the main line or a branch line, providing a clear basis for fault isolation, significantly shortening fault investigation and repair time, reducing power outage losses, and improving the reliability of power supply in the power system.

[0044] The data required for this application comes from existing power system measurement equipment (such as RTU, DTU, FTU, TTU, PMU, micro-PMU, and other IED devices with data measurement functions). The data can be obtained from existing substations, master stations, or waveform recording devices, without the need for additional investment in new equipment. Fault location functions can be achieved simply by upgrading the software of existing master stations or intelligent terminals. The upgrade is simple, cost-effective, and easy to promote and apply across the entire network.

[0045] This application does not rely on specific fault scenarios or fault point environments, and does not need to consider the complex background of actual faults. It makes fault judgments based on the probability distribution characteristics of the data itself, and can adapt to various actual operating environments such as high noise, complex terrain, and variable climate, providing stable and reliable fault location services for different types of power lines. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the main trunk line node distribution;

[0047] Figure 2 This is a schematic diagram of the node distribution with branch lines;

[0048] Figure 3 This is a schematic diagram of the monitoring interval with branch lines. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0050] Example 1

[0051] The method for locating faults in power system lines includes the following steps:

[0052] S1. Obtain power frequency electrical quantity data collected by measuring equipment at each node in the power system line;

[0053] The measurement equipment in S1 includes Remote Terminal Units (RTUs), Data Transmission Units (DTUs), Feeder Terminal Units (FTUs), Transformer Monitoring Units (TTUs), Phasor Measurement Units (PMUs), Micro-Phasor Measurement Units (micro-PMUs), and other intelligent electronic devices (IEDs) with data measurement capabilities. Data can be obtained from the substations or master stations connected to the equipment, or directly from the waveform recording device.

[0054] S2. Calculate the data probability distribution divergence T between adjacent nodes based on the collected power frequency electrical quantity data. The data probability distribution divergence is obtained by fusing the variance, standard deviation and logarithmic operation of the electrical quantity data of the two nodes.

[0055] Specifically, in S2, for any two adjacent nodes on a power line, denoted as node a and node b, the arrays of power frequency electrical quantities (such as voltage, current, etc.) measured by the measuring devices at nodes a and b are x, respectively. a and x b It is represented as follows:

[0056] ;

[0057] ;

[0058] in, For array x a The i-th data, For array x b The j-th data point in the array, where n is the array length;

[0059] Divergence of the probability distributions of the data corresponding to node a and node b Represented as:

[0060] ;

[0061] in, , x a and x b variance , and Standard deviation and log are respectively. n It is of order n, with n=10 by default.

[0062] S3. For two adjacent monitoring intervals formed by three adjacent nodes, calculate the divergence difference of the data probability distribution between these two adjacent monitoring intervals. .

[0063] Let a, b, and k be adjacent nodes on a power line equipped with measuring devices, and nodes a, b, and k be arranged in sequence, with b being the middle node among the three nodes. Then, the two monitoring intervals formed by them are ab and bk. This represents the divergence of the probability distribution of the data within the monitoring interval ab. Let bk represent the divergence of the probability distribution of the data in monitoring interval bk. Then, the difference in divergence between the probability distributions of the data in two adjacent monitoring intervals... The calculation formula is:

[0064] .

[0065] S4. Introduce a flag function S to measure the divergence of the probability distribution of the data. The analysis was performed, and a positioning time threshold was set to determine whether a fault existed.

[0066] Considering the impact of factors such as output fluctuations of the Distribution Generator (DG) and measurement errors caused by equipment aging in actual operation, the flag functions corresponding to nodes a, b, and k are... Represented as:

[0067] .

[0068] In other words, if the probability distribution divergence of data from two adjacent monitoring intervals is similar, then Within a certain range Internal fluctuations. If a line fault occurs, then... It deviates significantly from the normal range.

[0069] Therefore, the criterion for determining whether a fault exists in S4 is: if the data probability distribution divergence of all monitoring intervals is poor. The values ​​are all outside the normal fluctuation range. If the duration exceeds the positioning time threshold, a fault is determined to exist; otherwise, the system operates normally.

[0070] in, Standard deviation of measurement data under normal conditions and mean The composition, and its calculation formula is:

[0071] .

[0072] Since the transient process caused by normal operation of the power system, such as the switching on and off of photovoltaic / wind turbines, is less than 8 cycles, or 160ms, the positioning time threshold is set to 0.16s in this embodiment.

[0073] S5. Based on the value relationship of the flag function S, determine the specific location of the fault on the main line or branch line.

[0074] Due to real-time changes in user electricity load, inherent accuracy errors of measuring equipment, and external environmental influences such as line electromagnetic interference and temperature fluctuations, power frequency electrical quantities such as voltage and current will always have small and continuous random fluctuations, and there are no absolutely stable constant values. In other words, power system measurement data is volatile.

[0075] Therefore, the divergence T of the data probability distributions in two adjacent monitoring intervals must differ, resulting in a divergence difference. T must not be 0, then the flag function The value can be represented as:

[0076] .

[0077] In practical applications, reasonable threshold values ​​(such as 10) can be set based on the accuracy of the measuring equipment, the operating conditions of the system, and other actual conditions. -8 etc); if If the absolute value of T is lower than the threshold, the system can be determined to be in an extremely stable ideal state, and the method described in this application is not applicable.

[0078] The rules for determining the specific location of a main line fault are as follows:

[0079] If the results of the flag function S of two adjacent monitoring intervals are inconsistent, the fault is located in the segment between the common node of these two monitoring intervals.

[0080] If the results of the flag function S of two adjacent monitoring intervals are consistent and both are -1, then the fault is located at the beginning of the main line (i.e., the line section from the substation outgoing line to the first measuring device). If both are 1, then the fault is located at the end of the main line (i.e., the line section from the measuring device to the user end or the front end of the user's distribution transformer).

[0081] The rules for determining the specific location of a branch line fault are as follows: when the identified fault section includes a branch line and the branch line is equipped with measuring equipment, count the number of times the measuring points in the fault section participate in the calculation; if the number of times they participate in the calculation exceeds 2 and the results of the monitoring interval flag function associated with the main line and the branch line are inconsistent, then the fault is located on the main line; otherwise, the fault is located on the branch line.

[0082] Example 2

[0083] Reference Figure 1 In conjunction with Example 1, let a, b, k, and l be adjacent nodes on the main power line (without branch lines) where measuring equipment is installed. If nodes a, b, k, and l are arranged in sequence, then the detection interval formed is ab, bk, and kl.

[0084] If the data probability distribution has poor divergence None of these are within the normal fluctuation range. If the abnormality lasts for more than 0.16 seconds, it is determined that a fault has occurred; otherwise, it is determined that the system is in normal operation.

[0085] If the flag function from node a to node k The flag function that is not equal to the one from node b to node l If so, the fault is located in the segment between node b and node k.

[0086] when = If all values ​​are -1, the fault is located at the beginning of the line; if all values ​​are 1, the fault is located at the end of the line.

[0087] Example 3

[0088] Reference Figure 2 and Figure 3 The difference from Example 2 is that the segment between node b and node k has a branch line, and the branch line has a measuring device, with node p configured on the branch line. If the fault occurs in the segment between node b and node k, the number of calculations N involving node p in that segment is counted. When N > 2, and the flag function of node bpk... Flag function that is not equal to node pkb If the fault occurs on the main line, the fault is located on the branch line; otherwise, the fault is located on the branch line.

[0089] Example 4

[0090] This embodiment proposes a power system line fault location system to implement the power system line fault location method described in Embodiment 1.

[0091] The power system line fault location system includes a data acquisition module, a data processing module, and a fault location module;

[0092] The data acquisition module is used to execute S1 to collect power frequency electrical quantity data of each node through the measuring equipment;

[0093] The data processing module is used to execute S2-S4 to calculate the data probability distribution divergence T and the data probability distribution divergence difference. Calculate and perform flag function analysis;

[0094] The fault location module is used to execute S5 to determine the fault location and to distinguish between faults on the main line and branch lines.

[0095] The above description is merely an optional embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the content of the present invention under the concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for locating faults in power system lines, characterized in that, Includes the following steps: S1. Obtain power frequency electrical quantity data collected by measuring equipment at each node in the power system line; S2. Calculate the data probability distribution divergence T between adjacent nodes based on the collected power frequency electrical quantity data. The data probability distribution divergence T is obtained by fusing the variance, standard deviation, and logarithmic operation of the electrical quantity data of the two nodes. In S2, for any two adjacent nodes on the power line, denoted as node a and node b, the power frequency electrical quantity arrays measured by the measuring devices at nodes a and b are x and x, respectively. a and x b It is represented as follows: ； ； in, For array x a The i-th data, For array x b The j-th data point in the array, where n is the array length; Divergence of the probability distributions of the data corresponding to node a and node b Represented as: ； in, , x a and x b variance , and Standard deviation and log are respectively. n It is of order n; S3. For two adjacent monitoring intervals formed by three adjacent nodes, calculate the divergence difference of the data probability distribution between these two adjacent monitoring intervals. In S3, a, b, and k are adjacent nodes on the power line equipped with measuring devices, and nodes a, b, and k are arranged sequentially, forming two monitoring intervals ab and bk. This represents the divergence of the probability distribution of the data within the monitoring interval ab. Let bk represent the divergence of the probability distribution of the data in monitoring interval bk, then the difference in divergence of the probability distributions of the data in two adjacent monitoring intervals. The calculation formula is: ； S4. Introduce a flag function S to measure the divergence of the probability distribution of the data. Analyze the data and set a positioning time threshold to determine if a fault exists; The flag functions corresponding to nodes a, b, and k in S4 Represented as: ； The criterion for determining whether a fault exists in S4 is: if the data probability distribution of all monitoring intervals has poor divergence. The values ​​are all outside the normal fluctuation range. If the duration exceeds the positioning time threshold, a fault is determined to exist; otherwise, the system operates normally. S5. Based on the value relationship of the flag function S, determine the specific location of the fault on the main line or branch line; Based on the volatility of power system measurement data If T is not 0, and the flag function S takes the value of -1 or 1, then the rule for determining the specific location of the main line fault in S5 is as follows: If the results of the flag function S of two adjacent monitoring intervals are inconsistent, the fault is located in the segment between the common node of these two monitoring intervals. If the results of the flag function S of two adjacent monitoring intervals are the same and both are -1, then the fault is located at the beginning of the main line; if both are 1, then the fault is located at the end of the main line. The specific location determination rule for the branch line fault in S5 is as follows: when the identified fault section includes a branch line and the branch line is equipped with a measuring device, the number of times the measuring points in the fault section participate in the calculation is counted; if the number of times they participate in the calculation exceeds 2 and the results of the monitoring interval flag function associated with the main line and the branch line are inconsistent, then the fault is located on the main line; otherwise, the fault is located on the branch line.

2. The power system line fault location method according to claim 1, characterized in that, Normal fluctuation range middle, Standard deviation of measurement data under normal conditions and mean The composition, and its calculation formula is: 。 3. The power system line fault location method according to claim 1, characterized in that, The positioning time threshold is 0.16s.

4. A power system line fault location system, characterized in that, The method for locating faults in power system lines according to any one of claims 1-3 includes a data acquisition module, a data processing module, and a fault location module. The data acquisition module is used to execute S1 to collect power frequency electrical quantity data of each node through the measuring equipment; The data processing module is used to execute S2-S4 to calculate the data probability distribution divergence T and the data probability distribution divergence difference. Calculate and perform flag function analysis; The fault location module is used to execute S5 to determine the fault location and to distinguish between faults on the main line and branch lines.

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