Positioning method for single-phase earth fault of power distribution network

Abstract

The invention provides a positioning method for a single-phase earth fault of a power distribution network. The positioning method comprises the following steps: collecting transient zero-sequence current signals of detection points in real time; carrying out noise reduction processing on the collected transient zero-sequence current signal of each detection point; calculating the fuzzy entropy value of the transient zero-sequence current signal of each detection point after noise reduction processing by using a fuzzy entropy algorithm, and calculating the entropy value ratio between adjacent detection points; judging whether the entropy ratio of the adjacent detection points is within a normal threshold range in the region or not, if the entropy ratio exceeds the set normal threshold range in the region, judging that the fault occurs between the adjacent detection points, and if the entropy ratio does not exceed the set normal threshold range in the region, comparing the fuzzy entropy values of the bus outlet detection points, and judging that the fault occurs between the adjacent detection points; and if the fuzzy entropy value of the bus outgoing line head section detection point is greater than the fuzzy entropy value of the bus outgoing line tail section detection point, determining that the fault occurs at the line head section, otherwise, determining that the fault occurs at the line tail section. The method has the advantages of strong anti-interference capability, wide adaptability, high precision and low cost.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault detection, and in particular to a method for locating a single-phase grounding fault in a distribution network. Background Art

[0002] In China, the high-quality development of distribution networks has become a top priority of national policy. The Guiding Opinions on the High-quality Development of Distribution Networks under the New Situation clearly pointed out that distribution networks, as important public infrastructure, play an important role in ensuring power supply, supporting economic and social development, and serving to improve people's livelihood. Existing distribution network fault location methods have their own shortcomings. At present, the impedance-based fault location method relies on measuring the changes in current and voltage to determine the fault point, but its accuracy is easily affected by the imbalance of line impedance and is insensitive to high-resistance grounding faults. The traveling wave location method locates the fault point by detecting the electromagnetic traveling waves generated when the fault occurs, but it has extremely high requirements for the sampling rate and time synchronization of the equipment, and is easily interfered by external noise, resulting in positioning errors. Intelligent algorithm location methods use neural networks or other AI technologies to locate faults. Although they perform well under specific conditions, they are highly dependent on the quality and quantity of historical data, and their accuracy may decrease when facing new fault types. Finally, the distributed sensor location method monitors parameter changes in real time by arranging sensors in the distribution network. Although it can effectively improve the accuracy of fault location, the cost of sensors is high and is limited by the stability of the communication network. In summary, the existing fault location methods are accuracy dependent, affected by impedance, and insensitive to high-resistance faults; they have high sampling requirements and are affected by noise; they are data dependent, computationally complex, and have poor adaptability to new faults; they are costly and limited by the shortcomings of the communication network. Summary of the invention

[0003] In view of the technical problems of low accuracy, weak anti-interference ability, poor adaptability and high cost in the existing distribution network fault location methods, the present invention provides a location method for single-phase grounding faults in distribution networks, aiming to improve the anti-interference ability, adaptability, real-time performance, accuracy and reduce the cost of single-phase grounding fault location in distribution networks through a method based on zero-sequence current fuzzy entropy, realize real-time monitoring of the operating status of 10kV distribution networks, improve power supply reliability, enhance the technical level of fault location, reduce operating costs and promote the construction of smart grids.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0005] A method for locating a single-phase grounding fault in a distribution network comprises the following steps:

[0006] S1, real-time acquisition of transient zero-sequence current signals at each detection point;

[0007] S2, performing noise reduction processing on the transient zero-sequence current signals collected at each detection point;

[0008] S3, using a fuzzy entropy algorithm to calculate the fuzzy entropy value of the transient zero-sequence current signal of each detection point after noise reduction processing, and calculating the entropy value ratio between adjacent detection points;

[0009] S4. Determine whether the entropy value ratio of adjacent detection points is within the normal threshold range of the zone. If the entropy value ratio of adjacent detection points exceeds the set normal threshold range of the zone, it is determined that the fault occurs between the adjacent detection points; if the entropy value ratio of adjacent detection points does not exceed the set normal threshold range of the zone, compare the fuzzy entropy values ​​of the busbar outgoing line detection points. If the fuzzy entropy value of the busbar outgoing line first section detection point is greater than the fuzzy entropy value of the busbar outgoing line tail section detection point, it is determined that the fault occurs in the first section of the line, otherwise, the fault occurs in the tail section of the line.

[0010] Furthermore, in step S3, the fuzzy entropy algorithm is used to calculate the fuzzy entropy value of the transient zero-sequence current signal of each detection point after the noise reduction process, and the specific implementation steps are as follows:

[0011] S31. Express the zero-sequence current signal time series as Where x(i) represents the i-th value in the intercepted zero-sequence current signal time series, and N is the length of the signal sequence;

[0012] S32, select the embedding dimension m and the similarity tolerance r, and reconstruct the zero-sequence current signal time series into an m-dimensional zero-sequence current vector sequence:

[0013] X i ={x(i),x(i+1),…,x(i+m-1)}

[0014] Where, i = 1, 2, ..., N-m + 1;

[0015] S33. Define vector X i and X j The distance d(X i ,X j ), and calculate the maximum difference distance of different groups of zero-sequence current vector sequences:

[0016]

[0017] Where k represents the index offset;

[0018] Calculate the membership function μ(d(X i ,X j ),r):

[0019]

[0020] S34. Calculate the mean value of the fuzzy membership for all possible zero-sequence current vector sequences m (r):

[0021]

[0022] S35, increase the embedding dimension to m+1, repeat the above steps S31 to S34 to obtain φ m+1 (r), the fuzzy entropy of the zero-sequence current signal is defined as follows:

[0023]

[0024] This formula represents the rate of change of the fuzzy similarity of the zero-sequence current signal time series from m dimension to m+1 dimension, reflecting the complexity of the sequence.

[0025] Furthermore, in step S4, the normal threshold value range within the zone is 0.905 to 1.125.

[0026] Compared with the prior art, the method for locating single-phase grounding faults in a distribution network provided by the present invention, when a fault occurs in the distribution network, collects the transient zero-sequence current signal of each detection point in real time, performs noise reduction processing on the collected transient zero-sequence current signal to reduce the interference of environmental noise and ensure the accuracy and effectiveness of the signal, then uses a fuzzy entropy algorithm to calculate the fuzzy entropy value of the zero-sequence current signal of each detection point, and calculates the entropy value ratio between adjacent detection points, and determines the section where the fault occurs by analyzing the fuzzy entropy value and entropy value ratio of each measurement point, and specifically when the entropy value ratio of adjacent detection points exceeds the set normal threshold range in the area, the position of the fault point on the busbar can be determined. The present invention applies the fuzzy entropy algorithm to the single-phase grounding fault location of the distribution network for the first time. By utilizing the robustness and sensitivity of the algorithm to nonlinear signals, the complex features in the fault signal can be effectively processed, and the accuracy of fault location can be improved. Through simulation analysis under different grounding resistances and fault initial phase angles, a method for dynamically adjusting the threshold is proposed, so that the method can adapt to different working conditions and improve the applicability of fault location. The method performs standardized processing and fuzzy entropy calculation on the transient zero-sequence current signal, effectively reduces the influence of environmental noise and measurement errors, and exhibits strong anti-interference ability. The effectiveness of the method in the 10kV distribution network is verified by MATLAB / Simulink simulation. The results show that the method can accurately locate the fault under different fault conditions and has high engineering practicality. Therefore, the location method for the single-phase grounding fault of the distribution network proposed by the present invention has the advantages of strong anti-interference ability, wide adaptability, good real-time performance, high accuracy and low cost, and provides a new solution for the location of the single-phase grounding fault of the distribution network, which is conducive to improving power supply reliability, improving the technical level of fault location, reducing operating costs, promoting the construction of smart grids, and providing technical support for the implementation of smart grid planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention provides a flow chart of a method for locating a single-phase grounding fault in a distribution network.

[0028] Figure 2 It is a principle diagram of the wave recording type transient indicator involved in the present invention.

[0029] Figure 3 This is a schematic diagram of a 10kV single-phase grounding system provided by the present invention.

[0030] Figure 4 This is a model diagram of a 10kV single-phase grounding system provided by the present invention.

[0031] Figure 5 It is a schematic diagram of the entropy value ratio of adjacent detection points with different initial phase angles of faults provided by the present invention.

[0032] Figure 6 It is a schematic diagram of entropy value ratios of adjacent detection points of different grounding resistances provided by the present invention. DETAILED DESCRIPTION

[0033] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below with reference to specific diagrams.

[0034] Please refer to Figure 1 As shown, the present invention provides a method for locating a single-phase grounding fault in a distribution network, comprising the following steps:

[0035] S1. Real-time acquisition of transient zero-sequence current signals at each detection point. Specifically, the zero-sequence current signals can be acquired in real time through existing current sensing amplifiers such as INA214CIDCKT for current detection;

[0036] S2. Perform noise reduction processing on the transient zero-sequence current signals collected at each detection point. Specifically, existing modules such as AD7606 can be used to perform noise reduction processing and analog-to-digital conversion on the collected zero-sequence current signals to reduce the interference of environmental noise and output digital signals;

[0037] S3, using a fuzzy entropy algorithm to calculate the fuzzy entropy value of the transient zero-sequence current signal of each detection point after noise reduction processing, and calculate the entropy value ratio between adjacent detection points. Specifically, the digital signal after noise reduction processing can be input into an existing microcontroller such as STM32F103C8T6 to calculate the fuzzy entropy value and entropy value ratio;

[0038] S4. Determine whether the entropy value ratio of adjacent detection points is within the normal threshold range within the zone. If the entropy value ratio of adjacent detection points exceeds the set normal threshold range within the zone, it is determined that the fault occurs between the adjacent detection points; if the entropy value ratio of adjacent detection points does not exceed the set normal threshold range within the zone, compare the fuzzy entropy values ​​of the bus outgoing line detection points. If the fuzzy entropy value (FuzzyEn-M) of the bus outgoing line first section detection point is greater than the fuzzy entropy value (FuzzyEn-Q) of the bus outgoing line tail section detection point, it is determined that the fault occurs in the first section of the line, otherwise, the fault occurs in the tail section of the line.

[0039] Please refer to Figure 2 As shown in the figure, when a ground fault occurs in a small current grounding system, it will cause an imbalance in the three-phase system, which is equivalent to installing a zero-sequence voltage source between the fault sections NP. The response generated by this zero-sequence voltage source is distributed on both sides of the fault point. The line responses on the left and right sides do not interfere with each other and work independently. This independence makes the zero-sequence current characteristics on the left and right sides significantly different. Due to the different parameters and composition of the lines on both sides of the zero-sequence voltage source, the zero-sequence current signal flowing to the left side generally has a larger amplitude and a smaller frequency, and the waveform morphology is significantly different; while the waveforms of adjacent points on the same side are less different and the complexity is similar. Therefore, the fault section can be locked by utilizing the different information complexity on both sides of the fault point, that is, the different entropy values.

[0040] Specifically, fuzzy entropy (FuzzyEn) is a method for measuring the complexity of time series. It can be used to determine the change in the signal complexity of zero-sequence current over time. After a ground fault occurs, the orderliness of the zero-sequence current will change significantly within half a cycle, and the fuzzy entropy value will increase. Fuzzy entropy is based on fuzzy set theory and quantifies the complexity and uncertainty of a time series by calculating the fuzzy membership of the time series. Compared with approximate entropy (ApEn) and sample entropy (SampEn), fuzzy entropy performs better in processing zero-sequence current signals. By calculating fuzzy entropy, the complexity and dynamic characteristics of the zero-sequence current signal time series at different detection points can be effectively analyzed.

[0041] As a specific embodiment, in step S3, the fuzzy entropy algorithm is used to calculate the fuzzy entropy value of the transient zero-sequence current signal of each detection point after noise reduction processing, and the specific implementation steps are as follows:

[0042] S31. Express the zero-sequence current signal time series as Where x(i) represents the i-th value in the intercepted zero-sequence current signal time series, and N is the length of the signal sequence;

[0043] S32, select the embedding dimension m and the similarity tolerance r, and reconstruct the zero-sequence current signal time series into an m-dimensional zero-sequence current vector sequence:

[0044] Xi ={x(i),x(i+1),…,x(i+m-1)} Formula (1)

[0045] Where, i = 1, 2, ..., N-m + 1;

[0046] S33. Define vector X i and X j The distance d(X i ,X j ), and calculate the maximum difference distance of different groups of zero-sequence current vector sequences:

[0047]

[0048] Where k represents the index offset;

[0049] Calculate the membership function μ(d(X i ,X j ),r):

[0050]

[0051] S34. Calculate the mean value of the fuzzy membership for all possible zero-sequence current vector sequences m (r):

[0052]

[0053] S35, increase the embedding dimension to m+1, repeat the above steps S31 to S34 to obtain φ m+1 (r), the fuzzy entropy of the zero-sequence current signal is defined as follows:

[0054]

[0055] This formula represents the rate of change of the fuzzy similarity of the zero-sequence current signal time series from m dimension to m+1 dimension, reflecting the complexity of the sequence.

[0056] Specifically, a 10kV single-phase grounding system was built through MATLAB / Simulink to verify the feasibility of this method. The schematic diagram of the 10kV single-phase grounding system is shown in Figure 3 As shown, the 10kV single-phase grounding system model is as follows Figure 4 As shown in the figure, the lengths between the monitoring points are L 1 =10km, L 2 =15km, L 3 =8km; the positive and zero sequence impedances of the line are set as follows: the positive sequence impedance Z 1 =(0.75+j0.39)Ω / km, positive sequence to ground admittance b 1 =j3.043S / km; zero sequence impedance Z0 =(0.24+j1.62)Ω / km, zero-sequence admittance to ground b 0 =j1.784S / km.

[0057] The fault phase angle mainly affects the overall amplitude of the transient zero-sequence current of the fault. The closer the fault phase angle is to 90°, the larger the amplitude of the transient zero-sequence current of the fault, while the steady-state zero-sequence current only changes in phase and the amplitude remains unchanged. Therefore, the initial phase angle of the fault has a significant impact on the zero-sequence current, and its influence law needs to be studied. In a 10kV distribution network, although the initial phase angles of the voltage and current of the same phase usually do not change suddenly, there may be differences within a certain range (such as 15° to 90°). Therefore, it is necessary to verify the accuracy of the fuzzy entropy algorithm in fault line judgment under different initial phase angles. When the grounding resistance is 15Ω, it is assumed that the initial phase angles of the voltage and current generated by the power plant system are 15°, 30°, 60° and 90° respectively. The fuzzy entropy algorithm is simulated within the above phase angle range, and the results are shown in Tables 1 and 2.

[0058] As shown in Table 1, the application of fuzzy entropy algorithm in fault line judgment under different initial phase angles is simulated and analyzed, and the initial phase angle has a significant effect on the fuzzy entropy value. First, it can be seen from the data in Table 1 that the fuzzy entropy values ​​of each position (M, N, P, Q) show obvious changes when the initial phase angle is 15°, 30°, 60° and 90°. When the initial phase angle is 90°, the fuzzy entropy values ​​of positions M and N are 54.14 and 54.37, respectively, which are significantly higher than the fuzzy entropy values ​​under other initial phase angles. This shows that as the initial phase angle approaches 90°, the amplitude of the transient zero-sequence current of the fault increases, and the fuzzy entropy value also increases significantly.

[0059] As shown in Table 2, when the initial phase angle is 15°, 30°, 60° and 90°, the fuzzy entropy ratio of position N / P is 0.889, 0.874, 0.851 and 1.158 respectively. Compared with the fuzzy entropy ratios of positions M / N and P / Q, the difference between the fuzzy entropy ratio of position N / P and 1 is the largest, especially when the initial phase angle is 90°, the fuzzy entropy ratio of position N / P reaches 1.158, which is significantly higher than the ratios of other initial phase angles. In addition, the fuzzy entropy ratios of positions M / N and P / Q change relatively little at different initial phase angles. For example, when the initial phase angle is 15°, the fuzzy entropy ratios of positions M / N and P / Q are 0.987 and 1.004 respectively; while when the initial phase angle is 90°, the fuzzy entropy ratios of positions M / N and P / Q are 0.996 and 1.004 respectively. These ratios are all close to 1, indicating that the fault is between NP.

[0060] Table 1 Fuzzy entropy value table of fault initial phase angle change

[0061]

[0062] Table 2 Fuzzy entropy ratio table of fault initial phase angle change

[0063]

[0064] According to the current national plan, the line length of the 10kV distribution network should generally be less than 15 kilometers in principle. Within this length range, the land state does not change much, so the range of change of the grounding resistance should also be relatively small. According to the grounding resistance design principles of tower lines, lightning rods and bench-type equipment in the 10kV distribution network, the grounding resistance of bench-type equipment shall not be less than 4Ω, and the grounding resistance of tower-type equipment shall not be less than 10Ω. Therefore, when the initial phase angle of the fault is 90°, it is necessary to simulate and analyze the grounding resistance of 5Ω, 10Ω, 15Ω and 20Ω.

[0065] As shown in Table 3, the fuzzy entropy values ​​of each position (M, N, P, Q) vary significantly under different grounding resistances. When the grounding resistance is 10Ω, the fuzzy entropy values ​​of positions M and N reach 54.14 and 54.17, respectively, which are significantly higher than the fuzzy entropy values ​​under other grounding resistances. This shows that the larger the grounding resistance, the more obvious the fault characteristics, the increased complexity of the zero-sequence current signal time series, and the higher the fuzzy entropy value.

[0066] As shown in Table 4, when the grounding resistance is 5Ω, 10Ω, 15Ω and 20Ω, the fuzzy entropy ratio of position N / P is 1.132, 1.154, 0.897 and 0.895 respectively. Compared with the fuzzy entropy ratio of positions M / N and P / Q, the difference between the fuzzy entropy ratio of position N / P and 1 is the largest, especially when the grounding resistance is 5Ω and 10Ω, the fuzzy entropy ratio of position N / P is significantly higher than the ratio under other grounding resistances, reaching 1.132 and 1.154 respectively. This shows that the fault characteristics are most obvious between NP, and the fuzzy entropy algorithm can better identify the fault location. Secondly, the fuzzy entropy ratio of positions M / N and P / Q changes relatively little under different grounding resistances. For example, when the grounding resistance is 5Ω, the fuzzy entropy ratios of positions M / N and P / Q are 0.993 and 1.001 respectively, while when the grounding resistance is 20Ω, the fuzzy entropy ratios of positions M / N and P / Q are 0.982 and 1.010 respectively. These ratios are close to 1, indicating that no faults have occurred at these positions.

[0067] Table 3 Fuzzy entropy value table of ground resistance change

[0068]

[0069] Table 4 Ground resistance change fuzzy entropy ratio table

[0070]

[0071] like Figure 5 and Figure 6 As shown, the entropy ratio of adjacent detection points under different grounding resistances and initial fault phase angles is shown respectively. In both cases, the threshold range of the normal entropy ratio in the zone is within the upper and lower horizontal line areas. By taking the intersection of the normal thresholds in the two cases and adding an appropriate margin to avoid the process being affected by the grounding resistance and the initial fault phase angle, the fuzzy entropy algorithm can give full play to its effectiveness in locating faults in small current systems. According to the above principles, it is finally determined that the normal threshold range in the zone in step S4 is 0.905~1.125.

[0072] Compared with the prior art, the method for locating single-phase grounding faults in a distribution network provided by the present invention, when a fault occurs in the distribution network, collects the transient zero-sequence current signal of each detection point in real time, performs noise reduction processing on the collected transient zero-sequence current signal to reduce the interference of environmental noise and ensure the accuracy and effectiveness of the signal, then uses a fuzzy entropy algorithm to calculate the fuzzy entropy value of the zero-sequence current signal of each detection point, and calculates the entropy value ratio between adjacent detection points, and determines the section where the fault occurs by analyzing the fuzzy entropy value and entropy value ratio of each measurement point, and specifically when the entropy value ratio of adjacent detection points exceeds the set normal threshold range in the area, the position of the fault point on the busbar can be determined. The present invention applies the fuzzy entropy algorithm to the single-phase grounding fault location of the distribution network for the first time. By utilizing the robustness and sensitivity of the algorithm to nonlinear signals, the complex features in the fault signal can be effectively processed, and the accuracy of fault location can be improved. Through simulation analysis under different grounding resistances and fault initial phase angles, a method for dynamically adjusting the threshold is proposed, so that the method can adapt to different working conditions and improve the applicability of fault location. The method performs standardized processing and fuzzy entropy calculation on the transient zero-sequence current signal, effectively reduces the influence of environmental noise and measurement errors, and exhibits strong anti-interference ability. The effectiveness of the method in the 10kV distribution network is verified by MATLAB / Simulink simulation. The results show that the method can accurately locate the fault under different fault conditions and has high engineering practicality. Therefore, the location method for the single-phase grounding fault of the distribution network proposed by the present invention has the advantages of strong anti-interference ability, wide adaptability, good real-time performance, high accuracy and low cost, and provides a new solution for the location of the single-phase grounding fault of the distribution network, which is conducive to improving power supply reliability, improving the technical level of fault location, reducing operating costs, promoting the construction of smart grids, and providing technical support for the implementation of smart grid planning.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution 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 solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for locating a single-phase grounding fault in a distribution network, characterized in that: The following steps are involved: S1, real-time acquisition of transient zero-sequence current signals at each detection point; S2, performing noise reduction processing on the transient zero-sequence current signals collected at each detection point; S3, using a fuzzy entropy algorithm to calculate the fuzzy entropy value of the transient zero-sequence current signal of each detection point after noise reduction processing, and calculating the entropy value ratio between adjacent detection points; S4. Determine whether the entropy value ratio of adjacent detection points is within the normal threshold range of the zone. If the entropy value ratio of adjacent detection points exceeds the set normal threshold range of the zone, it is determined that the fault occurs between the adjacent detection points; if the entropy value ratio of adjacent detection points does not exceed the set normal threshold range of the zone, compare the fuzzy entropy values ​​of the busbar outgoing line detection points. If the fuzzy entropy value of the busbar outgoing line first section detection point is greater than the fuzzy entropy value of the busbar outgoing line tail section detection point, it is determined that the fault occurs in the first section of the line, otherwise, the fault occurs in the tail section of the line.

2. The method for locating a single-phase grounding fault in a distribution network according to claim 1, characterized in that: In step S3, the fuzzy entropy algorithm is used to calculate the fuzzy entropy value of the transient zero-sequence current signal of each detection point after noise reduction processing. The specific implementation steps are as follows: S31, the zero-sequence current signal time series is expressed as Where x(i) represents the i-th value in the intercepted zero-sequence current signal time series, and N is the length of the signal sequence; S32, select the embedding dimension m and the similarity tolerance r, and reconstruct the zero-sequence current signal time series into an m-dimensional zero-sequence current vector sequence: X i ={x(i),x(i+1),…,x(i+m-1)} Where, i = 1, 2, ..., N-m + 1; S33. Define vector X i and X j The distance d(X i ,X j ), and calculate the maximum difference distance of different groups of zero-sequence current vector sequences: Where k represents the index offset; Calculate the membership function μ(d(X i ,X j ),r): S34. Calculate the mean value of the fuzzy membership for all possible zero-sequence current vector sequences m (r): S35, increase the embedding dimension to m+1, repeat the above steps S31 to S34 to obtain φ m+1 (r), the fuzzy entropy of the zero-sequence current signal is defined as follows: This formula represents the rate of change of the fuzzy similarity of the zero-sequence current signal time series from m dimension to m+1 dimension, reflecting the complexity of the sequence.

3. The method for locating a single-phase grounding fault in a distribution network according to claim 1, characterized in that: The normal threshold range in the zone in step S4 is 0.905 to 1.125.

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