Medium-voltage cable fault section rapid positioning method based on moving average filtering and instantaneous symmetric component analysis, system equipment and storage medium
By employing a method combining moving average filtering and instantaneous symmetrical component analysis, the problem of inaccurate location caused by noise interference in cable distribution networks is solved, enabling rapid and accurate location of fault sections in medium-voltage cables. This method is applicable to 10~35kV cable lines.
Patent Information
- Application Number
- CN202511717275.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-21
- Publication Date
- 2026-02-06
AI Technical Summary
Existing fault location methods have poor noise immunity, low location accuracy, and poor real-time performance in cable distribution networks, making it difficult to meet the requirements for rapid and accurate fault location.
The method of moving average filtering and instantaneous symmetrical component analysis is adopted. By monitoring current data in real time, signal preprocessing is performed to remove high-frequency noise, instantaneous positive and negative sequence currents are calculated, and fault sections are judged in combination with topology.
It significantly improves the accuracy and real-time performance of fault location, is suitable for early fault diagnosis of cables, reduces deployment costs, and is applicable to 10~35kV medium voltage cable lines.
Smart Images
Figure CN121476829A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power system distribution network fault detection and location technology, specifically to a method, system equipment and storage medium for rapid location of medium-voltage cable fault sections based on moving average filtering and instantaneous symmetric component analysis. Background Technology
[0002] In real-world power distribution network environments, the acquired voltage and current signals are inevitably subject to various noise interferences. If these noises are not effectively suppressed, they can lead to significant errors in the calculated instantaneous positive-sequence and negative-sequence currents. In particular, the amplitude of the instantaneous negative-sequence component may be significantly amplified by noise, severely impacting the accuracy and reliability of fault segment identification and even causing misjudgments. Therefore, effectively suppressing noise and improving the robustness and usability of the instantaneous symmetrical component method in real-world power distribution network environments is a key issue that needs to be addressed to promote this method.
[0003] Currently, there are three main methods for fault location: impedance-based methods, traveling wave methods, and intelligent algorithms. Impedance-based methods estimate the fault distance by measuring the apparent impedance (voltage to current ratio) at the time of the fault and combining it with the impedance parameters per unit length of the line. However, they require high accuracy of line parameters (affected by temperature, aging, etc., parameter errors directly lead to location deviations); they are greatly affected by fault resistance (such as arcs and grounding resistance), and their uncertainty significantly reduces the accuracy of impedance calculation (especially for high-resistance grounding faults, where the error is even greater); load current and its fluctuations interfere with the accurate calculation of fault impedance; in scenarios with multiple branches, T-connected lines, and distributed power supply access, single-end measurements are prone to multiple suspected fault points or a decrease in the accuracy of traditional models. Traveling wave methods utilize the traveling wave signal (transient changes in current and voltage) generated by the fault to calculate the fault location through the time difference and wave velocity at different detection points. However, its equipment costs are high (requiring the installation of high-sampling-rate traveling wave detection devices and precise clock synchronization systems such as GPS, resulting in high initial investment and maintenance costs); it has extremely high requirements for sampling rate and synchronization accuracy (traveling waves propagate quickly, and sampling or synchronization errors can easily lead to large positioning deviations); the signal is prone to attenuation and distortion, making wavefront identification and differentiation between fault waves and interference waves difficult in complex networks (such as mixed lines, multiple branches, and multiple reflection points). Intelligent algorithms (such as genetic, ant colony, and particle swarm optimization algorithms) transform fault location into an optimization problem, using FTU fault information (overcurrent, undervoltage, etc.) to search for the optimal solution. However, it has a large computational load and slow convergence (the search space of complex networks is large, making it difficult to meet the needs of rapid positioning); it is prone to getting trapped in local optima (it is easy to converge to a local optimal solution too early during the search, reducing positioning accuracy); and it is sensitive to the completeness and accuracy of FTU information (incorrect or missing information directly leads to positioning result deviations). Summary of the Invention
[0004] Purpose of the invention: The purpose of this invention is to provide a method, system equipment and storage medium for rapid location of fault sections in medium-voltage cables based on moving average filtering and instantaneous symmetric component analysis, which solves the problems of poor noise resistance, low location accuracy and poor real-time performance of existing fault location methods in cable distribution networks.
[0005] Technical Solution: The present invention provides a method for rapid location of medium-voltage cable fault sections based on moving average filtering and instantaneous symmetrical component analysis, comprising the following steps: real-time monitoring of three-phase current data at each node in the distribution network; preprocessing the three-phase current data using a moving average filtering algorithm to smooth the signal and remove high-frequency noise; calculating instantaneous positive-sequence current and instantaneous negative-sequence current using the instantaneous symmetrical component method based on the filtered three-phase current data; performing a first-order difference between adjacent points on the instantaneous positive-sequence current and instantaneous negative-sequence current to obtain instantaneous positive-sequence current difference values and instantaneous negative-sequence current difference values; comparing the instantaneous negative-sequence current difference value with a preset threshold; when the instantaneous negative-sequence current difference value exceeds the preset threshold, further comparing the signs of the instantaneous negative-sequence current difference value and the instantaneous positive-sequence current difference value; and determining the location of the fault section based on the sign comparison results of the instantaneous negative-sequence current difference value and the topology of the distribution network.
[0006] Furthermore, the window size of the moving average filtering algorithm is adaptively selected based on the actual signal sampling rate, noise characteristics, and real-time requirements.
[0007] Furthermore, the instantaneous symmetrical component method extracts the instantaneous values of the positive and negative sequence components of the three-phase current in real time by constructing a rotating vector.
[0008] Furthermore, the sign comparison is as follows: when the instantaneous negative sequence current difference value has the opposite sign to the instantaneous positive sequence current difference value, the fault is determined to be located upstream of the current node.
[0009] Furthermore, fault segment identification is based on the sign combination of the instantaneous positive-sequence current difference value and the instantaneous negative-sequence current difference value of multiple nodes, combined with the distribution network topology to achieve accurate location.
[0010] The present invention discloses a rapid fault location system for medium-voltage cables based on moving average filtering and instantaneous symmetric component analysis, comprising:
[0011] Real-time monitoring module: Used for real-time monitoring of three-phase current data at each node in the distribution network;
[0012] Preprocessing module: Used to preprocess the three-phase current data using a moving average filtering algorithm to smooth the signal and remove high-frequency noise;
[0013] Instantaneous symmetrical component module: used to calculate instantaneous positive sequence current and instantaneous negative sequence current based on filtered three-phase current data using the instantaneous symmetrical component method;
[0014] Primary differential module: used to perform primary differential calculation between adjacent points on the instantaneous positive sequence current and the instantaneous negative sequence current respectively, to obtain the instantaneous positive sequence current differential value and the instantaneous negative sequence current differential value;
[0015] Comparison module: Used to compare the instantaneous negative sequence current difference value with a preset threshold. When the instantaneous negative sequence current difference value exceeds the preset threshold, the sign of the instantaneous negative sequence current difference value is further compared with that of the instantaneous positive sequence current difference value.
[0016] Judgment module: Used to determine the location of the fault section based on the sign comparison result of the instantaneous negative sequence current difference value and the instantaneous positive sequence current difference value, combined with the topology of the distribution network.
[0017] Furthermore, in the preprocessing module, the window size of the moving average filtering algorithm is adaptively selected based on the actual signal's sampling rate, noise characteristics, and real-time requirements.
[0018] Furthermore, in the instantaneous symmetrical component module, the instantaneous symmetrical component method extracts the instantaneous values of the positive-sequence and negative-sequence components of the three-phase current in real time by constructing a rotating vector.
[0019] Furthermore, in the comparison module, the sign comparison is as follows: when the instantaneous negative sequence current difference value has the opposite sign to the instantaneous positive sequence current difference value, it is determined that the fault is located upstream of the current node.
[0020] Furthermore, in the judgment module, the fault section judgment is based on the sign combination of the instantaneous positive sequence current difference value and the instantaneous negative sequence current difference value of multiple nodes, combined with the distribution network topology to achieve accurate positioning.
[0021] An electronic device according to the present invention includes a memory and a processor. The memory stores a computer program, and the processor executes the program to implement the steps of the method.
[0022] The present invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the method.
[0023] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: (1) Improved positioning accuracy: By performing moving average filtering in advance, the high-frequency interference in the field signal is effectively reduced, and the stability and accuracy of instantaneous negative sequence current calculation are significantly improved, thereby improving the overall fault location accuracy. (2) Applicable to early cable fault diagnosis: This method has a strong ability to identify early cable faults such as partial discharge, poor contact, and single-phase grounding. It can achieve sensitive detection through the change of weak negative sequence component when the current change is not completely significant. (3) Lightweight algorithm and easy to deploy: The moving average filtering and instantaneous component calculation require few resources and are easy to deploy in distribution automation terminals (such as FTU) or medium-voltage cable monitoring equipment to meet the real-time processing needs on site. (4) Wide applicability: It can be implemented on the existing distribution automation infrastructure without adding complex hardware or high-precision synchronization system. The deployment cost is low and it is applicable to various 10~35kV medium-voltage cable lines. Attached Figure Description
[0024] Figure 1 This is a flowchart of the present invention;
[0025] Figure 2 This is a schematic diagram of the fault location principle of the present invention. Detailed Implementation
[0026] The technical solution of the present invention will be further described below with reference to the accompanying drawings.
[0027] like Figure 1 As shown, this invention provides a rapid fault location method for medium-voltage cables based on moving average filtering and instantaneous symmetrical component analysis. Considering that medium-voltage cable systems often use single-end measurements, have significant signal attenuation, and are susceptible to interference, this method is particularly suitable for such environments, possessing advantages such as strong anti-interference capability, low false positive rate, and strong adaptability. This method consists of four modules: signal preprocessing (moving average filtering), obtaining instantaneous positive and negative sequence currents, determining the positive and negative sequence directions, and fault location. First, the real-time current waveform data monitored by the substation is processed by moving average filtering to eliminate or reduce high-frequency noise. Second, using the filtered data, the instantaneous positive and negative sequence currents are obtained using the instantaneous symmetrical component method. Third, the obtained instantaneous positive and negative sequence current data are subjected to abrupt change point difference analysis to determine the similarity or difference in their directions. Finally, the fault location of the distribution network is completed based on the distribution network topology and the direction information of the instantaneous positive and negative currents.
[0028] The complete process is as follows:
[0029] Step S1: Monitor the current data of each node, such as the substation, in real time.
[0030] Step S2: Preprocess the acquired real-time current data using a moving average filtering algorithm. This smooths the signal by calculating the average value of data points within a fixed window, removing high-frequency noise and resulting in a smoother current signal.
[0031] Where S2-1: Signal filtering processing
[0032] To improve the accuracy of instantaneous symmetrical component calculation while maintaining algorithm simplicity, this invention introduces a moving average filtering algorithm to process the acquired instantaneous values of the three-phase current. The moving average filter uses the arithmetic mean of consecutive sampling points of a certain length (window size N) as the filtered output value for the current point.
[0033] For discrete current sampling sequences (where x represents phases a, b, and c, and k is the sampling point number), its filtered output The calculation formula is as follows:
[0034]
[0035] Where N is the length of the sliding window. Choosing an appropriate window length N is crucial:
[0036] Smaller N values have limited effect on smoothing noise, but result in faster signal response and less delay.
[0037] A larger N value provides better noise smoothing, but introduces greater signal delay and may smooth out some useful high-frequency fault features. Therefore, the window length N needs to be determined experimentally based on trade-offs between the actual signal sampling rate, noise characteristics, and real-time requirements.
[0038] Through the By applying the moving average filter, the filtered current sequence can be obtained. These values will be used for subsequent calculations of instantaneous symmetric components. This method is computationally simple and easy to implement on hardware such as microcontrollers or DSPs.
[0039] Step S3: Calculate the instantaneous positive-sequence current and instantaneous negative-sequence current using the instantaneous symmetrical component method on the filtered real-time data.
[0040] Where S3-1: Calculation of instantaneous positive and negative sequence currents
[0041] Because the traditional instantaneous symmetric vector method has a certain time delay in calculation, affecting the real-time performance of the symmetric component transformation, this patent adopts an improved instantaneous symmetric vector method. The instantaneous values of the positive, negative, and zero-sequence components of the three-phase electrical quantities are obtained in real time by constructing a rotating vector.
[0042] For the instantaneous value of the three-phase unbalanced current:
[0043]
[0044] Let their corresponding rotating phasors be:
[0045]
[0046] The instantaneous current of phase A can be expressed as:
[0047]
[0048] Right now
[0049]
[0050] From this we can obtain
[0051]
[0052] And because
[0053]
[0054] Substituting (6) into (5) gives
[0055]
[0056] Similarly, we can obtain
[0057]
[0058]
[0059] The steady-state symmetric vector method is known to be:
[0060]
[0061] Substituting (2) into (10), and after simplification, we can obtain the instantaneous values of the positive sequence, negative sequence, and zero sequence components of the three-phase electrical quantities.
[0062]
[0063]
[0064] Step S4: Perform a difference between adjacent points of the instantaneous current data, compare the difference value of the instantaneous negative sequence current with a set threshold, and if it exceeds the set threshold, compare the signs of the difference value of the instantaneous negative sequence current with the difference value of the instantaneous positive sequence current.
[0065] Step S4-1: Instantaneous positive and negative sequence current direction determination
[0066] The instantaneous positive and negative sequence current sequences can be obtained from equations (11) and (12), so the direction of the instantaneous positive and negative sequence currents can be determined by a single difference method.
[0067]
[0068]
[0069] By multiplying equation (13) and equation (14), we can determine whether the directions of the instantaneous positive and negative sequence currents are the same:
[0070]
[0071] Step S5: Using the sign of the instantaneous positive and negative sequence current primary differential value combined with the distribution network topology, determine the fault section: when the instantaneous positive and negative sequence currents of a node are in opposite directions, the fault occurs upstream of that node.
[0072] Step S5-1: Location of fault sections in the distribution network
[0073] The fault section can be determined by the direction of the instantaneous positive and negative sequence currents at the nodes.
[0074] like Figure 2 As shown in the figure, when an asymmetrical fault occurs at the location indicated, negative sequence currents will be detected both upstream and downstream. When the instantaneous negative sequence current at CT2 is in the opposite direction to the instantaneous positive sequence current, i.e. And the instantaneous positive and negative sequence currents at CT1 are in the same direction, that is If so, it can be determined that the fault location is between CT1 and CT2.
[0075] This invention also provides a rapid fault location system for medium-voltage cables based on moving average filtering and instantaneous symmetric component analysis, comprising:
[0076] Real-time monitoring module: Used for real-time monitoring of three-phase current data at each node in the distribution network;
[0077] Preprocessing module: Used to preprocess the three-phase current data using a moving average filtering algorithm to smooth the signal and remove high-frequency noise;
[0078] Instantaneous symmetrical component module: used to calculate instantaneous positive sequence current and instantaneous negative sequence current based on filtered three-phase current data using the instantaneous symmetrical component method;
[0079] Primary differential module: used to perform primary differential calculation between adjacent points on the instantaneous positive sequence current and the instantaneous negative sequence current respectively, to obtain the instantaneous positive sequence current differential value and the instantaneous negative sequence current differential value;
[0080] Comparison module: Used to compare the instantaneous negative sequence current difference value with a preset threshold. When the instantaneous negative sequence current difference value exceeds the preset threshold, the sign of the instantaneous negative sequence current difference value is further compared with that of the instantaneous positive sequence current difference value.
[0081] Judgment module: Used to determine the location of the fault section based on the sign comparison result of the instantaneous negative sequence current difference value and the instantaneous positive sequence current difference value, combined with the topology of the distribution network.
[0082] In the preprocessing module, the window size of the moving average filtering algorithm is adaptively selected based on the actual signal sampling rate, noise characteristics, and real-time requirements.
[0083] In the instantaneous symmetrical component module, the instantaneous symmetrical component method extracts the instantaneous values of the positive and negative sequence components of the three-phase current in real time by constructing a rotating vector.
[0084] In the comparison module, the sign comparison is as follows: when the instantaneous negative sequence current difference value has the opposite sign to the instantaneous positive sequence current difference value, it is determined that the fault is located upstream of the current node.
[0085] In the judgment module, fault section identification is based on the sign combination of the instantaneous positive-sequence current difference values and instantaneous negative-sequence current difference values of multiple nodes, combined with the distribution network topology to achieve precise location.
[0086] This invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the steps of the method.
[0087] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method.
Claims
1. A method for rapid location of fault sections in medium-voltage cables based on moving average filtering and instantaneous symmetric component analysis, characterized in that, Includes the following steps: Real-time monitoring of three-phase current data at each node in the distribution network; preprocessing of the three-phase current data using a moving average filtering algorithm; calculation of instantaneous positive-sequence current and instantaneous negative-sequence current using the instantaneous symmetrical component method based on the filtered three-phase current data; and performing a first difference between adjacent points on the instantaneous positive-sequence current and instantaneous negative-sequence current to obtain the instantaneous positive-sequence current difference value and the instantaneous negative-sequence current difference value. The instantaneous negative sequence current difference value is compared with a preset threshold. When the instantaneous negative sequence current difference value exceeds the preset threshold, the signs of the instantaneous negative sequence current difference value and the instantaneous positive sequence current difference value are further compared. Based on the sign comparison results of the instantaneous negative sequence current difference value and the instantaneous positive sequence current difference value, combined with the topology of the distribution network, the location of the fault section is determined.
2. The method for rapid location of medium-voltage cable fault sections based on moving average filtering and instantaneous symmetric component analysis according to claim 1, characterized in that, The window size of the moving average filtering algorithm is adaptively selected based on the sampling rate of the actual signal, noise characteristics, and real-time requirements.
3. The method for rapid location of medium-voltage cable fault sections based on moving average filtering and instantaneous symmetric component analysis according to claim 1, characterized in that, The instantaneous symmetrical component method extracts the instantaneous values of the positive and negative sequence components of the three-phase current in real time by constructing a rotating vector.
4. The method for rapid location of medium-voltage cable fault sections based on moving average filtering and instantaneous symmetric component analysis according to claim 1, characterized in that, The sign comparison is as follows: when the signs of the instantaneous negative sequence current difference value and the instantaneous positive sequence current difference value are opposite, it is determined that the fault is located upstream of the current node.
5. The method for rapid location of medium-voltage cable fault sections based on moving average filtering and instantaneous symmetric component analysis according to claim 1, characterized in that, Fault segment identification is based on the sign combination of the instantaneous positive-sequence current difference value and the instantaneous negative-sequence current difference value of multiple nodes, combined with the distribution network topology to achieve accurate location.
6. A rapid fault location system for medium-voltage cables based on moving average filtering and instantaneous symmetric component analysis, characterized in that, include: Real-time monitoring module: Used for real-time monitoring of three-phase current data at each node in the distribution network; Preprocessing module: Used to preprocess the three-phase current data using a moving average filtering algorithm; Instantaneous symmetrical component module: used to calculate instantaneous positive sequence current and instantaneous negative sequence current based on filtered three-phase current data using the instantaneous symmetrical component method; Primary differential module: used to perform primary differential calculation between adjacent points on the instantaneous positive sequence current and the instantaneous negative sequence current respectively, to obtain the instantaneous positive sequence current differential value and the instantaneous negative sequence current differential value; Comparison module: Used to compare the instantaneous negative sequence current difference value with a preset threshold. When the instantaneous negative sequence current difference value exceeds the preset threshold, the sign of the instantaneous negative sequence current difference value is further compared with that of the instantaneous positive sequence current difference value. Judgment module: Used to determine the location of the fault section based on the sign comparison result of the instantaneous negative sequence current difference value and the instantaneous positive sequence current difference value, combined with the topology of the distribution network.
7. A rapid fault location system for medium-voltage cables based on moving average filtering and instantaneous symmetric component analysis according to claim 6, characterized in that, In the preprocessing module, the window size of the moving average filtering algorithm is adaptively selected based on the actual signal sampling rate, noise characteristics, and real-time requirements.
8. A rapid fault location system for medium-voltage cables based on moving average filtering and instantaneous symmetric component analysis according to claim 6, characterized in that, In the instantaneous symmetrical component module, the instantaneous symmetrical component method extracts the instantaneous values of the positive and negative sequence components of the three-phase current in real time by constructing a rotating vector.
9. A rapid fault location system for medium-voltage cables based on moving average filtering and instantaneous symmetric component analysis according to claim 6, characterized in that, In the comparison module, the sign comparison is as follows: when the instantaneous negative sequence current difference value has the opposite sign to the instantaneous positive sequence current difference value, it is determined that the fault is located upstream of the current node.
10. A rapid fault location system for medium-voltage cables based on moving average filtering and instantaneous symmetric component analysis according to claim 6, characterized in that, In the judgment module, the fault section judgment is based on the sign combination of the instantaneous positive sequence current difference value and the instantaneous negative sequence current difference value of multiple nodes, combined with the distribution network topology to achieve accurate location.
11. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the program to implement the steps of the method according to claims 1-5.
12. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, implements the steps of the method described in claims 1-5.