Power distribution network detection method and system based on time domain signal correlation and storage medium

Abstract

The invention discloses a power distribution network detection method and system based on time domain signal correlation, and a storage medium, belongs to the technical field of power distribution network fault detection, can comprehensively cover fault conditions, breaks through the adaptation bottleneck of a static template library, and improves the detection accuracy. A normalized high-resistance grounding fault current mode family containing different ignition angles and random resistance parameters is constructed, and a special circuit model capable of simulating insulator leakage current characteristics is matched, so that nonlinear fault current characteristics under different grounding media, environment humidity and initial fault conditions are completely covered; the core defect that in an existing correlation detection technology, the static template library scene adaptation range is limited is effectively overcome, the fault recognition adaptation capacity under the complex field working condition is greatly improved, fault feature matching is achieved based on normalized cross correlation coefficient calculation, the inherent anti-noise characteristic of correlation coefficients is utilized, and the fault recognition adaptation capacity is greatly improved. And the influence of electromagnetic transient disturbance and background noise of the power distribution network can be effectively suppressed in a low signal-to-noise ratio environment.

Description

Technical Field

[0001] This invention relates to the field of power distribution network fault detection technology, specifically to a power distribution network detection method, system, and storage medium based on time-domain signal correlation. Background Technology

[0002] As the terminal transmission network directly facing end users in the power system, the power supply reliability and operational safety of the distribution network are directly related to the stable and orderly conduct of social production and life. High-resistance grounding faults are a typical type of fault in the operation of distribution networks. They are mostly caused by the contact of distribution line conductors with high-resistance media such as dry soil, tree branches, and concrete buildings. The fault current amplitude of this type of fault is extremely small, and the current waveform exhibits strong nonlinearity and strong randomness, making it difficult for conventional protection methods to effectively identify. According to industry statistics, about 30% of power outages in distribution networks are caused by the failure to detect high-resistance grounding faults in a timely and accurate manner. The continued existence of the fault not only poses a serious risk of electric shock but also easily leads to major safety accidents such as electrical fires and the burning of distribution equipment, causing incalculable economic losses and social impact.

[0003] Currently, the mainstream detection technologies for high-resistance grounding faults in distribution networks are mainly divided into three categories: traditional relay protection methods, frequency domain harmonic analysis methods, and time-frequency transformation analysis methods. Among them, the detection method based on overcurrent protection relays triggers protection action by preset fixed current thresholds, but its core drawback is that the current amplitude of high-resistance grounding faults is usually much lower than the action threshold of conventional overcurrent protection, which easily leads to protection failure and missed fault detection. Frequency domain analysis methods based on harmonic component detection mostly use the second and fourth harmonic components as fault identification features. This method is easily affected by nonlinear load switching and capacitor bank start-up and shutdown in the distribution network, resulting in a high false detection rate. While time-frequency transformation analysis methods, represented by wavelet transform, can capture the transient signal characteristics at the time of fault occurrence, these methods have high computational complexity and cannot meet the real-time requirements of distribution network fault detection. At the same time, their detection effect is highly sensitive to the selection of the mother wavelet function and the setting of the signal decomposition level, resulting in serious inadequacy in the complex field operation environment of the distribution network.

[0004] In recent years, high-resistance grounding fault detection technology based on signal correlation analysis has gradually become a research hotspot in the industry. Its core principle is to pre-build a fault waveform template library, match the measured signals collected on-site with the template library, and complete fault identification based on signal correlation. However, existing detection technologies based on correlation analysis still have significant technical bottlenecks: First, the fault mode library is mostly a static template library, which fails to fully cover the dynamic fault characteristics under different fault ignition angles, line contact materials, and on-site environmental humidity, and the range of fault scenarios it can adapt to is limited. Second, the algorithm's noise and interference resistance is insufficient. Under common conditions such as unbalanced distribution network load and electromagnetic transient disturbances, background noise can cause the correlation coefficient to fluctuate significantly, directly reducing the reliability of fault detection. Third, the fault judgment mechanism is simple, generally using a fixed threshold to complete the judgment, which cannot effectively distinguish between high-resistance grounding faults and non-fault conditions with similar waveform characteristics such as transformer inrush current and capacitor switching, making it difficult to balance the accuracy of fault detection and the ability to prevent false tripping.

[0005] To address the core pain points of existing high-resistance grounding fault detection technologies in distribution networks, such as low detection accuracy, weak anti-interference capability, poor scenario adaptability, and insufficient real-time performance, a distribution network detection method, system, and storage medium based on time-domain signal correlation are proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a distribution network detection method, system, and storage medium based on time-domain signal correlation, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a distribution network detection method based on time-domain signal correlation, comprising the following steps:

[0008] S1. Construct a family of normalized high-resistance ground fault current modes that include different ignition angles and random resistances.

[0009] S2. Acquire the neutral point grounding wire current signal, filter out the fundamental frequency component and DC component, and extract the remaining current signal;

[0010] S3. Calculate the normalized cross-correlation coefficients between the residual current signal and each normalized waveform in the high-impedance ground fault current mode family.

[0011] S4. Set a safety margin threshold. If the maximum correlation coefficient is within two consecutive half-cycles... If so, it is determined to be a high-resistance grounding fault. Then it is determined to be a non-high-resistance grounding fault. If the value is between 0.25 and 0.45, then the determination is recalculated after a half-cycle delay.

[0012] As a further preferred embodiment of this technical solution: in S1, the range of the ignition angle is:

[0013]

[0014] in, Ignition angle;

[0015] In S1, the circuit model for constructing the high-resistance grounding fault mode family is as follows:

[0016] A1. It consists of a series resistor and two anti-parallel branches, and each branch consists of a DC source and a diode;

[0017] A2. The leakage resistor is connected in parallel to the circuit model to simulate the leakage current characteristics of the insulator.

[0018] As a further preferred embodiment of this technical solution: In S2, the neutral point grounding wire current signal:

[0019] The neutral point grounding wire current signal is the sum of the leakage current of each phase of the line. The current through the neutral point of the grounding transformer contains fundamental harmonics and noise. The fundamental frequency component and DC component are filtered out by a filter to extract the residual current signal.

[0020] As a further preferred embodiment of this technical solution: In S3, the normalized cross-correlation coefficient is... The calculation formula is:

[0021] (1)

[0022] In its formula (1), The number of sampling points. This is the preprocessed residual current signal. This is the normalized high-resistivity ground fault current mode;

[0023] In S3, the normalized correlation coefficient is:

[0024] Based on the impact of noise on the correlation coefficient, the robustness of the correlation coefficient is verified, including: when the signal contains only fundamental frequency harmonics, the neutral point current is the fundamental frequency component and is uncorrelated with the high-impedance ground fault current mode family; and when noise dominates the signal, the correlation coefficient... Its characteristics are:

[0025] (2)

[0026] In its formula (2), The mean, For variance, This represents the number of sampling points per half-cycle.

[0027] When the signal contains characteristics of a high-impedance ground fault, the neutral point current signal is highly correlated with the high-impedance ground fault current pattern family, and the influence of noise is suppressed. In this case, the variance of the correlation coefficient is:

[0028] (3)

[0029] In its formula (3), For signal-to-noise ratio, For signal power, This represents noise power.

[0030] As a further preferred embodiment of this technical solution: in S4, the safety margin threshold is set as follows:

[0031] Through multiple experimental tests on dry soil and grassland, it was determined that... To determine the lower threshold of high-resistance grounding faults, tests were conducted using transformer inrush current and load imbalance to determine the threshold for non-high-resistance grounding faults. To determine the upper limit threshold for non-high-resistance grounding faults, when If the value is between 0.25 and 0.45, the determination will be postponed to the next half-cycle.

[0032] Specifically, in S4, the delayed determination mechanism is as follows:

[0033] when When the correlation coefficient is between 0.25 and 0.45, it is necessary to continuously monitor the correlation coefficient for the next two half-cycles. If in any cycle... If the fault is high resistance, it is determined to be a ground fault; otherwise, it is determined to be a non-high resistance ground fault.

[0034] A distribution network detection system based on time-domain signal correlation includes a current transformer, a filter module, an embedded processor, and an alarm module.

[0035] The current transformer is installed at the zigzag grounding connection of the neutral point and is used to collect the neutral point current signal.

[0036] The filter module is used to filter out fundamental frequency harmonics and DC components;

[0037] The embedded processor has a pre-stored high-resistance grounding fault mode family database containing high-resistance grounding fault current waveform parameters corresponding to different surface humidity and contact materials, and performs correlation coefficient calculation and threshold determination.

[0038] The alarm module is used to trigger an alarm signal when a high-resistance grounding fault is detected.

[0039] As a further preferred embodiment of this technical solution, it also includes a computer device, which includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements a power distribution network detection method based on time-domain signal correlation.

[0040] A storage medium for distribution network detection based on time-domain signal correlation, wherein a computer program is stored on the storage medium, and when the computer program is executed by a processor, it implements the steps of any one of the distribution network detection methods based on time-domain signal correlation.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] 1. This invention can comprehensively cover fault conditions and overcome the adaptation bottleneck of static template library. By constructing a normalized high-resistance grounding fault current mode family containing different ignition angles and random resistance parameters, and in conjunction with a special circuit model that can simulate the leakage current characteristics of insulators, it fully covers the nonlinear fault current characteristics under different grounding media, environmental humidity, and fault initial conditions. It effectively solves the core defect of the limited scene adaptation range of static template library in the existing correlation detection technology and greatly improves the fault identification and adaptation capability under complex field conditions.

[0043] 2. This invention significantly improves anti-interference performance and effectively reduces the probability of missed and false fault detection. It achieves fault feature matching based on normalized cross-correlation coefficient calculation and utilizes the inherent noise immunity of correlation coefficients to effectively suppress the influence of electromagnetic transient disturbances and background noise in distribution networks under low signal-to-noise ratio environments. At the same time, by filtering out fundamental frequency and DC components in the signal preprocessing steps, it avoids interference from conventional operating conditions such as fundamental frequency harmonics and line load imbalance on the detection results. This greatly solves the industry problems of traditional overcurrent protection being prone to failure to operate and missed detection, and frequency domain harmonic analysis being prone to false detection due to nonlinear load interference, thus significantly improving the reliability of fault detection.

[0044] 3. This invention innovates a graded judgment mechanism that balances detection accuracy and anti-maloperation capability. It designs a graded fault judgment mechanism with dual threshold safety margin judgment and delayed half-cycle verification. Through the threshold range calibrated by actual measurement, it can accurately distinguish between high-resistance grounding faults and non-fault conditions with similar waveform characteristics, such as transformer inrush current and capacitor switching. This solves the defect of the traditional single fixed threshold judgment mechanism that cannot balance detection rate and anti-maloperation capability. While ensuring a high detection rate for high-resistance grounding faults, it eliminates the problem of protection maloperation under non-fault conditions.

[0045] 4. This invention simplifies the calculation and deployment scheme, balancing strong real-time performance with high economy. It only requires single-end acquisition of the neutral point grounding wire current signal to complete the entire detection process, eliminating the need for multi-end synchronous data acquisition, invasive sensors, or high-cost acquisition equipment, thus significantly reducing hardware deployment complexity and engineering application costs. At the same time, the algorithm calculation process is simple, without the need for complex time-frequency transformation calculations, and can complete fault determination within a single power cycle (≤20ms), perfectly adapting to the real-time requirements of distribution network fault detection. It can achieve online monitoring without interrupting the operation of the power grid and can be widely adapted to complex operating scenarios such as urban underground cables and rural overhead lines.

[0046] 5. This invention possesses strong engineering applicability, improving the operational safety level of distribution networks. It is simultaneously equipped with a modular detection system and a portable computer-readable storage medium solution. The algorithm program can be easily embedded into existing distribution network neutral point grounding protection devices, exhibiting strong compatibility and engineering scalability. This invention enables rapid and accurate identification of high-resistance grounding faults in distribution networks, effectively preventing safety accidents such as electric shock, electrical fires, and equipment burnout caused by persistent faults. It significantly reduces the incidence of power outages in distribution networks and substantially improves the reliability and operational safety of power supply. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the operation process of the power distribution network detection method based on time-domain signal correlation of the present invention;

[0048] Figure 2 This is a graph showing the maximum correlation coefficients of high-resistivity grounding fault current and transformer inrush current in an experimental example of the distribution network detection method, system, and storage medium based on time-domain signal correlation of this invention. Detailed Implementation

[0049] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0050] Example

[0051] Please see Figure 1 This invention provides a technical solution: a distribution network detection method based on time-domain signal correlation, comprising the following steps:

[0052] S1. Construct a family of normalized high-resistance ground fault current modes that include different ignition angles and random resistances.

[0053] The range of ignition angles is as follows:

[0054]

[0055] in, Ignition angle;

[0056] The circuit model for constructing the high-resistance grounding fault mode family is as follows:

[0057] A1. It consists of a series resistor and two anti-parallel branches, and each branch consists of a DC source and a diode;

[0058] A2. The leakage resistor is connected in parallel with the circuit model to simulate the leakage current characteristics of the insulator;

[0059] S2. Acquire the neutral point grounding wire current signal, filter out the fundamental frequency component and DC component, and extract the remaining current signal;

[0060] Among them, the neutral point grounding wire current signal:

[0061] The neutral point grounding wire current signal is the sum of the leakage current of each phase of the line. The current through the neutral point of the grounding transformer contains fundamental harmonics and noise. The fundamental frequency component and DC component are filtered out by a filter to extract the residual current signal.

[0062] S3. Calculate the normalized cross-correlation coefficients between the residual current signal and each normalized waveform in the high-impedance ground fault current mode family.

[0063] Among them, the normalized cross-correlation coefficient The calculation formula is:

[0064] (1)

[0065] In its formula (1), The number of sampling points. This is the preprocessed residual current signal. This is the normalized high-resistivity ground fault current mode;

[0066] Wherein, the normalized correlation coefficient is:

[0067] The robustness of the maximum correlation coefficient was verified by considering the impact of noise on the maximum correlation coefficient, including: when the signal contains only fundamental frequency harmonics (non-high-impedance ground fault scenario), the neutral point current is the fundamental frequency component and is uncorrelated with the high-impedance ground fault current mode family; and when noise dominates the signal, the correlation coefficient... Its characteristics are:

[0068] (2)

[0069] In its formula (2), The mean, For variance, This represents the number of sampling points per half-cycle.

[0070] When the signal contains characteristics of a high-impedance ground fault, the neutral point current signal is highly correlated with the high-impedance ground fault current pattern family, and the influence of noise is suppressed. In this case, the variance of the correlation coefficient is:

[0071] (3)

[0072] In its formula (3), For signal-to-noise ratio, For signal power, Noise power;

[0073] S4. Set a safety margin threshold. If the maximum correlation coefficient is within two consecutive half-cycles... If so, it is determined to be a high-resistance grounding fault. Then it is determined to be a non-high-resistance grounding fault. If the value is between 0.25 and 0.45, then a half-cycle delay is applied for reassessment.

[0074] The safety margin threshold is set as follows:

[0075] Through multiple experimental tests on dry soil and grassland, it was determined that... To determine the lower threshold of high-resistance grounding faults, tests for non-high-resistance grounding faults such as transformer inrush current and load imbalance are used. To determine the upper limit threshold for non-high-resistance grounding faults, when If the value is between 0.25 and 0.45, the determination will be postponed to the next half-cycle.

[0076] Specifically, in S4, the delayed determination mechanism is as follows:

[0077] when When the correlation coefficient is between 0.25 and 0.45, it is necessary to continuously monitor the correlation coefficient for the next two half-cycles. If in any cycle... If the fault is high resistance, it is determined to be a ground fault; otherwise, it is determined to be a non-high resistance ground fault.

[0078] In this embodiment, specifically: This invention proposes a distribution network detection method based on time-domain signal correlation. By constructing a dynamic normalized high-resistivity grounding fault current pattern family and a dual-threshold judgment mechanism, the accuracy and robustness of fault detection are significantly improved. Compared with traditional methods that rely on fixed thresholds or complex frequency domain analysis, the method of this invention only needs to collect the neutral point grounding wire current signal, extract the remaining current signal after filtering out the fundamental frequency harmonics and DC components, and calculate the normalized correlation coefficient with the predefined pattern family to achieve rapid judgment.

[0079] It eliminates the need for multi-terminal synchronous data acquisition or high-cost equipment, significantly reducing hardware complexity and deployment costs. It boasts high fault detection accuracy and excellent noise resistance. Furthermore, the method of this invention supports fault determination within a single cycle (≤20ms), without interrupting grid operation or requiring additional invasive sensors, thus significantly improving the safety and economy of the power distribution system.

[0080] Experimental Example

[0081] Please see Figure 2 In 63 actual experiments, the distribution of the maximum correlation coefficient between high-resistance ground fault current and transformer inrush current and the high-resistance ground fault mode family database shows that the maximum correlation coefficient of each experiment for high-resistance ground fault is greater than 0.45 for most experiments, while the maximum correlation coefficient of most experiments for transformer inrush current is less than 0.25.

[0082] The above method is applied to a distribution network detection system based on time-domain signal correlation, including a current transformer, a filter module, an embedded processor, and an alarm module:

[0083] A current transformer is installed at the zigzag grounding connection of the neutral point to collect the neutral point current signal.

[0084] The filter module is used to filter out fundamental frequency harmonics and DC components;

[0085] The embedded processor has a pre-stored database of high-resistance grounding fault mode families containing high-resistance grounding fault current waveform parameters corresponding to different surface humidity and contact materials (soil, grass, concrete). It performs correlation coefficient calculation and threshold determination. The sampling frequency of the embedded processor is ≥5kHz, which supports real-time processing of current signals and completes the determination within one power system cycle (≤20ms).

[0086] The alarm module is used to trigger an alarm signal when a high-resistance ground fault is detected.

[0087] In this embodiment, specifically: it also includes a computer device, which includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements a power distribution network detection method based on time-domain signal correlation.

[0088] In this embodiment, specifically: a computer program is stored on the storage medium, and when the computer program is executed by the processor, it implements the steps of any one of the distribution network detection methods based on time-domain signal correlation.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A distribution network detection method based on time-domain signal correlation, characterized in that, Includes the following steps: S1. Construct a family of normalized high-resistance ground fault current modes that include different ignition angles and random resistances. S2. Acquire the neutral point grounding wire current signal, filter out the fundamental frequency component and DC component, and extract the remaining current signal; S3. Calculate the normalized cross-correlation coefficients between the residual current signal and each normalized waveform in the high-impedance ground fault current mode family. S4. Set a safety margin threshold. If the maximum correlation coefficient is within two consecutive half-cycles... If so, it is determined to be a high-resistance grounding fault. Then it is determined to be a non-high-resistance grounding fault. If the value is between 0.25 and 0.45, then the determination is recalculated after a half-cycle delay.

2. The distribution network detection method based on time-domain signal correlation according to claim 1, characterized in that: In S1, the range of ignition angles is: in, Ignition angle; In S1, the circuit model for constructing the high-resistance grounding fault mode family is as follows: A1. It consists of a series resistor and two anti-parallel branches, and each branch consists of a DC source and a diode; A2. The leakage resistor is connected in parallel to the circuit model to simulate the leakage current characteristics of the insulator.

3. The distribution network detection method based on time-domain signal correlation according to claim 1, characterized in that: In S2, the neutral point grounding wire current signal is: The neutral point grounding wire current signal is the sum of the leakage current of each phase of the line. The current through the neutral point of the grounding transformer contains fundamental harmonics and noise. The fundamental frequency component and DC component are filtered out by a filter to extract the residual current signal.

4. The distribution network detection method based on time-domain signal correlation according to claim 1, characterized in that: In S3, the normalized cross-correlation coefficient The calculation formula is: （1） In its formula (1), The number of sampling points. This is the preprocessed residual current signal. This is the normalized high-resistivity ground fault current mode; In S3, the normalized correlation coefficient is: Based on the impact of noise on the correlation coefficient, the robustness of the correlation coefficient is verified, including: when the signal contains only fundamental frequency harmonics, the neutral point current is the fundamental frequency component and is uncorrelated with the high-impedance ground fault current mode family; and when noise dominates the signal, the correlation coefficient... Its characteristics are: （2） In its formula (2), The mean, For variance, This represents the number of sampling points per half-cycle. When the signal contains characteristics of a high-impedance ground fault, the neutral point current signal is highly correlated with the high-impedance ground fault current pattern family, and the influence of noise is suppressed. In this case, the variance of the correlation coefficient is: （3） In its formula (3), For signal-to-noise ratio, For signal power, This represents noise power.

5. The distribution network detection method based on time-domain signal correlation according to claim 1, characterized in that: In S4, the safety margin threshold is set as follows: Through multiple experimental tests on dry soil and grassland, it was determined that... To determine the lower threshold of a high-resistance grounding fault, tests were conducted using transformer inrush current and load imbalance to determine the threshold for a non-high-resistance grounding fault. To determine the upper limit threshold for non-high-resistance grounding faults, when If the value is between 0.25 and 0.45, the determination will be postponed to the next half-cycle. Specifically, in S4, the delayed determination mechanism is as follows: when When the correlation coefficient is between 0.25 and 0.45, it is necessary to continuously monitor the correlation coefficient for the next two half-cycles. If in any cycle... If the fault is high resistance grounding, it is determined to be a high resistance grounding fault; otherwise, it is determined to be a non-high resistance grounding fault.

6. A distribution network detection system based on time-domain signal correlation, characterized in that: Includes current transformers, filter modules, embedded processors, and alarm modules: The current transformer is installed at the zigzag grounding connection of the neutral point and is used to collect the neutral point current signal. The filter module is used to filter out fundamental frequency harmonics and DC components; The embedded processor has a pre-stored high-resistance grounding fault mode family database containing high-resistance grounding fault current waveform parameters corresponding to different surface humidity and contact materials, and performs correlation coefficient calculation and threshold determination. The alarm module is used to trigger an alarm signal when a high-resistance grounding fault is detected.

7. The distribution network detection system based on time-domain signal correlation according to claim 6, characterized in that: It also includes a computer device comprising a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement a power distribution network detection method based on time-domain signal correlation.

8. A storage medium for distribution network detection based on time-domain signal correlation, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the methods in the distribution network detection method based on time-domain signal correlation.

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