Earth lead instantaneous fault simulation data analysis method

Abstract

The invention discloses a grounding lead transient fault simulation data analysis method, which belongs to the technical field of electric power, and comprises the following steps: collecting and processing grounding lead transient fault signals, and determining grounding lead transient fault characteristic data; carrying out the analysis and mode recognition of the feature data of the instantaneous fault of the grounding wire, and determining a fault type diagnosis simulation result of the instantaneous fault simulation of the grounding wire; and determining the instantaneous fault location of the grounding wire based on a C-type traveling wave method, and taking treatment measures to process the instantaneous fault of the grounding wire according to the fault location and fault type diagnosis simulation result. According to the method, the problem that the operation stability and reliability of a power system are poor due to the fact that the efficiency and accuracy of detecting, positioning and processing the instantaneous fault of the grounding wire cannot be effectively improved in the prior art is solved. The method can effectively improve the detection, positioning and processing efficiency and accuracy of the instantaneous fault of the grounding wire, and can improve the operation stability and reliability of a power system.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, in particular to a method for analyzing grounding conductor transient fault simulation data. Background Art

[0002] When a grounding conductor experiences a transient fault, although the duration of the transient fault is short, the sudden rise and fall of voltage will damage the internal components of the equipment, shorten its service life, cause malfunction of the protection device, and trigger unnecessary power outages. Frequent transient faults will also affect the stability and reliability of the power system, bring inconvenience to industrial production and daily life, and increase economic losses. Therefore, it is particularly important to detect, locate and handle transient faults in grounding conductors.

[0003] Chinese patent publication number CN114355112B discloses a transient fault and defect discharge fault simulation test platform and data analysis method. By suspending the low-voltage side simulation conductor and grounding it with two uniform conductors, and then connecting two external uniform conductors, a uniform impedance transition is formed between the test sample and the low-voltage side simulation conductor. This makes the wave impedance of the entire test circuit continuous, effectively avoiding measurement errors and distortions caused by traveling wave refraction and reflection. However, this patent has the following drawbacks:

[0004] Existing technologies cannot effectively improve the efficiency and accuracy of detecting, locating and handling transient faults in ground conductors, resulting in poor operational stability and reliability of the power system. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for analyzing grounding conductor transient fault simulation data, which can effectively improve the efficiency and accuracy of detecting, locating and processing grounding conductor transient faults, improve the operating stability and reliability of the power system, and solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for analyzing ground conductor transient fault simulation data includes:

[0008] Collect and process the transient fault signal of the grounding conductor to determine the transient fault characteristic data of the grounding conductor;

[0009] The noise entropy weight factor is obtained by using the standard deviation of the energy ratio, and the amplitude of the instantaneous fault signal of the grounding conductor is amplified by using the noise entropy weight factor;

[0010] Establish a fault type diagnosis simulation model for the transient fault simulation of the grounding conductor to analyze and identify the characteristic data of the transient fault of the grounding conductor, and determine the fault type diagnosis simulation result of the transient fault simulation of the grounding conductor;

[0011] The transient fault location of the grounding conductor is determined based on the C-type traveling wave method. According to the fault location and fault type diagnosis simulation results, treatment measures are taken to deal with the transient fault of the grounding conductor.

[0012] Preferably, amplifying the amplitude of the ground conductor transient fault signal includes:

[0013] Real-time monitoring of the filtering result of the signal filter after filtering the instantaneous fault signal of the grounding conductor;

[0014] Extracting noise energy corresponding to noise of a specific frequency band in the ground conductor instantaneous fault signal from the filtering result;

[0015] Obtain the noise energy ratio between the noise energy corresponding to each filtering result and the noise energy of the previous filtering result;

[0016] Comparing the noise energy ratio with a preset noise energy ratio threshold;

[0017] When the noise energy ratio exceeds a preset noise energy ratio threshold, the noise energy ratio exceeding the noise energy ratio threshold is recorded as a target noise energy ratio;

[0018] When the number of the target noise energy ratios reaches and exceeds a preset number threshold, the target noise energy ratios are used to obtain the energy ratio standard deviation;

[0019] The energy ratio standard deviation is used to obtain a noise entropy weighting factor, and the noise entropy weighting factor is used to amplify the amplitude of the grounding conductor transient fault signal.

[0020] Preferably, the method of obtaining a noise entropy weighting factor by using the energy ratio standard deviation and amplifying the amplitude of the grounding conductor transient fault signal by using the noise entropy weighting factor comprises:

[0021] Retrieve the standard deviation of energy ratio;

[0022] Using the energy ratio standard deviation to obtain a noise entropy weight factor for dynamically adjusting the weight of the impact on the system;

[0023] Real-time monitoring of all noise entropy weighting factors generated during signal filtering;

[0024] Screening the noise entropy weight factors, and obtaining noise entropy weight factors whose factor values ​​exceed a preset factor threshold as target weight factors;

[0025] When there are multiple target weight factors, the factor standard deviation is obtained using the multiple target weight factors;

[0026] Retrieve and adjust the gain parameters of the signal amplifier;

[0027] Adjusting a gain parameter of a signal conditioning amplifier using the factor standard deviation to obtain an adjusted gain coefficient;

[0028] The amplitude of the grounding conductor transient fault signal is amplified using the adjusted gain coefficient.

[0029] Preferably, establishing a ground conductor transient fault simulation fault type diagnosis simulation model includes:

[0030] According to the needs of analyzing the grounding conductor transient fault simulation data, historical data of the grounding conductor transient fault is collected, and the collected historical data of the grounding conductor transient fault is divided into a training set and a test set;

[0031] Based on machine learning technology, a training set is used to train the machine learning model, so that the machine learning model can autonomously learn the fault type diagnosis behavior of the grounding conductor transient fault simulation from the training set, and determine the grounding conductor transient fault simulation fault type diagnosis simulation model based on machine learning;

[0032] The machine learning-based grounding conductor transient fault simulation fault type diagnosis simulation model is tested based on the test set to evaluate the performance of the machine learning-based grounding conductor transient fault simulation fault type diagnosis simulation model. It is determined whether the machine learning-based grounding conductor transient fault simulation fault type diagnosis simulation model can achieve the expected effect of diagnosing the transient fault simulation fault type of the grounding conductor, thereby determining the simulation model test evaluation results;

[0033] According to the simulation model test evaluation results, the parameters of the grounding conductor transient fault simulation fault type diagnosis simulation model based on machine learning are adjusted and optimized, and then the optimal grounding conductor transient fault simulation fault type diagnosis simulation model is determined to simulate different grounding conductor transient fault scenarios.

[0034] Preferably, analyzing and pattern-recognizing the grounding conductor transient fault characteristic data to determine the grounding conductor transient fault simulation type diagnosis simulation result includes:

[0035] Deploy a grounding conductor transient fault simulation fault type diagnosis simulation model, and deploy the grounding conductor transient fault simulation fault type diagnosis simulation model in an actual grounding conductor transient fault simulation fault type diagnosis simulation environment;

[0036] The transient fault characteristic data of the grounding conductor is input into the transient fault simulation fault type diagnosis simulation model of the grounding conductor. The transient fault characteristic data of the grounding conductor is analyzed and pattern recognized according to the transient fault simulation fault type diagnosis simulation model of the grounding conductor, and the transient fault type of the grounding conductor is diagnosed to determine the transient fault simulation fault type diagnosis simulation results of the grounding conductor, including strong transient fault, medium transient fault, and weak transient fault.

[0037] Preferably, the instantaneous fault location of the grounding conductor is determined based on the C-type traveling wave method, and according to the fault location and fault type diagnosis simulation results, treatment measures are taken to treat the instantaneous fault of the grounding conductor, including:

[0038] The distance of the grounding conductor instantaneous fault is measured based on the C-type traveling wave method. The round-trip time of the traveling wave signal between the fault point and the measurement point is detected to calculate the position of the grounding conductor instantaneous fault and determine the location of the grounding conductor instantaneous fault.

[0039] According to the results of the grounding conductor transient fault location and the grounding conductor transient fault simulation fault type diagnosis simulation, appropriate treatment measures are taken to deal with the grounding conductor transient fault;

[0040] Among them, the grounding conductor instantaneous fault line is isolated by a circuit breaker or a sectioner, and the damaged equipment is repaired in time according to the type of the grounding conductor instantaneous fault and the positioning result.

[0041] Preferably, collecting the instantaneous fault signal of the grounding conductor includes:

[0042] The current sensor is used to monitor the current parameters in the grounding system in real time and collect the current waveform when the grounding conductor has a transient fault.

[0043] The voltage parameters in the grounding system are monitored in real time using voltage sensors, and the voltage waveform when a transient fault occurs in the grounding conductor is collected;

[0044] The grounding conductor transient fault signal is determined based on the current waveform and voltage waveform when the grounding conductor transient fault occurs.

[0045] Preferably, processing the ground conductor transient fault signal includes:

[0046] Filtering the grounding conductor instantaneous fault signal based on a signal filter removes noise of a specific frequency band in the grounding conductor instantaneous fault signal, thereby suppressing and preventing noise interference in the grounding conductor instantaneous fault signal;

[0047] The instantaneous fault signal of the grounding conductor is amplified based on the signal amplifier, and the amplitude of the instantaneous fault signal of the grounding conductor is amplified by adjusting the gain parameter of the signal amplifier.

[0048] Preferably, processing the grounding conductor transient fault signal further includes:

[0049] Extract the characteristic quantity of the grounding conductor instantaneous fault signal, extract the characteristic quantity valuable for the analysis of the grounding conductor instantaneous fault simulation data from the grounding conductor instantaneous fault signal, and determine the characteristic data of the grounding conductor instantaneous fault, including time domain characteristics, frequency domain characteristics and high-low frequency energy ratio;

[0050] The high-frequency and low-frequency energy ratio is analyzed as a characteristic quantity to characterize the insulation degradation stage, among which the increase of the high-frequency energy ratio indicates the intensification of insulation aging.

[0051] Preferably, the method further includes: mining and analyzing historical grounding conductor transient fault data to discover fault patterns and characteristics for use in fault prediction and prevention;

[0052] The historical grounding conductor instantaneous fault data are collated to form a historical grounding conductor instantaneous fault data report. The historical grounding conductor instantaneous fault data report is mined and analyzed using big data analysis technology to discover the spatiotemporal distribution patterns of grounding conductor instantaneous faults, determine the patterns and characteristics of grounding conductor instantaneous faults, predict grounding conductor instantaneous faults based on the patterns and characteristics of grounding conductor instantaneous faults, and prevent possible future faults.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention monitors the current and voltage parameters in the grounding system in real time, records the current and voltage waveforms when the fault occurs, determines the instantaneous fault signal of the grounding conductor, processes the instantaneous fault signal of the grounding conductor, extracts characteristic quantities, determines the characteristic data of the instantaneous fault of the grounding conductor, establishes a simulation model for simulating the fault type of the instantaneous fault of the grounding conductor, analyzes and recognizes the characteristic data of the instantaneous fault of the grounding conductor, diagnoses the type of the instantaneous fault of the grounding conductor, determines the simulation result of the diagnosis of the simulated fault type of the instantaneous fault of the grounding conductor, measures the distance of the instantaneous fault of the grounding conductor based on the C-type traveling wave method, calculates the position of the instantaneous fault of the grounding conductor by detecting the round-trip time of the traveling wave signal between the fault point and the measurement point, determines the location of the instantaneous fault of the grounding conductor, takes treatment measures to treat the instantaneous fault of the grounding conductor based on the fault location and fault type diagnosis simulation results, and mines and analyzes historical instantaneous fault data of the grounding conductor to discover fault patterns and characteristics for fault prediction and prevention. The present invention can effectively improve the efficiency and accuracy of detecting, locating and treating instantaneous faults of the grounding conductor, and enhance the operational stability and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The figure is a flow chart of the grounding conductor transient fault simulation data analysis method of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] In order to solve the problem that the existing detection, location and treatment of transient grounding conductor faults cannot be effectively improved, resulting in poor operational stability and reliability of the power system, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0058] The method for analyzing ground conductor transient fault simulation data includes:

[0059] Collect and process the transient fault signal of the grounding conductor to determine the transient fault characteristic data of the grounding conductor;

[0060] In this embodiment, collecting the ground conductor instantaneous fault signal includes:

[0061] The current sensor is used to monitor the current parameters in the grounding system in real time and collect the current waveform when the grounding conductor has a transient fault.

[0062] The voltage parameters in the grounding system are monitored in real time using voltage sensors, and the voltage waveform when a transient fault occurs in the grounding conductor is collected;

[0063] The grounding conductor transient fault signal is determined based on the current waveform and voltage waveform when the grounding conductor transient fault occurs.

[0064] It should be noted that the current, voltage and other parameters in the grounding system are monitored in real time, and the current and voltage waveforms when the grounding conductor transient fault occurs are recorded for subsequent analysis.

[0065] In this embodiment, processing the ground conductor transient fault signal includes:

[0066] Filtering the grounding conductor instantaneous fault signal based on a signal filter removes noise of a specific frequency band in the grounding conductor instantaneous fault signal, thereby suppressing and preventing noise interference in the grounding conductor instantaneous fault signal;

[0067] The instantaneous fault signal of the grounding conductor is amplified based on the signal amplifier, and the amplitude of the instantaneous fault signal of the grounding conductor is amplified by adjusting the gain parameter of the signal amplifier.

[0068] Specifically, the amplitude of the transient fault signal of the grounding conductor is amplified, including:

[0069] Real-time monitoring of the filtering result of the signal filter after filtering the instantaneous fault signal of the grounding conductor;

[0070] Extracting noise energy corresponding to noise of a specific frequency band in the ground conductor instantaneous fault signal from the filtering result;

[0071] Obtain the noise energy ratio between the noise energy corresponding to each filtering result and the noise energy of the previous filtering result;

[0072] Comparing the noise energy ratio with a preset noise energy ratio threshold;

[0073] When the noise energy ratio exceeds a preset noise energy ratio threshold, the noise energy ratio exceeding the noise energy ratio threshold is recorded as a target noise energy ratio;

[0074] When the number of the target noise energy ratios reaches and exceeds a preset number threshold, the target noise energy ratios are used to obtain the energy ratio standard deviation;

[0075] The energy ratio standard deviation is used to obtain a noise entropy weighting factor, and the noise entropy weighting factor is used to amplify the amplitude of the grounding conductor transient fault signal.

[0076] The technical solution described above provides the following advantages: real-time monitoring of the signal filter's filtering results for the transient ground conductor fault signal, extracting the noise energy corresponding to the noise in a specific frequency band. This method allows for the acquisition of noise signature information related to the fault signal, providing a basis for subsequent analysis. The ratio of the noise energy corresponding to each filtering result to the previous noise energy is calculated and compared with a preset noise energy ratio threshold. This step helps determine changes in noise energy and whether an anomaly has occurred. When the noise energy ratio exceeds the preset threshold, the ratio is recorded as the target noise energy ratio. When the number of target noise energy ratios reaches or exceeds the preset threshold, it indicates that the noise energy change is significant and persistent, requiring further processing. The energy ratio standard deviation is calculated using the target noise energy ratios that reach the threshold. This standard deviation reflects the dispersion of the noise energy ratios. The noise entropy weighting factor is then derived from the energy ratio standard deviation. The noise entropy weighting factor measures the degree of noise disorder or uncertainty. Finally, the noise entropy weighting factor is used to amplify the amplitude of the transient ground conductor fault signal. This enhances the strength of the fault signal, making it easier to detect and analyze.

[0077] By monitoring the filtering results in real time and extracting the energy of noise at specific frequency bands, the system can accurately capture the noise characteristics associated with transient ground conductor fault signals, providing a reliable data foundation for subsequent analysis and processing. Calculating the noise energy ratio and comparing it with a preset threshold accurately determines changes in noise energy, promptly detecting abnormal noise energy fluctuations and improving the accuracy of fault signal detection. Calculating the energy ratio standard deviation and noise entropy weighting factor based on the target noise energy ratio precisely measures noise characteristics, providing a reasonable basis for signal amplitude amplification and further improving the accuracy of fault signal detection. Real-time monitoring and calculation of the noise energy ratio enables rapid response to changes in noise energy and promptly detects when the noise energy ratio exceeds the preset threshold, making the system highly sensitive to noise changes. By amplifying the amplitude of transient ground conductor fault signals, the system improves fault signal detectability, enabling the system to more sensitively detect weak fault signals and reducing the rate of missed fault detection. Using the noise entropy weighting factor to amplify the amplitude of transient ground conductor fault signals effectively suppresses noise interference, improving system stability in noisy environments and reducing the impact of noise on fault signal detection. By accurately extracting noise characteristics, judging noise changes and amplifying the fault signal amplitude, the reliability of fault signal detection is improved, enabling the system to stably detect instantaneous grounding wire fault signals in various complex noise environments.

[0078] Specifically, the noise entropy weighting factor is obtained by using the energy ratio standard deviation, and the amplitude of the grounding conductor transient fault signal is amplified by using the noise entropy weighting factor, including:

[0079] Retrieve the standard deviation of energy ratio;

[0080] Using the energy ratio standard deviation to obtain a noise entropy weight factor for dynamically adjusting the weight of the impact on the system;

[0081] The noise entropy weight factor is obtained by the following formula:

[0082]

[0083] Among them, S represents the noise entropy weighting factor; k represents the preset entropy sensitivity coefficient, which is used to control the sensitivity of noise energy changes. The larger the value, the more intense the weight response to noise energy fluctuations, and the value range of the entropy sensitivity coefficient is 0.5≤k≤2.0; σ represents the energy ratio standard deviation; among them, σ (energy ratio standard deviation) reflects the degree of dispersion of the target noise energy ratio. At the physical level, it measures the fluctuation of noise energy changes. The larger the standard deviation, the more dispersed the distribution of the noise energy ratio, that is, the more unstable the noise energy change; the smaller the standard deviation, the relatively concentrated the noise energy ratio, and the relatively stable noise energy change. k (preset entropy sensitivity coefficient) controls the sensitivity of noise energy changes. In a physical sense, it determines the degree of response of the system to noise energy fluctuations. When the k value is large, the system will have a stronger response to small fluctuations in noise energy, and the weight factor will change rapidly with the change of noise energy; when the k value is small, the system's response to noise energy fluctuations is relatively gentle. The formula uses the Sigmoid function form. The sigmoid function is characterized by its ability to map input values ​​to a range between 0 and 1. Here, by taking the product of the energy ratio standard deviation σ and the entropy sensitivity coefficient k as input, the physical quantity of noise energy variation is converted into a noise entropy weighting factor S, which ranges between 0 and 1. This mapping process can be understood as determining the weight of the noise impact on the system based on the degree of noise energy fluctuation (represented by σ) and the system's sensitivity to fluctuations (determined by k). Values ​​closer to 1 indicate a greater noise impact, while values ​​closer to 0 indicate a smaller noise impact. By setting the entropy sensitivity coefficient k, the system's sensitivity to noise energy changes can be flexibly adjusted according to actual needs. For complex and variable noise environments with frequent noise energy fluctuations, the k value can be appropriately increased to make the system more sensitive to noise changes. For relatively stable noise environments, the k value can be reduced to avoid overreaction to noise. This flexible adjustment mechanism allows the noise entropy weighting factor to better adapt to different noise environments, improving the rationality of the setting. By using the energy ratio standard deviation σ as a quantitative indicator reflecting noise energy fluctuations and combining it with the sigmoid function mapping, the noise entropy weighting factor can be objectively and accurately determined based on the actual noise energy fluctuations. This avoids arbitrary subjective weighting, making the weighting factor determination more consistent with the physical laws of noise variation and enhancing the rationality of the noise entropy weighting factor setting. The noise entropy weighting factor is calculated directly based on the energy ratio standard deviation σ, closely matching the actual noise fluctuation characteristics. When the noise energy fluctuates significantly, the weighting factor changes accordingly, adjusting the amplification level of the transient ground conductor fault signal amplitude, ensuring that the signal processing process is highly adapted to the noise characteristics. As the noise environment changes, the energy ratio standard deviation σ changes in real time, and the entropy sensitivity coefficient k dynamically adjusts the system's response to these changes. This enables the noise entropy weighting factor to dynamically adapt to noise changes in real time, maintaining a good fit with the noise environment in which the fault signal occurs, and ensuring the effectiveness and accuracy of fault signal amplitude amplification.

[0084] Real-time monitoring of all noise entropy weighting factors generated during signal filtering;

[0085] Screening the noise entropy weight factors, and obtaining noise entropy weight factors whose factor values ​​exceed a preset factor threshold as target weight factors;

[0086] When there are multiple target weight factors, the factor standard deviation is obtained using the multiple target weight factors;

[0087] Retrieve and adjust the gain parameters of the signal amplifier;

[0088] Adjusting a gain parameter of a signal conditioning amplifier using the factor standard deviation to obtain an adjusted gain coefficient;

[0089] The adjusted gain coefficient is obtained by the following formula:

[0090]

[0091] Where G represents the adjusted gain coefficient; G0 represents the gain parameter of the signal amplifier; S represents the noise entropy weight factor; E s Indicates that the noise energy ratio exceeds the preset noise energy ratio threshold value corresponding to the instantaneous signal energy average of the grounding conductor instantaneous fault signal; E n represents the average noise energy of the ground conductor transient fault signal corresponding to a noise energy ratio exceeding a preset noise energy ratio threshold; X(t) represents the time domain signal function of the unfiltered ground conductor transient fault signal with respect to time t; λ represents the high-order differential suppression coefficient, and the value range of the suppression coefficient is 0.03≤λ≤0.1, which is used to suppress overshoot distortion caused by signal mutations. A larger value indicates a stronger suppression of high-frequency transient components. The value of this part will change accordingly, thereby adjusting the gain factor. Its principle is to dynamically change the degree of signal amplification according to the uncertainty or disorder of the noise to adapt to different noise environments. The ratio of the fault signal's average instantaneous energy to the average noise energy is combined with the hyperbolic tangent function (tanh) to consider the relative relationship between the fault signal energy and the noise energy. When the fault signal energy is higher than the noise energy, the gain factor is adjusted accordingly, reflecting the need to adjust the signal amplification level at different signal-to-noise energy ratios. middle The part represents the third-order derivative of the unfiltered fault signal with respect to time t, reflecting the high-frequency transient changes of the signal. λ is the high-order differential suppression coefficient. When the product of λ and the third-order derivative increases, the value of the exp function decreases, which has a suppressive effect on the gain coefficient. The physical principle is to suppress the overshoot distortion caused by the sudden change of the signal and ensure the stability of the signal amplification process by controlling the high-frequency transient components of the signal. The above formula combines the noise characteristics (reflected by S), the relationship between the signal and the noise energy (reflected by E s 、E nand related operations), signal high-frequency transient suppression (embodied by λ and third-order derivative operations), and other factors adjust the gain coefficient. Comprehensively cover the key physical factors that affect signal amplification, so that the gain coefficient adjustment is more in line with the actual signal processing needs, and improve the rationality of the adjustment. By setting adjustable parameters (such as λ and S affected by k, etc.), it can be flexibly adjusted according to different fault signal characteristics and noise environments. For example, in the case of strong high-frequency noise interference, increase λ to suppress high-frequency transient components; for scenarios with changing noise characteristics, adjust S by adjusting k to adapt and ensure the rationality of the gain coefficient adjustment. According to the instantaneous signal energy average value of the fault signal, the noise energy average value and the noise entropy weighting factor, the gain coefficient is adjusted to closely fit the actual characteristics of the fault signal and noise. Under different fault signals and noise environments, the appropriate gain coefficient can be calculated through the formula, so that the signal amplification is highly adapted to the signal and noise characteristics, effectively enhancing the detectability of the fault signal. With the dynamic changes of the fault signal and the noise environment, the parameters in the formula (such as E s 、E n , S, etc.) will change in real time, thus dynamically adjusting the gain coefficient. For example, when the noise energy changes, S and E n The gain coefficient is adjusted accordingly to maintain good adaptability to fault signals and noise environments, thus ensuring signal processing effects.

[0092] The amplitude of the grounding conductor transient fault signal is amplified using the adjusted gain coefficient.

[0093] The technical effect of the above technical solution is as follows: The energy ratio standard deviation is obtained from the previous calculation results. It reflects the dispersion of the target noise energy ratio and serves as the basis for subsequent calculations. It also provides a data foundation reflecting the dispersion of the target noise energy ratio, which is a necessary input for the subsequent calculation of the noise entropy weighting factor. The noise entropy weighting factor is calculated based on the energy ratio standard deviation. The noise entropy weighting factor measures the uncertainty or disorder of the noise and dynamically adjusts its impact on the system, reflecting the impact of noise characteristics on signal processing. Furthermore, the noise characteristics are quantified, and the impact weight of the noise on the system is determined, providing a basis for signal amplitude amplification. The noise entropy weighting factor is compared with a preset factor threshold, and factors with values ​​exceeding the threshold are selected as target weighting factors. This step is used to select weighting factors that have a significant effect on signal amplitude amplification and eliminate interference from factors with less significant effects. Weighting factors that have an insignificant effect on signal amplitude amplification are removed, focusing on effective factors and improving the targeted signal processing. When there are multiple target weighting factors, their factor standard deviations are calculated to further quantify the dispersion of the target weighting factors, providing a more precise basis for adjusting the gain parameters of the signal amplifier. At the same time, the discrete characteristics of multiple target weight factors are further refined to prepare for precise adjustment of gain parameters. The original gain parameters of the signal amplifier are obtained. These are the current operating parameters of the signal amplifier and serve as the basis for subsequent adjustments. By obtaining the existing operating parameters of the signal amplifier, the adjustment starting point is clearly defined. The gain parameters of the signal amplifier are adjusted based on the factor standard deviation to obtain the adjusted gain coefficient. Based on the discreteness of the target weight factors, as reflected by the factor standard deviation, the gain coefficient is appropriately adjusted to adapt to the current noise characteristics. The adjusted gain coefficient is used to amplify the amplitude of the transient fault signal of the ground conductor, enhancing the fault signal strength and facilitating subsequent detection and analysis.

[0094] Furthermore, calculating the noise entropy weighting factor based on the standard deviation of the energy ratio comprehensively and accurately measures noise characteristics, providing a reliable basis for signal processing. This ensures that signal amplification more closely matches the actual noise conditions and avoids over- or under-amplification. Adjusting the gain factor based on the factor standard deviation precisely controls the amplification level of the signal amplifier, improving the accuracy of signal amplification and making the amplified signal more conducive to accurate fault detection. Real-time adjustment of the gain factor based on the noise entropy weighting factor rapidly responds to changes in noise characteristics and promptly adjusts the signal amplification level, ensuring sensitive detection of fault signals in diverse noise environments. Accurately adjusted amplified fault signals enable the detection system to more sensitively perceive weak fault signals, improving fault signal detection sensitivity and reducing the risk of missed detections. Adjusting the signal amplifier gain based on noise characteristics effectively suppresses noise interference, enabling the system to stably process fault signals even in complex noise environments and enhancing its anti-interference capabilities. This precise signal amplification process ensures stable fault signal processing, reduces signal fluctuations and misjudgments, and enables the system to reliably detect transient ground conductor fault signals.

[0095] In this embodiment, processing the ground conductor transient fault signal further includes:

[0096] Extract the characteristic quantity of the grounding conductor instantaneous fault signal, extract the characteristic quantity valuable for the analysis of the grounding conductor instantaneous fault simulation data from the grounding conductor instantaneous fault signal, and determine the characteristic data of the grounding conductor instantaneous fault, including time domain characteristics, frequency domain characteristics and high-low frequency energy ratio;

[0097] The high-frequency and low-frequency energy ratio is analyzed as a characteristic quantity to characterize the insulation degradation stage, among which the increase of the high-frequency energy ratio indicates the intensification of insulation aging.

[0098] It should be noted that the characteristic quantity of the instantaneous fault signal of the grounding conductor is extracted, and the high-frequency and low-frequency energy ratio is analyzed as a characteristic quantity to characterize the insulation degradation stage. By analyzing the line conditions before and after the fault, it is easy to find the fault line.

[0099] Establish a fault type diagnosis simulation model for the transient fault simulation of the grounding conductor to analyze and identify the characteristic data of the transient fault of the grounding conductor, and determine the fault type diagnosis simulation result of the transient fault simulation of the grounding conductor;

[0100] In this embodiment, a ground conductor transient fault simulation fault type diagnosis simulation model is established, including:

[0101] According to the needs of analyzing the grounding conductor transient fault simulation data, historical data of the grounding conductor transient fault is collected, and the collected historical data of the grounding conductor transient fault is divided into a training set and a test set;

[0102] Based on machine learning technology, a training set is used to train the machine learning model, so that the machine learning model can autonomously learn the fault type diagnosis behavior of the grounding conductor transient fault simulation from the training set, and determine the grounding conductor transient fault simulation fault type diagnosis simulation model based on machine learning;

[0103] The machine learning-based grounding conductor transient fault simulation fault type diagnosis simulation model is tested based on the test set to evaluate the performance of the machine learning-based grounding conductor transient fault simulation fault type diagnosis simulation model. It is determined whether the machine learning-based grounding conductor transient fault simulation fault type diagnosis simulation model can achieve the expected effect of diagnosing the transient fault simulation fault type of the grounding conductor, thereby determining the simulation model test evaluation results;

[0104] According to the simulation model test evaluation results, the parameters of the grounding conductor transient fault simulation fault type diagnosis simulation model based on machine learning are adjusted and optimized, and then the optimal grounding conductor transient fault simulation fault type diagnosis simulation model is determined to simulate different grounding conductor transient fault scenarios.

[0105] It should be noted that the establishment of a fault type diagnosis simulation model for transient fault simulation of the grounding conductor can be used to simulate different fault scenarios.

[0106] In this embodiment, the ground conductor transient fault characteristic data is analyzed and pattern recognized to determine the ground conductor transient fault simulation type diagnosis simulation result, including:

[0107] Deploy a grounding conductor transient fault simulation fault type diagnosis simulation model, and deploy the grounding conductor transient fault simulation fault type diagnosis simulation model in an actual grounding conductor transient fault simulation fault type diagnosis simulation environment;

[0108] The transient fault characteristic data of the grounding conductor is input into the transient fault simulation fault type diagnosis simulation model of the grounding conductor. The transient fault characteristic data of the grounding conductor is analyzed and pattern recognized according to the transient fault simulation fault type diagnosis simulation model of the grounding conductor, and the transient fault type of the grounding conductor is diagnosed to determine the transient fault simulation fault type diagnosis simulation results of the grounding conductor, including strong transient fault, medium transient fault, and weak transient fault.

[0109] It should be noted that strong transient faults, such as short circuit faults, manifest as drastic changes in current and voltage; medium transient faults, such as intermittent ground faults, have smaller signal changes; weak transient faults, such as high-resistance ground faults, have unclear signal characteristics.

[0110] Determine the location of the grounding conductor transient fault based on the C-type traveling wave method, and take treatment measures to deal with the grounding conductor transient fault according to the fault location and fault type diagnosis simulation results;

[0111] In this embodiment, the transient fault location of the grounding conductor is determined based on the C-type traveling wave method. According to the fault location and fault type diagnosis simulation results, treatment measures are taken to treat the transient fault of the grounding conductor, including:

[0112] The distance of the grounding conductor instantaneous fault is measured based on the C-type traveling wave method. The round-trip time of the traveling wave signal between the fault point and the measurement point is detected to calculate the position of the grounding conductor instantaneous fault and determine the location of the grounding conductor instantaneous fault.

[0113] According to the results of the grounding conductor transient fault location and the grounding conductor transient fault simulation fault type diagnosis simulation, appropriate treatment measures are taken to deal with the grounding conductor transient fault;

[0114] Among them, the grounding conductor instantaneous fault line is isolated by a circuit breaker or a sectioner, and the damaged equipment is repaired in time according to the type of the grounding conductor instantaneous fault and the positioning result.

[0115] Mining and analyzing historical ground conductor transient fault data to discover fault patterns and characteristics for fault prediction and prevention.

[0116] In this embodiment, historical ground conductor transient fault data is mined and analyzed to discover fault patterns and characteristics for fault prediction and prevention, including:

[0117] The historical grounding conductor instantaneous fault data are collated to form a historical grounding conductor instantaneous fault data report. The historical grounding conductor instantaneous fault data report is mined and analyzed using big data analysis technology to discover the spatiotemporal distribution patterns of grounding conductor instantaneous faults, determine the patterns and characteristics of grounding conductor instantaneous faults, predict grounding conductor instantaneous faults based on the patterns and characteristics of grounding conductor instantaneous faults, and prevent possible future faults.

[0118] In summary, by real-time monitoring of the current and voltage parameters in the grounding system, recording the current and voltage waveforms when the fault occurs, and determining the instantaneous fault signal of the grounding conductor, the instantaneous fault signal of the grounding conductor is processed and feature quantities are extracted to determine the characteristic data of the instantaneous fault of the grounding conductor. A simulation model for simulating the fault type diagnosis of the instantaneous fault of the grounding conductor is established to analyze and pattern recognize the characteristic data of the instantaneous fault of the grounding conductor, diagnose the type of the instantaneous fault of the grounding conductor, and determine the simulation results of the fault type diagnosis of the instantaneous fault of the grounding conductor. The instantaneous fault of the grounding conductor is measured based on the C-type traveling wave method. The instantaneous fault position of the grounding conductor is calculated by detecting the round-trip time of the traveling wave signal between the fault point and the measurement point, and the instantaneous fault location of the grounding conductor is determined. Based on the fault location and fault type diagnosis simulation results, treatment measures are taken to deal with the instantaneous fault of the grounding conductor. Historical instantaneous fault data of the grounding conductor is mined and analyzed to discover fault patterns and characteristics for fault prediction and prevention. This can effectively improve the efficiency and accuracy of detecting, locating and handling instantaneous faults of the grounding conductor, and improve the operational stability and reliability of the power system.

[0119] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0120] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing ground conductor transient fault simulation data, characterized in that: include: Collect and process the transient fault signal of the grounding conductor to determine the transient fault characteristic data of the grounding conductor; The noise entropy weight factor is obtained by using the standard deviation of the energy ratio, and the amplitude of the instantaneous fault signal of the grounding conductor is amplified by using the noise entropy weight factor; Establish a fault type diagnosis simulation model for the transient fault simulation of the grounding conductor to analyze and identify the characteristic data of the transient fault of the grounding conductor, and determine the fault type diagnosis simulation result of the transient fault simulation of the grounding conductor; The transient fault location of the grounding conductor is determined based on the C-type traveling wave method. According to the fault location and fault type diagnosis simulation results, treatment measures are taken to deal with the transient fault of the grounding conductor.

2. The method for analyzing ground conductor transient fault simulation data according to claim 1, wherein: Amplify the amplitude of the ground conductor transient fault signal, including: Real-time monitoring of the filtering result of the signal filter after filtering the instantaneous fault signal of the grounding conductor; Extracting noise energy corresponding to noise of a specific frequency band in the ground conductor instantaneous fault signal from the filtering result; Obtain the noise energy ratio between the noise energy corresponding to each filtering result and the noise energy of the previous filtering result; Comparing the noise energy ratio with a preset noise energy ratio threshold; When the noise energy ratio exceeds a preset noise energy ratio threshold, the noise energy ratio exceeding the noise energy ratio threshold is recorded as a target noise energy ratio; When the number of the target noise energy ratios reaches and exceeds a preset number threshold, the target noise energy ratios are used to obtain the energy ratio standard deviation; The energy ratio standard deviation is used to obtain a noise entropy weighting factor, and the noise entropy weighting factor is used to amplify the amplitude of the grounding conductor transient fault signal.

3. The method for analyzing ground conductor transient fault simulation data according to claim 2, wherein: Obtaining a noise entropy weighting factor using the energy ratio standard deviation, and amplifying the amplitude of the grounding conductor instantaneous fault signal using the noise entropy weighting factor, including: Retrieve the standard deviation of energy ratio; Using the energy ratio standard deviation to obtain a noise entropy weight factor for dynamically adjusting the weight of the impact on the system; Real-time monitoring of all noise entropy weighting factors generated during signal filtering; Screening the noise entropy weight factors, and obtaining noise entropy weight factors whose factor values ​​exceed a preset factor threshold as target weight factors; When there are multiple target weight factors, the factor standard deviation is obtained using the multiple target weight factors; Retrieve and adjust the gain parameters of the signal amplifier; Adjusting a gain parameter of a signal conditioning amplifier using the factor standard deviation to obtain an adjusted gain coefficient; The amplitude of the grounding conductor transient fault signal is amplified using the adjusted gain coefficient.

4. The method for analyzing ground conductor transient fault simulation data according to claim 1, wherein: Establish a ground conductor transient fault simulation fault type diagnosis simulation model, including: According to the needs of analyzing the grounding conductor transient fault simulation data, historical data of the grounding conductor transient fault is collected, and the collected historical data of the grounding conductor transient fault is divided into a training set and a test set; Based on machine learning technology, a training set is used to train the machine learning model, so that the machine learning model can autonomously learn the fault type diagnosis behavior of the grounding conductor transient fault simulation from the training set, and determine the grounding conductor transient fault simulation fault type diagnosis simulation model based on machine learning; The machine learning-based grounding conductor transient fault simulation fault type diagnosis simulation model is tested based on the test set to evaluate the performance of the machine learning-based grounding conductor transient fault simulation fault type diagnosis simulation model. It is determined whether the machine learning-based grounding conductor transient fault simulation fault type diagnosis simulation model can achieve the expected effect of diagnosing the transient fault simulation fault type of the grounding conductor, thereby determining the simulation model test evaluation results; According to the simulation model test evaluation results, the parameters of the grounding conductor transient fault simulation fault type diagnosis simulation model based on machine learning are adjusted and optimized, and then the optimal grounding conductor transient fault simulation fault type diagnosis simulation model is determined to simulate different grounding conductor transient fault scenarios.

5. The method for analyzing ground conductor transient fault simulation data according to claim 4, wherein: Analyze and pattern-recognize the transient fault characteristic data of the grounding conductor to determine the fault type diagnosis simulation results of the transient fault of the grounding conductor, including: Deploy a grounding conductor transient fault simulation fault type diagnosis simulation model, and deploy the grounding conductor transient fault simulation fault type diagnosis simulation model in an actual grounding conductor transient fault simulation fault type diagnosis simulation environment; The transient fault characteristic data of the grounding conductor is input into the transient fault simulation fault type diagnosis simulation model of the grounding conductor. The transient fault characteristic data of the grounding conductor is analyzed and pattern recognized according to the transient fault simulation fault type diagnosis simulation model of the grounding conductor, and the transient fault type of the grounding conductor is diagnosed to determine the transient fault simulation fault type diagnosis simulation results of the grounding conductor, including strong transient fault, medium transient fault, and weak transient fault.

6. The method for analyzing ground conductor transient fault simulation data according to claim 1, wherein: The transient fault location of the grounding conductor is determined based on the C-type traveling wave method. Based on the fault location and fault type diagnosis simulation results, treatment measures are taken to deal with the transient fault of the grounding conductor, including: The distance of the grounding conductor instantaneous fault is measured based on the C-type traveling wave method. The round-trip time of the traveling wave signal between the fault point and the measurement point is detected to calculate the position of the grounding conductor instantaneous fault and determine the location of the grounding conductor instantaneous fault. According to the results of the grounding conductor transient fault location and the grounding conductor transient fault simulation fault type diagnosis simulation, appropriate treatment measures are taken to deal with the grounding conductor transient fault; Among them, the grounding conductor instantaneous fault line is isolated by a circuit breaker or a sectioner, and the damaged equipment is repaired in time according to the type of the grounding conductor instantaneous fault and the positioning result.

7. The method for analyzing ground conductor transient fault simulation data according to claim 1, wherein: Collect instantaneous fault signals of ground conductors, including: The current sensor is used to monitor the current parameters in the grounding system in real time and collect the current waveform when the grounding conductor has a transient fault. The voltage parameters in the grounding system are monitored in real time using voltage sensors, and the voltage waveform when a transient fault occurs in the grounding conductor is collected; The grounding conductor transient fault signal is determined based on the current waveform and voltage waveform when the grounding conductor transient fault occurs.

8. The method for analyzing ground conductor transient fault simulation data according to claim 1, wherein: Processing of instantaneous grounding conductor fault signals, including: Filtering the grounding conductor instantaneous fault signal based on a signal filter removes noise of a specific frequency band in the grounding conductor instantaneous fault signal, thereby suppressing and preventing noise interference in the grounding conductor instantaneous fault signal; The instantaneous fault signal of the grounding conductor is amplified based on the signal amplifier, and the amplitude of the instantaneous fault signal of the grounding conductor is amplified by adjusting the gain parameter of the signal amplifier.

9. The method for analyzing ground conductor transient fault simulation data according to claim 8, wherein: Processing of instantaneous fault signals of ground conductors also includes: Extract the characteristic quantity of the grounding conductor instantaneous fault signal, extract the characteristic quantity valuable for the analysis of the grounding conductor instantaneous fault simulation data from the grounding conductor instantaneous fault signal, and determine the characteristic data of the grounding conductor instantaneous fault, including time domain characteristics, frequency domain characteristics and high-low frequency energy ratio; The high-frequency and low-frequency energy ratio is analyzed as a characteristic quantity to characterize the insulation degradation stage, among which the increase of the high-frequency energy ratio indicates the intensification of insulation aging.

10. The method for analyzing ground conductor transient fault simulation data according to claim 1, wherein: Also includes: Mining and analyzing historical ground conductor transient fault data to discover fault patterns and characteristics for fault prediction and prevention; The historical grounding conductor instantaneous fault data are collated to form a historical grounding conductor instantaneous fault data report. The historical grounding conductor instantaneous fault data report is mined and analyzed using big data analysis technology to discover the spatiotemporal distribution patterns of grounding conductor instantaneous faults, determine the patterns and characteristics of grounding conductor instantaneous faults, predict grounding conductor instantaneous faults based on the patterns and characteristics of grounding conductor instantaneous faults, and prevent possible future faults.

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