A method and system for detecting partial discharge in cables under high noise conditions

Abstract

This invention relates to the field of power equipment testing technology, specifically disclosing a method and system for detecting partial discharge in cables under high noise conditions. The method involves dividing the cable into several monitoring areas and using a current sensor to collect current pulse signals from each area in real time, including current pulse frequency and repetition rate. Current pulse frequency fluctuation characteristics and current pulse repetition rate characteristics are extracted, and corresponding characteristic indices are generated. Then, by comprehensively analyzing the characteristic indices of each area, a comprehensive index is calculated and compared with a preset threshold to determine whether partial discharge exists in that area. For areas identified as having partial discharge, the deviation between their real-time discharge frequency and historical normal discharge data is further analyzed, and an isolated forest model is used for anomaly detection to determine whether the discharge is abnormal. When an abnormal discharge is detected, the system automatically activates an early warning and control mechanism to adjust the voltage or control the temperature to prevent equipment damage or malfunction.

Description

Technical Field

[0001] This invention relates to the field of power equipment testing technology, specifically to a method and system for detecting partial discharge in cables under high noise conditions. Background Technology

[0002] In the operation of modern power systems, communication networks, and high-precision equipment, cables serve as crucial connecting components, carrying out the functions of power and data transmission. However, during prolonged use, cables may be affected by external environmental factors, aging, and mechanical damage, leading to partial discharge. Partial discharge not only degrades the electrical performance of cables but can also cause insulation aging and insulation breakdown, potentially resulting in equipment failure or safety accidents in severe cases. Therefore, timely detection and diagnosis of partial discharge are essential for ensuring the safe and stable operation of power equipment and communication facilities.

[0003] Methods for detecting partial discharge mainly include traditional electrical detection methods, optical detection methods, and ultrasonic detection methods. While these methods can detect partial discharge to a certain extent, they face some problems in practical applications. Existing technologies still suffer from signal interference in high-noise environments, insufficient detection accuracy, and low levels of automation.

[0004] First, in high-noise environments, these traditional methods are susceptible to external interference, leading to false alarms or missed alarms. Second, traditional detection methods typically rely on manual analysis and judgment, lacking sufficient real-time capability and intelligence, making it difficult to comprehensively and accurately monitor and analyze discharge conditions. Furthermore, traditional methods generally have low spatial resolution, making it difficult to effectively locate and determine the specific position and state of the discharge, especially over long distances or in complex environments. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for detecting partial discharge in cables under high noise conditions, so as to solve the problems mentioned above.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A method for detecting partial discharge in cables under high noise conditions includes the following steps:

[0008] S1: During the monitoring period, the cable is divided into several identical areas, and the current pulse signal in each area is acquired.

[0009] The current pulse signal is acquired by a current sensor and includes the current pulse frequency and the current pulse repetition rate.

[0010] S2: Preprocess the current pulse signal in each region, extract the current pulse frequency fluctuation characteristics and current pulse repetition rate characteristics, and generate the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index.

[0011] S3: Perform a comprehensive analysis of the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index in each region, calculate the comprehensive index of each region, and determine whether the corresponding region is a partial discharge region.

[0012] S4: Compare and analyze the real-time discharge frequency and discharge repetition rate of the identified partial discharge area with the historical normal discharge frequency and discharge repetition rate to determine whether the partial discharge is abnormal.

[0013] S5: Based on the judgment results, when an abnormal partial discharge is detected, the system activates the early warning and adjustment mechanism.

[0014] As a further aspect of the present invention: the process of obtaining the current pulse frequency fluctuation characteristic index specifically includes:

[0015] Acquire current pulse frequency data for each region;

[0016] Preprocess the current pulse frequency data;

[0017] Identify all local maxima and local minima in current pulse frequency data;

[0018] The local maxima and minima are connected using spline interpolation to generate the upper and lower envelopes.

[0019] Calculate the mean of the upper and lower envelopes;

[0020] The first intrinsic mode function is extracted from the current pulse frequency data using the empirical mode decomposition method.

[0021] Repeat the steps for the remaining signal to continue extracting all intrinsic mode functions;

[0022] Perform a Hilbert transform on each intrinsic mode function to obtain the analytic signal;

[0023] Calculate the instantaneous amplitude and instantaneous phase of the analytic signal;

[0024] Calculate the instantaneous frequency by taking the derivative of the instantaneous phase with respect to time, and then calculate the standard deviation σ of the instantaneous frequency. f and average value μ f The current pulse frequency fluctuation characteristic index is calculated based on the standard deviation and average value of the instantaneous frequency.

[0025] As a further aspect of the present invention: the process of obtaining the current pulse repetition rate characteristic index specifically includes:

[0026] Acquire the current pulse repetition rate data for each region to form a data matrix X;

[0027] The current pulse repetition rate data is standardized to obtain the standardized data matrix Z.

[0028] Calculate the covariance matrix C based on the standardized data matrix Z;

[0029] Eigenvalue decomposition is performed on the covariance matrix C to obtain eigenvalues ​​and eigenvectors;

[0030] Select the k largest eigenvalues ​​and their corresponding eigenvectors to form the principal component matrix V. k ;

[0031] Where k represents the number of eigenvalues;

[0032] The standardized data is projected onto the principal component space to obtain the dimensionality-reduced data Z. k ;

[0033] The original data is reconstructed based on the principal component projection results, resulting in the reconstructed data matrix Z. s ;

[0034] Calculate the reconstruction error matrix E;

[0035] Calculate the reconstruction error norm RE for each data point i ;

[0036] The expression for calculating the reconstruction error norm is as follows:

[0037] RE i =||E i ||;

[0038] In the formula, i represents the number of data points, RE i E represents the norm of the reconstruction error for the i-th data point. i This represents the reconstruction error matrix for the i-th data point;

[0039] Calculate the mean μ of the reconstruction error norm. F and standard deviation σ F The mean and standard deviation of the reconstruction error norm were normalized, and the characteristic index of the current pulse repetition rate was calculated.

[0040] As a further aspect of the present invention: the comprehensive analysis of the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index in each region, and the calculation of the comprehensive index for each region, specifically includes:

[0041] The current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index of each region are obtained, and the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index of each region are normalized to calculate the comprehensive index of each region.

[0042] As a further aspect of the present invention: the determination of whether the corresponding region is a partial discharge region specifically includes:

[0043] Determine whether the comprehensive index is less than the preset comprehensive index threshold. If yes, record it as a partial discharge region; otherwise, record it as a non-partial discharge region.

[0044] As a further aspect of the present invention: the comparison and analysis of the real-time discharge frequency and discharge repetition rate of the determined partial discharge region with the historical normal discharge frequency and discharge repetition rate to determine whether the partial discharge is abnormal specifically includes:

[0045] The historical normal discharge frequency and discharge repetition rate of multiple collection points were obtained according to the time series and recorded as historical normal data.

[0046] Standardize historical data;

[0047] The processed historical data is used as a training set to train the isolated forest model;

[0048] The average isolation degree of the corresponding historical data points is calculated by using the isolation depth of multiple trees.

[0049] Isolation forests divide historical data into multiple tree segments, ultimately generating an isolation score for each historical data point.

[0050] The formula for calculating the isolation score is as follows:

[0051]

[0052] In the formula, h(x) represents the isolation score of historical data point x, H(x) represents the average isolation depth in the isolation tree of historical data point x, m represents the number of historical data in the dataset, and c(m) represents the maximum isolation depth in the isolation tree.

[0053] The normal discharge frequency and discharge repetition rate of each region are collected in real time according to the time series and recorded as the real-time data of the region.

[0054] Standardize the real-time data;

[0055] The processed real-time data from each region is input into the trained Isolation Forest model for detection.

[0056] The isolation score of real-time data points in each region is calculated using the isolated forest model.

[0057] For each region, calculate the average isolation score of all real-time data points, and denot it as the real-time region average isolation score;

[0058] For each region, calculate the average isolation score of all historical data points, and denot it as the historical region average isolation score;

[0059] The difference between the real-time regional average isolation score and the historical regional average isolation score is calculated, and the absolute value is removed, which is recorded as the regional deviation value.

[0060] Compare the region deviation value with the region deviation value threshold;

[0061] If the deviation value of a region is greater than or equal to the region deviation value threshold, it is recorded as an abnormal region;

[0062] If the deviation value of a region is less than the region deviation value threshold, it is recorded as a normal region.

[0063] As a further aspect of the present invention: when a partial discharge anomaly is detected based on the judgment result, the system activates an early warning adjustment mechanism, specifically including:

[0064] Based on the abnormal area, the system will automatically take alarm action;

[0065] Upon receiving an alarm, the system automatically takes control measures, including:

[0066] Calculate the standard deviation and average value of the voltage in the corresponding area for a period of time before the warning, and calculate the voltage fluctuation ratio by comparing the standard deviation and the average value of the voltage.

[0067] The voltage fluctuation ratio is compared with a preset threshold.

[0068] If the voltage fluctuation ratio is greater than or equal to the preset threshold, the real-time voltage will be adjusted according to the historical normal voltage range.

[0069] If the voltage fluctuation ratio is less than the preset threshold, the insulation layer temperature is detected to determine whether the insulation layer temperature is greater than or equal to the preset temperature threshold. If so, the temperature is reduced through the temperature control system.

[0070] After adjusting the voltage and temperature of the cable during operation, the discharge situation in the corresponding area is reassessed. If abnormal discharge still exists in the area, power outage isolation measures are taken.

[0071] A partial discharge detection system for cables under high noise conditions, comprising:

[0072] The data acquisition module is used to divide the cable into several identical areas during the monitoring period and acquire the current pulse signal in each area.

[0073] The data preprocessing module preprocesses the current pulse signal in each region, extracts the current pulse frequency fluctuation characteristics and current pulse repetition rate characteristics, and generates the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index.

[0074] The partial discharge region determination module comprehensively analyzes the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index in each region, calculates the comprehensive index of each region, and determines whether the corresponding region is a partial discharge region.

[0075] The partial discharge anomaly analysis module is used to compare and analyze the real-time discharge frequency and discharge repetition rate of the determined partial discharge area with the historical normal discharge frequency and discharge repetition rate to determine whether the partial discharge is abnormal.

[0076] The early warning adjustment module, based on the judgment result, activates the early warning adjustment mechanism when an abnormal partial discharge is detected.

[0077] A cable partial discharge detection system under high noise conditions, wherein the data preprocessing module further includes at least:

[0078] A current pulse frequency fluctuation characteristic index calculation unit calculates the current pulse frequency fluctuation characteristic index by processing the current pulse frequency.

[0079] The current pulse repetition rate characteristic index calculation unit calculates the current pulse repetition rate characteristic index by processing the repetition of current pulses.

[0080] A partial discharge detection system for cables under high noise conditions, wherein the partial discharge region determination module further includes at least:

[0081] The comprehensive index calculation unit calculates the comprehensive index by comprehensively calculating the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index.

[0082] The beneficial effects of this invention are:

[0083] (1) By dividing the cable into regions and installing current sensors in each region, partial discharge signals can be accurately captured. This method combines real-time monitoring of current pulse frequency and repetition rate. Through advanced signal preprocessing technology, noise interference can be effectively removed and accurate discharge characteristics can be extracted. This method enables cable partial discharge detection to have high spatial resolution, which can detect early discharge anomalies in time, thereby effectively avoiding equipment damage or safety hazards. Compared with traditional methods, this method can provide more accurate discharge detection in high-noise environments, avoiding the misjudgment or missed judgment caused by noise in traditional methods. It significantly improves the stability and monitoring accuracy of the system and has strong practical application value.

[0084] (2) This invention improves the intelligence and accuracy of discharge anomaly detection by introducing comprehensive index analysis and the isolated forest model. When a partial discharge anomaly is detected, the system can automatically activate the early warning and control mechanism and take timely emergency response measures for the abnormal area. This mechanism includes real-time adjustment of voltage and temperature, and can determine whether there are abnormal fluctuations based on historical data, thereby taking corresponding control measures to avoid long-term damage to the equipment caused by partial discharge. Through this intelligent adjustment mechanism, the system can not only improve the fault response speed, but also intervene before the equipment malfunctions, thereby improving the safety and reliability of the equipment. Attached Figure Description

[0085] The invention will now be further described with reference to the accompanying drawings.

[0086] Figure 1 This is a flowchart illustrating the specific steps of a method for detecting partial discharge in cables under high noise conditions according to the present invention.

[0087] Figure 2 This is a flowchart of a cable partial discharge detection system under high noise conditions according to the present invention. Detailed Implementation

[0088] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0089] Please see Figure 1 As shown, this invention provides a method for detecting partial discharge in cables under high noise conditions, comprising the following steps:

[0090] S1: During the monitoring period, the cable is divided into several identical areas, and the current pulse signal in each area is acquired.

[0091] The current pulse signal is acquired by a current sensor and includes the current pulse frequency and the current pulse repetition rate.

[0092] S2: Preprocess the current pulse signal in each region, extract the current pulse frequency fluctuation characteristics and current pulse repetition rate characteristics, and generate the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index.

[0093] S3: Perform a comprehensive analysis of the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index in each region, calculate the comprehensive index of each region, and determine whether the corresponding region is a partial discharge region.

[0094] S4: Compare and analyze the real-time discharge frequency and discharge repetition rate of the identified partial discharge area with the historical normal discharge frequency and discharge repetition rate to determine whether the partial discharge is abnormal.

[0095] S5: Based on the judgment results, when an abnormal partial discharge is detected, the system activates the early warning and adjustment mechanism.

[0096] In S1: During the monitoring period, the cable is divided into several identical areas, and the current pulse signal in each area is acquired, specifically including:

[0097] During the monitoring period, the entire cable is divided into several monitoring areas of equal length;

[0098] It should be noted that the length of each monitoring area is selected based on parameters such as the total cable length, monitoring accuracy requirements, and equipment sensitivity, to ensure that each area can representatively reflect the distribution of partial discharge signals after being divided.

[0099] After dividing the area into zones, the system installs a current sensor in each zone;

[0100] The current pulse signal is captured in real time by a current sensor, including the real-time acquisition of the current pulse frequency and current pulse repetition rate.

[0101] Before data acquisition, the current sensors need to be calibrated to the same sampling frequency to ensure time synchronization of the acquired data and the accuracy of the acquired data.

[0102] It should be noted that during the acquisition process, the raw current pulse signal captured by the sensor includes the ground discharge signal generated by partial discharge and the environmental noise signal;

[0103] The system will automatically remove DC bias and low-frequency interference, and initially separate out the useful signal;

[0104] The data from each monitoring area independently reflects the local discharge situation;

[0105] At the end of each monitoring cycle, the system automatically stores the current pulse frequency and current pulse repetition rate for each region.

[0106] In S2, the current pulse signal in each region is preprocessed to extract the current pulse frequency fluctuation characteristics and current pulse repetition rate characteristics, generating the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index, specifically including:

[0107] Acquire current pulse frequency data for each region;

[0108] Preprocess the current pulse frequency data, including filtering and noise reduction;

[0109] Identify all local maxima and local minima in current pulse frequency data;

[0110] The local maxima and minima are connected using spline interpolation to generate the upper and lower envelopes.

[0111] Calculate the mean of the upper and lower envelopes;

[0112] The first intrinsic mode function is extracted from the current pulse frequency data using the empirical mode decomposition method.

[0113] Repeat the steps for the remaining signal to continue extracting all intrinsic mode functions;

[0114] Perform a Hilbert transform on each intrinsic mode function to obtain the analytic signal;

[0115] Calculate the instantaneous amplitude and instantaneous phase of the analytic signal;

[0116] Calculate the instantaneous frequency by taking the derivative of the instantaneous phase with respect to time, and then calculate the standard deviation σ of the instantaneous frequency. f and average value μ f The current pulse frequency fluctuation characteristic index is calculated based on the standard deviation and average value of the instantaneous frequency.

[0117] The expression for calculating the current pulse frequency fluctuation characteristic index is as follows:

[0118]

[0119] In the formula, I f The index representing the frequency fluctuation characteristic of current pulses, σ f μ represents the standard deviation of instantaneous frequency. f It represents the average value of the instantaneous frequency.

[0120] Acquire the current pulse repetition rate data for each region to form a data matrix X;

[0121] The current pulse repetition rate data is standardized to obtain the standardized data matrix Z.

[0122] Based on the standardized data matrix Z, the covariance matrix C is calculated using the following expression:

[0123]

[0124] In the formula, n represents the number of data points, where n is a positive integer greater than 1, C represents the covariance matrix, and Z... T This represents the transpose of the data matrix Z;

[0125] Eigenvalue decomposition is performed on the covariance matrix C to obtain eigenvalues ​​and eigenvectors;

[0126] Select the k largest eigenvalues ​​and their corresponding eigenvectors to form the principal component matrix V. k ;

[0127] Where k represents the number of eigenvalues;

[0128] The standardized data is projected onto the principal component space to obtain the dimensionality-reduced data Z. k ;

[0129] The original data is reconstructed based on the principal component projection results, resulting in the reconstructed data matrix Z. s The calculation expression is:

[0130]

[0131] In the formula, V represents the principal component matrix k The transpose of Z s Z represents the reconstructed data matrix. k Z represents the data after dimensionality reduction. k ;

[0132] The reconstruction error matrix E is calculated using the following expression:

[0133] E = ZZ s ;

[0134] In the formula, E represents the reconstruction error matrix, and Z represents the standardized data matrix;

[0135] Calculate the reconstruction error norm RE for each data point i The calculation expression is:

[0136] RE i =||E i ||;

[0137] In the formula, i represents the number of data points, RE iE represents the reconstruction error norm of the i-th data point. i This represents the reconstruction error matrix for the i-th data point;

[0138] Calculate the mean μ of the reconstruction error norm. F and standard deviation σ F The mean and standard deviation of the reconstruction error norm are normalized, and the characteristic index of the current pulse repetition rate is calculated. The calculation expression is as follows:

[0139]

[0140] In the formula, I F The characteristic index representing the repetition rate of current pulses, μ F σ represents the mean of the reconstruction error norm. F denoted by , the standard deviation of the reconstruction error norm, and e represents the logarithm of the natural number base.

[0141] In S3, the current pulse frequency fluctuation characteristic index and current pulse repetition rate characteristic index of each region are comprehensively analyzed to calculate the comprehensive index of each region and determine whether the corresponding region is a partial discharge region. Specifically, this includes:

[0142] Obtain the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index for each region, and normalize the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index for each region to calculate the comprehensive index for each region.

[0143] The formula for calculating the comprehensive index is as follows:

[0144]

[0145] In the formula, j represents the number of regions, and P j I represents the composite index within the j-th region. fj I represents the current pulse frequency fluctuation characteristic index in the j-th region. Fj The characteristic index of the current pulse repetition rate in the j-th region is represented by a1 and a2, which are preset proportional coefficients, and both a1 and a2 are greater than 0.

[0146] It should be noted that the comprehensive index reflects the discharge situation in each monitoring area, and the smaller the comprehensive index, the more likely the corresponding area is to experience partial discharge.

[0147] The composite index for each region is compared with a preset composite index threshold.

[0148] If the comprehensive index is less than the preset comprehensive index threshold, it indicates that the corresponding area is a partial discharge area.

[0149] If the comprehensive index is greater than or equal to the preset comprehensive index threshold, it indicates that the corresponding area is a non-partial discharge area.

[0150] In S4, the real-time discharge frequency and discharge repetition rate of the identified partial discharge region are compared and analyzed with the historical normal discharge frequency and discharge repetition rate to determine whether the partial discharge is abnormal. Specifically, this includes:

[0151] The historical normal discharge frequency and discharge repetition rate of multiple collection points are obtained according to the time series (e.g., per minute) and recorded as historical normal data;

[0152] Standardize historical data;

[0153] The processed historical data is used as the training set to train the Isolation Forest model, specifically including:

[0154] By generating multiple random trees, each tree divides historical data by randomly selecting features and split points;

[0155] Divide each historical data point into segments and calculate the depth to which the corresponding historical data point is isolated in the tree;

[0156] The shallower the isolation depth of a historical data point, the more likely it is to be isolated, and it may be an outlier.

[0157] The average isolation degree of the corresponding historical data points is calculated by using the isolation depth of multiple trees.

[0158] Isolation forests divide historical data into multiple tree segments, ultimately generating an isolation score for each historical data point.

[0159] The formula for calculating the isolation score is as follows:

[0160]

[0161] In the formula, h(x) represents the isolation score of historical data point x, H(x) represents the average isolation depth in the isolation tree of historical data point x, m represents the number of historical data in the dataset, and c(m) represents the maximum isolation depth in the isolation tree.

[0162] The normal discharge frequency and discharge repetition rate of each region are collected in real time according to the time series (e.g., every minute), and recorded as the real-time data of the region;

[0163] Real-time data from each region is input into the trained Isolation Forest model for detection.

[0164] The Isolation Forest model calculates the isolation score for each region's real-time data points as the model's output;

[0165] For each region, calculate the average isolation score of all real-time data points, and denot it as the real-time region average isolation score;

[0166] For each region, calculate the average isolation score of all historical data points, and denot it as the historical region average isolation score;

[0167] The difference between the real-time regional average isolation score and the historical regional average isolation score is calculated, and the absolute value is removed, which is recorded as the regional deviation value.

[0168] Compare the region deviation value with the region deviation value threshold;

[0169] If the deviation value of a region is greater than or equal to the deviation value threshold of a region, it indicates that the discharge situation in the corresponding region is abnormal and is recorded as an abnormal region.

[0170] If the deviation value of a region is less than the region deviation value threshold, it indicates that the discharge situation in the corresponding region is normal and is recorded as an abnormal region or a normal region.

[0171] It should be noted that the regional deviation value reflects whether the discharge situation in the region is abnormal, and the lower the regional deviation value, the lower the degree of discharge abnormality in the corresponding region.

[0172] In S5, based on the judgment result, when an abnormal partial discharge is detected, the system activates an early warning and adjustment mechanism, which specifically includes:

[0173] Based on the abnormal area, the system will automatically take alarm action;

[0174] Upon receiving an alarm, the system automatically takes control measures, including:

[0175] Calculate the standard deviation and average value of the voltage in the corresponding area during the period before the warning (e.g., one hour), and calculate the voltage fluctuation ratio by comparing the standard deviation and the average value of the voltage.

[0176] The voltage fluctuation ratio is compared with a preset threshold.

[0177] If the voltage fluctuation ratio is greater than or equal to the preset threshold, it indicates that the voltage in the corresponding area is abnormal. In this case, the real-time voltage will be adjusted according to the historical normal voltage range.

[0178] If the voltage fluctuation ratio is less than the preset threshold, it indicates that the voltage in the corresponding area is normal. Then, the insulation layer temperature is detected to determine whether the insulation layer temperature is greater than or equal to the preset temperature threshold. If so, the temperature is reduced through the temperature control system.

[0179] After adjusting the voltage and temperature of the cable during operation, the discharge situation in the corresponding area is reassessed. If abnormal discharge still exists in the area, power outage isolation measures are taken.

[0180] Please see Figure 2 As shown, a cable partial discharge detection system under high noise conditions includes:

[0181] The data acquisition module is used to divide the cable into several identical areas during the monitoring period and acquire the current pulse signal in each area.

[0182] The data preprocessing module preprocesses the current pulse signal in each region, extracts the current pulse frequency fluctuation characteristics and current pulse repetition rate characteristics, and generates the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index.

[0183] The partial discharge region determination module comprehensively analyzes the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index in each region, calculates the comprehensive index of each region, and determines whether the corresponding region is a partial discharge region.

[0184] The partial discharge anomaly analysis module is used to compare and analyze the real-time discharge frequency and discharge repetition rate of the determined partial discharge area with the historical normal discharge frequency and discharge repetition rate to determine whether the partial discharge is abnormal.

[0185] The early warning adjustment module, based on the judgment result, activates the early warning adjustment mechanism when an abnormal partial discharge is detected.

[0186] The working principle of this invention is as follows: By dividing the cable into multiple monitoring zones and collecting the current pulse signal of each zone in real time using current sensors, including pulse frequency and repetition rate, partial discharge can be detected. First, in the preprocessing stage, the current pulse signal of each zone is denoised and features are extracted to obtain the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index. Next, these characteristic indices are used for comprehensive analysis to calculate the comprehensive index of each zone and determine whether it is a partial discharge zone. Subsequently, the real-time discharge frequency and repetition rate are compared with historical normal data to determine whether the partial discharge is abnormal. If an abnormality is detected, the system will activate an early warning and adjustment mechanism. This mechanism includes adjusting parameters such as voltage and temperature to avoid the discharge affecting the equipment. This method can accurately detect partial discharge under high noise interference conditions and effectively prevent equipment failure caused by discharge. This invention not only improves monitoring accuracy but also optimizes the emergency response mechanism, ensuring the safe operation of power equipment.

[0187] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0188] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0189] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0190] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0191] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1. A method for detecting partial discharge in cables under high noise conditions, characterized in that, Includes the following steps: S1: During the monitoring period, the cable is divided into several identical areas, and the current pulse signal in each area is acquired. The current pulse signal is acquired by a current sensor and includes the current pulse frequency and the current pulse repetition rate. S2: Preprocess the current pulse signal in each region, extract the current pulse frequency fluctuation characteristics and current pulse repetition rate characteristics, and generate the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index. S3: Perform a comprehensive analysis of the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index in each region, calculate the comprehensive index of each region, and determine whether the corresponding region is a partial discharge region. S4: Compare and analyze the real-time discharge frequency and discharge repetition rate of the identified partial discharge area with the historical normal discharge frequency and discharge repetition rate to determine whether the partial discharge is abnormal. S5: Based on the judgment results, when an abnormal partial discharge is detected, the system activates the early warning and adjustment mechanism.

2. The method for detecting partial discharge in cables under high noise conditions according to claim 1, characterized in that, The process of obtaining the current pulse frequency fluctuation characteristic index specifically includes: Acquire current pulse frequency data for each region; Preprocess the current pulse frequency data; Identify all local maxima and local minima in current pulse frequency data; The local maxima and minima are connected using spline interpolation to generate the upper and lower envelopes. Calculate the mean of the upper and lower envelopes; The first intrinsic mode function is extracted from the current pulse frequency data using the empirical mode decomposition method. Repeat the steps for the remaining signal to continue extracting all intrinsic mode functions; Perform a Hilbert transform on each intrinsic mode function to obtain the analytic signal; Calculate the instantaneous amplitude and instantaneous phase of the analytic signal; Calculate the instantaneous frequency by taking the derivative of the instantaneous phase with respect to time, and then calculate the standard deviation σ of the instantaneous frequency. f and average value μ f The current pulse frequency fluctuation characteristic index is calculated based on the standard deviation and average value of the instantaneous frequency.

3. The method for detecting partial discharge in cables under high noise conditions according to claim 1, characterized in that, The process of obtaining the current pulse repetition rate characteristic index specifically includes: Acquire the current pulse repetition rate data for each region to form a data matrix X; The current pulse repetition rate data is standardized to obtain the standardized data matrix Z. Calculate the covariance matrix C based on the standardized data matrix Z; Eigenvalue decomposition is performed on the covariance matrix C to obtain eigenvalues ​​and eigenvectors; Select the k largest eigenvalues ​​and their corresponding eigenvectors to form the principal component matrix V. k ; Where k represents the number of eigenvalues; The standardized data is projected onto the principal component space to obtain the dimensionality-reduced data Z. k ; The original data is reconstructed based on the principal component projection results, resulting in the reconstructed data matrix Z. s ; Calculate the reconstruction error matrix E; Calculate the reconstruction error norm RE for each data point i ; The expression for calculating the reconstruction error norm is as follows: RE i =||E i ||; In the formula, i represents the number of data points, RE i E represents the reconstruction error norm of the i-th data point. i This represents the reconstruction error matrix for the i-th data point; Calculate the mean μ of the reconstruction error norm. F and standard deviation σ F The mean and standard deviation of the reconstruction error norm were normalized, and the characteristic index of the current pulse repetition rate was calculated.

4. The method for detecting partial discharge in cables under high noise conditions according to claim 1, characterized in that, The process involves comprehensively analyzing the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index within each region to calculate a comprehensive index for each region. Specifically, this includes: The current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index of each region are obtained, and the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index of each region are normalized to calculate the comprehensive index of each region.

5. The method for detecting partial discharge in cables under high noise conditions according to claim 1, characterized in that, The determination of whether the corresponding region is a partial discharge region specifically includes: Determine whether the comprehensive index is less than the preset comprehensive index threshold. If yes, record it as a partial discharge region; otherwise, record it as a non-partial discharge region.

6. The method for detecting partial discharge in cables under high noise conditions according to claim 1, characterized in that, The process of comparing and analyzing the real-time discharge frequency and discharge repetition rate of the identified partial discharge region with historical normal discharge frequencies and discharge repetition rates to determine whether the partial discharge is abnormal includes: The historical normal discharge frequency and discharge repetition rate of multiple collection points were obtained according to the time series and recorded as historical normal data. Standardize historical data; The processed historical data is used as a training set to train the isolated forest model; The average isolation degree of the corresponding historical data points is calculated by using the isolation depth of multiple trees. Isolation forests divide historical data into multiple tree segments, ultimately generating an isolation score for each historical data point. The formula for calculating the isolation score is as follows: In the formula, h(x) represents the isolation score of historical data point x, H(x) represents the average isolation depth in the isolation tree of historical data point x, m represents the number of historical data in the dataset, and c(m) represents the maximum isolation depth in the isolation tree. The normal discharge frequency and discharge repetition rate of each region are collected in real time according to the time series and recorded as the real-time data of the region. Standardize the real-time data; The processed real-time data from each region is input into the trained Isolation Forest model for detection. The isolation score of real-time data points in each region is calculated using the isolated forest model. For each region, calculate the average isolation score of all real-time data points, and denot it as the real-time region average isolation score; For each region, calculate the average isolation score of all historical data points, and denot it as the historical region average isolation score; The difference between the real-time regional average isolation score and the historical regional average isolation score is calculated, and the absolute value is removed, which is recorded as the regional deviation value. Compare the region deviation value with the region deviation value threshold; If the deviation value of a region is greater than or equal to the region deviation value threshold, it is recorded as an abnormal region; If the deviation value of a region is less than the region deviation value threshold, it is recorded as a normal region.

7. The method for detecting partial discharge in cables under high noise conditions according to claim 1, characterized in that, Based on the judgment result, when an abnormal partial discharge is detected, the system activates an early warning and adjustment mechanism, which specifically includes: Based on the abnormal area, the system will automatically take alarm action; Upon receiving an alarm, the system automatically takes control measures, including: Calculate the standard deviation and average value of the voltage in the corresponding area for a period of time before the warning, and calculate the voltage fluctuation ratio by comparing the standard deviation and the average value of the voltage. The voltage fluctuation ratio is compared with a preset threshold. If the voltage fluctuation ratio is greater than or equal to the preset threshold, the real-time voltage will be adjusted according to the historical normal voltage range. If the voltage fluctuation ratio is less than the preset threshold, the insulation layer temperature is detected to determine whether the insulation layer temperature is greater than or equal to the preset temperature threshold. If so, the temperature is reduced through the temperature control system. After adjusting the voltage and temperature of the cable during operation, the discharge situation in the corresponding area is reassessed. If abnormal discharge still exists in the area, power outage isolation measures are taken.

8. A cable partial discharge detection system under high noise conditions, characterized in that, A method for detecting partial discharge in cables under high noise conditions as described in any one of claims 1-7, comprising: The data acquisition module is used to divide the cable into several identical areas during the monitoring period and acquire the current pulse signal in each area. The data preprocessing module preprocesses the current pulse signal in each region, extracts the current pulse frequency fluctuation characteristics and current pulse repetition rate characteristics, and generates the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index. The partial discharge region determination module comprehensively analyzes the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index in each region, calculates the comprehensive index of each region, and determines whether the corresponding region is a partial discharge region. The partial discharge anomaly analysis module is used to compare and analyze the real-time discharge frequency and discharge repetition rate of the determined partial discharge area with the historical normal discharge frequency and discharge repetition rate to determine whether the partial discharge is abnormal. The early warning adjustment module, based on the judgment result, activates the early warning adjustment mechanism when an abnormal partial discharge is detected.

9. The cable partial discharge detection system under high noise conditions according to claim 8, characterized in that, The data preprocessing module also includes at least: A current pulse frequency fluctuation characteristic index calculation unit calculates the current pulse frequency fluctuation characteristic index by processing the current pulse frequency. The current pulse repetition rate characteristic index calculation unit calculates the current pulse repetition rate characteristic index by processing the repetition of current pulses.

10. A cable partial discharge detection system under high noise conditions according to claim 8, characterized in that, The partial discharge region determination module also includes at least: The comprehensive index calculation unit calculates the comprehensive index by comprehensively calculating the current pulse frequency fluctuation characteristic index and the current pulse repetition rate characteristic index.

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