Power transmission line anomaly recognition model construction method combined with edge calculation

Abstract

The invention relates to the technical field of power transmission line anomaly recognition, and particularly discloses a power transmission line anomaly recognition model construction method combined with edge calculation, which comprises the following steps of: gradually increasing electromagnetic interference intensity test, acquiring current and voltage signals in real time, and monitoring electric field intensity and signal change by combining a high-precision sensor; dynamically calculating an influence value of electromagnetic interference; recursively estimating the dynamic influence of the electromagnetic interference through a Kalman filter, accurately modeling the influence of the electromagnetic interference by combining the system state and measurement data updating, and realizing the real-time evaluation of the influence of the interference on the system by calculating an electromagnetic interference coefficient and an electromagnetic shielding efficiency expected deviation; according to the method, a spectral analysis and high-frequency current and voltage fluctuation feature extraction method is adopted, and accidental interference and system faults are effectively distinguished by calculating a spectral ratio; through multi-level data analysis and optimization, real fault signals and electromagnetic interference signals can be accurately distinguished, and false alarms and missing alarms are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line anomaly identification, and in particular to a method for constructing a power transmission line anomaly identification model combined with edge computing. Background Art

[0002] In the power system, the normal operation of the transmission line is of vital importance. However, electromagnetic interference and system failures often have a serious impact on its stability and safety. Electromagnetic interference usually comes from the external environment, such as lightning, radio waves, equipment switching operations and other factors. It may cause fluctuations in the current and voltage of the transmission line, affect the signal quality, and even cause false alarms or missed alarms. System failures, such as short circuits, overloads and ground faults, are the main causes of damage to the power system and potential safety hazards. Due to the overlap of signal characteristics of electromagnetic interference and system failures, it is difficult for traditional fault detection systems to accurately distinguish between these two types of signals, which in turn affects the diagnosis of faults and the stable operation of the system. Therefore, how to accurately identify real fault signals and interference signals in a complex electromagnetic environment and avoid misjudgment and missed judgment has become a technical problem in the current transmission line abnormality identification.

[0003] Existing power system anomaly identification methods mostly rely on time domain signal processing technology, but in the case of strong electromagnetic interference or high-frequency current and voltage fluctuations, traditional methods often have difficulty coping with complex interference patterns. Especially in the case of high electromagnetic interference intensity, it is difficult for existing systems to effectively extract effective features and accurately distinguish between faults and interference signals. In addition, traditional fault identification algorithms lack dynamic analysis and optimization of interference effects, resulting in reduced system identification accuracy in high electromagnetic interference environments. Therefore, there is an urgent need for a new method that can comprehensively consider the dual effects of electromagnetic interference and system failures, and improve the system robustness and fault identification accuracy through precise models and algorithms. Summary of the invention

[0004] The purpose of the present invention is to provide a method for constructing a power transmission line anomaly identification model combined with edge computing to solve the problems in the above background.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] The method for constructing a transmission line anomaly recognition model combined with edge computing includes the following steps:

[0007] S1: In the process of identifying abnormalities in the transmission line, the electromagnetic frequency range and electromagnetic intensity level to be monitored are set according to the abnormality types detected in the transmission line, including short circuit, overload, and ground fault, so as to simulate the actual electromagnetic interference situation;

[0008] S2: Real-time collection of current and voltage signals of the transmission line, gradually increasing the electromagnetic interference intensity within the set electromagnetic frequency range, recording the electric field intensity and current and voltage change data at each electromagnetic frequency, and using electromagnetic field detectors to monitor the absorption, reflection and transmission characteristics of the signal, and recording the impact of electromagnetic interference on the signal;

[0009] Wherein, the signal includes: a current signal and a voltage signal;

[0010] S3: Classify the collected signals in real time, mark them as normal signals, abnormal signals and signals to be determined, and mark and save the signals to be determined so as to analyze whether they are false signals caused by electromagnetic interference or real fault signals in the future, so as to ensure the accuracy of subsequent signal analysis;

[0011] S4: Start system diagnosis, and analyze whether the signal is accidental interference or caused by system failure, based on the electromagnetic interference source, signal characteristics and system operation status. If it is an accidental abnormality, repeat the test; if it is a systemic problem, stop the test and troubleshoot the electromagnetic shielding equipment or system to repair the hidden fault and ensure the effectiveness of subsequent tests;

[0012] S5: Perform detailed analysis on the signal data marked as qualified signals and compare them with the expected performance indicators to evaluate the performance of the system under different electromagnetic interference intensities, optimize the recognition process, and improve the system's fault recognition capability under high-frequency current and voltage fluctuation conditions to ensure that real fault signals and electromagnetic interference signals can be accurately distinguished.

[0013] As a further solution of the present invention: the electric field strength and current and voltage change data at each electromagnetic frequency are obtained, and after obtaining, the impact value of electromagnetic interference is calculated by analyzing the electric field strength and current and voltage change data, and compared with the expected electromagnetic shielding efficiency, so as to calculate the expected deviation of the electromagnetic shielding efficiency.

[0014] As a further solution of the present invention: the process of obtaining the expected deviation of the electromagnetic shielding efficiency is as follows:

[0015] Use linear equations to represent dynamic systems of electromagnetic interference;

[0016] Wherein, the calculation expression of the linear equation is:

[0017] x k =Ax k-1 +Bu k +w k ;

[0018] In the formula, k represents the acquisition time point, x k represents the changes in electric field strength, current and voltage affected by electromagnetic interference at time point k, A represents the system state transfer matrix, uk represents the external control input, B represents the input matrix, w k represents the process noise with covariance Q, x k-1 Indicates the changes in electric field strength, current and voltage affected by electromagnetic interference at time point k-1;

[0019] The current and voltage signals measured by the sensor are expressed by the measurement equation;

[0020] Wherein, the calculation expression of the measurement equation is:

[0021] z k =Hx k +v k ;

[0022] In the formula, z k represents the change data of electric field strength, current and voltage measured by the sensor at time point k, H represents the measurement matrix, v k represents the process noise with covariance R;

[0023] At each new sampling, according to the state x k-1 and input u k Predict the state at the current time point k, recorded as the predicted state;

[0024] The calculation expression of the predicted state is:

[0025]

[0026] In the formula, is the predicted state;

[0027] The error of the predicted state is estimated to obtain the predicted covariance matrix, and the calculation expression is:

[0028]

[0029] In the formula, represents the predicted state covariance matrix, Q represents the process noise covariance matrix;

[0030] Combined with the actual measured value z k To update the state estimate and calculate the Kalman gain, the calculation expression is:

[0031]

[0032] In the formula, K k represents the Kalman gain of the measured data and the predicted data, H represents the measurement matrix, and R represents the measurement noise covariance matrix;

[0033] Calculate the updated state estimate and calculate the expression:

[0034]

[0035] In the formula, represents the updated state estimate;

[0036] According to the updated state estimation of the Kalman filter, the impact value of electromagnetic interference on the transmission line signal is calculated. The calculation expression of the impact value of electromagnetic interference is:

[0037]

[0038] In the formula, I k Represents the impact value of electromagnetic interference at time point k;

[0039] The difference between the actual electromagnetic interference effect and the expected electromagnetic shielding efficiency is calculated and the absolute value is taken to obtain the deviation value, which is recorded as the expected deviation of electromagnetic shielding efficiency F. k ;

[0040] The expected electromagnetic shielding efficiency is a system-determined value.

[0041] As a further solution of the present invention: the influence value of the electromagnetic interference and the expected deviation of the electromagnetic shielding efficiency at the time point k are obtained, the influence value of the electromagnetic interference and the expected deviation of the electromagnetic shielding efficiency are standardized, and the electromagnetic interference coefficient is calculated.

[0042] As a further solution of the present invention: the real-time classification of the collected signals and marking them as normal signals, abnormal signals and signals to be determined specifically includes:

[0043] Compare the electromagnetic interference coefficient with a preset threshold value to determine whether the electromagnetic interference coefficient is greater than or equal to the preset threshold value;

[0044] If yes, it means that the electromagnetic interference affects the signal and it is impossible to judge whether it is normal or abnormal, and the signal is marked as a signal to be determined;

[0045] If not, it means that the electromagnetic interference will not affect the judgment of whether the signal is normal or abnormal, then the collected signal is identified as abnormal to determine whether the current signal is a normal signal or an abnormal signal.

[0046] As a further solution of the present invention: the analysis of whether the signal is caused by accidental interference or system failure specifically includes:

[0047] In the process of identifying abnormalities in transmission lines, the signals are distinguished by Fourier transform and spectrum analysis;

[0048] Convert the time domain signal to the frequency domain signal. For each time point k, the Fourier transform calculation expression is:

[0049]

[0050] In the formula, f represents frequency, X(f) represents the spectrum of the signal at different frequency components, x(k) represents the time domain signal at time point k, j represents the imaginary unit, and e represents the logarithm of the base of the natural number;

[0051] The spectrum of the signal is obtained by Fourier transform, and the spectrum amplitude is calculated. The calculation expression is:

[0052]

[0053] In the formula, represents the real part of the spectrum X(f), represents the imaginary part of the spectrum X(f);

[0054] The main frequency is calculated by the peak frequency of the spectrum. The calculation expression is:

[0055] f 0 = argmax|X(f)|;

[0056] In the formula, f 0 represents the main frequency, argmax|X(f)| represents the maximum frequency of the given spectrum;

[0057] Calculate the spectrum ratio, the calculation expression is:

[0058]

[0059] Where T represents the spectrum ratio, f low Represents the frequency range of the low-frequency region, f high Represents the frequency range of the high-frequency region, f max Indicates the maximum frequency of the signal;

[0060] Determine whether the spectrum ratio is greater than or equal to a preset threshold. If so, it is recorded as accidental interference. If not, it is recorded as a system failure.

[0061] As a further solution of the present invention: the signal data marked as qualified signals are analyzed in detail and compared with expected performance indicators, the performance of the system under different electromagnetic interference intensities is evaluated, and the system recognition accuracy coefficient is calculated, which specifically includes:

[0062] Obtain a data set marked as qualified signals;

[0063] The qualified signal is a signal that has been preliminarily screened, indicating that the system is not interfered with or misjudged under the current electromagnetic interference;

[0064] For each signal, record the waveforms of current and voltage, as well as the corresponding timestamps;

[0065] Extracting time domain features from qualified signals includes: peak value, mean value and RMS value;

[0066] The main frequency of the qualified signal is obtained, and the spectrum density is calculated. The calculation expression of the spectrum density is:

[0067]

[0068] Where S(f) represents the spectrum density;

[0069] The ratio of the spectrum density to the main frequency is calculated to obtain the system identification accuracy coefficient.

[0070] As a further solution of the present invention: the accurate distinction between the real fault signal and the electromagnetic interference signal specifically includes: determining whether the system recognition accuracy coefficient is greater than or equal to a preset threshold value, if the system recognition accuracy coefficient is greater than or equal to the preset threshold value, it means that the corresponding system accurately distinguishes the real fault signal from the electromagnetic interference signal, and can accurately determine the fault signal according to the qualified signal;

[0071] If the system recognition accuracy coefficient is less than the preset threshold, it means that the corresponding system cannot accurately distinguish between the real fault signal and the electromagnetic interference signal, and cannot accurately judge the fault signal based on the qualified signal.

[0072] Beneficial effects of the present invention:

[0073] (1) The present invention innovatively adopts a test method of gradually increasing the electromagnetic interference intensity, and combines high-precision sensors to monitor the changes in current and voltage signals in real time, so as to comprehensively capture the impact of electromagnetic interference on system signals; the dynamic impact value of electromagnetic interference is recursively estimated by using a Kalman filter, and the impact of electromagnetic interference is accurately modeled and tracked by fusing state prediction and actual measurement data; at the same time, the calculation method of the electromagnetic interference impact value and the electromagnetic shielding efficiency deviation proposed by the present invention can dynamically evaluate the impact of electromagnetic interference on system performance at different intensities, and optimize the system's response strategy by standardizing the calculation of the electromagnetic interference coefficient, ensuring that good fault identification capabilities are maintained in a high-intensity interference environment, and significantly reducing false alarms and missed alarms caused by accidental interference. On this basis, the present invention realizes the precise distinction between fault signals and electromagnetic interference signals, and greatly improves the system's fault detection accuracy and robustness through multi-level data analysis and dynamic optimization. Especially in complex electromagnetic environments, the system can adapt to changes in various interference types and intensities, providing a strong guarantee for the stability and reliability of the power system, reflecting the technical advancement and broad application prospects of the present invention in terms of intelligent response and real-time processing capabilities;

[0074] (2) In practical applications, electromagnetic interference and system fault signals often have similar time domain characteristics. Therefore, how to effectively distinguish between the two is the key to the stable operation of the power system. The present invention performs detailed frequency domain analysis of the signal through Fourier transform and spectrum analysis, and judges the interference nature of the signal based on the spectrum ratio, so as to accurately distinguish accidental interference from system faults; by calculating the spectrum amplitude, main frequency and spectrum ratio, the system can identify the different characteristics of low-frequency fault signals and high-frequency interference signals at the frequency domain level, providing a more accurate basis for subsequent fault diagnosis and processing; in addition, the system classifies and optimizes the signal characteristics through machine learning algorithms, and combines actual measurement data to further enhance the ability to identify complex fault modes, ensuring the efficient operation of the system under different electromagnetic interference intensities; this method can not only improve the recognition accuracy, but also respond quickly when accidental interference occurs, reducing the risk of misjudgment caused by accidental interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The present invention will be further described below in conjunction with the accompanying drawings.

[0076] Figure 1 It is a flowchart of the specific steps of the method for constructing a power transmission line anomaly identification model combined with edge computing of the present invention;

[0077] Figure 2 It is a flowchart of the calculation process of the electromagnetic interference coefficient in the present invention. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0079] See also Figure 1 As shown, the present invention is a method for constructing a power transmission line abnormality identification model combined with edge computing, comprising the following steps:

[0080] S1: In the process of identifying abnormalities in the transmission line, the electromagnetic frequency range and electromagnetic intensity level to be monitored are set according to the abnormality types detected in the transmission line, including short circuit, overload, and ground fault, so as to simulate the actual electromagnetic interference situation;

[0081] S2: Real-time collection of current and voltage signals of the transmission line, gradually increasing the electromagnetic interference intensity within the set electromagnetic frequency range, recording the electric field intensity and current and voltage change data at each electromagnetic frequency, and using electromagnetic field detectors to monitor the absorption, reflection and transmission characteristics of the signal, and recording the impact of electromagnetic interference on the signal;

[0082] Wherein, the signal includes: a current signal and a voltage signal;

[0083] S3: Classify the collected signals in real time, mark them as qualified signals, abnormal signals and signals to be determined, and mark and save the signals to be determined so as to analyze whether they are false signals caused by electromagnetic interference or real fault signals in the future, so as to ensure the accuracy of subsequent signal analysis;

[0084] S4: Start system diagnosis, and analyze whether the signal is accidental interference or caused by system failure, based on the electromagnetic interference source, signal characteristics and system operation status; if it is an accidental abnormality, repeat the test; if it is a systemic problem, stop the test and troubleshoot the electromagnetic shielding equipment or system to repair the hidden fault and ensure the effectiveness of subsequent tests;

[0085] S5: Conduct a detailed analysis of the test data marked as qualified signals and compare them with the expected performance indicators to evaluate the performance of the system under different electromagnetic interference intensities, optimize the identification process, and improve the system's fault identification capability under high-frequency current and voltage fluctuation conditions, ensuring that real fault signals and electromagnetic interference signals can be accurately distinguished, reducing the possibility of false alarms and missed alarms.

[0086] In S1, during the transmission line abnormality identification process, according to the abnormality types detected by the transmission line, including short circuit, overload, and ground fault, the monitored electromagnetic frequency range and electromagnetic intensity level are set to simulate the actual electromagnetic interference situation, including:

[0087] In the process of identifying abnormalities in transmission lines, first, the electromagnetic frequency range and electromagnetic intensity level to be monitored are set according to the type of abnormality to be detected (such as short circuit, overload, ground fault);

[0088] The above abnormal types will cause current and voltage fluctuations in different frequency bands, so it is necessary to set a suitable frequency range for each fault;

[0089] Exemplary: The low frequency band is used to simulate short circuits and ground faults, between 50Hz and 500Hz; while the high frequency band is used to simulate electromagnetic interference, above 2kHz;

[0090] According to the current and voltage fluctuation frequencies of these fault types, the frequency range and electromagnetic intensity level are selected to ensure that different types of abnormal signals can be accurately identified and electromagnetic interference can be distinguished;

[0091] Next, after setting the electromagnetic frequency range, the electromagnetic interference intensity is gradually increased for testing. During this process, high-precision sensors are used to monitor the current and voltage signals in real time, and the changes in the electric field intensity at each frequency are recorded.

[0092] Please participate Figure 2As shown, in S2, the electric field strength and current and voltage change data at each electromagnetic frequency are obtained. After obtaining, the impact value caused by electromagnetic interference is calculated by analyzing the electric field strength and current and voltage change data, and compared with the expected electromagnetic shielding efficiency, so as to calculate the expected deviation of the electromagnetic shielding efficiency;

[0093] The process of obtaining the expected deviation of the electromagnetic shielding efficiency is as follows:

[0094] Use linear equations to represent dynamic systems of electromagnetic interference;

[0095] Wherein, the calculation expression of the linear equation is:

[0096] x k =Ax k-1 +Bu k +w k ;

[0097] In the formula, k represents the acquisition time point, x k represents the changes in electric field strength, current and voltage affected by electromagnetic interference at time point k, A represents the system state transfer matrix, u k represents the external control input, B represents the input matrix, w k represents the process noise with covariance Q, x k-1 Indicates the changes in electric field strength, current and voltage affected by electromagnetic interference at time point k-1;

[0098] The current and voltage signals measured by the sensor are expressed by the measurement equation;

[0099] Wherein, the calculation expression of the measurement equation is:

[0100] z k =Hx k +v k ;

[0101] In the formula, z k represents the change data of electric field strength, current and voltage measured by the sensor at time point k, H represents the measurement matrix, v k represents the process noise with covariance R;

[0102] At each new sampling, according to the state x k-1 and input u k Predict the state at the current time point k, recorded as the predicted state;

[0103] The calculation expression of the predicted state is:

[0104]

[0105] In the formula, is the predicted state;

[0106] The error of the predicted state is estimated to obtain the predicted covariance matrix, and the calculation expression is:

[0107]

[0108] In the formula, represents the predicted state covariance matrix, Q represents the process noise covariance matrix;

[0109] Combined with the actual measured value z k To update the state estimate and calculate the Kalman gain, the calculation expression is:

[0110]

[0111] In the formula, K k represents the Kalman gain of the measured data and the predicted data, H represents the measurement matrix, and R represents the measurement noise covariance matrix;

[0112] Calculate the updated state estimate and calculate the expression:

[0113]

[0114] In the formula, represents the updated state estimate;

[0115] According to the updated state estimation of the Kalman filter, the impact value of electromagnetic interference on the transmission line signal is calculated. The calculation expression of the impact value of electromagnetic interference is:

[0116]

[0117] In the formula, I k Represents the impact value of electromagnetic interference at time point k;

[0118] The difference between the actual electromagnetic interference effect and the expected electromagnetic shielding efficiency is calculated and the absolute value is taken to obtain the deviation value, which is recorded as the expected deviation of electromagnetic shielding efficiency F. k ;

[0119] Wherein, the expected electromagnetic shielding efficiency is a system-determined value;

[0120] It should be noted that by processing the impact value of electromagnetic interference through the Kalman filter algorithm, the deviation of electromagnetic shielding efficiency can be effectively estimated, and an accurate basis can be provided for subsequent fault detection and signal processing. Kalman filtering accurately captures the impact of electromagnetic interference on current and voltage through recursive estimation of system state, combined with actual measurement values ​​and prediction results, thereby ensuring the stability and accuracy of the power system in the electromagnetic environment.

[0121] If it is an accidental abnormality, repeat the test; if it is a systemic problem, stop the test and troubleshoot the electromagnetic shielding equipment or system to repair the hidden fault and ensure the effectiveness of subsequent tests;

[0122] Obtaining the impact value of electromagnetic interference and the expected deviation of electromagnetic shielding efficiency at time point k, standardizing the impact value of electromagnetic interference and the expected deviation of electromagnetic shielding efficiency, and calculating the electromagnetic interference coefficient;

[0123] Wherein, the calculation expression of the electromagnetic interference coefficient is:

[0124]

[0125] In the formula, G k Represents the electromagnetic interference coefficient, I k represents the impact value of electromagnetic interference at time point k, F k represents the expected deviation of electromagnetic shielding efficiency at time point k, a 1 and a 2 is the preset proportional coefficient;

[0126] It should be noted that the electromagnetic interference coefficient reflects the degree of interference of electromagnetic interference on the identification of transmission line faults, and the larger the value of the electromagnetic interference coefficient, the higher the corresponding degree of electromagnetic interference, and accidental interference refers to accidental and non-continuous electromagnetic interference signals in the power system or communication system; it is usually caused by external factors, equipment failures or instantaneous events, and is random, short-lived and irregular.

[0127] In S3, the collected signals are classified in real time and marked as normal signals, abnormal signals and signals to be determined, which specifically includes:

[0128] Compare the electromagnetic interference coefficient with a preset threshold value to determine whether the electromagnetic interference coefficient is greater than or equal to the preset threshold value;

[0129] If yes, it means that the electromagnetic interference affects the signal and it is impossible to judge whether it is normal or abnormal, and the signal is marked as a signal to be determined;

[0130] If not, it means that electromagnetic interference will not affect the judgment of whether the signal is normal or abnormal, then the collected signal is identified as abnormal to determine whether the current signal is a normal signal or an abnormal signal;

[0131] In S4, whether the analysis signal is caused by accidental interference or system failure specifically includes:

[0132] In the process of identifying abnormalities in transmission lines, the signals are distinguished by Fourier transform and spectrum analysis;

[0133] Convert the time domain signal to the frequency domain signal. For each time point k, the Fourier transform calculation expression is:

[0134]

[0135] In the formula, f represents frequency, X(f) represents the spectrum of the signal at different frequency components, x(k) represents the time domain signal at time point k, j represents the imaginary unit, and e represents the logarithm of the base of the natural number;

[0136] The spectrum of the signal is obtained by Fourier transform, and the spectrum amplitude is calculated. The calculation expression is:

[0137]

[0138] In the formula, represents the real part of the spectrum X(f), represents the imaginary part of the spectrum X(f);

[0139] The main frequency is calculated by the peak frequency of the spectrum. The calculation expression is:

[0140] f 0 = argmax|X(f)|;

[0141] In the formula, f 0 represents the main frequency, argmax|X(f)| represents the maximum frequency of the given spectrum;

[0142] Calculate the spectrum ratio, the calculation expression is:

[0143]

[0144] Where T represents the spectrum ratio, f low Represents the frequency range of the low-frequency region, f high Represents the frequency range of the high-frequency region, f max Indicates the maximum frequency of the signal;

[0145] Determine whether the spectrum ratio is greater than or equal to a preset threshold, if so, record it as accidental interference, if not, record it as a system failure;

[0146] It should be noted that by calculating the spectrum ratio, accidental interference and system failure can be accurately identified, and the larger the value of the spectrum ratio, the more accidental the interference.

[0147] In S5, the signal data marked as qualified signals are analyzed in detail and compared with expected performance indicators, the performance of the system under different electromagnetic interference intensities is evaluated, and the system recognition accuracy coefficient is calculated, which specifically includes:

[0148] Obtain a data set marked as qualified signals;

[0149] The qualified signal is a signal that has been preliminarily screened, indicating that the system is not interfered with or misjudged under the current electromagnetic interference;

[0150] For each signal, record the waveforms of current and voltage, as well as the corresponding timestamps;

[0151] Extracting time domain features from qualified signals includes: peak value, mean value and RMS value;

[0152] The main frequency of the qualified signal is obtained, and the spectrum density is calculated. The calculation expression of the spectrum density is:

[0153]

[0154] Where S(f) represents the spectrum density;

[0155] Calculate the ratio of the spectrum density to the main frequency to obtain the system identification accuracy coefficient;

[0156] The accurate distinction between real fault signals and electromagnetic interference signals specifically includes:

[0157] Determine whether the system recognition accuracy coefficient is greater than or equal to a preset threshold. If the system recognition accuracy coefficient is greater than or equal to the preset threshold, it means that the corresponding system can accurately distinguish between real fault signals and electromagnetic interference signals, and can accurately determine fault signals based on qualified signals;

[0158] If the system recognition accuracy coefficient is less than the preset threshold, it means that the corresponding system cannot accurately distinguish between the real fault signal and the electromagnetic interference signal, and cannot accurately judge the fault signal based on the qualified signal.

[0159] The working principle of the present invention is to improve the fault identification ability in the electromagnetic interference environment through multi-level analysis and optimization steps. First, in the process of transmission line abnormality identification, according to different abnormal types including short circuit, overload, and ground fault, set the appropriate electromagnetic frequency range and electromagnetic intensity level to simulate the actual electromagnetic interference situation. Then, by real-time acquisition of current and voltage signals, and gradually increasing the electromagnetic interference intensity, recording the electric field intensity and current and voltage change data at each frequency, and using the electromagnetic field detector to monitor the absorption, reflection and transmission characteristics of the signal, the impact value of electromagnetic interference on the signal is calculated. Then, the impact value of electromagnetic interference is recursively estimated in combination with the Kalman filter algorithm, and then compared with the expected electromagnetic shielding efficiency, the expected deviation of the electromagnetic shielding efficiency is calculated, and the electromagnetic interference coefficient is calculated by standardization. In the classification process, the collected signals are classified in real time and marked as qualified signals, abnormal signals or signals to be determined. Based on the comparison of the electromagnetic interference coefficient with the preset threshold, it is judged whether the signal is affected by interference and further analysis is taken. For whether the signal is accidental interference or system failure, frequency features are extracted through Fourier transform and spectrum analysis, and accidental interference and fault signals are accurately distinguished by calculating the spectrum ratio. Finally, the marked qualified signal is compared with the expected performance indicators to evaluate the performance of the system under different electromagnetic interference intensities and optimize the fault identification algorithm. By calculating the system identification accuracy coefficient, it is ensured that the system can effectively distinguish between real fault signals and electromagnetic interference signals, reduce false alarms and missed alarms, and improve the accuracy of fault identification and the robustness of the system. Through comprehensive analysis, algorithm optimization and real-time monitoring, this method improves the fault detection capability of transmission lines in complex electromagnetic environments, providing important guarantees for the stability and safety of power systems.

[0160] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0161] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present application is generated in whole or in part. 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). 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 contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0162] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0163] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean 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 the present application.

[0164] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for constructing a transmission line anomaly recognition model combined with edge computing, characterized in that: The following steps are involved: S1: In the process of identifying abnormalities in the transmission line, the electromagnetic frequency range and electromagnetic intensity level to be monitored are set according to the abnormality types detected in the transmission line, including short circuit, overload, and ground fault, so as to simulate the actual electromagnetic interference situation; S2: Real-time collection of current and voltage signals of the transmission line, gradually increasing the electromagnetic interference intensity within the set electromagnetic frequency range, recording the electric field intensity and current and voltage change data at each electromagnetic frequency, and using electromagnetic field detectors to monitor the absorption, reflection and transmission characteristics of the signal, and recording the impact of electromagnetic interference on the signal; Wherein, the signal includes: a current signal and a voltage signal; S3: Classify the collected signals in real time, mark them as normal signals, abnormal signals and signals to be determined, and mark and save the signals to be determined so as to analyze whether they are false signals caused by electromagnetic interference or real fault signals in the future, so as to ensure the accuracy of subsequent signal analysis; S4: Start system diagnosis, and analyze whether the signal is accidental interference or caused by system failure, based on the electromagnetic interference source, signal characteristics and system operation status. If it is an accidental abnormality, repeat the test; if it is a systemic problem, stop the test and troubleshoot the electromagnetic shielding equipment or system to repair the hidden fault and ensure the effectiveness of subsequent tests; S5: Perform detailed analysis on the signal data marked as qualified signals and compare them with the expected performance indicators to evaluate the performance of the system under different electromagnetic interference intensities, optimize the recognition process, and improve the system's fault recognition capability under high-frequency current and voltage fluctuation conditions to ensure that real fault signals and electromagnetic interference signals can be accurately distinguished.

2. The method for constructing a power transmission line abnormality identification model combined with edge computing according to claim 1, characterized in that: The electric field strength and current and voltage change data at each electromagnetic frequency are obtained. After obtaining, the impact value of electromagnetic interference is calculated by analyzing the electric field strength and current and voltage change data, and compared with the expected electromagnetic shielding efficiency, so as to calculate the expected deviation of electromagnetic shielding efficiency.

3. The method for constructing a power transmission line abnormality identification model combined with edge computing according to claim 2, characterized in that: The process of obtaining the expected deviation of the electromagnetic shielding efficiency is as follows: Use linear equations to represent dynamic systems of electromagnetic interference; Wherein, the calculation expression of the linear equation is: x k =Ax k-1 +Bu k +w k ; In the formula, k represents the acquisition time point, x k represents the changes in electric field strength, current and voltage affected by electromagnetic interference at time point k, A represents the system state transfer matrix, u k represents the external control input, B represents the input matrix, w k represents the process noise with covariance Q, x k-1 Indicates the changes in electric field strength, current and voltage affected by electromagnetic interference at time point k-1; The current and voltage signals measured by the sensor are expressed by the measurement equation; Wherein, the calculation expression of the measurement equation is: z k =Hx k +v k ; In the formula, z k represents the change data of electric field strength, current and voltage measured by the sensor at time point k, H represents the measurement matrix, v k represents the process noise with covariance R; At each new sampling, according to the state x k-1 and input u k Predict the state at the current time point k, recorded as the predicted state; The calculation expression of the predicted state is: In the formula, is the predicted state; The error of the predicted state is estimated to obtain the predicted covariance matrix, and the calculation expression is: In the formula, represents the predicted state covariance matrix, Q represents the process noise covariance matrix; Combined with the actual measured value z k To update the state estimate and calculate the Kalman gain, the calculation expression is: In the formula, K k represents the Kalman gain of the measured data and the predicted data, H represents the measurement matrix, and R represents the measurement noise covariance matrix; Calculate the updated state estimate and calculate the expression: In the formula, represents the updated state estimate; According to the updated state estimation of the Kalman filter, the impact value of electromagnetic interference on the transmission line signal is calculated. The calculation expression of the impact value of electromagnetic interference is: In the formula, I k Represents the impact value of electromagnetic interference at time point k; The difference between the actual electromagnetic interference effect and the expected electromagnetic shielding efficiency is calculated and the absolute value is taken to obtain the deviation value, which is recorded as the expected deviation of electromagnetic shielding efficiency F. k ; The expected electromagnetic shielding efficiency is a system-determined value.

4. The method for constructing a power transmission line anomaly identification model combined with edge computing according to claim 3 is characterized in that: The impact value of electromagnetic interference and the expected deviation of electromagnetic shielding efficiency at time point k are obtained, the impact value of electromagnetic interference and the expected deviation of electromagnetic shielding efficiency are standardized, and the electromagnetic interference coefficient is calculated.

5. The method for constructing a power transmission line anomaly identification model combined with edge computing according to claim 1, characterized in that: The real-time classification of the collected signals and marking them as normal signals, abnormal signals and signals to be determined specifically includes: Compare the electromagnetic interference coefficient with a preset threshold value to determine whether the electromagnetic interference coefficient is greater than or equal to the preset threshold value; If yes, it means that the electromagnetic interference affects the signal and it is impossible to judge whether it is normal or abnormal, and the signal is marked as a signal to be determined; If not, it means that the electromagnetic interference will not affect the judgment of whether the signal is normal or abnormal, then the collected signal is identified as abnormal to determine whether the current signal is a normal signal or an abnormal signal.

6. The method for constructing a power transmission line anomaly identification model combined with edge computing according to claim 1, characterized in that: Whether the analysis signal is accidental interference or caused by system failure, specifically including: In the process of identifying abnormalities in transmission lines, the signals are distinguished by Fourier transform and spectrum analysis; Convert the time domain signal to the frequency domain signal. For each time point k, the Fourier transform calculation expression is: In the formula, f represents frequency, X(f) represents the spectrum of the signal at different frequency components, x(k) represents the time domain signal at time point k, j represents the imaginary unit, and e represents the logarithm of the base of the natural number; The spectrum of the signal is obtained by Fourier transform, and the spectrum amplitude is calculated. The calculation expression is: In the formula, represents the real part of the spectrum X(f), represents the imaginary part of the spectrum X(f); The main frequency is calculated by the peak frequency of the spectrum. The calculation expression is: f0=argmax|X(f)|; In the formula, f0 represents the main frequency, argmax|X(f)| represents the maximum frequency of the given spectrum; Calculate the spectrum ratio, the calculation expression is: Where T represents the spectrum ratio, f low Represents the frequency range of the low-frequency region, f high Represents the frequency range of the high-frequency region, f max Indicates the maximum frequency of the signal; Determine whether the spectrum ratio is greater than or equal to a preset threshold. If so, it is recorded as accidental interference. If not, it is recorded as a system failure.

7. The method for constructing a power transmission line anomaly identification model combined with edge computing according to claim 1, characterized in that: The signal data marked as qualified signals are analyzed in detail and compared with expected performance indicators to evaluate the performance of the system under different electromagnetic interference intensities and calculate the system recognition accuracy coefficient, specifically including: Obtain a data set marked as qualified signals; The qualified signal is a signal that has been preliminarily screened, indicating that the system is not interfered with or misjudged under the current electromagnetic interference; For each signal, record the waveforms of current and voltage, as well as the corresponding timestamps; Extracting time domain features from qualified signals includes: peak value, mean value and RMS value; The main frequency of the qualified signal is obtained, and the spectrum density is calculated. The calculation expression of the spectrum density is: Where S(f) represents the spectrum density; The ratio of the spectrum density to the main frequency is calculated to obtain the system identification accuracy coefficient.

8. The method for constructing a power transmission line anomaly identification model combined with edge computing according to claim 1, characterized in that: The accurate distinction between the real fault signal and the electromagnetic interference signal specifically includes: determining whether the system recognition accuracy coefficient is greater than or equal to a preset threshold value, if the system recognition accuracy coefficient is greater than or equal to the preset threshold value, it indicates that the corresponding system accurately distinguishes the real fault signal from the electromagnetic interference signal, and can accurately determine the fault signal according to the qualified signal; If the system recognition accuracy coefficient is less than the preset threshold, it means that the corresponding system cannot accurately distinguish between the real fault signal and the electromagnetic interference signal, and cannot accurately judge the fault signal based on the qualified signal.

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