A method for detecting line faults

Abstract

This invention relates to the field of power technology and discloses a line fault detection method, comprising: setting the first and second time-varying coefficients in a polynomial model to 0, and using the square of the absolute value as a residual model, so as to dynamically update the polynomial model according to the constructed residual model, thereby enabling the dynamically updated polynomial model to dynamically restore the time domain of the current distorted signal. This method can effectively eliminate the dynamic nonlinear influence of the current transformer on the time domain of the measured signal, accurately restore the real fault information, and thus improve the detection accuracy of hidden faults in overhead lines.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a method for detecting line faults. Background Technology

[0002] With the continuous development of power systems, the safety and reliability of overhead distribution lines, as a key component of the power supply network, are receiving increasing attention. However, in actual operation, overhead distribution lines are affected by both external environmental factors and inherent line characteristics, leading to frequent faults such as high-resistance grounding faults and hidden faults. Traditional fault detection methods mainly rely on the direct measurement and analysis of line electrical quantities. However, in complex electromagnetic environments, signals often exhibit nonlinear distortion, making accurate extraction of fault characteristics extremely difficult, resulting in low accuracy in detecting hidden faults.

[0003] As a commonly used signal acquisition device in overhead lines, the non-ideal characteristics of instrument transformers have a dynamic nonlinear effect on the time domain of the measured signal. This dynamic nonlinear effect changes with time and environment, which can lead to distortion of fault signals, making it impossible for the detection system to accurately reproduce the true fault information, thus affecting the accuracy of fault detection. Summary of the Invention

[0004] Based on this, it is necessary to propose a line fault detection method to address the above problems. This method can effectively eliminate the dynamic nonlinear influence of the current transformer on the measured signal in the time domain, accurately restore the true fault information, and thus improve the detection accuracy of hidden faults in overhead lines.

[0005] To achieve the above objectives, the present invention provides a line fault detection method in a first aspect, the method comprising:

[0006] Acquire the current distortion signal and historical distortion signal of the current transformer, as well as the experimental reconstruction signal corresponding to the historical distortion signal;

[0007] A polynomial model is constructed based on the historical distortion signal and the experimental restored signal.

[0008] Set the first and second time-varying coefficients in the polynomial model to 0, and use the square of their absolute values ​​as the residual model. Substitute the data point at time t in the current distorted signal into the polynomial model to obtain the data point at time t in the target restored signal. Update each time-varying coefficient in the polynomial model according to the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model to obtain the updated polynomial model. Use the updated polynomial model as the polynomial model. Here, t takes integer values ​​greater than 0 until t equals the total number of times in the current distorted signal to obtain the target restored signal.

[0009] The target restored signal is subjected to multi-feature extraction to obtain a signal feature vector, and the signal feature vector is input into a preset machine learning model to obtain the fault classification and detection result.

[0010] Optionally, updating each time-varying coefficient of the polynomial model based on the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model to obtain the updated polynomial model includes:

[0011] Substitute the data point at time t in the current distorted signal into the residual model to obtain the residual at time t;

[0012] Based on the residual at time t, the data point at time t in the current distorted signal, and the nth time-varying coefficient in the polynomial model, determine the nth time-varying coefficient at time t+1; where n takes integer values ​​greater than -1 in sequence until n equals the total number of time-varying coefficients minus 1, thus obtaining each time-varying coefficient at time t+1.

[0013] Based on each time-varying coefficient at time t+1, each time-varying coefficient in the polynomial model is updated to obtain the updated polynomial model.

[0014] Optionally, substituting the data point at time t in the current distorted signal into the residual model to obtain the residual at time t includes:

[0015] Using the expression of the residual model Obtain the residual at time t;

[0016] in, Let be the residual at time t. This refers to the (n+1)th time-varying coefficient in the polynomial model. Subtract 1 from the total number of time-varying coefficients in the polynomial model. Let be the data point at time t in the current distorted signal.

[0017] Optionally, determining the nth time-varying coefficient at time t+1 based on the residual at time t, the data point at time t in the current distorted signal, and the nth time-varying coefficient in the polynomial model includes:

[0018] Using formula Determine the nth time-varying coefficient at time t+1;

[0019] in, This is the nth time-varying coefficient at time t+1. For the nth time-varying coefficient, To preset the learning rate, Let be the residual at time t. Let be the data point at time t in the current distorted signal.

[0020] Optionally, substituting the data point at time t in the current distorted signal into the polynomial model to obtain the data point at time t in the target restored signal includes:

[0021] Using the expression of the polynomial model

[0022] Determine the target restored signal;

[0023] in, Let be the data point at time t in the target restored signal. For the (n+1)th time-varying coefficient, Subtract 1 from the total number of time-varying coefficients. Let be the data point at time t in the current distorted signal.

[0024] Optionally, constructing a polynomial model based on the historical distortion signal and the experimental restored signal includes:

[0025] The historical distortion signal and the experimental restored signal are standardized respectively to obtain the standard distortion signal and the standard restored signal;

[0026] Using the least squares method, multiple time-varying coefficients are determined based on the data points at each time step in the standard distorted signal and the data points at each time step in the standard restored signal.

[0027] The polynomial model is constructed based on each time-varying coefficient.

[0028] Optionally, after constructing the polynomial model based on the historical distortion signal and the experimentally restored signal, the method further includes:

[0029] Fourier transforms are performed on the current distorted signal, the historical distorted signal, and the experimental restored signal, respectively, to obtain the current frequency domain distorted signal, the historical frequency domain distorted signal, and the experimental frequency domain restored signal;

[0030] Based on the historical frequency domain distortion signal and the experimental frequency domain restored signal, a frequency response transfer function is constructed.

[0031] The frequency response transfer function is transformed to obtain a frequency domain form function, and the target frequency domain restored signal is determined based on the amplitude attenuation coefficient and phase offset of the frequency domain form function, as well as the amplitude and phase of each frequency in the current frequency domain distorted signal.

[0032] Perform an inverse Fourier transform on the target frequency domain reconstructed signal to obtain the initial reconstructed signal;

[0033] The initial restored signal is used as the current distortion signal.

[0034] Optionally, the expression for the frequency response transfer function is:

[0035] ;

[0036] in, ;

[0037] In the above formula, The frequency response transfer function is... For the Laplace operator, The transfer coefficient of the (m+1)th molecule. Subtract 1 from the total number of numerator or denominator transfer coefficients. The transitivity coefficient of the (m+1)th denominator. The imaginary unit, Pi For frequency.

[0038] Optionally, the expression for the frequency domain form function is:

[0039] ;

[0040] in, For the frequency domain form function, The amplitude attenuation coefficient at frequency f is... It is a natural constant. The imaginary unit, The phase offset is the frequency f.

[0041] Optionally, determining the target frequency domain reconstructed signal based on the amplitude attenuation coefficient and phase offset of the frequency domain form function, and the amplitude and phase of each frequency in the current frequency domain distorted signal, includes:

[0042] Using formula Determine the target frequency domain reconstructed signal;

[0043] in, The frequency domain component of the k-th frequency in the target frequency domain reconstructed signal. For the kth frequency, Let x be the amplitude of the k-th frequency in the current frequency domain distorted signal. For frequency f k The amplitude attenuation coefficient, It is a natural constant. The imaginary unit, The phase of the k-th frequency in the current frequency domain distorted signal. For frequency f k The phase offset.

[0044] To achieve the above objectives, the present invention provides a line fault detection device in a second aspect, the device comprising:

[0045] The acquisition module is used to acquire the current distortion signal and historical distortion signal of the current transformer, as well as the experimental reconstruction signal corresponding to the historical distortion signal;

[0046] A construction module is used to construct a polynomial model based on the historical distortion signal and the experimental restored signal;

[0047] The substitution and update module is used to set the first and second time-varying coefficients in the polynomial model to 0, and after squaring their absolute values, use them as the residual model. It then substitutes the data point at time t in the current distorted signal into the polynomial model to obtain the data point at time t in the target restored signal. Based on the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model, it updates each time-varying coefficient in the polynomial model to obtain an updated polynomial model. This updated polynomial model is then used as the final polynomial model. Here, t takes values ​​of integers greater than 0 until t equals the total number of times in the current distorted signal, thus obtaining the target restored signal.

[0048] The extraction and prediction module is used to extract multiple features from the target restored signal to obtain a signal feature vector, and input the signal feature vector into a preset machine learning model to obtain a fault classification detection result.

[0049] To achieve the above objectives, the present invention provides, in a third aspect, a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the line fault detection method as described in any one of the first aspects.

[0050] To achieve the above objectives, the present invention provides a computer device in a fourth aspect, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the line fault detection method as described in any one of the first aspects.

[0051] The present invention provides the following advantages: The method acquires the current distortion signal and historical distortion signal of the transformer, as well as the experimentally restored signal corresponding to the historical distortion signal. Then, based on the historical distortion signal and the experimentally restored signal, a polynomial model is constructed. The first and second time-varying coefficients in the polynomial model are set to 0, and the square of their absolute values ​​is used as the residual model. The data point at time t in the current distortion signal is then substituted into the polynomial model to obtain the data point at time t in the target restored signal. Each time-varying coefficient in the polynomial model is updated based on the residual model, the data point at time t in the current distortion signal, and each time-varying coefficient in the polynomial model, resulting in an updated polynomial model. This updated polynomial model is used as the polynomial model, where t takes values ​​greater than 0 sequentially until t equals the total number of times in the current distortion signal, thus obtaining the target restored signal. Finally, multi-feature extraction is performed on the target restored signal to obtain the signal feature vector. The signal feature vector is input into a preset machine learning model to obtain the fault classification and detection results. Specifically, by setting the first and second time-varying coefficients in the polynomial model to 0 and taking the square of the absolute value as the residual model, the data point at time t in the current distorted signal is substituted into the polynomial model to obtain the data point at time t in the target restored signal. Based on the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model, each time-varying coefficient in the polynomial model is updated to obtain the updated polynomial model. The updated polynomial model can be used as the polynomial model, and the polynomial model can be dynamically updated based on the constructed residual model. Thus, the dynamically updated polynomial model can dynamically restore the time domain of the current distorted signal. This method can effectively eliminate the dynamic nonlinear influence of the transformer on the time domain of the measured signal, accurately restore the real fault information, and thus improve the detection accuracy of hidden faults in overhead lines. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] in:

[0054] Figure 1 This is a schematic diagram of a line fault detection method according to an embodiment of this application;

[0055] Figure 2 This is a schematic diagram of a line fault detection device according to an embodiment of this application;

[0056] Figure 3 This is a diagram showing the internal structure of a computer device in some embodiments. Detailed Implementation

[0057] 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.

[0058] With the continuous development of power systems, the safety and reliability of overhead distribution lines, as a key component of the power supply network, are receiving increasing attention. However, in actual operation, overhead distribution lines are affected by both external environmental factors and inherent line characteristics, leading to frequent faults such as high-resistance grounding faults and hidden faults. Traditional fault detection methods mainly rely on the direct measurement and analysis of line electrical quantities. However, in complex electromagnetic environments, signals often exhibit nonlinear distortion, making accurate extraction of fault characteristics extremely difficult, resulting in low accuracy in detecting hidden faults.

[0059] As a commonly used signal acquisition device in overhead lines, the non-ideal characteristics of instrument transformers have a dynamic nonlinear effect on the time domain of the measured signal. This dynamic nonlinear effect changes with time and environment, which can lead to distortion of fault signals, making it impossible for the detection system to accurately reproduce the true fault information, thus affecting the accuracy of fault detection.

[0060] To address the aforementioned issues, this application proposes a line fault detection method that can effectively eliminate the dynamic nonlinear influence of the instrument transformer on the time domain of the measured signal, accurately reconstruct the true fault information, thereby improving the detection accuracy of hidden faults in overhead lines. The specific implementation principle will be described in detail in the following embodiments.

[0061] It should be noted that this application describes the nonlinear effects of the non-ideal characteristics of the instrument transformer on the measured signal in three stages: the first stage is the nonlinear effects of the non-ideal characteristics of the instrument transformer on the amplitude and phase of the measured signal, which is generally caused by the characteristics of the instrument transformer such as the core permeability and winding inductance; the second stage is the nonlinear effects of the non-ideal characteristics of the instrument transformer on the time domain of the measured signal, which is generally caused by the characteristics of the core saturation and hysteresis; and the third stage is the dynamic nonlinear effects of the non-ideal characteristics of the instrument transformer on the time domain of the measured signal, which is generally caused by the characteristics of the instrument transformer such as temperature drift and core aging.

[0062] This application provides a method for detecting line faults in its first aspect.

[0063] Please see Figure 1 The diagram below illustrates a line fault detection method according to an embodiment of this application. The method includes:

[0064] Step 110: Obtain the current distortion signal and historical distortion signal of the current transformer, as well as the experimental restoration signal corresponding to the historical distortion signal.

[0065] Here, the signal can be an electrical signal, including but not limited to current signals, voltage signals, etc.

[0066] It should be noted that the current distortion signal of the current transformer can be obtained by measuring the current and / or voltage in the line in real time; the historical distortion signal can be obtained by inputting the experimental restoration signal into the current transformer and then outputting the signal from the current transformer; the experimental restoration signal can be obtained by the operator using a signal generator.

[0067] Regarding the generation method of the experimental restoration signal, in some embodiments, the operating frequency range of the current transformer can be obtained, the signal generator can be controlled to generate a signal, and within the operating frequency range, the frequency of the generated signal can be adjusted sequentially by a preset step size to obtain a signal with multiple frequencies, which is the experimental restoration signal.

[0068] Regarding the value of the operating frequency range, in some embodiments, this application preferably sets the operating frequency range to 0.1Hz to 10kHz.

[0069] Step 120: Construct a polynomial model based on the historical distorted signal and the experimentally restored signal.

[0070] In some embodiments, multiple time-varying coefficients can be determined based on data points at various times in the historical distorted signal and data points at various times in the experimentally restored signal to construct a polynomial model.

[0071] In some embodiments, a multi-order polynomial can be used to determine multiple time-varying coefficients and construct a polynomial model. Multiple coefficients of the multi-order polynomial are determined based on data points at various times in the historical distorted signal and the experimentally restored signal. These multiple coefficients are then used as time-varying coefficients to obtain the constructed polynomial model. The order of the multi-order polynomial can be predetermined by the operator based on extensive experience, experiments, or statistics, or it can be predetermined by the operator according to actual needs.

[0072] It should be noted that the non-ideal characteristics of the current transformer not only have a dynamic nonlinear effect on the time domain of the measured signal, but also have a nonlinear effect on the amplitude and phase of the measured signal. This nonlinear effect on amplitude and phase can also lead to distortion of the fault signal, making it impossible for the detection system to accurately restore the true fault information, thereby affecting the accuracy of fault detection. Therefore, after constructing the polynomial model, that is, after step 120, in some other embodiments, this application can also compensate for the frequency domain distortion of amplitude and phase of the current distorted signal.

[0073] Step 130: Set the first and second time-varying coefficients in the polynomial model to 0, and use the square of their absolute values ​​as the residual model. Substitute the data point at time t in the current distorted signal into the polynomial model to obtain the data point at time t in the target restored signal. Update each time-varying coefficient in the polynomial model according to the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model to obtain the updated polynomial model. Use the updated polynomial model as the polynomial model. Here, t takes integer values ​​greater than 0 in sequence until t equals the total number of times in the current distorted signal to obtain the target restored signal.

[0074] It should be noted that, due to the non-ideal characteristics of the transformer, the measured signal has a dynamic nonlinear effect in the time domain. To address this, this application constructs a polynomial model, sets the first and second time-varying coefficients in the polynomial model to 0, and uses the square of their absolute values ​​as the residual model. The data point at time t in the current distorted signal is substituted into the polynomial model to obtain the data point at time t in the target restored signal. Based on the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model, each time-varying coefficient in the polynomial model is updated. The updated polynomial model is then used as the new polynomial model. In other words, by dynamically updating the polynomial model, dynamic compensation for time-domain distortion is performed on the data points at each time in the current distorted signal, thereby obtaining the target restored signal through dynamic compensation for time-domain distortion.

[0075] It should be further explained that, since each time-varying coefficient in the polynomial model is updated at each time step, and the residual model is based on the polynomial model, correspondingly, each time-varying coefficient in the residual model is also updated along with each time-varying coefficient in the polynomial model.

[0076] Furthermore, it should be noted that in step 130, in some other embodiments, this application may also compensate for time-domain distortion of the current distorted signal.

[0077] In other embodiments, the data point at time t in the current distorted signal can be substituted into the polynomial model to obtain the data point at time t in the target restored signal. Here, t takes the value of an integer greater than 0 in turn until t is equal to the total number of times in the current distorted signal, thus obtaining the target restored signal.

[0078] In this application, a polynomial model is constructed by solving for the time-varying coefficients. Then, the data points at each time step in the current distorted signal are sequentially substituted into the polynomial model. Time-domain distortion compensation is performed on the data points at each time step in the current distorted signal. Through time-domain distortion compensation, the target restored signal is obtained. This can effectively eliminate the nonlinear influence of the current transformer on the measured signal in the time domain, accurately restore the real fault information, and thus improve the detection accuracy of hidden faults in overhead lines.

[0079] In some embodiments, the determination of the data point at time t in the target reconstruction signal can utilize the expression of a polynomial model. The target reconstruction signal is obtained; where, To reconstruct the data point at time t in the target signal, For the (n+1)th time-varying coefficient, Subtract 1 from the total number of time-varying coefficients. Let t be the data point at time t in the current distorted signal.

[0080] In this application, the data points of the target reconstructed signal at each moment are accurately determined by a polynomial model expression, which effectively improves the accuracy and reliability of signal reconstruction.

[0081] Step 140: Perform multi-feature extraction on the target restored signal to obtain the signal feature vector, and input the signal feature vector into the preset machine learning model to obtain the fault classification and detection result.

[0082] Here, the preset machine learning model refers to a pre-trained model used to predict the output fault classification detection result based on the input signal feature vector.

[0083] For signal feature types extracted from multiple features contained in a signal feature vector, in some embodiments, the signal feature vector includes spectral center frequency, bandwidth, short-time energy, time-domain waveform change, impulse index, kurtosis, margin factor, waveform factor, frequency standard deviation, mean square frequency, energy entropy, etc.

[0084] Regarding the acquisition method of the preset machine learning model, in some embodiments, a large number of experimental reconstruction signals can be acquired, and multi-feature extraction can be performed on the experimental reconstruction signals to obtain experimental signal feature vectors. The labels corresponding to the experimental signal feature vectors can be determined, and then a large number of experimental signal feature vectors and their corresponding labels can be input into the initial machine learning model for training to obtain the preset machine learning model. In other embodiments, a large number of historical distortion signals can also be acquired (i.e., they can be obtained based on experimental reconstruction signals or from line measurements), and the signals can be reconstructed through the above steps 110 to 130 to obtain historical reconstruction signals. Then, multi-feature extraction can be performed on the historical reconstruction signals to obtain historical signal feature vectors, and the labels corresponding to the historical signal feature vectors can be determined. Finally, a large number of historical signal feature vectors and their corresponding labels can be input into the initial machine learning model for training to obtain the preset machine learning model.

[0085] In this embodiment, by setting the first and second time-varying coefficients in the polynomial model to 0 and squaring their absolute values ​​to obtain the residual model, and then substituting the data point at time t in the current distorted signal into the polynomial model, the data point at time t in the target restored signal is obtained. Based on the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model, each time-varying coefficient in the polynomial model is updated to obtain the updated polynomial model. Using the updated polynomial model as the polynomial model, the polynomial model can be dynamically updated based on the constructed residual model. This allows the dynamically updated polynomial model to dynamically restore the time domain of the current distorted signal. This method can effectively eliminate the dynamic nonlinear influence of the transformer on the time domain of the measured signal, accurately restore the real fault information, and thus improve the detection accuracy of hidden faults in overhead lines.

[0086] In addition to the previously mentioned advantages of effectively eliminating the influence of instrument transformers on the time-domain dynamic nonlinearity of measurement signals, accurately restoring true fault information, and improving the detection accuracy of concealed faults in overhead lines, this line fault detection method also has the following advantages: Comprehensive consideration of multiple nonlinear effects: It elaborates on the nonlinear effects of the non-ideal characteristics of instrument transformers on the measurement signals in terms of amplitude, phase, and time-domain static and dynamic aspects. These factors are comprehensively considered in the method design. After constructing a polynomial model, frequency-domain distortion compensation for amplitude and phase, as well as time-domain distortion compensation, are performed on the current distorted signal. The distorted signal is processed from multiple dimensions, making the restored target signal closer to the actual signal. The method utilizes realistic signals, improving the completeness and accuracy of signal reconstruction. It dynamically updates the polynomial model by constructing a residual model and dynamically updating the time-varying coefficients of the polynomial model based on the residual model, the current distorted signal data points, and the time-varying coefficients in the polynomial model. This dynamic update mechanism adapts to the non-ideal characteristics of the transformer changing with time and environment, ensuring the polynomial model maintains a good fit and thus more accurately reconstructs the distorted signal at different times, enhancing the method's applicability in complex and variable environments. Furthermore, it accurately determines the target reconstructed signal data points by using the polynomial model expression to precisely determine the data points of the target reconstructed signal at each time point, providing a basis for subsequent... Fault detection provides a high-quality signal foundation, effectively improving the accuracy and reliability of signal reconstruction and helping to analyze line fault conditions more accurately. Multi-feature extraction enhances fault identification capabilities: multi-feature extraction of the target reconstructed signal yields signal feature vectors containing various feature types such as spectral center frequency, bandwidth, and short-time energy. These rich features reflect signal characteristics from different perspectives, providing more comprehensive and detailed information for fault classification and detection. This helps machine learning models more accurately identify different types of faults, improving the accuracy and reliability of fault detection. Flexible acquisition of preset machine learning models: two methods are provided for acquiring preset machine learning models. Training can be done through experimentally reconstructed signals or using historically reconstructed signals obtained from historically distorted signals. This flexibility allows for the selection of appropriate methods to build models based on available data resources and actual conditions in practical applications, improving the method's practicality and operability. Furthermore, the use of a large amount of experimental or historical data for model training helps improve the model's generalization ability, enabling it to achieve better fault detection results in different scenarios. Adaptability to complex electromagnetic environments: traditional fault detection methods suffer from difficulties in fault feature extraction and low detection accuracy in complex electromagnetic environments due to signal nonlinear distortion. This method effectively eliminates the various effects caused by the non-ideal characteristics of instrument transformers, restores the true fault information, and can maintain high fault detection accuracy even in complex electromagnetic environments, thereby enhancing the operational stability and reliability of power systems in complex environments.

[0087] In one feasible implementation, step 130 in the above embodiment, which updates each time-varying coefficient in the polynomial model based on the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model to obtain an updated polynomial model, includes: substituting the data point at time t in the current distorted signal into the residual model to obtain the residual at time t; determining the nth time-varying coefficient at time t+1 based on the residual at time t, the data point at time t in the current distorted signal, and the nth time-varying coefficient in the polynomial model; wherein n takes integer values ​​greater than -1 sequentially until n equals the total number of time-varying coefficients minus 1, to obtain each time-varying coefficient at time t+1; and updating each time-varying coefficient in the polynomial model based on each time-varying coefficient at time t+1 to obtain the updated polynomial model.

[0088] In this embodiment, by dynamically updating the time-varying coefficients of the polynomial model, dynamic nonlinear compensation in the time domain for the current distorted signal is achieved, significantly improving the signal restoration accuracy and thus eliminating the influence of the non-ideal characteristics of the transformer on the detection accuracy.

[0089] Understandably, residual-driven dynamic adjustment involves substituting the current distorted signal into the residual model (constructed based on the squared absolute value of a polynomial model). By calculating the residual at the current time step, and combining it with the current distorted signal value and the current time-varying coefficients, the time-varying coefficients at the next time step are dynamically derived. For example, the residual at time t is used to update the nth time-varying coefficient at time t+1. This process iterates until all coefficients are updated. The residual model directly reflects the degree of signal distortion, ensuring that the direction of time-varying coefficient adjustment is consistent with the actual error, and avoiding the failure of the static model due to environmental changes. Point-by-point optimization of time-varying coefficients involves using a recursive update mechanism (e.g., n iterates from 0 to the total number of time-varying coefficients). The time-varying coefficients at each time step are optimized based on the data from previous time steps and the current residual. For example, the residual at time t+1 is used to update the nth time-varying coefficient. The updating of time-varying coefficients depends on the residual at time t, the current distorted signal value, and the preceding coefficients, forming a closed-loop feedback. The coefficient updates are matched with the time-varying characteristics of the signal in real time, effectively compensating for dynamic nonlinear distortion caused by temperature drift, core aging, etc., and improving the model's adaptability. Polynomial model iterative reconstruction: The updated time-varying coefficients are substituted into the original polynomial model to generate a new model that adapts to the signal characteristics at the current time, and it is applied cyclically to the data compensation at subsequent times. For example, after each time-time coefficient update is completed, the new model is used to process the data at the next time-time until the entire signal time period is covered. The dynamic reconstruction of the model ensures that the compensation at each time-time is based on the latest signal characteristics, eliminates accumulated errors, and the time-domain dynamic distortion compensation accuracy of the final restored signal (target restored signal) is significantly better than that of the static model.

[0090] Furthermore, this update method achieves the ultimate elimination of the time-domain dynamic nonlinear effects of the instrument transformer through a three-step closed loop of residual quantization distortion, recursive optimization coefficients, and dynamic model reconstruction. This enables the fault signal restoration accuracy to break through the limitations of traditional static compensation, providing a more reliable data foundation for the detection of hidden faults in overhead lines.

[0091] In one feasible implementation, substituting the data point at time t in the current distorted signal into the residual model to obtain the residual at time t in the above embodiment includes:

[0092] Using the expression of the residual model Obtain the residual at time t;

[0093] in, Let be the residual at time t. This refers to the (n+1)th time-varying coefficient in the polynomial model. Subtract 1 from the total number of time-varying coefficients in the polynomial model. Let t be the data point at time t in the current distorted signal.

[0094] In this embodiment, the residual at time t is determined by using the residual model expression, which provides a key basis for dynamically updating the time-varying coefficients of the polynomial model, effectively improving the signal restoration accuracy and thus eliminating the influence of the non-ideal characteristics of the transformer on the detection accuracy.

[0095] Understandably, in the process of compensating for the impact of non-ideal characteristics of current transformers on the time-domain dynamic nonlinearity of measurement signals, a residual model is constructed and combined with a polynomial model. The residual at time t is accurately calculated through the residual model expression. This residual reflects the degree of deviation between the current distorted signal and the actual signal at that time. Based on this residual value, combined with the data point at time t in the current distorted signal and each time-varying coefficient in the polynomial model, the time-varying coefficients at time t+1 can be accurately determined. The polynomial model is dynamically adjusted based on these updated time-varying coefficients, so that the polynomial model can better adapt to the changes of the signal at different times. This allows for more accurate dynamic compensation of time-domain distortion for the data points at each time in the current distorted signal, ultimately obtaining a signal that is closer to the real fault information, and significantly improving the detection accuracy of hidden faults in overhead lines.

[0096] In one feasible implementation, determining the nth time-varying coefficient at time t+1 based on the residual at time t, the data point at time t in the current distorted signal, and the nth time-varying coefficient in the polynomial model, in the above embodiment includes:

[0097] Using formula Determine the nth time-varying coefficient at time t+1;

[0098] in, This is the nth time-varying coefficient at time t+1. For the nth time-varying coefficient, To preset the learning rate, Let be the residual at time t. Let t be the data point at time t in the current distorted signal.

[0099] The preset learning rate can be set in advance by the operator based on a large amount of experience, experiments or statistics. Of course, it can also be set in advance by the operator according to actual needs.

[0100] In this embodiment, the time-varying coefficients of the polynomial model are adaptively updated using a dynamic formula, which significantly improves the accuracy and efficiency of time-domain dynamic nonlinear compensation.

[0101] Understandably, the dynamic adjustment mechanism involves: introducing a preset learning rate into the formula, dynamically adjusting the time-varying coefficients through the product of the residual and the current data point, enabling the model to optimize compensation parameters in real time according to the degree of signal distortion, avoiding slow convergence or oscillation problems; residual-driven optimization: the residual directly reflects the deviation between the model's prediction and the actual signal, serving as a weight term to ensure that the update direction of the time-varying coefficients always points to the goal of minimizing distortion, thereby improving the targeting of compensation; historical data utilization: combining the residual from the previous moment with the current data point, forming a feedback loop in the time series, enabling the model to capture the trend of the dynamic nonlinear characteristics of the transformer changing with time / environment, enhancing the robustness of compensation; parameter adaptive capability: the preset learning rate can be flexibly configured, allowing operators to adjust the update sensitivity according to the actual scenario (such as temperature drift rate, core aging degree), balancing compensation accuracy and computational efficiency to meet diverse application needs.

[0102] Furthermore, this design tightly integrates the physical layer distortion characteristics with the algorithm layer dynamic compensation through mathematical formulas, breaking through the limitations of traditional static models in compensating for dynamic nonlinear effects, and providing a more accurate signal restoration basis for overhead line fault detection.

[0103] In one feasible implementation, step 130 in the above embodiment, which involves substituting the data point at time t in the current distorted signal into the polynomial model to obtain the data point at time t in the target restored signal, includes:

[0104] Expression using a polynomial model Determine the target reconstruction signal;

[0105] in, To reconstruct the data point at time t in the target signal, For the (n+1)th time-varying coefficient, Subtract 1 from the total number of time-varying coefficients. Let t be the data point at time t in the current distorted signal.

[0106] In this embodiment, the data points of the target restored signal at each moment are accurately determined by the polynomial model expression, which significantly improves the accuracy and reliability of time-domain dynamic nonlinear compensation and eliminates the influence of the non-ideal characteristics of the transformer on the detection accuracy.

[0107] Understandably, the system employs dynamic compensation for time-varying characteristics: the multinomial model dynamically updates time-varying coefficients to track the changes in the non-ideal characteristics of the transformer over time and with the environment, ensuring that each data point is compensated based on the current optimal model, thus avoiding the accumulation of errors caused by characteristic drift in the static model; residual-driven adaptive optimization: the residual model is used to quantify and compensate for errors, and the time-varying coefficients are dynamically adjusted in conjunction with a preset learning rate, so that the model parameters gradually approach the true values, significantly reducing the impact of time-domain dynamic nonlinear distortion and improving the accuracy of signal reconstruction; full-process closed-loop correction: from initial signal acquisition to final reconstruction, through layered processing of frequency domain distortion compensation, time-domain static compensation, and time-domain dynamic compensation, a complete signal reconstruction closed loop is formed, ensuring that the final output target reconstructed signal is highly close to the real fault information, providing a reliable basis for fault detection.

[0108] In one feasible implementation, step 120 in the above embodiment, which involves constructing a polynomial model based on the historical distortion signal and the experimental restored signal, includes: standardizing the historical distortion signal and the experimental restored signal to obtain a standard distortion signal and a standard restored signal; using the least squares method, determining multiple time-varying coefficients based on the data points at each time point in the standard distortion signal and the data points at each time point in the standard restored signal; and constructing a polynomial model based on the time-varying coefficients.

[0109] In some embodiments, the determination of the standard distortion signal and the standard restored signal can be achieved by standardizing the historical distortion signal and the experimental restored signal respectively, thereby obtaining the standard distortion signal and the standard restored signal. The first mean and the first standard deviation of the historical distortion signal are determined, and the second mean and the second standard deviation of the experimental restored signal are determined. Then, based on the first mean and the first standard deviation, the data points at each time point in the historical distortion signal are standardized to obtain the standard distortion signal, and based on the second mean and the second standard deviation, the data points at each time point in the experimental restored signal are standardized to obtain the standard restored signal.

[0110] In this application, the standardization process based on mean and standard deviation improves the standardization and comparability of signal data, laying a solid foundation for the subsequent accurate determination of time-varying coefficients and the construction of polynomial models.

[0111] In this embodiment, the time-varying coefficients of the polynomial model are determined by standardization and the least squares method, which improves the efficiency and accuracy of model construction and thus enhances the effect of time-domain distortion compensation or time-domain distortion dynamic compensation.

[0112] Understandably, standardizing the historical distorted signal and the experimentally reconstructed signal separately yields the standard distorted signal and the standard reconstructed signal. Standardization eliminates dimensional influences and outlier interference, making the signal data more standardized and uniform, providing a more stable data foundation for subsequent model construction. Using the least squares method, multiple time-varying coefficients are determined based on data points at various times in the standard distorted signal and the standard reconstructed signal. The least squares method is a classic mathematical optimization method that finds the best function match for the data by minimizing the sum of squared errors, effectively handling noise and errors in the signal data, making the determined time-varying coefficients more accurate and reliable. A polynomial model is then constructed based on these accurate time-varying coefficients. Because the accuracy of the time-varying coefficients is guaranteed, the constructed polynomial model can more accurately describe the relationship between the historical distorted signal and the experimentally reconstructed signal of the instrument transformer. Therefore, when performing time-domain distortion compensation or dynamic time-domain distortion compensation on the target reconstructed signal, it can more effectively eliminate the nonlinear influence or dynamic nonlinearity of the non-ideal characteristics of the instrument transformer on the signal time domain, improving the accuracy of fault detection.

[0113] In one feasible implementation, step 120 in the above embodiments, after constructing a polynomial model based on the historical distortion signal and the experimental restored signal, further includes: performing Fourier transforms on the current distortion signal, the historical distortion signal, and the experimental restored signal respectively to obtain the current frequency domain distortion signal, the historical frequency domain distortion signal, and the experimental frequency domain restored signal; constructing a frequency response transfer function based on the historical frequency domain distortion signal and the experimental frequency domain restored signal; transforming the frequency response transfer function to obtain a frequency domain form function, and determining the target frequency domain restored signal based on the amplitude attenuation coefficient and phase offset of the frequency domain form function, as well as the amplitude and phase of each frequency in the current frequency domain distortion signal; performing an inverse Fourier transform on the target frequency domain restored signal to obtain an initial restored signal; and using the initial restored signal as the current distortion signal.

[0114] It should be noted that the frequency response transfer function of this application belongs to the s-domain of the Laplace transform, while the frequency form function belongs to the frequency domain of the Fourier transform. Therefore, in some embodiments, the frequency response transfer function can be converted between the s-domain and the frequency domain to obtain the frequency domain form function; where s is the Laplace operator.

[0115] It should be further noted that, since the non-ideal characteristics of the transformer have a non-linear effect on the amplitude and phase of the measured signal, this application compensates for the amplitude and phase of each frequency in the current frequency-domain distorted signal by using the amplitude attenuation coefficient and phase offset of the frequency-domain form function, so as to obtain the target frequency-domain restored signal through the compensation of frequency-domain distortion.

[0116] Furthermore, it should be noted that the current distortion signal initially measured by the current transformer in this application is a time-domain signal, while the target frequency-domain restored signal is a frequency-domain signal. Therefore, in some embodiments, the target frequency-domain restored signal can be subjected to inverse Fourier transform in both the frequency and time domains to obtain the initial restored signal in the time domain.

[0117] Regarding the construction method of the frequency response transfer function, in some embodiments, the frequency response of each frequency can be determined based on the frequency domain components of each frequency in the historical frequency domain distortion signal and the frequency domain components of each frequency in the experimental frequency domain reconstruction signal. Based on the frequency response of each frequency and the preset objective function, multiple numerator transfer coefficients and multiple denominator transfer coefficients are obtained to construct the frequency response transfer function. The preset objective function can be obtained and preset by the operator based on a large amount of experience, experiments or statistics. Of course, it can also be preset by the operator according to actual needs.

[0118] It should be noted that the purpose of the preset objective function is to find the multiple numerator transfer coefficients and multiple denominator transfer coefficients corresponding to each frequency response when the frequency response of each frequency reaches its minimum, so that the frequency response transfer function can be constructed based on the multiple numerator transfer coefficients and multiple denominator transfer coefficients.

[0119] Furthermore, regarding the construction methods of the preset objective function and the frequency response transfer function, in some embodiments, the transfer function can be constructed by having multiple polynomials in both the numerator and denominator. Then, the preset objective function is constructed based on the transfer function and the frequency response at each frequency. The frequency response at each frequency is known, while in the transfer function, only the coefficients of the multiple polynomials in the numerator and denominator are unknown and adjustable. Therefore, by adjusting the coefficients of the multiple polynomials in the numerator and denominator to minimize the frequency response at each frequency, the adjusted coefficients of the multiple polynomials in the numerator are used as numerator transfer coefficients, and the coefficients of the multiple polynomials in the denominator are used as denominator transfer coefficients. Substituting these multiple numerator and denominator transfer coefficients into the transfer function yields the constructed frequency response transfer function.

[0120] Furthermore, regarding the construction method of the frequency response transfer function, in some embodiments, the quotient between the frequency domain component of the d-th frequency in the historical frequency domain distorted signal and the frequency domain component of the d-th frequency in the experimental frequency domain reconstructed signal can be used as the frequency response of the d-th frequency. Here, d takes integer values ​​greater than 0 in sequence until d equals the total number of frequencies in the historical frequency domain distorted signal or the experimental frequency domain reconstructed signal, thus obtaining the frequency response of each frequency. Then, using the LM method, under the condition of satisfying the preset objective function, the transfer coefficients of each numerator and each denominator in the preset objective function are adjusted according to the frequency response of each frequency to minimize the function value of the preset objective function, thus obtaining multiple transfer coefficients of each numerator and multiple transfer coefficients of each denominator. Finally, the frequency response transfer function is constructed based on the transfer coefficients of each numerator and each denominator. Here, LM stands for Levenberg-Marquardt, also known as the Levenberg-Marquardt method.

[0121] In this embodiment, the construction process of the frequency response transfer function is optimized by using the LM method, which significantly improves the convergence speed and accuracy of the transfer coefficient solution. At the same time, it enhances the model's adaptability to complex nonlinear distortions, providing a more reliable foundation for subsequent signal restoration and fault detection.

[0122] Furthermore, the expression for the preset objective function is:

[0123] ;in, In the above formula, The function value of the preset objective function, The total number of frequencies in the historical frequency domain distorted signal or the experimental frequency domain restored signal. Let d be the frequency response at the d-th frequency. Let d be the Laplace operator corresponding to the d-th frequency. The transfer coefficient of the (m+1)th molecule. Subtract 1 from the total number of numerator or denominator transfer coefficients. The transitivity coefficient of the (m+1)th denominator. The imaginary unit, Pi This is the d-th frequency.

[0124] In this embodiment, by constructing a composite objective function based on frequency response, the fitting accuracy of the transfer function to the non-ideal characteristics of the transformer is significantly improved, while the anti-interference capability of the model in complex electromagnetic environments is enhanced, providing a more reliable mathematical basis for subsequent signal restoration.

[0125] In this embodiment, the frequency response of each frequency is determined based on the frequency components of each frequency in the historical frequency-domain distorted signal and the frequency components of each frequency in the experimental frequency-domain restored signal. Multiple numerator transfer coefficients and multiple denominator transfer coefficients are obtained based on the frequency responses of each frequency and a preset objective function to construct a frequency response transfer function. Then, based on the amplitude attenuation coefficient and phase offset of the frequency domain transfer function corresponding to the constructed frequency response transfer function, the amplitude and phase of the current frequency-domain distorted signal are restored. This not only effectively eliminates the nonlinear effects of the transformer on the measured signal in the time domain, as well as the dynamic nonlinear effects in the time domain, but also further eliminates its nonlinear effects on the amplitude and phase of the measured signal, thereby restoring the true fault information with extreme accuracy and improving the detection accuracy of hidden faults in overhead lines.

[0126] In one feasible implementation, the expression for the frequency response transfer function in the above embodiments is:

[0127] ;

[0128] in, ;

[0129] In the above formula, The frequency response transfer function, For the Laplace operator, The transfer coefficient of the (m+1)th molecule. Subtract 1 from the total number of numerator or denominator transfer coefficients. The transitivity coefficient of the (m+1)th denominator. The imaginary unit, Pi For frequency.

[0130] In this embodiment, by constructing a frequency response transfer function based on the Laplace domain, high-precision mathematical modeling of the non-ideal characteristics of the transformer is achieved, which significantly improves the amplitude / phase accuracy of signal reconstruction and enhances the model's adaptability to nonlinear distortion, providing a reliable signal basis for subsequent fault feature extraction.

[0131] Understandably, the full-band dynamic compensation capability is achieved by using a multi-order polynomial structure in the transfer function, which can accurately fit the amplitude and phase frequency characteristics of the transformer within the operating frequency range. The complex domain parameter optimization mechanism is achieved by introducing the imaginary unit and the Laplace operator, which allows the transfer function to simultaneously optimize both the real part (amplitude response) and the imaginary part (phase response).

[0132] In one feasible implementation, the expression for the frequency domain form function in the above embodiments is:

[0133] ;

[0134] in, It is a function in the frequency domain. The amplitude attenuation coefficient at frequency f is... It is a natural constant. The imaginary unit, This is the phase offset at frequency f.

[0135] In this embodiment, an independent compensation mechanism for amplitude attenuation and phase shift is implemented by constructing a frequency domain form function, which significantly improves the accuracy and flexibility of frequency domain distortion compensation. At the same time, it provides high-precision initial conditions for subsequent operations and effectively solves the distortion problem caused by the non-ideal characteristics of the transformer.

[0136] Understandably, the amplitude-phase decoupling compensation mechanism decouples the amplitude attenuation coefficient and phase offset into independent parameters in the frequency domain form, allowing for precise compensation for amplitude-frequency and phase-frequency characteristic distortions of the transformer, respectively. The complex domain dynamic modeling capability introduces natural constants and imaginary units, allowing the frequency domain form to be expressed in complex form, thus simultaneously representing the real part (amplitude) and imaginary part (phase) of the signal. This modeling method enables the model to accurately match the nonlinear offset caused by the transformer. The frequency band adaptive compensation characteristic, where both the amplitude attenuation coefficient and phase offset are functions of frequency, dynamically adapts to the differences in nonlinear characteristics of the transformer in different frequency bands.

[0137] In one feasible implementation, determining the target frequency domain reconstructed signal based on the amplitude attenuation coefficient and phase offset of the frequency domain formal function, and the amplitude and phase of each frequency in the current frequency domain distorted signal, in the above embodiments includes:

[0138] Using formula Determine the target frequency domain reconstructed signal;

[0139] in, To reconstruct the frequency domain component of the target frequency domain signal at the k-th frequency, For the kth frequency, Let be the amplitude of the k-th frequency in the current frequency domain distorted signal. For frequency f k The amplitude attenuation coefficient, It is a natural constant. The imaginary unit, The phase of the k-th frequency in the current frequency domain distorted signal. For frequency f k The phase offset.

[0140] In this embodiment, the amplitude attenuation and phase shift caused by the mutual inductor are accurately and independently compensated by the frequency domain component compensation formula, which significantly improves the restoration accuracy of the frequency domain distortion signal and provides a high-fidelity signal basis for subsequent fault feature extraction.

[0141] Understandably, the amplitude-phase decoupling compensation mechanism decouples the amplitude compensation term from the phase compensation term in the formula, allowing for independent optimization of the amplitude-frequency characteristic distortion and phase-frequency characteristic distortion of the current transformer, respectively. The complex domain dynamic modeling capability introduces natural constants and imaginary units, allowing the formula to be expressed in complex form, thus simultaneously representing the real part (amplitude) and the imaginary part (phase) of the signal. This modeling method enables the model to accurately match the nonlinear offset caused by the current transformer. The frequency band adaptive compensation characteristic is also noteworthy: both the amplitude attenuation coefficient and the phase offset are functions of frequency, allowing for dynamic adaptation to the differences in nonlinear characteristics of the current transformer in different frequency bands.

[0142] In one feasible implementation, the process of performing a Fourier transform on the current distorted signal to obtain the current frequency domain distorted signal in the above embodiments includes: performing a Fourier transform on the current distorted signal to obtain an initial frequency domain distorted signal; determining the maximum frequency based on the experimentally restored signal; determining the cutoff frequency based on the maximum frequency and a preset safety factor; and performing frequency filtering on the initial frequency domain distorted signal based on the cutoff frequency to obtain the current frequency domain distorted signal.

[0143] The preset safety factor can be set in advance by the operator based on a large amount of experience, experiments or statistics. Of course, it can also be set in advance by the operator according to actual needs.

[0144] Regarding the value of the preset safety factor, in some embodiments, this application preferably sets the preset safety factor to 1.2.

[0145] Regarding the method for determining the maximum frequency, in some embodiments, the experimental restored signal can be subjected to Fourier transform to obtain the experimental frequency domain restored signal, and then the highest frequency among the frequency domain components of the experimental frequency domain restored signal can be taken as the maximum frequency; in other embodiments, the operating frequency range of the current transformer can also be used to determine the maximum frequency, such as when the operating frequency range of the current transformer is 0.1Hz to 10kHz, 10kHz can be taken as the maximum frequency.

[0146] Regarding the method for determining the cutoff frequency, in some embodiments, the product of the maximum frequency and the preset safety factor can be used as the cutoff frequency.

[0147] In some embodiments, the frequency components with frequencies greater than the cutoff frequency in the frequency components of the initial frequency-domain distortion signal can be removed to obtain the current frequency-domain distortion signal.

[0148] In this embodiment, the frequency filtering mechanism effectively suppresses high-frequency noise interference, significantly improving the signal-to-noise ratio of the fault signal and the accuracy of subsequent feature extraction.

[0149] Understandably, the adaptive frequency band constraint—determining the maximum frequency based on the experimental frequency domain reconstructed signal and setting the cutoff frequency with a safety factor of 1.2—preserves the effective fault characteristic frequency band while avoiding high-frequency noise contamination of the signal, making it more adaptable than a fixed frequency band scheme; improved anti-aliasing effect—precisely eliminating components outside the cutoff frequency after Fourier transform, effectively preventing high-frequency noise from aliasing into the baseband frequency band, providing a cleaner input signal for subsequent frequency domain distortion compensation; and transformer characteristic adaptation—the cutoff frequency mechanism can automatically match the equipment characteristics to the differences in the operating frequency bands of different transformers, avoiding the characteristic loss or noise introduction problems caused by a fixed frequency band.

[0150] In one feasible implementation, step 140 in the above embodiment, which involves extracting multiple features from the target reconstructed signal to obtain a signal feature vector, includes: performing wavelet transform on the target reconstructed signal to obtain a first time-frequency diagram; performing short-time Fourier transform on the target reconstructed signal to obtain a second time-frequency diagram; performing multiple feature extraction on the first and second time-frequency diagrams to obtain the spectral center frequency, bandwidth, short-time energy, and time-domain waveform change; and using the spectral center frequency, bandwidth, short-time energy, and time-domain waveform change as the signal feature vector.

[0151] In this embodiment, the feature extraction is fused using a multi-time-frequency analysis method, which significantly improves the comprehensiveness of fault signal feature representation and the accuracy of fault classification and detection.

[0152] Understandably, the enhanced time-frequency joint representation combines the complementary characteristics of wavelet transform and short-time Fourier transform, retaining the time-frequency localization analysis capability of wavelet transform while utilizing the frequency resolution advantage of short-time Fourier transform, thus constructing a more complete signal time-frequency feature space. Multi-dimensional feature fusion extracts four core features: spectral center frequency, bandwidth, short-time energy, and time-domain waveform variation, forming a multi-dimensional feature vector covering the frequency, time, and energy domains, effectively solving the problem of insufficient representation of complex fault modes by single features. Improved anti-interference capability enhances the ability of time-frequency analysis methods to handle non-stationary fault signals. Natural adaptability: Time-frequency plot processing can suppress noise interference, and the extracted bandwidth and time-domain waveform changes are more sensitive to hidden faults; Enhanced feature interpretability: All four extracted features have clear physical meaning and are strongly correlated with fault types, providing interpretable input features for machine learning models and helping to improve the credibility of fault classification results; Optimized computational efficiency: A standardized time-frequency transformation process is adopted, and both wavelet transform and short-time Fourier transform can be implemented with fast algorithms. The feature extraction process maintains linear complexity, ensuring feature quality while meeting the requirements of real-time detection.

[0153] In a second aspect, this application provides a line fault detection device.

[0154] Please see Figure 2 This is a schematic diagram of a line fault detection device according to an embodiment of this application. The device 210 includes:

[0155] The acquisition module 211 is used to acquire the current distortion signal and historical distortion signal of the current transformer, as well as the experimental reconstruction signal corresponding to the historical distortion signal;

[0156] Module 212 is used to construct a polynomial model based on historical distorted signals and experimentally restored signals;

[0157] The substitution and update module 213 is used to set the first and second time-varying coefficients in the polynomial model to 0, and after taking the square of the absolute value, it becomes the residual model. The data point at time t in the current distorted signal is substituted into the polynomial model to obtain the data point at time t in the target restored signal. Based on the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model, each time-varying coefficient in the polynomial model is updated to obtain the updated polynomial model. The updated polynomial model is used as the polynomial model. Here, t takes integer values ​​greater than 0 in sequence until t is equal to the total number of times in the current distorted signal to obtain the target restored signal.

[0158] The extraction and prediction module 214 is used to extract multiple features from the target restored signal to obtain the signal feature vector, and input the signal feature vector into the preset machine learning model to obtain the fault classification detection result.

[0159] In this embodiment of the application, the relevant contents of the above-mentioned acquisition module 211, construction module 212, substitution and update module 213 and extraction and prediction module 214 can be found in the following references. Figure 1 The contents of the illustrated embodiments will not be repeated here.

[0160] It should be noted that the device 210 of this application also includes other modules. It can be understood that the method of this application and the device 210 have a one-to-one correspondence. Therefore, the other modules of the device 210 of this application are the contents corresponding to the method of this application in the above embodiments.

[0161] In this embodiment, by setting the first and second time-varying coefficients in the polynomial model to 0 and squaring their absolute values ​​to obtain the residual model, and then substituting the data point at time t in the current distorted signal into the polynomial model, the data point at time t in the target restored signal is obtained. Based on the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model, each time-varying coefficient in the polynomial model is updated to obtain the updated polynomial model. Using the updated polynomial model as the polynomial model, the polynomial model can be dynamically updated based on the constructed residual model. This allows the dynamically updated polynomial model to dynamically restore the time domain of the current distorted signal. This device can effectively eliminate the dynamic nonlinear influence of the transformer on the time domain of the measured signal, accurately restore the real fault information, and thus improve the detection accuracy of hidden faults in overhead lines.

[0162] In addition to the previously mentioned advantages of effectively eliminating the influence of instrument transformers on the time-domain dynamic nonlinearity of measurement signals, accurately restoring true fault information, and improving the detection accuracy of hidden faults in overhead lines, this line fault detection device also has the following advantages: It comprehensively considers multiple nonlinear effects: It elaborates on the nonlinear effects of the non-ideal characteristics of instrument transformers on the measurement signals in terms of amplitude, phase, and time-domain static and dynamic aspects. These factors are comprehensively considered in the device design. After constructing a polynomial model, it also performs frequency-domain distortion compensation for amplitude and phase, as well as time-domain distortion compensation, on the current distorted signal. This multi-dimensional processing of the distorted signal makes the restored target signal more accurate. The use of realistic signals improves the completeness and accuracy of signal reconstruction. Dynamic updates to the polynomial model: By constructing a residual model and dynamically updating the time-varying coefficients of the polynomial model based on the residual model, the current distorted signal data points, and the time-varying coefficients in the polynomial model, this dynamic update mechanism adapts to the non-ideal characteristics of the transformer changing with time and environment, ensuring the polynomial model maintains a good fit and thus more accurately reconstructs distorted signals at different times, enhancing the device's applicability in complex and variable environments. Precise determination of target reconstructed signal data points: The polynomial model expression is used to precisely determine the data points of the target reconstructed signal at each time point, providing a basis for subsequent... Fault detection provides a high-quality signal foundation, effectively improving the accuracy and reliability of signal reconstruction and helping to analyze line fault conditions more accurately. Multi-feature extraction enhances fault identification capabilities: multi-feature extraction of the target reconstructed signal yields signal feature vectors containing various feature types such as spectral center frequency, bandwidth, and short-time energy. These rich features reflect signal characteristics from different perspectives, providing more comprehensive and detailed information for fault classification and detection. This helps machine learning models more accurately identify different types of faults, improving the accuracy and reliability of fault detection. Flexible acquisition of preset machine learning models: two methods are provided for acquiring preset machine learning models. Training can be done through experimentally reconstructed signals or using historically reconstructed signals obtained from historically distorted signals. This flexibility allows for the selection of appropriate methods to build models based on available data resources and actual conditions in practical applications, improving the device's practicality and operability. Furthermore, the use of a large amount of experimental or historical data for model training helps improve the model's generalization ability, enabling it to achieve better fault detection results in different scenarios. Adaptability to complex electromagnetic environments: Traditional fault detection devices suffer from difficulty in fault feature extraction and low detection accuracy in complex electromagnetic environments due to signal nonlinear distortion. This device effectively eliminates the various effects caused by the non-ideal characteristics of the instrument transformer, restores the true fault information, and can maintain high fault detection accuracy even in complex electromagnetic environments, thereby enhancing the operational stability and reliability of the power system in complex environments.

[0163] In a third aspect, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the line fault detection method as described in any one of the first aspects.

[0164] This application provides a computer device in a fourth aspect, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform a line fault detection method as described in any one of the first aspects.

[0165] Figure 3 The diagram illustrates the internal structure of a computer device in some embodiments. This computer device may specifically be a terminal, a server, or a gateway. Figure 3 As shown, the computer device includes a processor, memory, and network interface connected via a system bus.

[0166] The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When executed by a processor, this computer program causes the processor to perform the steps in the above method embodiments. The internal memory may also store a computer program, which, when executed by a processor, causes the processor to perform the steps in the above method embodiments. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0167] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods.

[0168] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0169] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0170] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for detecting line faults, characterized in that, The method includes: Acquire the current distortion signal and historical distortion signal of the current transformer, as well as the experimental reconstruction signal corresponding to the historical distortion signal; A polynomial model is constructed based on the historical distortion signal and the experimental restored signal. Set the first and second time-varying coefficients in the polynomial model to 0, and use the square of their absolute values ​​as the residual model. Substitute the data point at time t in the current distorted signal into the polynomial model to obtain the data point at time t in the target restored signal. Update each time-varying coefficient in the polynomial model according to the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model to obtain the updated polynomial model. Use the updated polynomial model as the polynomial model. Here, t takes integer values ​​greater than 0 until t equals the total number of times in the current distorted signal to obtain the target restored signal. The target restored signal is subjected to multi-feature extraction to obtain a signal feature vector, and the signal feature vector is input into a preset machine learning model to obtain a fault classification and detection result; in, The step of updating each time-varying coefficient of the polynomial model based on the residual model, the data point at time t in the current distorted signal, and each time-varying coefficient in the polynomial model to obtain the updated polynomial model includes: Substitute the data point at time t in the current distorted signal into the residual model to obtain the residual at time t; Based on the residual at time t, the data point at time t in the current distorted signal, and the nth time-varying coefficient in the polynomial model, determine the nth time-varying coefficient at time t+1; where n takes integer values ​​greater than -1 in sequence until n equals the total number of time-varying coefficients minus 1, thus obtaining each time-varying coefficient at time t+1. Based on each time-varying coefficient at time t+1, each time-varying coefficient in the polynomial model is updated to obtain the updated polynomial model. The step of substituting the data point at time t in the current distorted signal into the residual model to obtain the residual at time t includes: Using the expression of the residual model Obtain the residual at time t; in, Let be the residual at time t. This refers to the (n+1)th time-varying coefficient in the polynomial model. Subtract 1 from the total number of time-varying coefficients in the polynomial model. Let be the data point at time t in the current distorted signal.

2. The line fault detection method according to claim 1, characterized in that, The step of determining the nth time-varying coefficient at time t+1 based on the residual at time t, the data point at time t in the current distorted signal, and the nth time-varying coefficient in the polynomial model includes: Using formula Determine the nth time-varying coefficient at time t+1; in, This is the nth time-varying coefficient at time t+1. For the nth time-varying coefficient, To preset the learning rate, Let be the residual at time t. Let be the data point at time t in the current distorted signal.

3. The line fault detection method according to claim 1, characterized in that, The step of substituting the data point at time t in the current distorted signal into the polynomial model to obtain the data point at time t in the target restored signal includes: Using the expression of the polynomial model Determine the target restored signal; in, Let be the data point at time t in the target restored signal. For the (n+1)th time-varying coefficient, Subtract 1 from the total number of time-varying coefficients. Let be the data point at time t in the current distorted signal.

4. The line fault detection method according to claim 1, characterized in that, The step of constructing a polynomial model based on the historical distortion signal and the experimental restored signal includes: The historical distortion signal and the experimental restored signal are standardized respectively to obtain the standard distortion signal and the standard restored signal; Using the least squares method, multiple time-varying coefficients are determined based on the data points at each time step in the standard distorted signal and the data points at each time step in the standard restored signal. The polynomial model is constructed based on each time-varying coefficient.

5. The line fault detection method according to claim 1, characterized in that, After constructing the polynomial model based on the historical distortion signal and the experimental restored signal, the method further includes: Fourier transforms are performed on the current distorted signal, the historical distorted signal, and the experimental restored signal, respectively, to obtain the current frequency domain distorted signal, the historical frequency domain distorted signal, and the experimental frequency domain restored signal; Based on the historical frequency domain distortion signal and the experimental frequency domain restored signal, a frequency response transfer function is constructed. The frequency response transfer function is transformed to obtain a frequency domain form function, and the target frequency domain restored signal is determined based on the amplitude attenuation coefficient and phase offset of the frequency domain form function, as well as the amplitude and phase of each frequency in the current frequency domain distorted signal. Perform an inverse Fourier transform on the target frequency domain reconstructed signal to obtain the initial reconstructed signal; The initial restored signal is used as the current distortion signal.

6. The line fault detection method according to claim 5, characterized in that, The expression for the frequency response transfer function is: ； in, ; In the above formula, The frequency response transfer function is... For the Laplace operator, The transfer coefficient of the (m+1)th molecule. Subtract 1 from the total number of numerator or denominator transfer coefficients. The transitivity coefficient of the (m+1)th denominator. The imaginary unit, Pi For frequency.

7. The line fault detection method according to claim 5, characterized in that, The expression for the frequency domain form function is: ； in, For the frequency domain form function, The amplitude attenuation coefficient at frequency f is... It is a natural constant. The imaginary unit, The phase offset is the frequency f.

8. The line fault detection method according to claim 5, characterized in that, The step of determining the target frequency domain reconstructed signal based on the amplitude attenuation coefficient and phase offset of the frequency domain form function, and the amplitude and phase of each frequency in the current frequency domain distorted signal, includes: Using formula Determine the target frequency domain reconstructed signal; in, The frequency domain component of the k-th frequency in the target frequency domain reconstructed signal. For the kth frequency, Let x be the amplitude of the k-th frequency in the current frequency domain distorted signal. For frequency f k The amplitude attenuation coefficient, It is a natural constant. The imaginary unit, The phase of the k-th frequency in the current frequency domain distorted signal. For frequency f k The phase offset.

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