Short-circuit current zero-crossing point prediction method based on least squares method

Abstract

The present invention relates to the technical field of circuit breaker interruption, and more specifically, to a short-circuit current zero-crossing point prediction method based on the least squares method. The method is used to solve the problems in the prior art of being unable to adaptively decompose the short-circuit current waveform, reducing model adaptability, failing to achieve accurate parameter estimation and rapid convergence, and reducing waveform description capability and zero-crossing point prediction reliability. The present invention adopts an EMD algorithm to adaptively decompose the short-circuit current waveform, avoids pre-set basis functions, enhances model adaptability, uses sine function superposition for high frequencies, a polynomial model for low frequencies, and a linear combination of wavelet functions for medium frequencies. The method combines the least squares method with the Levenberg-Marquardt algorithm to achieve accurate parameter estimation and rapid convergence. The final model has high fitting accuracy, each IMF component has a clear physical meaning, and significantly improves waveform description capability and zero-crossing point prediction reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit breaker disconnection, and more particularly to a method for predicting a short-circuit current zero-crossing point based on a least squares method. Background Art

[0002] In power systems, when a circuit breaker receives a trip command to interrupt a short-circuit current, it typically operates immediately. However, if the circuit breaker trips at the peak of the short-circuit current, it generates a significant instantaneous interrupting current, resulting in enormous arcing energy and severe damage to the breaker contacts. To reduce arcing energy, improve the circuit breaker's breaking capacity, and extend its service life, the ideal operation is to complete the trip before the current crosses zero. This minimizes the arcing duration and intensity, effectively protecting the breaker contacts. Therefore, accurately calculating the short-circuit current's zero-crossing time is crucial.

[0003] Patent application with reference publication number CN119312588A discloses a correction method for online calculation of short-circuit current zero-crossing time based on the least squares method, comprising: changing the DC attenuation component according to the preset ratio requirement of the low-frequency power system and performing short-circuit fault simulation calculations respectively, to obtain a first relationship between the zero-crossing time prediction error and the DC attenuation component ratio; changing the fault initial phase according to the preset phase adjustment requirement of the low-frequency power system and performing short-circuit fault simulation calculations respectively, to obtain a second relationship between the short-circuit zero-crossing time prediction error and the fault initial phase; obtaining an error compensation formula for the predicted current zero-crossing time based on the first relationship and the second relationship; using the least squares method to calculate the predicted short-circuit current zero-crossing time and the short-circuit fault current parameters of the short-circuit fault, and recording the calculation time to calculate the compensated current zero-crossing time; compensating the predicted short-circuit current zero-crossing time based on the compensated current zero-crossing time to obtain a corrected short-circuit current zero-crossing time;

[0004] However, the above-mentioned reference patent improves the short-circuit current zero-crossing prediction accuracy and algorithm convergence speed by constructing a zero-crossing prediction error compensation formula, solves the problems of low prediction accuracy and long calculation time, and meets the actual needs of phase selection and disconnection. However, it cannot achieve real-time and accurate short-circuit fault monitoring and identification, cannot provide early warning of potential risks, cannot provide time guarantee for the timely start of protection measures, and cannot adaptively decompose the short-circuit current waveform, reducing the adaptability of the model, and cannot achieve accurate parameter estimation and rapid convergence, reducing the waveform description ability and zero-crossing prediction reliability.

[0005] To this end, we propose a short-circuit current zero-crossing point prediction method based on the least squares method to address the above problem. Summary of the Invention

[0006] The purpose of the present invention is to provide a short-circuit current zero-crossing point prediction method based on the least squares method, which solves the problems that the existing technology cannot achieve real-time and accurate short-circuit fault monitoring and identification, cannot provide early warning of potential risks, cannot provide time guarantee for the timely activation of protection measures, and cannot adaptively decompose the short-circuit current waveform, reducing model adaptability, and cannot achieve accurate parameter estimation and rapid convergence, which reduces the waveform description ability and zero-crossing point prediction reliability.

[0007] The purpose of the present invention is achieved through the following technical solutions:

[0008] The short-circuit current zero-crossing point prediction method based on the least squares method includes the following steps:

[0009] Step 1: collecting a short-circuit current waveform signal through a current transformer, and performing a preprocessing operation on the collected short-circuit current waveform signal;

[0010] Step 2: Real-time monitoring of short-circuit assessment parameters of the power system to monitor and identify short-circuit faults in the power system;

[0011] Step 3: Decompose the pre-processed short-circuit current waveform signal and use the least square method to construct a mathematical model to fit the short-circuit current waveform;

[0012] Step 4: Based on the model obtained by least squares fitting, predict the zero-crossing time of the short-circuit current;

[0013] Step 5: Generate corresponding control instructions in advance based on the predicted zero-crossing time and the inherent opening time of the circuit breaker.

[0014] As a preferred embodiment of the present invention, the specific process of monitoring and identifying short-circuit faults in the power system in step 2 is as follows:

[0015] Obtain historical short-circuit assessment parameters of the power system, including operating current, operating voltage, and operating frequency, generate a monitoring cycle, and divide the monitoring cycle into multiple monitoring periods;

[0016] Obtain the current mutation rate of the power system in multiple monitoring periods, and construct a set A of current mutation rates based on this. The mean of the difference between the largest subset and the smallest subset in set A is recorded as the current mutation rate difference DTC.

[0017] Obtain the voltage sag rate of the power system in multiple monitoring periods to construct a set B of voltage sag rates, and record the average of the difference between the largest subset and the smallest subset in set B as the voltage sag rate difference DDC;

[0018] The frequency change rate of the power system in multiple monitoring periods is obtained to construct a set C of frequency change rates, and the average of the differences between the largest subset and the smallest subset in set C is recorded as the frequency change rate difference PBC.

[0019] As a preferred embodiment of the present invention, the current mutation rate difference DTC, the voltage drop rate difference DDC and the frequency change rate difference PBC are obtained, and the current mutation rate difference DTC, the voltage drop rate difference DDC and the frequency change rate difference PBC are combined to construct a short circuit monitoring feature matrix DJ;

[0020] The constructed short-circuit monitoring feature matrix DJ is used as the input of the machine learning model, and the label vector v is used as the output of the machine learning model. The label vector v indicates whether a short circuit occurs in the power system. The output of the label vector v is 0 or 1. 0 indicates that a short circuit does not occur in the power system, and 1 indicates that a short circuit occurs in the power system. The label vector v is used as the prediction target, and the sum of the prediction errors of all training data is minimized as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the training is stopped. The machine learning model that predicts the label vector v is obtained.

[0021] Obtain the real-time short-circuit assessment parameters of the power system, process them to construct the real-time short-circuit monitoring feature matrix SJ, and use the trained machine learning model to identify whether a short-circuit fault has occurred in the power system;

[0022] If the label vector v output by the machine learning model is 0, it means that there is no short circuit in the power system;

[0023] If the label vector v output by the machine learning model is 1, it indicates that a short circuit has occurred in the power system.

[0024] As a preferred embodiment of the present invention, the specific process of using the least squares method to construct a mathematical model to fit the short-circuit current waveform in step three is as follows:

[0025] After a short circuit is detected in the power system, the pre-processed short-circuit current waveform signal x(t) is immediately obtained and the pre-processed short-circuit current waveform signal is decomposed using the EMD algorithm;

[0026] The specific steps of the EMD algorithm are as follows:

[0027] S1: Find all local maxima and minima in the signal;

[0028] S2: Use cubic spline interpolation to connect the local maximum and minimum values to obtain the upper envelope and lower envelope respectively;

[0029] S3: Calculate the average value of the upper and lower envelopes as the average envelope m1(t);

[0030] S4: Subtract the average envelope from the original signal to obtain the first IMF: h1(t)=x(t)-m1(t). If h1(t) meets the IMF conditions, it is used as the first IMF. Otherwise, h1(t) is used as the new original signal and S1 to S4 are repeated until the IMF conditions are met.

[0031] S5: Subtract the first IMF from the original signal to obtain the residual r1(t)=x(t)-h1(t);

[0032] S6: Take the residual r1(t) as the new original signal and repeat S1 to S5 until the residual meets the stopping condition, obtaining multiple IMFs and the final residual.

[0033] As a preferred embodiment of the present invention, the result of EMD decomposition is expressed by the following formula:

[0034] ;

[0035] IMF i (t) is the i-th IMF, representing the signal with different frequency components, r n (t) is the final residual, representing the trend or low-frequency component of the signal, and n is the number of IMFs;

[0036] After decomposition using the EMD algorithm, a series of IMF components IMF1(t), IMF2(t), ..., IMF n (t) and a residual term r n (t), use the least squares method to fit these components, and choose a suitable mathematical model based on the frequency characteristics and shape of the IMF components.

[0037] As a preferred embodiment of the present invention, the mathematical model is selected as follows:

[0038] High-frequency IMFs: These components contain the high-frequency portion of the signal and are fitted using a superposition of sinusoidal functions:

[0039] ;

[0040] Among them A ik is the amplitude of the kth sine wave in the ith IMF, f ik is the frequency of the kth sinusoid in the ith IMF, φ ik is the phase of the kth sinusoid in the ith IMF, K i is the number of sine waves used to fit the i-th IMF;

[0041] Low-frequency IMFs and residuals: These components contain the low-frequency content or trend of the signal and are fitted using a polynomial function:

[0042] ;

[0043] where a k are the polynomial coefficients, P is the order of the polynomial;

[0044] Intermediate frequency IMFs: These components contain the intermediate frequency components of the signal, which are located between high and low frequencies and are fitted using a linear combination of wavelet functions:

[0045] ;

[0046] where ψ jk (t) is the wavelet basis function of different scales (j) and positions (k), c k are the wavelet coefficients.

[0047] As a preferred embodiment of the present invention, for each selected model, the least squares method is used to estimate the model parameters. When the sine model is used to fit the high-frequency IMF, the least squares objective function is:

[0048] ;

[0049] Where N is the number of data points, t j is the time of the jth data point;

[0050] is the parameter vector of the i-th IMF model;

[0051] Iteratively update the parameter vector θ using the Levenberg-Marquardt algorithm i , until convergence;

[0052] When using a polynomial model to fit the low-frequency IMF, the least squares objective function is:

[0053] ;

[0054] Let X be the design matrix whose elements are , y is IMF i (t) is represented by a vector, then the parameter vector a=[a0,a1,…a p ] T The least squares solution of is:

[0055] a=(X T X) -1 X T y.

[0056] As a preferred embodiment of the present invention, the residual term represents the trend or low-frequency component of the signal, so it can be fitted using a model similar to the low-frequency IMF. The polynomial fitting process is the same as that of the low-frequency IMF, except that the IMF i (t) is replaced by r n (t);

[0057] When using the wavelet model to fit the intermediate frequency IMF, the least squares objective function is:

[0058] ;

[0059] This objective function is a quadratic function of the wavelet coefficient c, so an analytical solution can be obtained:

[0060] c=(Ψ T Ψ) -1 Ψ T y;

[0061] Where Ψ is the design matrix composed of wavelet basis functions, y is the IMF i Vector representation of (t);

[0062] The fitting models of all IMF components and the fitting models of the residual terms are combined to obtain the final short-circuit current waveform model:

[0063] .

[0064] As a preferred embodiment of the present invention, the specific process of predicting the zero-crossing time of the short-circuit current in step 4 is as follows:

[0065] Obtain the final short-circuit current waveform model based on least squares fitting. When predicting the zero-crossing time of the short-circuit current, find the t value that satisfies x(t)=0. Use the Newton-Raphson method to solve. The steps of the Newton-Raphson method are as follows:

[0066] T1: Select an initial guess value t0;

[0067] T2: Repeat the following steps until the accuracy requirement is met: Calculate ,in is the derivative of x(t);

[0068] T3: Final t n This is the approximate value of the zero-crossing time.

[0069] As a preferred embodiment of the present invention, the specific process of generating the corresponding control instructions in advance in step 5 is as follows:

[0070] Get the predicted short-circuit current zero-crossing time t n, obtain the inherent opening time t of the circuit breaker f In order to safely open the circuit breaker near the zero crossing point, the inherent opening time t of the circuit breaker must be considered in advance. f , control instruction issuance time t k The calculation of is as follows:

[0071] t k =t n -t f ;

[0072] Calculate the time t when the control command is issued k After that, in t k The corresponding control instructions are issued before the time.

[0073] Compared with the prior art, the advantages of the present invention are:

[0074] (1) In the present invention, a feature matrix is constructed through multi-parameter analysis, and a machine learning model is combined to achieve real-time and accurate short-circuit fault monitoring and identification. This method has the advantages of high automation, strong scalability, and strong early warning capability. It significantly improves the accuracy and efficiency of short-circuit fault identification, and can provide early warning of potential risks, providing time guarantee for the timely activation of protection measures.

[0075] (2) In the present invention, the EMD algorithm is used to adaptively decompose the short-circuit current waveform, avoiding the preset basis function and enhancing the adaptability of the model. Sine function superposition is used for high frequency, polynomial model is used for low frequency, and linear combination of wavelet function is used for medium frequency. The least squares method and Levenberg-Marquardt algorithm are combined to achieve accurate parameter estimation and rapid convergence. The final model has high fitting accuracy, each IMF component has a clear physical meaning, and the waveform description ability and zero-crossing point prediction reliability are significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 Flowchart of the short-circuit current zero-crossing point prediction method of the present invention;

[0077] Figure 2 Flow chart of the steps of the EMD algorithm in the present invention;

[0078] Figure 3 This is a flow chart of the steps for solving the Newton-Raphson method in the present invention. DETAILED DESCRIPTION

[0079] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work shall fall within the scope of protection of the present invention.

[0080] Example 1: Figure 1 and Figure 2 As shown, the short-circuit current zero-crossing point prediction method based on the least squares method proposed in the present invention includes the following steps:

[0081] Step 1: collecting a short-circuit current waveform signal through a current transformer, and performing a preprocessing operation on the collected short-circuit current waveform signal, wherein the preprocessing operation includes but is not limited to filtering and denoising, baseline correction, outlier detection processing, data smoothing processing, and normalization processing;

[0082] Filtering and denoising the short-circuit current waveform signal, performing baseline correction, outlier detection, data smoothing, and normalization can significantly improve data quality and analysis reliability. These preprocessing steps effectively remove noise, compensate for drift, correct erroneous data, reduce fluctuations, and scale the data to a specific range, thereby improving the accuracy and stability of zero-crossing detection and enhancing the performance and safety of circuit breaker control systems.

[0083] Step 2: Real-time monitoring of short-circuit assessment parameters of the power system to monitor and identify short-circuit faults in the power system;

[0084] The specific process of step 2, monitoring and identifying short-circuit faults in the power system, is as follows:

[0085] Obtain historical short-circuit assessment parameters of the power system, including operating current, operating voltage, and operating frequency, generate a monitoring cycle, and divide the monitoring cycle into multiple monitoring periods;

[0086] Obtain the current mutation rate of the power system during multiple monitoring periods. The current mutation rate refers to the rate of change of current over time, that is, the amount of change in current per unit time. Based on this, a set A of current mutation rates is constructed, and the mean of the difference between the largest subset and the smallest subset in set A is recorded as the current mutation rate difference DTC;

[0087] Obtain the voltage sag rate of the power system during multiple monitoring periods. The voltage sag rate refers to the rate of change of voltage over time, that is, the amount of voltage change per unit time. Based on this, a set B of voltage sag rates is constructed, and the mean of the difference between the largest subset and the smallest subset in set B is recorded as the voltage sag rate difference DDC.

[0088] Obtain the frequency change rate of the power system over multiple monitoring periods. The frequency change rate refers to the rate of change of the system frequency over time, that is, the amount of change in frequency per unit time. Based on this, a set C of frequency change rates is constructed, and the mean of the difference between the largest subset and the smallest subset in set C is recorded as the frequency change rate difference PBC;

[0089] Obtain the current mutation rate difference DTC, voltage drop rate difference DDC and frequency change rate difference PBC, and combine the current mutation rate difference DTC, voltage drop rate difference DDC and frequency change rate difference PBC to construct a short circuit monitoring feature matrix DJ;

[0090] The constructed short-circuit monitoring feature matrix DJ is used as the input of the machine learning model, and the label vector v is used as the output of the machine learning model. The label vector v indicates whether a short circuit occurs in the power system. The output of the label vector v is 0 or 1. 0 indicates that a short circuit does not occur in the power system, and 1 indicates that a short circuit occurs in the power system. The label vector v is used as the prediction target, and the sum of the prediction errors of all training data is minimized as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the training is stopped. The machine learning model that predicts the label vector v is obtained.

[0091] Obtain the real-time short-circuit assessment parameters of the power system, process them to construct the real-time short-circuit monitoring feature matrix SJ, and use the trained machine learning model to identify whether a short-circuit fault has occurred in the power system;

[0092] If the label vector v output by the machine learning model is 0, it means that there is no short circuit in the power system;

[0093] If the label vector v output by the machine learning model is 1, it means that a short circuit has occurred in the power system;

[0094] By constructing a feature matrix through multi-parameter analysis and combining it with a machine learning model, real-time and accurate short-circuit fault monitoring and identification can be achieved. This system has the advantages of high automation, strong scalability, and strong early warning capabilities. It significantly improves the accuracy and efficiency of short-circuit fault identification, and can provide early warning of potential risks, providing time guarantee for the timely initiation of protection measures.

[0095] Step 3: Decompose the pre-processed short-circuit current waveform signal and use the least square method to construct a mathematical model to fit the short-circuit current waveform;

[0096] The specific process of step 3 using the least squares method to construct a mathematical model to fit the short-circuit current waveform is as follows:

[0097] After a short circuit is detected in the power system, the pre-processed short-circuit current waveform signal x(t) is immediately obtained and the pre-processed short-circuit current waveform signal is decomposed using the EMD algorithm;

[0098] The EMD algorithm is an adaptive data-driven method that decomposes the signal into a series of components called intrinsic mode functions. Each IMF is a locally stationary signal with a clear physical meaning. The specific steps of the EMD algorithm are as follows:

[0099] S1: Find all local maxima and minima in the signal;

[0100] S2: Use cubic spline interpolation to connect the local maximum and minimum values to obtain the upper envelope and lower envelope respectively;

[0101] S3: Calculate the average value of the upper and lower envelopes as the average envelope m1(t);

[0102] S4: Subtract the mean envelope from the original signal to obtain the first IMF: h1(t)=x(t)-m1(t). If h1(t) meets the IMF conditions (the number of local extreme values is finite and the upper and lower envelopes are symmetric about the zero mean), then it is used as the first IMF. Otherwise, h1(t) is used as the new original signal and S1 to S4 are repeated until the IMF conditions are met.

[0103] S5: Subtract the first IMF from the original signal to obtain the residual r1(t)=x(t)-h1(t);

[0104] S6: Use the residual r1(t) as the new original signal and repeat S1 to S5 until the residual meets the stopping condition (for example, the residual energy is less than the preset threshold or the number of iterations reaches the upper limit), and obtain multiple IMFs and the final residual;

[0105] The result of EMD decomposition is expressed by the following formula:

[0106] ;

[0107] IMF i (t) is the i-th IMF, representing the signal with different frequency components, r n (t) is the final residual, representing the trend or low-frequency component of the signal, and n is the number of IMFs;

[0108] After decomposition using the EMD algorithm, a series of IMF components IMF1(t), IMF2(t), ..., IMF n (t) and a residual term r n (t) Use the least squares method to fit these components and select an appropriate mathematical model based on the frequency characteristics and shape of the IMF components;

[0109] The mathematical model is selected as follows:

[0110] High-frequency IMFs: These components contain the high-frequency portion of the signal and are fitted using a superposition of sinusoidal functions:

[0111] ;

[0112] Among them A ikis the amplitude of the kth sine wave in the ith IMF, f ik is the frequency of the kth sinusoid in the ith IMF, φ ik is the phase of the kth sinusoid in the ith IMF, K i is the number of sine waves used to fit the i-th IMF;

[0113] Low-frequency IMFs and residuals: These components contain the low-frequency content or trend of the signal and are fitted using a polynomial function:

[0114] ;

[0115] where a k are the polynomial coefficients, P is the order of the polynomial;

[0116] Intermediate frequency IMFs: These components contain the intermediate frequency components of the signal, which are located between high and low frequencies and are fitted using a linear combination of wavelet functions:

[0117] ;

[0118] where ψ jk (t) is the wavelet basis function of different scales (j) and positions (k), c k are the wavelet coefficients, and the choice of wavelet basis function depends on the characteristics of the IMF;

[0119] For each selected model, the least squares method is used to estimate the model parameters. When the sinusoidal model is used to fit the high-frequency IMF, the least squares objective function is:

[0120] ;

[0121] Where N is the number of data points, t j is the time of the jth data point;

[0122] is the parameter vector of the i-th IMF model;

[0123] Iteratively update the parameter vector θ using the Levenberg-Marquardt algorithm i , until convergence, the Levenberg-Marquardt algorithm is a common algorithm in the prior art, and the parameter vector θ is not iteratively updated here. i The process is elaborated in detail;

[0124] When using a polynomial model to fit the low-frequency IMF, the least squares objective function is:

[0125] ;

[0126] Let X be the design matrix whose elements are , y is IMF i (t) is represented by a vector, then the parameter vector a=[a0,a1,…a p ] T The least squares solution of is:

[0127] a=(X T X) -1 X T y;

[0128] The residual term represents the trend or low-frequency component of the signal, so it can be fitted using a model similar to the low-frequency IMF. The polynomial fitting process is the same as that of the low-frequency IMF, except that the IMF i (t) is replaced by r n (t);

[0129] When using the wavelet model to fit the intermediate frequency IMF, the least squares objective function is:

[0130] ;

[0131] This objective function is a quadratic function of the wavelet coefficient c, so an analytical solution can be obtained:

[0132] c=(Ψ T Ψ) -1 Ψ T y;

[0133] Where Ψ is the design matrix composed of wavelet basis functions, y is the IMF i Vector representation of (t);

[0134] The fitting models of all IMF components and the fitting models of the residual terms are combined to obtain the final short-circuit current waveform model:

[0135] ;

[0136] The EMD algorithm is used to adaptively decompose the short-circuit current waveform, avoiding preset basis functions and enhancing model adaptability. Sine function superposition is used for high frequencies, polynomial models are used for low frequencies, and linear combinations of wavelet functions are used for medium frequencies. The least squares method and the Levenberg-Marquardt algorithm are combined to achieve accurate parameter estimation and rapid convergence. The final model has high fitting accuracy, and each IMF component has a clear physical meaning, which significantly improves the waveform description capability and the reliability of zero-crossing point prediction.

[0137] Embodiment 2: The technical solution of this embodiment of the present invention differs from that of Embodiment 1 in that:

[0138] like Figure 1 and Figure 3As shown, step 4: predict the zero-crossing time of the short-circuit current based on the model obtained by least squares fitting;

[0139] The specific process of step 4 predicting the zero-crossing time of the short-circuit current is as follows:

[0140] Obtain the final short-circuit current waveform model based on least squares fitting. When predicting the zero-crossing time of the short-circuit current, find the t value that satisfies x(t)=0. Use the Newton-Raphson method to solve. The steps of the Newton-Raphson method are as follows:

[0141] T1: Select an initial guess value t0;

[0142] T2: Repeat the following steps until the accuracy requirement is met: Calculate ,in is the derivative of x(t);

[0143] T3: Final t n This is the approximate value of the zero-crossing time;

[0144] The Newton-Raphson method is used to iteratively solve the zero-crossing point of the short-circuit current waveform model. This method has the advantages of high precision, high efficiency, and wide applicability, and can quickly and accurately predict the zero-crossing time.

[0145] Step 5: Generate corresponding control instructions in advance based on the predicted zero-crossing time and the inherent opening time of the circuit breaker;

[0146] The specific process of generating corresponding control instructions in advance in step 5 is as follows:

[0147] Get the predicted short-circuit current zero-crossing time t n , obtain the inherent opening time t of the circuit breaker f In order to safely open the circuit breaker near the zero crossing point, the inherent opening time t of the circuit breaker must be considered in advance. f , control instruction issuance time t k The calculation of is as follows:

[0148] t k =t n -t f ;

[0149] If the calculated t k If the value is less than zero, it means that the predicted zero-crossing time has passed. In this case, you need to take countermeasures based on the actual situation. The countermeasures are as follows:

[0150] If the system can predict the time of the next zero crossing, it can wait for the next zero crossing to perform the tripping operation;

[0151] If missing the current zero crossing does not cause serious consequences, you can choose to ignore it and wait for the next zero crossing;

[0152] In some emergency situations, forced tripping may be necessary, but this is generally not recommended as it may result in greater shock and potential risks;

[0153] Calculate the time t when the control command is issued k After that, in t k The corresponding control instructions are issued before the time. The specific content of the control instructions is:

[0154] Clearly specify which circuit breaker needs to perform the action;

[0155] Clearly indicate the actions to be performed;

[0156] Including the predicted zero crossing time t n and the instruction issuing time t k , t n Used for internal reference of circuit breaker control system, t k Determine when the instruction is to be issued;

[0157] Depending on actual needs, other parameters may be included, such as tripping mode, current limit, etc.

[0158] By precalculating the time to issue control instructions and providing multiple response strategies (such as waiting for the next zero crossing or ignoring the current zero crossing), the circuit breaker is ensured to safely open near the zero crossing, thereby improving system reliability, response speed and safety. The complete control instruction contains all necessary information, avoiding real-time calculation delays.

[0159] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes based on the technical solutions and improved concepts of the present invention within the technical scope disclosed by the present invention, and they should be covered by the scope of protection of the present invention.

Claims

1. A short-circuit current zero-crossing point prediction method based on the least squares method is characterized by: The following steps are involved: Step 1: collecting a short-circuit current waveform signal through a current transformer, and performing a preprocessing operation on the collected short-circuit current waveform signal; Step 2: Real-time monitoring of short-circuit assessment parameters of the power system to monitor and identify short-circuit faults in the power system; The process of monitoring short-circuit faults in the power system is as follows: Obtain historical short-circuit assessment parameters of the power system, including operating current, operating voltage, and operating frequency, generate a monitoring cycle, and divide the monitoring cycle into multiple monitoring periods; Obtain the current mutation rate of the power system in multiple monitoring periods to construct a set A of current mutation rates, and record the average of the difference between the largest subset and the smallest subset in set A as the current mutation rate difference DTC; Obtain the voltage sag rate of the power system during multiple monitoring periods to construct a set B of voltage sag rates, and record the mean of the difference between the largest subset and the smallest subset in set B as the voltage sag rate difference DDC; Obtain the frequency change rate of the power system in multiple monitoring periods, and use it to construct a set C of frequency change rates. The average of the differences between the largest subset and the smallest subset in set C is recorded as the frequency change rate difference PBC. Obtain the current mutation rate difference DTC, voltage drop rate difference DDC and frequency change rate difference PBC, and combine the current mutation rate difference DTC, voltage drop rate difference DDC and frequency change rate difference PBC to construct a short circuit monitoring feature matrix DJ; Step 3: Decompose the pre-processed short-circuit current waveform signal and use the least square method to construct a mathematical model to fit the short-circuit current waveform; The process of using the least squares method to construct a mathematical model to fit the short-circuit current waveform is as follows: When using a sinusoidal model to fit high-frequency IMF, the least squares objective function is: ； Where N is the number of data points, t j is the time of the jth data point; is the parameter vector of the i-th IMF model; Iteratively update the parameter vector θ using the Levenberg-Marquardt algorithm i , until convergence; When using a polynomial model to fit the low-frequency IMF, the least squares objective function is: ； Let X be the design matrix whose elements are , y is IMF i (t) is represented by a vector, then the parameter vector a=[a0,a1,…a p ] T The least squares solution of is: a=(X T X) -1 X T y; When using the wavelet model to fit the intermediate frequency IMF, the least squares objective function is: ； This objective function is a quadratic function of the wavelet coefficient c, so an analytical solution can be obtained: c=(Ψ T (W) -1 P T y; Where Ψ is the design matrix composed of wavelet basis functions, y is the IMF i Vector representation of (t); Step 4: Based on the model obtained by least squares fitting, predict the zero-crossing time of the short-circuit current; Step 5: Generate corresponding control instructions in advance based on the predicted zero-crossing time and the inherent opening time of the circuit breaker.

2. The short-circuit current zero-crossing point prediction method based on the least squares method according to claim 1, characterized in that: The process of identifying the short-circuit fault of the power system in step 2 is as follows: The constructed short-circuit monitoring feature matrix DJ is used as the input of the machine learning model, and the label vector v is used as the output of the machine learning model. The label vector v indicates whether a short circuit occurs in the power system. The output of the label vector v is 0 or 1. 0 indicates that a short circuit does not occur in the power system, and 1 indicates that a short circuit occurs in the power system. The label vector v is used as the prediction target, and the sum of the prediction errors of all training data is minimized as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the training is stopped. The machine learning model that predicts the label vector v is obtained. Obtain the real-time short-circuit assessment parameters of the power system, process them to construct the real-time short-circuit monitoring feature matrix SJ, and use the trained machine learning model to identify whether a short-circuit fault has occurred in the power system; If the label vector v output by the machine learning model is 0, it means that there is no short circuit in the power system; If the label vector v output by the machine learning model is 1, it indicates that a short circuit has occurred in the power system.

3. The short-circuit current zero-crossing point prediction method based on the least squares method according to claim 1, characterized in that: The process of decomposing the pre-processed short-circuit current waveform signal in step 3 is as follows: After a short circuit is detected in the power system, the pre-processed short-circuit current waveform signal x(t) is immediately obtained and the pre-processed short-circuit current waveform signal is decomposed using the EMD algorithm; The specific steps of the EMD algorithm are as follows: S1: Find all local maxima and minima in the signal; S2: Use cubic spline interpolation to connect the local maximum and minimum values to obtain the upper envelope and lower envelope respectively; S3: Calculate the average value of the upper and lower envelopes as the average envelope m1(t); S4: Subtract the average envelope from the original signal to obtain the first IMF: h1(t)=x(t)-m1(t). If h1(t) meets the IMF conditions, it is used as the first IMF. Otherwise, h1(t) is used as the new original signal and S1 to S4 are repeated until the IMF conditions are met. S5: Subtract the first IMF from the original signal to obtain the residual r1(t)=x(t)-h1(t); S6: Take the residual r1(t) as the new original signal and repeat S1 to S5 until the residual meets the stopping condition, obtaining multiple IMFs and the final residual.

4. The short-circuit current zero-crossing point prediction method based on the least squares method according to claim 3, characterized in that: The result of EMD decomposition is expressed by the following formula: ； IMF i (t) is the i-th IMF, representing the signal with different frequency components, r n (t) is the final residual, representing the trend or low-frequency component of the signal, and n is the number of IMFs; After decomposition using the EMD algorithm, a series of IMF components IMF1(t), IMF2(t), ..., IMF n (t) and a residual term r n (t), use the least squares method to fit these components, and choose a suitable mathematical model based on the frequency characteristics and shape of the IMF components.

5. The short-circuit current zero-crossing point prediction method based on the least squares method according to claim 4, characterized in that: The mathematical model is selected as follows: High-frequency IMFs: These components contain the high-frequency portion of the signal and are fitted using a superposition of sinusoidal functions: ； Among them A ik is the amplitude of the kth sine wave in the ith IMF, f ik is the frequency of the kth sinusoid in the ith IMF, φ ik is the phase of the kth sinusoid in the ith IMF, K i is the number of sine waves used to fit the i-th IMF; Low-frequency IMFs and residuals: These components contain the low-frequency content or trend of the signal and are fitted using a polynomial function: ； where a k are the polynomial coefficients, P is the order of the polynomial; Intermediate frequency IMFs: These components contain the intermediate frequency components of the signal, which are located between high and low frequencies and are fitted using a linear combination of wavelet functions: ； where ψ jk (t) is the wavelet basis function of different scales (j) and positions (k), c k are the wavelet coefficients.

6. The short-circuit current zero-crossing point prediction method based on the least squares method according to claim 5, characterized in that: The residual term represents the trend or low-frequency component of the signal, so it can be fitted using a model similar to the low-frequency IMF. The polynomial fitting process is the same as that of the low-frequency IMF, except that the IMF i (t) is replaced by r n (t); The fitting models of all IMF components and the fitting models of the residual terms are combined to obtain the final short-circuit current waveform model: 。 7. The short-circuit current zero-crossing point prediction method based on the least squares method according to claim 1, characterized in that: The specific process of predicting the zero-crossing time of the short-circuit current in step 4 is as follows: Obtain the final short-circuit current waveform model based on least squares fitting. When predicting the zero-crossing time of the short-circuit current, find the t value that satisfies x(t)=0. Use the Newton-Raphson method to solve. The steps of the Newton-Raphson method are as follows: T1: Select an initial guess value t0; T2: Repeat the following steps until the accuracy requirement is met: Calculate ,in is the derivative of x(t); T3: Final t n This is the approximate value of the zero-crossing time.

8. The short-circuit current zero-crossing point prediction method based on the least squares method according to claim 1, characterized in that: The specific process of generating the corresponding control instructions in advance in step 5 is as follows: Get the predicted short-circuit current zero-crossing time t n , obtain the inherent opening time t of the circuit breaker f In order to safely open the circuit breaker near the zero crossing point, the inherent opening time t of the circuit breaker must be considered in advance. f , control instruction issuance time t k The calculation of is as follows: t k =t n -t f ; Calculate the time t when the control command is issued k After that, at t k The corresponding control instructions are issued before the time.

Images