A short-circuit current zero-crossing point prediction method and system fusing LS-UKF

Abstract

The application discloses a short-circuit current zero-crossing point prediction method and system based on LS-UKF, and a multi-parameter short-circuit current signal model containing DC attenuation, multi-order harmonic and noise components is constructed after short-circuit fault is identified; a linearization model obtained through a trigonometric transformation and Taylor expansion is preliminarily estimated by using a least square method, and initial prior values of characteristic parameters are obtained; the initial prior values are taken as initial states of unscented Kalman filtering, and recursive estimation of the multi-parameter model is carried out to obtain final estimated values of the characteristic parameters; and a waveform is reconstructed based on the final estimated values to solve the zero-crossing point. According to the application, the multi-parameter model is constructed to accurately represent complex transient characteristics, the least square method short-window estimation is combined to accelerate filter convergence, and the unscented Kalman filtering is directly used for recursive estimation to avoid linearization error, so that the prediction accuracy and speed of the zero-crossing point are simultaneously improved.

Description

Technical Field

[0001] This invention belongs to the field of phase-controlled short-circuit fault current interruption technology, and particularly relates to a method and system for predicting the zero-crossing point of short-circuit current by integrating LS-UKF. Background Technology

[0002] As my country's power system develops towards higher voltage, larger capacity, and regional interconnection, the problem of excessive short-circuit current is becoming increasingly serious, leading to an increase in system short-circuit capacity and insufficient circuit breaker breaking capacity. Furthermore, the increased breaking current also exacerbates the erosion of circuit breaker contacts. Simultaneously, with the steady progress of the construction of new power systems, the equivalent DC component and harmonic content of the system are continuously increasing during short-circuit faults, resulting in zero-point drift of the short-circuit current, a greater rate of current rise, increased risk of circuit breaker failure, and intensified transient impacts.

[0003] Phase-controlled switching technology is an intelligent control technology that controls circuit breakers to open and close at specific current or voltage phases based on load characteristics to reduce transient impacts. Among these technologies, short-circuit fault current phase-controlled interruption technology reconstructs the short-circuit current waveform through fitting, predicting in advance the target zero-crossing point that will achieve the most favorable arc extinguishing effect. Based on this prediction, it controls the circuit breaker's opening timing and arcing time, preventing prolonged high-current arcing from eroding the circuit breaker contacts. This improves both the electrical life and breaking capacity of the circuit breaker. The key to phase-controlled interruption technology lies in quickly and accurately predicting the zero-crossing point of the short-circuit current that meets the interruption requirements.

[0004] Existing short-circuit current prediction methods mainly employ digital signal processing (DSC) algorithms and artificial intelligence (AI) algorithms. DSC algorithms, primarily based on least squares and Fourier transforms, solve for characteristic parameters using a mathematical model of the short-circuit current to reconstruct the waveform and then estimate the zero-crossing point. Under ideal conditions, these algorithms generally achieve good prediction results. However, due to the simplification of short-circuit current components, including ignoring harmonics, noise, and linearized DC components, their accuracy suffers under complex waveform conditions such as high harmonics and strong DC attenuation. Furthermore, batch processing, redundant matrix calculations, and invalid parameter updates result in slow speed. AI algorithms, primarily based on neural networks, learn the mapping relationship between input and output through extensive offline training on historical short-circuit currents, achieving a certain degree of rapid prediction of the short-circuit current's zero-crossing point. However, their generalization ability depends on the conditions covered by the training data, and prediction accuracy may decrease under new conditions. Moreover, neural networks are typical black-box models, lacking application to the physical model of the short-circuit current, resulting in weak interpretability of current characteristic parameters. Therefore, the real-time performance, versatility, and prediction stability of these methods still need improvement. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a short-circuit current zero-crossing prediction method and system that integrates LS-UKF. By establishing a multi-parameter signal model of the short-circuit current, an unscented Kalman filter is used to directly estimate the state of the signal model, avoiding linearization errors and improving the accuracy of nonlinear prediction. Furthermore, the least squares method short-window initialization is combined to improve the filtering convergence performance, thereby achieving a synergistic improvement in zero-crossing prediction accuracy and speed while ensuring the integrity of the short-circuit current model.

[0006] This invention provides a short-circuit current zero-crossing prediction method that integrates LS-UKF: Step 1: Monitor the line current signal in real time. If no short circuit fault is detected, continue monitoring. If a short circuit fault is detected, proceed to Step 2. Step 2: Considering the high-order harmonics and random noise present in the real power grid, establish a multi-parameter short-circuit current signal model; Step 3: Based on the multi-parameter short-circuit current signal model, the linearized short-circuit current signal model is initially estimated using the least squares method to obtain the initial prior values ​​of the characteristic parameters. Step 4: Use the initial prior value as the initial state of the unscented Kalman filter, and use the unscented Kalman filter algorithm to recursively estimate the multi-parameter short-circuit current signal model to obtain the final estimated value of the characteristic parameters. Step 5: Substitute the final estimated value into the multi-parameter short-circuit current signal model to reconstruct the short-circuit current waveform and solve for the zero-crossing point of the short-circuit current.

[0007] As an optional implementation, in step 2, the method for constructing the multi-parameter short-circuit current signal model is as follows: A short-circuit fault signal model is established using a single-phase short-circuit fault model. High-order harmonics and random noise are introduced into the short-circuit fault signal model to establish a multi-parameter short-circuit current signal model.

[0008] As an optional implementation, step 3, the method for obtaining the initial prior values ​​of the feature parameters includes: Step 3.1: By performing trigonometric transformation on the AC component in the multi-parameter short-circuit current signal model and Taylor expansion on the DC component exponent term while retaining the first two terms, a linearized short-circuit current signal model is obtained. Step 3.2: Discretize the linearized short-circuit fault signal model according to the sampling time to obtain the short-circuit current equation set; Step 3.3: Solve the short-circuit current equations using the least squares method to obtain the estimated values ​​of the characteristic parameter vector.

[0009] As an optional implementation, step 4, the method for obtaining the final estimated values ​​of the feature parameters based on the initial prior values, includes: Step 4.1: Use the characteristic parameters that need to be estimated as system state variables and the sampled short-circuit current values ​​as system observation variables to construct state equations and observation equations; Step 4.2: Use the initial prior value as the initial prior value of the unscented Kalman filter state vector, and set the initial value of the covariance matrix; Step 4.3: Construct the Sigma point set based on the mean of the posterior state estimate at the current time and the corresponding error covariance matrix, and calculate the weight corresponding to each Sigma point; Step 4.4: Based on the Sigma point set and the corresponding weights, calculate the predicted value of the system observation at sampling point j and the predicted mean of the 0th to 2nth sampling points, as well as the observation prediction covariance matrix and the cross-covariance between the state and the observation. Step 4.5: Calculate the Kalman gain based on the observation prediction covariance matrix and the cross-covariance between the state and the observation, and update the system state variables and error covariance based on the predicted values ​​of the system observations at sampling point j, the predicted mean values ​​of the 0th to 2nth sampling points, and the Kalman gain.

[0010] As an optional implementation, in step 3.2, the linearized short-circuit current signal model is discretized according to the sampling time to establish the short-circuit current equation set Y=HX, where Y is the short-circuit current sampling observation vector, X is the characteristic parameter vector, and H is the observation matrix; Step 3.3: Solve the linear equation system using the least squares method to obtain the initial estimate of the characteristic parameter vector. ,in H + Observation matrix H The pseudo-inverse of the preliminary estimate X 0 is used as the initial prior value.

[0011] This invention also proposes a short-circuit current zero-crossing prediction system integrating LS-UKF, including a model building module, a preliminary estimation module, a recursive estimation module, and a zero-crossing solution module: The model building module, after identifying a short-circuit fault, constructs a multi-parameter short-circuit current signal model, which includes a DC attenuation component, multiple harmonic components, and noise components. The preliminary estimation module, based on the multi-parameter short-circuit current signal model, uses the least squares method to perform a preliminary estimation of the linearized short-circuit current signal model and obtain the initial prior values ​​of the characteristic parameters. The linearized short-circuit current signal model is obtained by performing a trigonometric transformation on the AC component of the multi-parameter short-circuit current signal model and a Taylor expansion on the DC component exponent term, retaining the first two terms.

[0012] The linearized short-circuit current signal model is discretized according to the sampling time, and the short-circuit current equation system Y=HX is established, where Y is the short-circuit current sampling observation vector, X is the characteristic parameter vector, and H is the observation matrix. The linear equations are solved using the least squares method to obtain the estimated values ​​of the characteristic parameter vector. ,in H + Observation matrix H The pseudo-inverse of the preliminary estimate X 0 is used as the initial prior value.

[0013] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0014] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0015] The recursive estimation module uses the initial prior value as the initial state of the unscented Kalman filter, and uses the unscented Kalman filter algorithm to recursively estimate the multi-parameter short-circuit current signal model to obtain the final estimated value of the characteristic parameters. The zero-crossing point solving module reconstructs the short-circuit current waveform based on the final estimated value and solves for the zero-crossing point of the short-circuit current.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention addresses the problems of insufficient prediction accuracy due to model simplification and the limitation of prediction speed caused by batch computation and redundant iteration in traditional short-circuit current prediction algorithms. It proposes a short-circuit current zero-crossing prediction method that integrates least squares (LS) and unscented Kalman filter (UKF). By constructing a multi-parameter short-circuit current model including DC attenuation components, higher harmonics, and noise components, it achieves accurate characterization of complex fault transient current characteristics. Simultaneously, it utilizes unscented transformation and filtering recursion to directly estimate nonlinear state variables, avoiding the Jacobian matrix solution and linearization error problems in traditional linearization methods.

[0017] 2. This invention introduces the least squares algorithm to quickly estimate the initial values ​​of the short-circuit current characteristic parameters within a short time window during the initial stage of a fault, and uses these values ​​as the initial prior values ​​for the unscented Kalman filter algorithm, thereby improving the convergence performance and dynamic tracking capability in the early stage of filtering. At the same time, combined with the recursive state update mechanism, it realizes real-time online estimation of the short-circuit current parameters, which improves the accuracy and speed of short-circuit current zero-crossing prediction while ensuring the integrity of the short-circuit current model.

[0018] 3. This invention demonstrates high prediction accuracy and robustness under conditions containing harmonics and noise, as well as under real fault recording data. Compared with existing typical prediction algorithms, this invention has significant advantages in short-circuit current zero-crossing prediction accuracy, waveform fitting degree, and prediction speed. It can effectively meet the real-time and accuracy requirements of high-voltage AC circuit breakers for rapid phase-controlled breaking, thereby reducing arcing time and contact erosion risk, and improving the reliability and engineering application value of circuit breakers. Attached Figure Description

[0019] Figure 1 This is a flowchart of a short-circuit current zero-crossing prediction method integrating LS-UKF according to the present invention; Figure 2 This is a schematic diagram of the method flow for a short-circuit current zero-crossing prediction method integrating LS-UKF proposed in this invention; Figure 3 This is a schematic diagram showing the results of the comparative verification of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0021] like Figure 1 As shown, this invention proposes a short-circuit current zero-crossing prediction method integrating LS-UKF, comprising the following steps: Step 1: Monitor the line current signal in real time. If no short circuit fault is detected, continue monitoring. If a short circuit fault is detected, proceed to Step 2. Step 2: Considering the high-order harmonics and random noise present in the real power grid, establish a multi-parameter short-circuit current signal model; The method for constructing the multi-parameter short-circuit current signal model is as follows: A short-circuit fault signal model is established using a single-phase short-circuit fault model. High-order harmonics and random noise are introduced into the short-circuit fault signal model to establish a multi-parameter short-circuit current signal model.

[0022] Step 3: Based on the multi-parameter short-circuit current signal model, the linearized short-circuit current signal model is initially estimated using the least squares method to obtain the initial prior values ​​of the characteristic parameters. Step 3.1: By performing trigonometric transformation on the AC component in the multi-parameter short-circuit current signal model and Taylor expansion on the DC component exponent term while retaining the first two terms, a linearized short-circuit current signal model is obtained. Step 3.2: Discretize the linearized short-circuit fault signal model according to the sampling time to obtain the short-circuit current equation set Y=HX, where Y is the short-circuit current sampling observation vector, X is the characteristic parameter vector, and H is the observation matrix; Step 3.3: Solve the linear equation system using the least squares method to obtain the estimated values ​​of the characteristic parameter vector. ,in H + Observation matrix H The pseudo-inverse of the preliminary estimate X 0 is used as the initial prior value.

[0023] Step 4: Use the initial prior value as the initial state of the unscented Kalman filter, and use the unscented Kalman filter algorithm to recursively estimate the multi-parameter short-circuit current signal model to obtain the final estimated value of the characteristic parameters. Step 4.1: Use the characteristic parameters that need to be estimated as system state variables and the sampled short-circuit current values ​​as system observation variables to construct state equations and observation equations; Step 4.2: Use the initial prior value as the initial prior value of the unscented Kalman filter state vector, and set the initial value of the covariance matrix; Step 4.3: Construct the Sigma point set based on the mean of the posterior state estimate at the current time and the corresponding error covariance matrix, and calculate the weight corresponding to each Sigma point; Step 4.4: Based on the Sigma point set and the corresponding weights, calculate the predicted value of the system observation at sampling point j and the predicted mean of the 0th to 2nth sampling points, as well as the observation prediction covariance matrix and the cross-covariance between the state and the observation. Step 4.5: Calculate the Kalman gain based on the observation prediction covariance matrix and the cross-covariance between the state and the observation, and update the system state variables and error covariance based on the predicted values ​​of the system observations at sampling point j, the predicted mean values ​​of the 0th to 2nth sampling points, and the Kalman gain.

[0024] Step 5: Substitute the final estimated value into the multi-parameter short-circuit current signal model to reconstruct the short-circuit current waveform and solve for the zero-crossing point of the short-circuit current.

[0025] This invention also proposes a short-circuit current zero-crossing prediction system integrating LS-UKF, including a model building module, a preliminary estimation module, a recursive estimation module, and a zero-crossing solution module: The model building module, after identifying a short-circuit fault, constructs a multi-parameter short-circuit current signal model, which includes a DC attenuation component, multiple harmonic components, and noise components. The preliminary estimation module, based on the multi-parameter short-circuit current signal model, uses the least squares method to perform a preliminary estimation of the linearized short-circuit current signal model and obtain the initial prior values ​​of the characteristic parameters. The linearized short-circuit current signal model is obtained by performing a trigonometric transformation on the AC component of the multi-parameter short-circuit current signal model and a Taylor expansion on the DC component exponent term, retaining the first two terms.

[0026] The linearized short-circuit current signal model is discretized according to the sampling time, and the short-circuit current equation system Y=HX is established, where Y is the short-circuit current sampling observation vector, X is the characteristic parameter vector, and H is the observation matrix. The linear equations are solved using the least squares method to obtain the estimated values ​​of the characteristic parameter vector. ,in H + Observation matrix H The pseudo-inverse of the preliminary estimate X 0 is used as the initial prior value.

[0027] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0028] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0029] The recursive estimation module uses the initial prior value as the initial state of the unscented Kalman filter, and uses the unscented Kalman filter algorithm to recursively estimate the multi-parameter short-circuit current signal model to obtain the final estimated value of the characteristic parameters. The zero-crossing point solving module reconstructs the short-circuit current waveform based on the final estimated value and solves for the zero-crossing point of the short-circuit current.

[0030] Example 1 This invention proposes a short-circuit current zero-crossing prediction method integrating LS-UKF, such as... Figure 2 As shown, the method includes the following steps: Step 1, Current signal monitoring and fault identification: Monitor the line current signal in real time at a sampling frequency of 10kHz. If no short circuit fault is detected, continue monitoring. If a short circuit fault is detected, proceed to the subsequent prediction stage, while maintaining a sampling frequency of 10kHz to collect short circuit current samples.

[0031] The methods for identifying short-circuit faults include, but are not limited to, methods where the detected current value exceeds a set threshold (e.g., 1.2 times or more of the rated current), or where the rate of change of the detected current value exceeds a set threshold within a short period of time (e.g., within half a cycle). Since fault identification methods are mature existing technologies, they will not be described in detail in this embodiment.

[0032] Step 2, Construct a multi-parameter short-circuit fault signal model: A signal model is constructed based on the short-circuit fault mechanism of the power system. High-voltage power systems generally use a directly grounded neutral point. In short-circuit fault analysis, the system is usually equivalent to an infinite power source, the system load connection is star-grounded, and the circuit breakers operate independently for each phase. For phase-controlled interruption, the prediction target is the zero-crossing point of the short-circuit current in each phase. Multi-phase short circuits can be decomposed into independent prediction problems of the current in each phase. Therefore, a single-phase short-circuit fault model can be used to analyze the fault current, and the short-circuit fault signal model is established as follows: (1) In the formula, A 0 represents the initial value of DC component attenuation. t The DC component decay time constant, A n The amplitude of the fundamental component. i The phase angle of the fundamental component at the moment the fault occurs.

[0033] Furthermore, considering the high-order harmonics and random noise present in a real power grid, a multi-parameter short-circuit current signal model is established as follows: (2) In the formula, A n The amplitudes of the fundamental and harmonic components are... oh It is the power frequency angular frequency. i n The phase angle of the fundamental and harmonic components at the moment the fault occurs. N The total harmonic order is... i noise ( t ) represents the Gaussian white noise component.

[0034] Step 3, Least Squares (LS) Preliminary Estimation: The sampling time is T1, N1 short-circuit current samples are continuously collected, the multi-parameter short-circuit fault signal model is linearized, and the characteristic parameters are solved using the least squares method. Details are as follows: Step 3.1, Linearize the multi-parameter short-circuit fault signal model. According to equation (2) in step 2, perform a trigonometric transformation on the AC component, and perform a Taylor expansion on the exponential term of the DC component, retaining the first two terms, to obtain the linearized short-circuit fault signal model, expressed as: (3) In the formula: (4) in, The sine component coefficients of the nth harmonic are... The coefficients of the cosine component of the nth harmonic are... This is the DC bias component. For linear drift component coefficients, This is the initial amplitude of the DC component. Let n be the amplitude of the nth harmonic. It is the time constant of the DC component.

[0035] Step 3.2: Establish a system of least squares linear equations, and transform the linearized short-circuit fault signal model from Step 3.1 according to the sampling time Δ. t Discretization, let Y = [ i ( t ) i ( t + Δt ... i ( t + kΔt )] T , X = [ X 1n X 2n X 3 X 4] T ,and (5) The short-circuit current equations can then be expressed as: (6) Step 3.3: Solve equation (6) using the least squares method to obtain a preliminary estimate of the short-circuit current characteristic parameter vector. X 0, when the number of sampling points k When the number of state variables is greater than or equal to the number of state variables, multiply both sides by . H pseudo-reversal H + Then we obtain the estimated value of the feature parameter vector. X 0: (7) Step 4: Unscented Kalman Filter (UKF) recursive estimation. After the initial estimation, the sampling time is T2, and N2 short-circuit current samples are collected point by point. The unscented Kalman filter algorithm is used to directly perform recursive estimation on the multi-parameter short-circuit current signal model, and the characteristic parameters are further solved. Details are as follows: Step 4.1, establish the unscented Kalman filter equation set, which describes the state-space model of the stochastic discrete nonlinear system as follows: (8) In the formula, for any time k , X k It is a system state variable. Z k These are system observation variables. f It is a function of the state equation of a nonlinear system. h It is the observation equation function of a nonlinear system. W k It has a covariance matrix Q Process noise, V k It has a covariance matrix R Measurement noise.

[0036] According to equation (2) in step 2, it is sorted by sampling time. Δt Discretization involves separating the feature parameters that need to be estimated. A 0、 t、A n , i n The sampled short-circuit current value will be used as a system state variable. i ( k As observed variables of the system, construct the state equation and observation equation: (9) Step 4.2, parameter initialization, using the estimated feature parameter vector values ​​obtained in step 3.3. X 0 is used as the initial prior value for the unscented Kalman filter state vector, and an appropriate initial value for the covariance matrix is ​​set. P 0 is used as the starting point for the iteration.

[0037] Step 4.3, Unscented Transformation: Estimate the mean based on the posterior state at time k. and the corresponding error covariance matrix Construction 2 m Add one more Sigma point to obtain the Sigma point set, and calculate the weight corresponding to each Sigma point: (10) In the formula, the subscript k Indicates the first k At any given moment, starting from the initial value of 0. m This represents the dimension of the state vector.

[0038] The formula for calculating the weight of each Sigma point is as follows: (11) In the formula, The weights are the state / observation mean. The superscript represents the weights of the covariance. j Indicates the number of sampling points; l Scale factor; α This is the scaling factor, which controls the distance of the Sigma point relative to the mean of the state variables; β These are state distribution parameters, used to introduce prior information about the statistical properties of state variables.

[0039] Step 4.4, calculate the predicted state vector and covariance matrix, based on k Calculate the Sigma point set and corresponding weights at time 1. k The prior estimate of the Sigma point at time +1 is obtained by weighted summation. k Predicted system state variables and covariance matrix at time +1: (12) Step 4.5, update the predicted values ​​of the observation vectors and the covariance matrix, based on... k Substituting the short-circuit current sample value at time +1 and the prior estimate at Sigma point into the observation equation, we obtain the predicted value of the observed quantity at Sigma point. Then, by weighted summation, we obtain the predicted value of the system observation at sampling point j. and the predicted mean of the 0th to 2nth sampling points and the observed-predicted covariance matrix Cross-covariance between state and observation : (13) Step 4.6, Calculate the Kalman gain. And obtain the updated system state variables. and error covariance : (14) Step 4.7: For each new short-circuit current sample, perform iterative calculations in steps 4.3 to 4.5 until the sampling time reaches T2, i.e., the number of sampling points reaches N2.

[0040] Step 5, reconstruct the waveform and solve for the zero-crossing point: After completing the unscented Kalman filter iteration, substitute the obtained characteristic parameter estimates into equation (2) to reconstruct the short-circuit current waveform and use the linear interpolation method to solve for the zero-crossing point.

[0041] Figure 3 The proposed prediction method is validated using waveform recording of short-circuit current data from a real power grid and compared with existing common algorithms. After sampling the short-circuit current signal, the prediction algorithm is used to obtain the expression coefficients of the short-circuit current signal model, thereby obtaining characteristic parameters such as AC component amplitude, fault phase angle, and DC decay time constant. The short-circuit current waveform is then reconstructed, and the zero-crossing point is calculated. The proposed method completes the prediction of short-circuit current in a real power grid in only 5ms. Compared with other algorithms, it has higher prediction accuracy under the same sampling time and can meet the requirements of phase-controlled short-circuit current interruption.

[0042] Example 2 This invention also proposes a short-circuit current zero-crossing prediction system integrating LS-UKF, including a model building module, a preliminary estimation module, a recursive estimation module, and a zero-crossing solution module: The model building module, after identifying a short-circuit fault, constructs a multi-parameter short-circuit current signal model, which includes a DC attenuation component, multiple harmonic components, and noise components. The preliminary estimation module, based on the multi-parameter short-circuit current signal model, uses the least squares method to perform a preliminary estimation of the linearized short-circuit current signal model and obtain the initial prior values ​​of the characteristic parameters. The recursive estimation module uses the initial prior value as the initial state of the unscented Kalman filter, and uses the unscented Kalman filter algorithm to recursively estimate the multi-parameter short-circuit current signal model to obtain the final estimated value of the characteristic parameters. The zero-crossing point solving module reconstructs the short-circuit current waveform based on the final estimated value and solves for the zero-crossing point of the short-circuit current.

[0043] In the preliminary estimation module, the linearized short-circuit current signal model is obtained by performing a trigonometric transformation on the AC component of the multi-parameter short-circuit current signal model and a Taylor expansion on the DC component exponent term, retaining the first two terms.

[0044] The linearized short-circuit current signal model is discretized according to the sampling time, and the short-circuit current equation system Y=HX is established, where Y is the short-circuit current sampling observation vector, X is the characteristic parameter vector, and H is the observation matrix. The linear equations are solved using the least squares method to obtain preliminary estimates of the characteristic parameter vectors. ,in H + Observation matrix H The pseudo-inverse of the preliminary estimateX 0 is used as the initial prior value.

[0045] Example 3 The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0046] Example 4 The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0047] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0048] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0049] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0050] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A short-circuit current zero-crossing prediction method integrating LS-UKF, characterized in that, include: Step 1: Monitor the line current signal in real time. If no short circuit fault is detected, continue monitoring. If a short circuit fault is detected, proceed to step 2; Step 2: Considering the high-order harmonics and random noise present in the real power grid, establish a multi-parameter short-circuit current signal model; Step 3: Based on the multi-parameter short-circuit current signal model, the linearized short-circuit current signal model is initially estimated using the least squares method to obtain the initial prior values ​​of the characteristic parameters. Step 4: Use the initial prior value as the initial state of the unscented Kalman filter, and use the unscented Kalman filter algorithm to recursively estimate the multi-parameter short-circuit current signal model to obtain the final estimated value of the characteristic parameters. Step 5: Substitute the final estimated value into the multi-parameter short-circuit current signal model to reconstruct the short-circuit current waveform and solve for the zero-crossing point of the short-circuit current.

2. The short-circuit current zero-crossing prediction method integrating LS-UKF as described in claim 1, characterized in that: In step 2, the method for constructing the multi-parameter short-circuit current signal model is as follows: A short-circuit fault signal model is established using a single-phase short-circuit fault model. High-order harmonics and random noise are introduced into the short-circuit fault signal model to establish a multi-parameter short-circuit current signal model.

3. The short-circuit current zero-crossing prediction method integrating LS-UKF as described in claim 1, characterized in that: In step 3, the methods for obtaining the initial prior values ​​of the feature parameters include: Step 3.1: By performing trigonometric transformation on the AC component in the multi-parameter short-circuit current signal model and Taylor expansion on the DC component exponent term while retaining the first two terms, a linearized short-circuit current signal model is obtained. Step 3.2: Discretize the linearized short-circuit fault signal model according to the sampling time to obtain the short-circuit current equation set; Step 3.3: Solve the short-circuit current equations using the least squares method to obtain the estimated values ​​of the characteristic parameter vector.

4. The short-circuit current zero-crossing prediction method integrating LS-UKF as described in claim 3, characterized in that: In step 3.2, the linearized short-circuit current signal model is discretized according to the sampling time, and the short-circuit current equation system Y=HX is established, where Y is the short-circuit current sampling observation vector, X is the characteristic parameter vector, and H is the observation matrix; Step 3.3: Solve the linear equation system using the least squares method to obtain the initial estimate of the characteristic parameter vector. ,in H + Observation matrix H The pseudo-inverse of the preliminary estimate X 0 is used as the initial prior value.

5. The short-circuit current zero-crossing prediction method integrating LS-UKF according to claim 1, characterized in that: In step 4, the methods for obtaining the final estimates of the feature parameters based on the initial prior values ​​include: Step 4.1: Use the characteristic parameters that need to be estimated as system state variables and the sampled short-circuit current values ​​as system observation variables to construct state equations and observation equations; Step 4.2: Use the initial prior value as the initial prior value of the unscented Kalman filter state vector, and set the initial value of the covariance matrix; Step 4.3: Construct the Sigma point set based on the mean of the posterior state estimate at the current time and the corresponding error covariance matrix, and calculate the weight corresponding to each Sigma point; Step 4.4: Based on the Sigma point set and the corresponding weights, calculate the predicted value of the system observation at sampling point j and the predicted mean of the 0th to 2nth sampling points, as well as the observation prediction covariance matrix and the cross-covariance between the state and the observation. Step 4.5: Calculate the Kalman gain based on the observation prediction covariance matrix and the cross-covariance between the state and the observation, and update the system state variables and error covariance based on the predicted values ​​of the system observations at sampling point j, the predicted mean values ​​of the 0th to 2nth sampling points, and the Kalman gain.

6. A short-circuit current zero-crossing prediction system using the method described in any one of claims 1-5, comprising a model building module, a preliminary estimation module, a recursive estimation module, and a zero-crossing solution module, characterized in that: The model building module, after identifying a short-circuit fault, constructs a multi-parameter short-circuit current signal model, which includes a DC attenuation component, multiple harmonic components, and noise components. The preliminary estimation module, based on the multi-parameter short-circuit current signal model, uses the least squares method to perform a preliminary estimation of the linearized short-circuit current signal model and obtain the initial prior values ​​of the characteristic parameters. The recursive estimation module uses the initial prior value as the initial state of the unscented Kalman filter, and uses the unscented Kalman filter algorithm to recursively estimate the multi-parameter short-circuit current signal model to obtain the final estimated value of the characteristic parameters. The zero-crossing point solution module reconstructs the short-circuit current waveform based on the final estimated value and solves for the zero-crossing point of the short-circuit current.

7. A short-circuit current zero-crossing prediction system integrating LS-UKF according to claim 6, characterized in that: In the preliminary estimation module, the linearized short-circuit current signal model is obtained by performing a trigonometric transformation on the AC component of the multi-parameter short-circuit current signal model and a Taylor expansion on the DC component exponent term, retaining the first two terms.

8. A short-circuit current zero-crossing prediction system integrating LS-UKF according to claim 6, characterized in that: In the preliminary estimation module, the linearized short-circuit current signal model is discretized according to the sampling time, and the short-circuit current equation system Y=HX is established, where Y is the short-circuit current sampling observation vector, X is the characteristic parameter vector, and H is the observation matrix; The linear equations are solved using the least squares method to obtain the estimated values ​​of the characteristic parameter vector. ,in H + Observation matrix H The pseudo-inverse of the preliminary estimate X 0 is used as the initial prior value.

9. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-5.

Images