A current transformer error online monitoring method considering load effect and related device
By constructing a set of overdetermined regression equations and using an elastic network regression method, the real-time and accuracy issues of current transformer error detection are solved, enabling stable monitoring and rapid identification under load conditions, adapting to complex operating conditions, and meeting the high-efficiency monitoring needs of smart substations.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- MARKETING SERVICE CENT (MEASURING CENT) OF STATE GRID SHAANXI ELECTRIC POWER CO LTD
- Filing Date
- 2026-03-02
- Publication Date
- 2026-06-26
AI Technical Summary
Existing methods for detecting errors in current transformers are inefficient and costly, making it difficult to achieve real-time monitoring and accurate diagnosis. Furthermore, they cannot accurately identify subtle error shifts under load conditions. Existing methods are sensitive to linear correlation, have unstable values, and cannot adapt to complex operating conditions.
A hybrid regularized loss function is constructed using a method based on overdetermined regression equations and elastic network regression. The impedance parameter matrix of the current transformer is solved by the trust region reflection algorithm to obtain the impedance parameter increment and realize online monitoring.
It operates stably under load, converges quickly, accurately identifies subtle error deviations, adapts to complex operating conditions, meets the real-time monitoring and precise diagnosis requirements of smart substations, and reduces sensitivity to initial values.
Smart Images
Figure CN122283572A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of electronic information technology, and relates to the field of power current transformer error monitoring, and particularly to an online monitoring method and related device for current transformer errors that takes into account load effects. Background Technology
[0002] In power scenarios such as substations, current transformers (CTs) convert large primary currents into small secondary currents for use by secondary equipment through the principle of electromagnetic induction. They are the core equipment for current measurement, relay protection and control in power systems, and their measurement accuracy directly affects the safety and stability of power system operation.
[0003] With the continuous development of power systems towards intelligence and automation, the demand for real-time status monitoring of current transformers is becoming increasingly urgent. Explained, the errors (ratio error and phase error) of current transformers gradually increase due to factors such as equipment aging, changes in ambient temperature, and fluctuations in secondary loads. If the error exceeds the allowable range, inaccurate metering, maloperation or failure to operate protection systems may occur, potentially leading to serious power accidents.
[0004] Currently, traditional methods for detecting current transformer errors mainly rely on manual inspections (for example, maintenance personnel periodically visit the site to visually inspect current transformers and other equipment, compare instrument readings, and perform measurements with handheld testing instruments, combining this with their maintenance experience to determine if there are any abnormalities). This method requires access to the test line during power outages or isolation, lacking the ability to capture errors in continuous operation. It is not only inefficient but also costly, failing to meet the real-time monitoring and accurate diagnostic requirements of smart substations. Furthermore, existing high-voltage substation transformer error detection devices introduce reference standard transformers or incorporate precision current sources to compare the tested transformer with standard signals, using sampling and calculation modules to obtain ratio and phase angle error parameters. However, these devices generally rely on external standard equipment, lack intelligent online monitoring solutions during the testing process, and struggle to achieve automatic diagnostics during operation.
[0005] Specifically, Chinese invention application CN202310990348.9 discloses a method, device, and electronic equipment for verifying the relative error of current transformers in substations. It presents a method for monitoring the error of multiple current transformers. This method uses the least squares method to solve for the parameters, providing unbiased optimal linear estimates. However, it is extremely sensitive to the linear correlation between explanatory variables. When there is a high correlation (i.e., multicollinearity) between the secondary current signals of multiple transformers, the transpose product of the design matrix in the least squares method approaches singularity, leading to numerical instability in the solution process, a significant increase in the variance of parameter estimates, and even significant distortion. This phenomenon impairs the model's ability to identify faults. The problems manifest as decreased identification accuracy, large fluctuations in results, and inability to effectively identify subtle error shifts, severely impacting the online monitoring capability of current transformer errors. Furthermore, when the current transformer is close to the substation and the line is short, it operates in a zero-flux state, and the aforementioned ratio and angle difference models can effectively monitor its errors. However, when the current transformer is installed far from the substation's secondary equipment, the impedance in the secondary circuit cannot be ignored, and the current transformer operates under load. In this case, the electrical parameters of the secondary branch will couple to the excitation branch, causing corresponding fluctuations in the ratio and angle difference. Therefore, error monitoring of the current transformer cannot be achieved through regression calculations of the ratio and angle difference. Summary of the Invention
[0006] The purpose of this invention is to provide a method and related apparatus for online monitoring of current transformer errors considering load effects, in order to solve one or more of the aforementioned technical problems. Specifically, the technical solution disclosed in this invention is a novel solution that can operate stably under load conditions, is insensitive to initial values, and converges quickly, thus meeting the requirements for online monitoring of current transformer errors under complex operating conditions.
[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for online monitoring of current transformer errors considering load effects, comprising the following steps: Step 1: Based on the selected target bus, obtain the secondary fundamental current phasor value of the current transformer in each branch line of the target bus; Step 2: Based on the phasor values of the secondary fundamental current of the current transformer obtained in Step 1, construct the overdetermined regression equation set. Step 3: Based on the overdetermined regression equation set constructed in Step 2, introduce the elastic network regression problem to construct the hybrid regularized loss function, and solve the elastic network regression problem to minimize the hybrid regularized loss function, thereby obtaining the current transformer impedance parameter matrix of the overdetermined regression equation set. Step 4: Select a reference current transformer based on the current transformers in each branch of the target busbar; obtain the impedance parameter increments of other current transformers compared to the reference current transformer according to the current transformer impedance parameter matrix; obtain the online monitoring results of current transformer error based on the comparison results of the impedance parameter increments of each current transformer with the preset increment threshold.
[0008] A further improvement to the technical solution of this invention is that, in step 2, the constructed overdetermined regression equation system is expressed as: ; In the formula, This represents the phasor value of the second fundamental current. Indicates the first q The first current transformer p Group secondary current data; This is the impedance parameter matrix of the current transformer. For the first q The impedance parameters of a current transformer.
[0009] A further improvement to the technical solution of the present invention is that, in the impedance parameter matrix of the current transformer, the first... i Impedance parameters of a current transformer Represented as: ; In the formula, , The first i The resistance and leakage reactance of the secondary winding of a current transformer i =1, 2, ..., q ; , The first i The excitation resistance and excitation reactance of each current transformer; For the first i The secondary load impedance of the current transformer.
[0010] A further improvement to the technical solution of the present invention lies in that, the first i The process of obtaining the secondary load impedance of the first current transformer includes: acquiring the second... i The secondary voltage of the voltage transformer corresponding to each current transformer is used as the ratio of the secondary voltage to the secondary current as the secondary load impedance.
[0011] A further improvement to the technical solution of the present invention is that the hybrid regularization loss function is expressed as: ; In the formula, QE is the hybrid regularization loss function; To make the first qSubstituting the impedance parameters of each current transformer into the residuals of the over-deterministic regression equations This is the original least squares loss; For regularity; The mixing coefficient; It is an L1 norm. It is the L2 norm. A This is the impedance parameter matrix of the current transformer.
[0012] A further improvement of the technical solution of the present invention is that, in step 3, in the process of solving the elastic network regression problem to minimize the hybrid regularization loss function, the trust region reflection algorithm, gradient descent algorithm, or Gauss-Newton iteration algorithm is adopted.
[0013] A second aspect of the present invention provides an online error monitoring system for current transformers that takes into account load effects, comprising: The data acquisition module is used to acquire the secondary fundamental current phasor values of the current transformers in each branch line of the selected target bus. The equation system construction module is used to construct an overdetermined regression equation system based on the obtained secondary fundamental current phasor values of the current transformer. The parameter optimization module is used to construct a hybrid regularized loss function based on the constructed overdetermined regression equation set, introduce the elastic network regression problem, solve the elastic network regression problem to minimize the hybrid regularized loss function, and obtain the current transformer impedance parameter matrix of the overdetermined regression equation set. The monitoring result acquisition module is used to select a reference current transformer based on the current transformers in each branch line of the target bus; obtain the impedance parameter increment of other current transformers compared with the reference current transformer according to the current transformer impedance parameter matrix; and obtain the online monitoring result of current transformer error based on the comparison result of the impedance parameter increment of each current transformer with the preset increment threshold.
[0014] In a third aspect, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the online monitoring method for current transformer error considering load effects as described in any one of the first aspects of the present invention.
[0015] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the online monitoring method for current transformer error considering load effects as described in any one of the first aspects of the present invention.
[0016] In a fifth aspect, the present invention provides a computer program product comprising computer instructions which, when executed by a processor, implement the steps of the online monitoring method for current transformer errors considering load effects as described in any one of the first aspects of the present invention.
[0017] Compared with the prior art, the present invention has the following beneficial effects: The technical solution disclosed in this invention first obtains the phasor values of the secondary fundamental current of the current transformers in each branch line of the target bus, providing basic data for subsequent analysis. Then, an overdetermined regression equation system is constructed, and a loss function is built and solved by introducing an elastic network regression problem to obtain the impedance parameter matrix of the current transformers. This process does not rely on external standard equipment, achieving intelligent online monitoring and overcoming the shortcomings of existing traditional solutions. After obtaining the impedance parameter matrix, a reference current transformer is selected, and the impedance parameter increments of other current transformers relative to the reference are obtained. By comparing the impedance parameter increments of each current transformer with a preset increment threshold, the online error monitoring results are obtained. This new method overcomes the sensitivity of the least squares method to linear correlation of variables, is numerically stable under multicollinearity, provides accurate parameter estimation, and can effectively identify subtle error shifts. Simultaneously, it considers load effects, adapts to complex operating conditions, operates stably under load, is insensitive to initial values, and converges quickly, meeting the requirements of smart substations for real-time monitoring and accurate diagnosis. In summary, this invention can overcome the multicollinearity problem and adapt to different operating conditions. In addition, even when the current transformer is operating under load and the electrical parameters of the secondary side branch are coupled, causing fluctuations in the ratio and angle differences, the error can be accurately monitored, realizing stable and fast convergence of online monitoring under complex operating conditions. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in this 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 some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0019] Figure 1 This is a flowchart illustrating an online error monitoring method for current transformers that takes load effects into account, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the bus current vector balance principle in an embodiment of the present invention; Figure 3 This is a schematic diagram of the parameter model of the current transformer in an embodiment of the present invention; Figure 4 This is a schematic diagram of the trust region reflection algorithm in an embodiment of the present invention; Figure 5This is a schematic diagram comparing error diagnosis results in a specific embodiment of the present invention; Figure 6 This is a schematic diagram of an online current transformer error monitoring system that takes load effects into account, according to an embodiment of the present invention. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention; obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0021] Based on the technical solutions disclosed in the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.
[0022] Please see Figure 1 The present invention provides an online error monitoring method for current transformers considering load effects, comprising the following steps: Step 1: Based on the selected target bus, obtain the secondary fundamental current phasor value of the current transformer in each branch line of the target bus; Step 2: Based on the phasor values of the secondary fundamental current of the current transformer obtained in Step 1, construct the overdetermined regression equation set. Step 3: Based on the overdetermined regression equation set constructed in Step 2, introduce the elastic network regression problem to construct the hybrid regularized loss function, solve the elastic network regression problem to minimize the hybrid regularized loss function, and obtain the current transformer impedance parameter matrix of the overdetermined regression equation set. Step 4: Select a reference current transformer based on the current transformers in each branch of the target busbar; obtain the impedance parameter increments of other current transformers compared to the reference current transformer according to the current transformer impedance parameter matrix; obtain the online monitoring results of current transformer error based on the comparison results of the impedance parameter increments of each current transformer with the preset increment threshold.
[0023] Traditional manual inspection methods require maintenance personnel to periodically conduct on-site visual inspections and compare instrument readings. This not only necessitates connecting test lines during power outages or isolation, making real-time error capture during continuous operation impossible, but is also extremely inefficient and costly, failing to meet the high demands of smart substations for real-time monitoring and accurate diagnosis. Existing high-voltage substation transformer error detection devices, while incorporating reference standard transformers or built-in precision current sources for comparative testing, generally rely on external standard equipment. The testing process lacks intelligent online monitoring solutions, making automatic diagnosis difficult during operation. Current detection methods using the least squares method are highly sensitive to linear correlations between explanatory variables. In cases of multicollinearity, the solution process becomes numerically unstable, with increased or even distorted parameter estimation variance, leading to decreased accuracy and large fluctuations in fault identification results. This makes it difficult to effectively identify subtle error shifts, severely impacting online monitoring capabilities. When current transformers are installed far from substation secondary equipment and operate under load, the coupling effect of secondary branch electrical parameters causes fluctuations in ratio and angle differences, which existing methods cannot monitor through regression calculations.
[0024] The technical solution disclosed in this invention first obtains the phasor values of the secondary fundamental current of the current transformers in each branch of the target bus, providing accurate data for subsequent analysis. Then, it constructs a set of overdetermined regression equations, introduces an elastic network regression problem to construct a loss function and solves it, obtaining the impedance parameter matrix of the current transformers. This process does not rely on external standard equipment, achieving intelligent online monitoring and overcoming the dependency problem of existing devices. After obtaining the impedance parameter matrix, a reference current transformer is selected, and the impedance parameter increments of other current transformers relative to the reference are obtained. The online error monitoring results are obtained by comparing these increments with a preset increment threshold. This method effectively overcomes the multicollinearity problem of the least squares method, ensuring numerical stability and accurate parameter estimation under multicollinearity conditions. It can effectively identify subtle error shifts, improve fault identification accuracy, and reduce result fluctuations. Simultaneously, it considers the load effect; when the current transformer is under load, the coupling effect of the secondary branch electrical parameters does not affect error monitoring. It can adapt to complex operating conditions, operate stably under load, is insensitive to initial values, and converges quickly, meeting the requirements of smart substations for real-time monitoring and accurate diagnosis.
[0025] In a specific exemplary technical solution of the present invention, in step 2, the step of constructing a set of overdetermined regression equations based on the phasor values of the secondary fundamental current of the current transformer obtained in step 1... The overdetermined regression equations are expressed as follows: ; In the formula, This represents the phasor value of the second fundamental current. Indicates the measured number of qThe first current transformer p Group secondary current data; This is the impedance parameter matrix of the current transformer. For the first q The impedance parameters of a current transformer.
[0026] For a more detailed explanation, please refer to Figure 2 , Figure 2 This diagram illustrates the circuit connections of a substation busbar and its lines. Due to the physical connection, line 1 on the busbar connects to line 2. q The in-phase currents satisfy the KCL theorem, that is, the sum of the current phasors is equal to zero. Based on this principle, an error diagnosis model for current transformers is established.
[0027] Based on the T-type equivalent circuit of the current transformer, an equivalent circuit parameter model of the electromagnetic current transformer is established. The relationship between the secondary current and the primary current is then:
[0028] In the formula, and These are the excitation resistance and the excitation reactance, respectively. and These are the resistance and leakage reactance of the secondary winding, respectively. This is the secondary load impedance.
[0029] If the secondary voltage of a voltage transformer is collected, the secondary load impedance value Z can be obtained by comparing its ratio with the secondary current. Given Z, the KCL equation can be reconstructed using the above formula, and nonlinear least squares regression can be performed to determine which current transformer's parameters have shifted, thus achieving error identification. Specifically: make By collecting and calculating p Group q The secondary fundamental phasor value of each current transformer is used to establish a... q The system of regression equations with over-limit solutions; where the first dimensional equation is the impedance parameter matrix of the current transformer. i Impedance parameters of a current transformer , i =1, 2, ..., q .
[0030] Please see Figure 3 Based on the principle of current transformers, and taking the T-type equivalent circuit of current transformers as a reference, an equivalent circuit parameter model of electromagnetic current transformers is established; for explanation, since the primary side of a current transformer can be regarded as a current source, its primary impedance can be ignored. Figure 3 middle, and These are the excitation resistance and the excitation reactance, respectively. and These are the resistance and leakage reactance of the secondary winding, respectively. Z This is the secondary load impedance.
[0031] Please see Figure 4 This paper illustrates the trust region reflection algorithm flow. The technical solution of this invention uses the trust region reflection method to iteratively solve the error diagnosis model under the established current transformer parameter model. Explained, the core idea of the trust region reflection method is to define a local region (called the trust region) in each iteration, replace the objective function with an approximate model within this local region, and update the iteration points by optimizing the approximate model. By dynamically adjusting the size of the trust region, the trust region reflection method ensures the consistency between the approximate model and the objective function, thereby guaranteeing the convergence and robustness of the algorithm. Figure 4 middle, f (x) is the objective function to be optimized. x 0 represents the initial value of the parameter matrix to be determined. s 0 represents the initial radius of the trust region. The convergence condition value, and The threshold for adjusting the dynamic radius of the trust region is used. The trust region reflection algorithm is employed to solve the elastic network regression problem: L1 regularization compresses some regression coefficients, making the model simpler, more optimized, and still accurate. L1 regularization compresses the error of non-faulty current transformers to zero; while L2 regularization addresses the problem of high correlation (i.e., multicollinearity) among the secondary current signals of multiple transformers. By balancing the constraints of L1 and L2 regularization terms, elastic network regression combines the advantages of both, achieving a more accurate regression of the current transformer error diagnosis model. After obtaining the increments of the impedance parameters of the error-biased transformers through regression, faulty transformer identification can be performed. Interpretably, the aforementioned trust region reflection method can be replaced by suitable solution algorithms such as gradient descent or Gauss-Newton iteration.
[0032] In this embodiment of the invention, elastic network regression is introduced to construct a hybrid regularized loss function Q. E : ; In the formula, QE is the hybrid regularization loss function; To make the first q Substituting the impedance parameters of each current transformer into the residuals of the over-deterministic regression equations This is the original least squares loss; For regularity; The mixing coefficient; It is an L1 norm. It is the L2 norm. A This is the impedance parameter matrix of the current transformer.
[0033] Please see Figure 5 , Figure 5 The simulation results of error diagnosis according to a specific embodiment of the present invention are shown for comparison. CT3 is set as a faulty transformer, and its... R 2. X 2. R m and X m Regression calculations are then performed. Interpretatively, since the absolute values of the four parameters of the current transformer are generally unknown, the first transformer is used as a benchmark to calculate the parameter increments of the other transformers relative to it. The results of the regression calculations are as follows: Figure 5 As shown in the figure, the calculation results show that the absolute error of the regression calculation of each impedance parameter of the current transformer is within 1%, which can realize the identification of the error of the current transformer and find the offset parameter.
[0034] Please refer to Tables 1 and 2, which show the comparison between the actual and estimated incremental values of CT1 when some parameters are simulated to change. To test experimental data under the parameter model, identical known loads were added to the secondary sides of the three current transformers during the experiment. Since the excitation resistance Rm and excitation reactance Xm of the current transformers are difficult to change under experimental conditions, the changes in secondary winding resistance R2 and secondary winding leakage reactance X2 are taken as examples. CT1 is set as a fault transformer, and a 50 mΩ resistor and a 0.157 mH inductor (with a reactance of 49.3 mΩ) are connected in series on the secondary side to simulate changes in secondary winding resistance R2 and secondary winding leakage reactance X2. Experimental data for simulating changes in secondary winding resistance R2 and secondary winding leakage reactance X2 are collected and recorded. The comparison between the estimated and actual parameter values of the fault transformer CT1 obtained using a nonlinear regression method combining elastic networks and trust region reflection is shown in the table. Due to the effect of hybrid regularization, the algorithm compresses the parameters of non-faulty current transformers CT2 and CT3 to 0, and they are not listed here. The calculation results in the table show that the absolute error of the regression calculation of each impedance parameter of the current transformer is within 1%, which can identify the error of the current transformer and find the offset parameters.
[0035] Table 1. Simulated Changes R Comparison of actual and estimated values of CT1 parameter increments at 2 o'clock
[0036] Table 2. Simulated Changes X Comparison of actual and estimated values of CT1 parameter increments at 2 o'clock
[0037] In summary, when the current transformer is installed far from the secondary equipment of the substation, the impedance in the secondary circuit cannot be ignored, and the current transformer operates under load. Therefore, error monitoring of the current transformer cannot be achieved through regression calculations of phase difference and angle difference. This invention, however, achieves error monitoring of the current transformer through regression calculations of the current transformer's parameter model. The novel solution disclosed in this invention has the following significant improvements: (1) Low sensitivity to the initial value of the current transformer error; Explanation: When the initial value of the set parameter is far from the true value, this method can still reach the convergence point, and has low sensitivity to the initial value. (2) Obtain the absolute value of the current transformer error; Explanatoryly, for the possible multiple solutions of the current transformer error model, when the method of the present invention encounters the constraint boundary, the algorithm will automatically "reflect" back to the constraint range to ensure the feasibility of the solution, thereby obtaining the absolute value of the current transformer error; (3) Fast convergence and good real-time performance; Explainedly, due to the strategy of dynamically adjusting the trust region radius, the method of this invention accelerates convergence in the later stage of iteration, and the convergence speed is significantly better than the traditional gradient descent method. It only requires a single-digit number of iterations, while the traditional gradient descent method requires hundreds of iterations, and the computation time differs by two orders of magnitude. The real-time performance of the algorithm is significantly improved.
[0038] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.
[0039] Please see Figure 6 In this embodiment of the invention, an online error monitoring system for a current transformer considering load effects is provided, comprising: The data acquisition module is used to acquire the secondary fundamental current phasor values of the current transformers in each branch line of the selected target bus. The equation system construction module is used to construct an overdetermined regression equation system based on the obtained secondary fundamental current phasor values of the current transformer. The parameter optimization module is used to construct a hybrid regularized loss function based on the constructed overdetermined regression equation set, introduce the elastic network regression problem, solve the elastic network regression problem to minimize the hybrid regularized loss function, and obtain the current transformer impedance parameter matrix of the overdetermined regression equation set. The monitoring result acquisition module is used to select a reference current transformer based on the current transformers in each branch line of the target bus; obtain the impedance parameter increment of other current transformers compared with the reference current transformer according to the current transformer impedance parameter matrix; and obtain the online monitoring result of current transformer error based on the comparison result of the impedance parameter increment of each current transformer with the preset increment threshold.
[0040] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used to execute the operation of an online monitoring method for current transformer errors considering load effects.
[0041] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the online monitoring method for current transformer errors considering load effects in the above embodiments.
[0042] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.
[0043] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0044] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0045] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0046] 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 scope of protection of the claims of the present invention.
Claims
1. A method for online monitoring of current transformer errors considering load effects, characterized in that, Includes the following steps: Step 1: Based on the selected target bus, obtain the secondary fundamental current phasor value of the current transformer in each branch line of the target bus; Step 2: Based on the phasor values of the secondary fundamental current of the current transformer obtained in Step 1, construct the overdetermined regression equation set. Step 3: Based on the overdetermined regression equation set constructed in Step 2, introduce the elastic network regression problem to construct the hybrid regularized loss function, and solve the elastic network regression problem to minimize the hybrid regularized loss function, thereby obtaining the current transformer impedance parameter matrix of the overdetermined regression equation set. Step 4: Select a reference current transformer based on the current transformers in each branch of the target bus. Based on the impedance parameter matrix of the current transformer, obtain the impedance parameter increment of other current transformers compared to the reference current transformer; Based on the comparison results of the impedance parameter increment of each current transformer with the preset increment threshold, the online monitoring results of the current transformer error are obtained.
2. The online monitoring method for current transformer error considering load effects according to claim 1, characterized in that, In step 2, the constructed overdetermined regression equation system is expressed as: ； In the formula, This represents the phasor value of the second fundamental current. Indicates the first q The first current transformer p Group secondary current data; This is the impedance parameter matrix of the current transformer. For the first q The impedance parameters of a current transformer.
3. The online monitoring method for current transformer error considering load effects according to claim 2, characterized in that, In the impedance parameter matrix of the current transformer, the first... i Impedance parameters of a current transformer Represented as: ； In the formula, , The first i The resistance and leakage reactance of the secondary winding of a current transformer i =1, 2, ..., q ; , The first i The excitation resistance and excitation reactance of each current transformer; For the first i The secondary load impedance of the current transformer.
4. The online monitoring method for current transformer error considering load effects according to claim 3, characterized in that, No. i The process of obtaining the secondary load impedance of the first current transformer includes: acquiring the second... i The secondary voltage of the voltage transformer corresponding to each current transformer is used as the ratio of the secondary voltage to the secondary current as the secondary load impedance.
5. The online monitoring method for current transformer error considering load effects according to claim 1, characterized in that, The hybrid regularization loss function is expressed as follows: ； In the formula, QE is the hybrid regularization loss function; To make the first q Substituting the impedance parameters of each current transformer into the residuals of the over-deterministic regression equations This is the original least squares loss; For regularity; The mixing coefficient; It is an L1 norm. It is the L2 norm. A This is the impedance parameter matrix of the current transformer.
6. The online monitoring method for current transformer error considering load effects according to claim 1, characterized in that, In step 3, in the process of solving the elastic network regression problem to minimize the hybrid regularization loss function, the trust region reflection algorithm, gradient descent algorithm, or Gauss-Newton iteration algorithm are used.
7. An online error monitoring system for a current transformer considering load effects, characterized in that, include: The data acquisition module is used to acquire the secondary fundamental current phasor values of the current transformers in each branch line of the selected target bus. The equation system construction module is used to construct an overdetermined regression equation system based on the obtained secondary fundamental current phasor values of the current transformer. The parameter optimization module is used to construct a hybrid regularized loss function based on the constructed overdetermined regression equation set, introduce the elastic network regression problem, solve the elastic network regression problem to minimize the hybrid regularized loss function, and obtain the current transformer impedance parameter matrix of the overdetermined regression equation set. The monitoring result acquisition module is used to select a reference current transformer based on the current transformers in each branch of the target bus. Based on the impedance parameter matrix of the current transformer, obtain the impedance parameter increment of other current transformers compared to the reference current transformer; Based on the comparison results of the impedance parameter increment of each current transformer with the preset increment threshold, the online monitoring results of the current transformer error are obtained.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the online monitoring method for current transformer errors considering load effects as described in any one of claims 1 to 6.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the online monitoring method for current transformer errors considering load effects as described in any one of claims 1 to 6.
10. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the online monitoring method for current transformer errors considering load effects as described in any one of claims 1 to 6.