A method for verifying the effectiveness of a current limiting and detuning device for an electromagnetic voltage transformer

Abstract

The present invention provides a method for verifying the effectiveness of a current-limiting and harmonic elimination device for an electromagnetic voltage transformer, belonging to the technical field of electromagnetic voltage transformers. The method comprises: first, establishing a mathematical model that takes into account multiple key parameters, including a set of equations for electromagnetic induction, voltage balance, impedance, error, harmonic analysis, and the like. Then, an orthogonal test method is used to design a parameter combination scheme, and experimental verification and optimization of the mathematical model are performed. Next, an effectiveness evaluation function is constructed that covers multiple indicators such as ratio difference, angular difference, and harmonic distortion. Sample data is generated through Monte Carlo simulation, and a support vector machine algorithm is used to establish an effectiveness prediction model. Finally, the model can input the key parameters of the device to be tested and output its comprehensive effectiveness evaluation. By establishing a comprehensive mathematical model and optimizing it in combination with experimental data, the present invention solves the technical problem that the effectiveness judgment of existing current-limiting and harmonic elimination devices often requires a large number of experiments, which is time-consuming and labor-intensive, and may also lead to equipment loss.

Description

Technical Field

[0001] The present invention belongs to the technical field of electromagnetic voltage transformers, and in particular relates to a method for verifying the effectiveness of a current limiting and harmonic elimination device of an electromagnetic voltage transformer. Background Art

[0002] Electromagnetic voltage transformers (EMVTs) are common voltage measurement devices in power systems, widely used in grid operation monitoring, relay protection, metering, and other fields. Compared to traditional mechanical VTs, EMVTs offer advantages such as small size, light weight, and long service life, making them popular in many applications. However, EMVTs also face numerous operational challenges.

[0003] First, electromagnetic voltage transformers can experience core saturation. When the input voltage is excessive, the transformer's core can enter a saturated state, resulting in output voltage distortion and reduced measurement accuracy. This can hinder the proper operation of relay protection devices and affect the accuracy of energy metering. To prevent core saturation, a current-limiting resistor is typically connected in series with the transformer's secondary side. However, this resistor introduces additional losses, increasing power consumption.

[0004] Secondly, electromagnetic voltage transformers (EVTs) are subject to measurement errors. Transformer ratio errors and phase errors can affect voltage measurement accuracy, particularly in complex power grids containing harmonic components, significantly reducing measurement accuracy. To address this issue, detuning capacitors have traditionally been used to compensate for the EVT's phase error. However, selecting the detuning capacitor's capacitance requires careful consideration.

[0005] Furthermore, the dynamic response characteristics of electromagnetic voltage transformers under transient conditions are also crucial. When a grid fault or sudden load change occurs, the transformer must be able to quickly track voltage changes and promptly reflect the grid status. However, the transformer's inherent time constant affects its dynamic response performance.

[0006] In practical applications, electromagnetic voltage transformers often face problems such as overvoltage and ferromagnetic resonance. These problems not only affect measurement accuracy but can also lead to equipment damage and system instability. To address these issues, current-limiting and detuning devices have emerged.

[0007] Existing current-limiting and detuning devices for electromagnetic voltage transformers typically include current-limiting resistors and detuning capacitors. The current-limiting resistors suppress overcurrent, while the detuning capacitors suppress ferromagnetic resonance. However, determining the effectiveness of these current-limiting and detuning devices often requires extensive experimentation, which is time-consuming and labor-intensive, and may also result in equipment loss. Summary of the Invention

[0008] In view of this, the present invention provides a method for verifying the effectiveness of an electromagnetic voltage transformer current limiting and harmonic elimination device, which can solve the technical problem that the effectiveness judgment of existing current limiting and harmonic elimination devices often requires a large number of experiments, is time-consuming and labor-intensive, and may also cause equipment loss.

[0009] The present invention is achieved in that:

[0010] The present invention provides a method for verifying the effectiveness of a current limiting and harmonic elimination device of an electromagnetic voltage transformer, comprising the following steps:

[0011] S10. Establishing a mathematical model of an electromagnetic voltage transformer current limiting and harmonic elimination device taking into account multiple key parameters, wherein the multiple key parameters include transformer parameters, a current limiting resistor, a harmonic elimination capacitor, and electrical parameters, and the mathematical model includes an electromagnetic induction equation group, a voltage balance equation group, an impedance equation group, an error equation group, a harmonic analysis equation group, an iron core saturation equation group, a temperature rise equation group, and a dynamic response equation group;

[0012] S20, setting the initial parameter range of the mathematical model, including the value ranges of the mutual inductor parameters, the current limiting resistor, the detuning capacitor, and the variation range of the electrical parameters;

[0013] S30. Use the orthogonal test method to design a parameter combination scheme to ensure that the influence of each parameter is fully considered in a limited number of tests, and conduct an electromagnetic voltage transformer current limiting and detuning experiment based on the designed parameter combination scheme to obtain the experimental electrical parameters;

[0014] S40, optimizing the mathematical model using the experimental data to obtain an optimized mathematical model;

[0015] S50, constructing an effectiveness evaluation function, including multiple effectiveness evaluation indicators and the weight of each effectiveness evaluation indicator, wherein the effectiveness evaluation indicators include ratio difference, angle difference, harmonic distortion, core saturation, temperature rise, and response time;

[0016] S60, using the optimization mathematical model, within the set initial parameter range, generating multiple groups of sample data through a Monte Carlo simulation method, and calculating the effectiveness evaluation function value of each group of sample data;

[0017] S70, using a support vector machine algorithm and using the effectiveness evaluation function value corresponding to the generated sample data machine as a training set, to establish an effectiveness prediction model for the electromagnetic voltage transformer current limiting and detuning device;

[0018] S80. Collect the transformer parameters, current limiting resistor, and detuning capacitor of the electromagnetic voltage transformer current limiting and detuning device to be tested, input them into the electromagnetic voltage transformer current limiting and detuning device effectiveness prediction model, obtain the effectiveness evaluation function value of the electromagnetic voltage transformer current limiting and detuning device to be tested, and output it.

[0019] Among them, the transformer parameters include the number of primary winding turns, the number of secondary winding turns, the core cross-sectional area, the average magnetic path length, the air gap length, and the leakage inductance; the electrical parameters include the input voltage, the output voltage, the input current, the output current, the phase angle, the core temperature, the ambient temperature, and the harmonic content.

[0020] The electromagnetic induction equations are used to calculate the induced electromotive force and magnetization characteristics of the mutual inductor, including: a primary induced electromotive force equation, a secondary induced electromotive force equation and a magnetization curve equation.

[0021] The voltage balance equations are used to describe the voltage relationship between various parts of the transformer, including the primary winding voltage balance equation, the secondary winding voltage balance equation, the current limiting resistor voltage drop equation, and the detuning capacitor voltage equation.

[0022] The impedance equations are used to calculate the impedance characteristics of each part of the transformer, including the primary winding impedance equation, the secondary winding impedance equation, the current limiting resistor impedance equation, the detuning capacitor impedance equation and the equivalent impedance equation.

[0023] The error equation group is used to calculate the measurement error of the mutual inductor, including a ratio difference calculation equation and an angle difference calculation equation.

[0024] The harmonic analysis equations are used to analyze the harmonic characteristics of the transformer output, including the harmonic voltage equation and the total harmonic distortion calculation equation.

[0025] The core saturation equation group is used to evaluate the saturation degree of the transformer core, including a magnetic flux density calculation equation and a saturation judgment equation.

[0026] The temperature rise equations are used to calculate the temperature changes of various parts of the transformer, including the winding temperature rise equation, the core temperature rise equation and the ambient temperature influence equation.

[0027] The dynamic response equation group is used to describe the response characteristics of the mutual inductor under transient conditions, including a transient response equation and a response time calculation equation.

[0028] Specifically, step S10 includes: establishing a mathematical model of the electromagnetic voltage transformer current limiting and harmonic elimination device that takes into account multiple key parameters, the mathematical model including an electromagnetic induction equation group, a voltage balance equation group, an impedance equation group, an error equation group, a harmonic analysis equation group, an iron core saturation equation group, a temperature rise equation group, and a dynamic response equation group. Each equation group involves multiple key parameters such as transformer parameters, current limiting resistors, harmonic elimination capacitors, and electrical parameters, and can more comprehensively describe the physical characteristics and working state of the electromagnetic voltage transformer current limiting and harmonic elimination device.

[0029] Specifically, step S20 includes: setting the initial value range of each key parameter in the aforementioned mathematical model, including the value range of mutual inductor parameters such as the number of primary winding turns, the number of secondary winding turns, the core cross-sectional area, the average magnetic path length, the air gap length, the leakage inductance, the value range of the current limiting resistor, the value range of the detuning capacitor, and the variation range of electrical parameters such as input voltage, output voltage, input current, output current, phase angle, core temperature, ambient temperature, and harmonic content, laying the foundation for subsequent parameter optimization and performance analysis.

[0030] Specifically, step S30 includes: using the orthogonal experimental method to design multiple groups of different parameter combination schemes, covering the variation range of the aforementioned key parameters, and conducting actual electromagnetic voltage transformer current limiting and deharmonic elimination experiments according to the designed parameter combination schemes, measuring and recording the input voltage, output voltage, input current, output current, phase angle, core temperature, ambient temperature, harmonic content and other electrical parameters under each set of experimental conditions, providing experimental data support for the subsequent optimization of the mathematical model.

[0031] Specifically, step S40 includes: using the experimental data obtained in step S30, adjusting the values of each parameter in the mathematical model through optimization algorithms such as least squares method and Newton method, so that the model output can better match the experimental results, obtaining an optimized mathematical model, and ensuring that the model can accurately reflect the characteristics of the actual device.

[0032] Specifically, step S50 includes: constructing a comprehensive effectiveness evaluation function including multiple performance indicators such as ratio difference, angle difference, harmonic distortion, core saturation, temperature rise, response time, etc., and setting a reasonable weight coefficient for each indicator to reflect the importance of each indicator in the overall performance, providing a basis for subsequent performance prediction.

[0033] Specifically, step S60 includes: using the mathematical model optimized in step S40, within the parameter value range set in step S20, using the Monte Carlo simulation method to generate a large number of parameter sample combinations, and calculating the effectiveness evaluation function value corresponding to each group of samples to form a larger sample data set, providing basic data for the machine learning training in step S70.

[0034] Specifically, step S70 includes: using a support vector machine algorithm, with the sample data generated in step S60 and its effectiveness evaluation function value as a training set, establishing a mapping relationship from device parameters to effectiveness indicators, and forming a performance prediction model for an electromagnetic voltage transformer current limiting and detuning device. The model has good generalization ability and can quickly predict various performance indicators under given device parameters.

[0035] Specifically, step S80 includes: collecting key parameters such as the transformer parameters, current limiting resistor, and detuning capacitor of the electromagnetic voltage transformer current limiting and detuning device to be tested, and inputting them into the performance prediction model established in step S70, so as to quickly obtain performance indicators such as the ratio difference, angle difference, harmonic distortion, core saturation, temperature rise, and response time of the device, providing a basis for subsequent device optimization.

[0036] Optionally, the electromagnetic induction equations in the mathematical model include the primary induced electromotive force equation, the secondary induced electromotive force equation, and the magnetization curve equation, which can describe the generation process of the induced electromotive force in the transformer winding and the magnetization characteristics of the iron core. The voltage balance equations include the primary winding voltage balance equation, the secondary winding voltage balance equation, the current-limiting resistor voltage drop equation, and the detuning capacitor voltage equation, which reflect the voltage relationship between the various parts of the transformer. The following is a detailed description of each equation group:

[0037] 1. Electromagnetic Induction Equations

[0038] 1.1 Primary induced electromotive force equation:

[0039]

[0040] Where, e1 is the induced electromotive force of the primary winding (unit: V); N1 is the number of turns of the primary winding; Φ is the magnetic flux (unit: Wb); B is the magnetic induction intensity (unit: T); A is the cross-sectional area of the core (unit: m 2 ); t is time (unit s).

[0041] 1.2 Secondary induced electromotive force equation:

[0042] Where e2 is the induced electromotive force of the secondary winding (unit: V); N2 is the number of turns of the secondary winding.

[0043] 1.3 Magnetization curve equation: H = f(B) = αB + βB 3 +γB 5 ;

[0044] Where H is the magnetic field intensity (unit: A / m); α, β, and γ are the fitting coefficients of the magnetization curve, which are obtained by fitting the experimental data.

[0045] 2. Voltage balance equations

[0046] 2.1 Primary winding voltage balance equation:

[0047] Where, v1 is the primary winding terminal voltage (unit: V); R1 is the primary winding resistance (unit: Ω); i1 is the primary winding current (unit: A); L1 is the primary winding self-inductance (unit: H); v Ris the voltage drop across the current limiting resistor (unit: V).

[0048] 2.2 Secondary winding voltage balance equation:

[0049] Where, v2 is the secondary winding terminal voltage (unit: V); R2 is the secondary winding resistance (unit: Ω); i2 is the secondary winding current (unit: A); L2 is the secondary winding self-inductance (unit: H); v C is the detuning capacitor voltage (unit V).

[0050] 2.3 Current limiting resistor voltage drop equation: v R =R L i1;

[0051] Where R L is the current limiting resistor value (unit: Ω).

[0052] 2.4 Detuning capacitor voltage equation:

[0053] Where C is the detuning capacitor value (unit: F).

[0054] 3. Impedance equations

[0055] 3.1 Primary winding impedance equation: Z1=R1+jωL1;

[0056] Where Z1 is the complex impedance of the primary winding (unit: Ω); ω is the angular frequency (unit: rad / s); and j is the imaginary unit.

[0057] 3.2 Secondary winding impedance equation: Z2=R2+jωL2;

[0058] 3.3 Current limiting resistor impedance equation: Z R =R L ;

[0059] 3.4 Detuning capacitor impedance equation:

[0060] 3.5 Equivalent impedance equation:

[0061] 4. Error equations

[0062] 4.1 Ratio difference calculation equation:

[0063] Where, ∈ is the ratio difference (unit %); K n is the rated transformation ratio of the transformer; |V1|, |V2| are the effective values of the primary and secondary voltages (unit: V).

[0064] 4.2 Angular difference calculation equation:

[0065] Where δ is the angular difference (unit: rad); are the real and imaginary parts of the complex voltage.

[0066] 5. Harmonic Analysis Equations

[0067] 5.1 Harmonic voltage equation:

[0068]

[0069] Where v(t) is the instantaneous voltage (unit V); V0 is the DC component (unit V); V n is the amplitude of the nth harmonic (unit: V); φ n is the nth harmonic phase angle (unit: rad).

[0070] 5.2 Total harmonic distortion calculation equation:

[0071] Where THD is the total harmonic distortion (unit: %).

[0072] 6. Core saturation equations

[0073] 6.1 Magnetic flux density calculation equation:

[0074] 6.2 Saturation judgment equation:

[0075] Where S is saturation; B sat is the core saturation magnetic induction intensity (unit T).

[0076] 7. Temperature Rise Equations

[0077] 7.1 Winding temperature rise equation:

[0078] Where θ w is the winding temperature (unit: °C); w is the winding heat capacity (unit J / ℃); P w is the winding power loss (unit W); K w is the winding heat dissipation coefficient (unit: W / ℃); θ a is the ambient temperature (unit: °C).

[0079] 7.2 Core temperature rise equation:

[0080] Where θ c is the core temperature (unit: °C); c is the core heat capacity (unit J / ℃); P c is the core loss power (unit W); K cis the core heat dissipation coefficient (unit: W / ℃).

[0081] 7.3 Ambient temperature influence equation: θ a =θ a0 +Δθ a sin(ω a t);

[0082] Where θ a0 is the average ambient temperature (unit: °C); Δθ a is the ambient temperature fluctuation amplitude (unit: °C); ω a is the angular frequency of ambient temperature change (unit: rad / s).

[0083] 8. Dynamic response equations

[0084] 8.1 Transient Response Equation: v2(t) = V m (1-e ；t / τ )sin(ωt+φ);

[0085] Where v2(t) is the instantaneous voltage of the secondary winding (unit: V); V m is the steady-state voltage amplitude (unit: V); τ is the time constant (unit: s); φ is the initial phase angle (unit: rad).

[0086] 8.2 Response time calculation equation:

[0087] Where, t r is the response time (unit: s), which is defined as the time required for the output to reach 90% of the steady-state value.

[0088] These equations constitute a complex mathematical model for the electromagnetic voltage transformer's current-limiting and detuning device. Each equation involves multiple parameters and involves mathematical concepts such as differentials, complex numbers, and powers. Obtaining the specific values of these parameters requires experimental measurement, data sheet consultation, or theoretical calculations. For example, winding parameters can be obtained through DC resistance measurements and AC impedance tests, core parameters can be determined through no-load tests and magnetization curve measurements, and thermal parameters can be measured through temperature rise tests.

[0089] Compared with the prior art, the method for verifying the effectiveness of an electromagnetic voltage transformer current limiting and harmonic elimination device provided by the present invention has the following beneficial effects:

[0090] 1. Comprehensiveness of evaluation: This method establishes mathematical models covering multiple aspects, including electromagnetic induction, voltage balance, impedance characteristics, error analysis, harmonic analysis, core saturation, temperature rise, and dynamic response. It comprehensively considers various factors that affect the performance of the current limiting and harmonic elimination device, greatly improving the comprehensiveness and accuracy of the evaluation.

[0091] 2. Efficiency improvement: The orthogonal experimental method is used to design parameter combination schemes, which greatly reduces the number of experiments required. At the same time, a large amount of sample data is generated through the Monte Carlo simulation method, which significantly improves the evaluation efficiency and shortens the evaluation cycle.

[0092] 3. Enhanced prediction capability: The support vector machine algorithm is used to establish a prediction model, which enables performance prediction of untested parameter combinations, providing strong support for device optimization and parameter selection.

[0093] 4. Strong adaptability: By constructing an effectiveness evaluation function that includes multiple evaluation indicators and their weights, the evaluation results are more objective and comprehensive, and can adapt to the needs of different application scenarios.

[0094] 5. Cost reduction: It reduces the number of actual experiments, reduces equipment loss and manpower and material resource consumption, and significantly reduces evaluation costs.

[0095] 6. Convenient optimization: Based on the optimized mathematical model and prediction model, the effects of different parameter combinations can be quickly simulated, providing a convenient way to optimize device parameters.

[0096] 7. Improved standardization: Unified mathematical models and evaluation methods provide a basis for industry standardization, which is conducive to improving the comparability of results from different manufacturers and laboratories.

[0097] In summary, the solution of the present invention solves the technical problem that the effectiveness judgment of the existing current limiting and harmonic elimination device often requires a large number of experiments, which is time-consuming and labor-intensive and may also cause equipment loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0099] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0100] like Figure 1 FIG. 1 is a flow chart of a method for verifying the effectiveness of an electromagnetic voltage transformer current limiting and harmonic elimination device provided by the present invention. The method comprises the following steps:

[0101] S10. Establishing a mathematical model of an electromagnetic voltage transformer current limiting and harmonic elimination device taking into account multiple key parameters, wherein the multiple key parameters include transformer parameters, current limiting resistors, harmonic elimination capacitors, and electrical parameters, and the mathematical model includes an electromagnetic induction equation group, a voltage balance equation group, an impedance equation group, an error equation group, a harmonic analysis equation group, an iron core saturation equation group, a temperature rise equation group, and a dynamic response equation group;

[0102] S20, setting the initial parameter range of the mathematical model, including the value range of the mutual inductor parameters, the current limiting resistor, the detuning capacitor, and the variation range of the electrical parameters;

[0103] S30. Use the orthogonal test method to design a parameter combination scheme to ensure that the influence of each parameter is fully considered in a limited number of tests, and conduct an electromagnetic voltage transformer current limiting and detuning experiment based on the designed parameter combination scheme to obtain the experimental electrical parameters;

[0104] S40, optimizing the mathematical model using the experimental data to obtain an optimized mathematical model;

[0105] S50, constructing an effectiveness evaluation function, including multiple effectiveness evaluation indicators and the weight of each effectiveness evaluation indicator, the effectiveness evaluation indicators including ratio difference, angle difference, harmonic distortion, core saturation, temperature rise, and response time;

[0106] S60, using the optimization mathematical model, within the set initial parameter range, generating multiple groups of sample data through a Monte Carlo simulation method, and calculating the effectiveness evaluation function value of each group of sample data;

[0107] S70, using a support vector machine algorithm and using the effectiveness evaluation function value corresponding to the generated sample data machine as a training set, to establish an effectiveness prediction model for the electromagnetic voltage transformer current limiting and detuning device;

[0108] S80, collecting the transformer parameters, current limiting resistor, and detuning capacitor of the electromagnetic voltage transformer current limiting and detuning device to be tested, inputting them into the electromagnetic voltage transformer current limiting and detuning device effectiveness prediction model, obtaining the effectiveness evaluation function value of the electromagnetic voltage transformer current limiting and detuning device to be tested, and outputting it.

[0109] The specific implementation of the above steps is described in detail below:

[0110] The specific implementation of step S10 is as follows: First, a mathematical model of the electromagnetic voltage transformer current limiting and detuning device is established taking into account multiple key parameters. This mathematical model includes the following equations:

[0111] 1. Electromagnetic Induction Equations: This set of equations is used to calculate the induced electromotive force and magnetization characteristics of a transformer. It includes the primary and secondary induced electromotive force equations, as well as the magnetization curve equation. The primary and secondary induced electromotive force equations describe the generation of the induced electromotive force in the transformer windings and are related to the number of turns, core cross-sectional area, and rate of change of magnetic induction intensity. The magnetization curve equation describes the magnetization characteristics of the transformer core and is derived by fitting experimental data.

[0112] 2. Voltage balance equations: This system describes the voltage relationships between various components of the transformer, including the primary winding voltage balance equation, the secondary winding voltage balance equation, the current-limiting resistor voltage drop equation, and the detuning capacitor voltage equation. These equations represent the voltage balance relationships between various components of the transformer and are related to parameters such as winding resistance, self-inductance, induced electromotive force, as well as the current-limiting resistor and detuning capacitor.

[0113] 3. Impedance Equations: This set of equations is used to calculate the impedance characteristics of each part of the transformer, including the primary winding impedance equation, the secondary winding impedance equation, the current-limiting resistor impedance equation, the detuning capacitor impedance equation, and the equivalent impedance equation. These equations describe the AC impedance of each part of the transformer and form the basis for subsequent calculations of parameters such as voltage and current.

[0114] 4. Error Equations: This set of equations is used to calculate the measurement error of the transformer, including the ratio error and the angle error. The ratio error reflects the transformer's ratio error, while the angle error reflects the phase error, both of which are important indicators for evaluating transformer performance.

[0115] 5. Harmonic Analysis Equations: This set of equations is used to analyze the harmonic characteristics of the transformer output, including the harmonic voltage equation and the total harmonic distortion calculation equation. Harmonics can affect the transformer's measurement accuracy and output quality, so they need to be analyzed.

[0116] 6. Core Saturation Equations: This set of equations is used to assess the saturation level of the transformer core. It includes equations for calculating magnetic flux density and determining saturation. Core saturation can cause changes in the transformer's characteristics and requires monitoring.

[0117] 7. Temperature Rise Equations: This set of equations is used to calculate the temperature changes in various parts of the transformer, including the winding temperature rise equation, the core temperature rise equation, and the ambient temperature effect equation. Temperature rise is a critical factor to consider during transformer operation, affecting its performance and reliability.

[0118] 8. Dynamic Response Equations: This set of equations describes the response characteristics of a transformer under transient conditions and includes the transient response equation and the response time calculation equation. A transformer's dynamic characteristics reflect its ability to track transient inputs and are another important indicator of transformer performance.

[0119] By establishing the above mathematical model covering multiple key parameters, the various physical characteristics and working conditions of the electromagnetic voltage transformer current limiting and detuning device can be described more comprehensively. This mathematical model provides a basis for subsequent analysis and optimization of the device performance.

[0120] The specific implementation of step S20 is: after establishing the mathematical model, it is necessary to set the initial parameter range of each key parameter. Specifically including:

[0121] 1. Initial range of transformer parameters: primary winding turns N1, secondary winding turns N2, core cross-sectional area A, average magnetic path length l m , air gap length l g , leakage inductance L σ These parameters involve the structure size and magnetic circuit design of the transformer, and need to be set within a reasonable value range based on actual conditions.

[0122] 2. Initial range of current limiting resistor: current limiting resistor R L The value range of R needs to consider the balance between current limiting capability and power loss. L The value range is between 1Ω and 100Ω.

[0123] 3. Initial range of detuning capacitor: The value range of detuning capacitor C needs to match the transformer parameters and operating voltage. Usually the value range of C is between 1μF and 100μF.

[0124] 4. Electrical parameter variation range: input voltage V1, output voltage V2, input current I1, output current I2, phase angle φ, core temperature θ c 、Ambient temperature θ a , harmonic content, etc. These parameters reflect the working status of the device and need to be set within a reasonable range according to the actual application scenario.

[0125] The selection of the initial value range of the above parameters needs to be combined with the design experience of the transformer and the actual application requirements to ensure that the mathematical model can cover various possible situations of the actual operation of the device.

[0126] The specific implementation of step S30 is: first, the orthogonal test method is used to design the parameter combination scheme. The orthogonal test method is an effective experimental design method that can fully consider the influence of each parameter in a limited number of experiments. Specifically, according to the orthogonal table L16 (4 5 ) or L32(4 9 ) and other design 16 or 32 groups of different parameter combination schemes, covering the variation range of transformer parameters, current limiting resistors, detuning capacitors and electrical parameters.

[0127] Secondly, current-limiting and harmonic elimination experiments were conducted on electromagnetic voltage transformers based on the designed parameter combination scheme. The electrical parameters under each set of experimental conditions were measured and recorded, including input voltage, output voltage, input current, output current, phase angle, core temperature, ambient temperature, and harmonic content. These experimental data will provide a basis for subsequent optimization of the mathematical model.

[0128] By designing parameter combinations using the orthogonal experiment method, we can effectively cover the value space of each parameter in a limited number of experiments and obtain the most experimental data reflecting the characteristics of the device. The collection of these experimental data lays the foundation for the optimization of the mathematical model in the subsequent steps.

[0129] The specific implementation of step S40 is: using the experimental data obtained in step S30 to optimize the mathematical model established in step S10. The specific implementation process is as follows:

[0130] First, the experimental data measured in step S30 are imported into the computer program, including various input parameters such as N1, N2, A, l m ,l g ,L σ ,R L ,C,V1,I1,φ,θ c ,θ a ,THD, etc., and the corresponding output response indicators such as V2,I2,∈,δ,THD,S,Δθ,t r wait.

[0131] Secondly, write an optimization algorithm, such as the least squares method, Newton's method, etc., the goal is to minimize the error between the mathematical model prediction value and the experimental value. In the optimization algorithm, adjust the values of the parameters in the mathematical model, such as the winding parameters N1, N2, the core parameters A, l m ,l g ,L σ , resistance and capacitance parameters R L , C, etc., until the model output is consistent with the experimental data to the required degree.

[0132] The specific optimization process is as follows:

[0133] 1) Initialize the values of each parameter, such as

[0134] 2) Substitute the initial parameters into the mathematical model established in step S10 to calculate the predicted values of various output response indicators.

[0135] 3) Calculate the error between the predicted value and the experimental value, such as the square error

[0136] 4) Use optimization algorithms such as least squares method or Newton method to adjust the values of each parameter according to the error gradient information to minimize the error.

[0137] 5) Repeat steps 2)-4) until the error meets the convergence condition.

[0138] Through multiple iterative optimizations, the optimized mathematical model parameters are finally obtained, so that the model prediction results can better reflect the characteristics of the actual device.

[0139] Finally, the optimized mathematical model is verified and its predictions are compared with new experimental data to ensure that the model can accurately describe the device behavior.

[0140] Through the above optimization process, the mathematical model can be made to better conform to the characteristics of the actual device, providing a reliable theoretical basis for subsequent performance evaluation and parameter optimization. The optimized mathematical model will be used for Monte Carlo simulation analysis in step S60.

[0141] The specific implementation of step S50 is: based on the optimized mathematical model, construct an effectiveness evaluation function for the electromagnetic voltage transformer current limiting and detuning device. This evaluation function includes the following indicators:

[0142] 1. Ratio difference ∈

[0143] The ratio difference reflects the ratio error of the transformer and should be controlled within |∈|≤0.5%. The ratio difference calculation formula is:

[0144] 2. Angular difference δ

[0145] The angular difference reflects the phase error of the mutual inductor and should be controlled within |δ|≤10′. The angular difference calculation formula is:

[0146] 3. Harmonic distortion THD

[0147] Harmonic distortion reflects the harmonic content of the output voltage and should be controlled within THD ≤ 3%. The formula for calculating harmonic distortion is:

[0148] 4. Core saturation S

[0149] Saturation reflects the saturation degree of the core and should be controlled below S≤0.8. The saturation calculation formula is:

[0150] 5. Temperature rise Δθ

[0151] The temperature rise reflects the temperature rise of each component of the device and should be controlled within Δθ≤40°C. The winding temperature rise and the core temperature rise can be calculated using the temperature rise equations in step S10.

[0152] 6. Response time t r

[0153] The response time reflects the speed at which the device responds to transient inputs and should be controlled within t r≤2.3τ is preferred, where τ is the time constant. The response time calculation formula is:

[0154] For each effectiveness evaluation indicator, its weight coefficient w also needs to be set ∈ ,w δ ,w THD ,w S ,w Δ θ, To reflect the importance of each indicator in the overall performance. The weight coefficient setting can be adjusted according to actual application requirements.

[0155] Combining various indicators and their weights, the following effectiveness evaluation function can be constructed:

[0156]

[0157] By establishing a comprehensive evaluation function that includes the above-mentioned multiple performance indicators, the effectiveness of the electromagnetic voltage transformer current limiting and detuning device can be more comprehensively evaluated. This evaluation function provides a basis for performance prediction in subsequent steps.

[0158] The specific implementation of step S60 is: using the mathematical model optimized in step S40, within the initial parameter range set in step S20, a Monte Carlo simulation method is used to generate multiple sets of sample data. Monte Carlo simulation is a numerical simulation method based on random sampling. The specific implementation process is as follows:

[0159] 1) According to the value range of each key parameter set in step S20, such as A random number generator is used to generate a large number of parameter sample combinations {N1, N2, A, l m ,l g ,L σ ,R L ,C,V1,I1,φ,θ c ,θ a ,THD}.

[0160] 2) Substitute each set of parameter samples into the mathematical model optimized in step S40 and calculate the corresponding output response index {V2,I2,∈,δ,THD,S,Δθ,t r}.

[0161] 3) Repeat steps 1)-2) above to generate enough parameter samples and corresponding output responses to form a large sample data set. Generally, a sample size of 1,000 or more is appropriate.

[0162] Monte Carlo simulation can quickly obtain a large amount of sample data and its performance indicators within the parameter range, providing the necessary sample set for training the performance prediction model in subsequent steps. This random sampling simulation method can effectively cover the parameter space and lay the foundation for statistical analysis of device performance.

[0163] The specific implementation of step S70 is to use a support vector machine (SVM) algorithm, using the sample data generated in step S60 and its corresponding effectiveness evaluation function values as a training set to establish an effectiveness prediction model for the electromagnetic voltage transformer current limiting and detuning device. Support vector machines are a commonly used machine learning algorithm with good generalization capabilities and are suitable for prediction and classification of various linear and nonlinear problems.

[0164] The specific implementation process is as follows:

[0165] 1) The sample data {N1, N2, A, l generated in step S60 m ,l g ,L σ ,R L ,C,V1,I1,φ,θ c ,θ a ,THD} and its corresponding effectiveness evaluation function value F are divided into training set and test set. Usually the training set accounts for 70% to 80% of the total samples.

[0166] 2) Select an appropriate SVM kernel function, such as the linear kernel K(x,y) = x·y, the polynomial kernel K(x,y) = (γx·y+r) d , radial basis function kernel K(x,y)=exp(-γ‖xy‖ 2 ) etc., and select an appropriate kernel function according to the complexity of the problem.

[0167] 3) Using the training data, tune the parameters of the SVM model, such as the penalty parameter C and the kernel parameter γ, to minimize the model's prediction error on the training data. Methods such as grid search or cross-validation can be used to find the optimal parameter combination.

[0168] 4) Train the optimized SVM model to make it fit the training set data better and establish the m ,l g ,L σ ,R L ,C,V1,I1,φ,θ c ,θ a ,THD} to the effectiveness evaluation function value F.

[0169] 5) Finally, the test set data is used to evaluate the prediction performance of the trained SVM model on new data to ensure that the model has good generalization ability.

[0170] The effectiveness prediction model established by the SVM algorithm can quickly and accurately predict the performance indicators under given device parameters, providing a basis for the subsequent optimization design of the device. The training process of the SVM model fully utilizes the large amount of sample data generated in step S60, ensuring the reliability of the prediction results.

[0171] The specific implementation of step S80 is to collect key parameters of the electromagnetic voltage transformer current limiting and harmonic elimination device under test, including transformer parameters, current limiting resistor, harmonic elimination capacitor, etc., and input them into the SVM prediction model established in step S70 to obtain the effectiveness evaluation function value of the device under test. The specific operation is as follows:

[0172] 1. Measure various parameters of the device under test, such as the number of turns of the primary winding N1, the number of turns of the secondary winding N2, the cross-sectional area of the core A, and the average magnetic path length l m , air gap length l g , leakage inductance L σ , and the current limiting resistor R L And detuning capacitor C, etc.

[0173] 2. Input the parameter values obtained from the above measurements into the SVM prediction model established in step S70. This SVM model has been trained and optimized with a large amount of sample data to establish a mapping relationship between device parameters and effectiveness evaluation indicators.

[0174] 3. Substitute the parameters of the device under test into the SVM model, and the model will quickly calculate the ratio error ∈, angle difference δ, harmonic distortion THD, core saturation S, temperature rise Δθ and response time t of the device. r And other performance indicators.

[0175] 4. Finally, the effectiveness evaluation indicators calculated by the SVM model are output. These indicators reflect the overall performance level of the device under test and can be directly compared and analyzed with the target indicators set in step S50.

[0176] Through this step, the effectiveness of the device under test can be quickly and accurately evaluated without the need for extensive experimental testing.The output of the SVM prediction model provides a basis for the subsequent optimization of device performance.

[0177] In general, the effectiveness verification method of this electromagnetic voltage transformer current limiting and detuning device includes the following core steps:

[0178] 1. Establish a mathematical model that considers multiple key parameters, including electromagnetic induction, voltage balance, impedance characteristics, measurement error, harmonic analysis, core saturation, temperature rise characteristics, and dynamic response.

[0179] 2. Set the initial value range of each parameter to lay the foundation for subsequent experimental design and model optimization.

[0180] 3. Use the orthogonal experimental method to design parameter combination schemes, conduct experimental tests and obtain key data.

[0181] 4. Use experimental data to optimize the mathematical model so that it can more accurately describe the actual device behavior.

[0182] 5. Construct a comprehensive effectiveness evaluation function that includes multiple performance indicators to provide a basis for subsequent performance prediction.

[0183] 6. Generate a large amount of sample data based on the Monte Carlo simulation method and train the SVM prediction model.

[0184] 7. Collect the parameters of the device to be tested and use the trained SVM model to quickly predict its effectiveness indicators.

[0185] This entire verification method leverages mathematical modeling, experimental testing, and data-driven machine learning to comprehensively evaluate the performance of electromagnetic voltage transformer current-limiting and detuning devices. This systematic verification process significantly improves the reliability and optimization efficiency of device design.

[0186] Specifically, the principle of this invention is to establish a comprehensive mathematical model that includes multiple key parameters, optimize the model based on experimental data, and then construct a comprehensive performance evaluation system and a performance prediction model based on Monte Carlo simulation and SVM algorithm. This method embodies the following important principles:

[0187] 1. The necessity of comprehensive multi-parameter modeling

[0188] The performance of electromagnetic voltage transformers is influenced by numerous factors, including winding parameters, core parameters, current-limiting resistors, detuning capacitors, and input and output voltages and currents. These parameters are complexly coupled, making single or partial modeling difficult to accurately reflect the actual device behavior. Therefore, establishing a comprehensive mathematical model encompassing multiple key parameters is essential for effectively describing and analyzing transformer performance.

[0189] 2. The importance of model optimization close to reality

[0190] Mathematical models developed on paper inevitably deviate from actual conditions, requiring optimization and correction based on experimental data. This present invention employs an orthogonal experimental design, which fully accounts for the influence of various parameters within a limited number of experiments, yielding rich experimental data reflecting device characteristics. Using this experimental data to adjust and optimize model parameters can ensure that the mathematical model better aligns with the actual device's behavior.

[0191] 3. Necessity of comprehensive performance evaluation

[0192] The performance of electromagnetic voltage transformers is reflected not only in measurement accuracy but also in multiple aspects, including core saturation, temperature rise, and dynamic response. These performance indicators influence each other, necessitating a comprehensive evaluation system to comprehensively measure the effectiveness of the device. The evaluation function constructed in this invention, which incorporates indicators such as ratio error, angle error, harmonic distortion, core saturation, temperature rise, and response time, can more accurately reflect the overall performance level of the device.

[0193] 4. Performance prediction based on simulation and machine learning

[0194] For complex electromagnetic voltage transformer systems, it is difficult to fully analyze their performance solely through mathematical models; computer simulation and other methods are required. This paper uses Monte Carlo simulation to rapidly generate a large amount of sample data within the model parameter space, providing a sufficient sample set for training the performance prediction model based on the Support Vector Machine (SVM).

[0195] To better understand and implement the present invention, the following is an example of a specific application scenario: a 220kV substation uses an electromagnetic voltage transformer that experiences measurement errors and requires performance evaluation and optimization. The substation's main technical parameters are as follows: primary winding turns N1 = 2000; secondary winding turns N2 = 100; core cross-sectional area A = 0.045 m 2 ; Average magnetic path length l m =1.2m; air gap length l g =2mm; leakage inductance L σ =50mH; current limiting resistor R L =20Ω; detuning capacitor C = 10μF; rated input voltage V1 = 220kV; rated output voltage V2 = 110V.

[0196] According to the effectiveness verification method of the electromagnetic voltage transformer current limiting and detuning device of the present invention, the transformer device is subjected to detailed performance analysis and optimized design.

[0197] Step S10: Establishing a mathematical model

[0198] First, a mathematical model considering multiple key parameters was established, including the following equations:

[0199] 1. Electromagnetic Induction Equations

[0200] Primary induced electromotive force equation:

[0201]

[0202] Secondary induced electromotive force equation:

[0203]

[0204] Magnetization curve equation:

[0205] H=f(B)=800B+0.2B 3 +0.005B 5 ;

[0206] 2. Voltage balance equations

[0207] Primary winding voltage balance equation:

[0208]

[0209] Secondary winding voltage balance equation:

[0210]

[0211] Current limiting resistor voltage drop equation: v R =20i1;

[0212] The voltage equation of the detuning capacitor is:

[0213] 3. Impedance equations Primary winding impedance equation: Z1 = 20 + jω0.05; Secondary winding impedance equation: Z2 = 2 + jω0.01; Current limiting resistor impedance equation: Z R =20;

[0214] The impedance equation of the detuning capacitor is:

[0215] Equivalent impedance equation:

[0216] 4. Error equations

[0217] Ratio difference calculation equation:

[0218] Angular difference calculation equation:

[0219] 5. Harmonic Analysis Equations

[0220] Harmonic voltage equation:

[0221]

[0222] Total harmonic distortion calculation equation:

[0223]

[0224] 6. Core saturation equations

[0225] Magnetic flux density calculation equation:

[0226]

[0227] Saturation judgment equation:

[0228]

[0229] 7. Temperature Rise Equations

[0230] Winding temperature rise equation:

[0231]

[0232] Core temperature rise equation:

[0233]

[0234] Ambient temperature influence equation:

[0235] θ a =25+5sin(2πt / 86400);

[0236] 8. Dynamic response equations

[0237] Transient response equation:

[0238] v2(t)=110(1-e ；t / 0.01 )sin(2π50t+0); Response time calculation equation:

[0239] t r =2.3·0.01=23ms;

[0240] The above mathematical model comprehensively describes the various physical characteristics and working conditions of the electromagnetic voltage transformer.

[0241] Step S20: Setting parameter range

[0242] According to the actual situation, the initial value ranges of various key parameters are set as follows:

[0243] Transformer parameter range:

[0244] 2000≤N1≤2500;

[0245] 80≤N2≤120;

[0246] 0.04≤A≤0.05m 2 ;

[0247] 1≤l m ≤1.5m;

[0248] 1≤l g ≤5mm;

[0249] 40≤L σ ≤60mH;

[0250] Current limiting resistor range:

[0251] 10≤R L ≤30Ω;

[0252] Detuning capacitor range:

[0253] 5≤C≤15μF;

[0254] Electrical parameter range:

[0255] 210≤V1≤230kV;

[0256] 0.9≤|V2 / V1|≤1.1;

[0257] 0.9≤cosφ≤1.0;

[0258] 20≤θ c ≤80°C;

[0259] 20≤θ a ≤40°C;

[0260] THD≤5%;

[0261] The setting of these parameter ranges takes into account the reasonable values of the actual application environment and device characteristics.

[0262] Step S30: Orthogonal experimental design

[0263] Orthogonal test method L16(4 5 ), 16 different parameter combination schemes were designed and actual electromagnetic voltage transformer current limiting and detuning experiments were carried out. The input voltage V1, output voltage V2, input current I1, output current I2, phase angle φ, core temperature θ under each experimental condition were measured and recorded. c 、Ambient temperature θ a As well as data such as total harmonic distortion THD, it provides a basis for subsequent model optimization.

[0264] The specific 16 groups of parameter combinations and experimental data are shown in Table 1 below:

[0265] Table 1 Experimental data table

[0266]

[0267]

[0268] Step S40: Model optimization

[0269] Using the experimental data obtained in step S30, the mathematical model established in step S10 was optimized using the least squares method. After multiple iterative adjustments, the optimized model parameters were finally obtained as follows:

[0270] N1=2200;

[0271] N2=100;

[0272] A=0.044m 2 ;

[0273] l m =1.2m;

[0274] l g =3mm;

[0275] L σ =50mH;

[0276] R L =20Ω;

[0277] C = 10 μF;

[0278] The optimized mathematical model can better reflect the physical characteristics and working state of the actual device. The optimized model was verified with new experimental data, and the predicted results were in good agreement with the measured data.

[0279] Step S50: Constructing an evaluation function

[0280] Based on the optimized mathematical model, a comprehensive evaluation function was established, including the following performance indicators:

[0281] 1. Ratio difference ∈

[0282] Target value |∈|≤0.5%, weight w ∈ =0.3

[0283] 2. Angular difference δ

[0284] Target value |δ|≤10′, weight w δ =0.2

[0285] 3. Harmonic distortion THD

[0286] Target value THD≤3%, weight w THD =0.2

[0287] 4. Core saturation S

[0288] Target value S≤0.8, weight w S =0.1

[0289] 5. Winding temperature rise Δθ w

[0290] Target value Δθ w ≤40℃,weight

[0291] 6. Response time t r

[0292] Target value t r ≤23ms, weight

[0293] The comprehensive evaluation function is:

[0294] F=0.3|∈|+0.2|δ|+0.2THD+0.1S+0.1Δθ w +0.1t r

[0295] Step S60: Monte Carlo simulation

[0296] Using the optimized mathematical model, within the parameter value range set in step S20, 1000 sets of sample data were generated using the Monte Carlo simulation method. The specific process is as follows:

[0297] 1) Based on the value range of each parameter, a random number generator was used to randomly generate 1000 sets of combinations of mutual inductor parameters, current limiting resistors, detuning capacitors, and electrical parameters to form a parameter sample set. As shown in Table 2:

[0298] Table 2 Parameter sample set

[0299]

[0300] 2) Substitute each set of parameter samples into the optimized mathematical model and calculate the corresponding performance indicators, such as ratio difference ∈, angle difference δ, harmonic distortion THD, core saturation S, winding temperature rise Δθ w , response time t r Etc., to form a performance evaluation sample set.

[0301] Through this Monte Carlo method, 1,000 sets of sample data that fully cover the parameter space and their corresponding performance indicators were obtained, providing a basis for subsequent SVM model training.

[0302] Step S70: SVM prediction model

[0303] Based on the sample data generated in step S60, a support vector machine (SVM) algorithm was used to establish an effectiveness prediction model for the electromagnetic voltage transformer current limiting and detuning device. The specific process is as follows:

[0304] 1) Randomly divide 1000 groups of sample data into training set (700 groups) and test set (300 groups).

[0305] 2) Select radial basis function kernel K(x,y)=e ；γ‖x；y‖2 As the kernel function of SVM, the optimal penalty parameter C = 100 and kernel function parameter γ = 0.1 are determined by grid search method.

[0306] 3) Using the training set data, the optimized SVM regression model is trained to establish the m ,l g ,L σ ,R L ,C,V1,I1,φ,θ c ,θ a ,THD} to the comprehensive performance evaluation function F.

[0307] 4) The test set data was used to evaluate the prediction performance of the trained SVM model on new data. The average absolute error was 2.8%, indicating that the model has good generalization ability.

[0308] Through this performance prediction model based on the SVM algorithm, the comprehensive performance index F under given device parameters can be quickly predicted, providing a reliable basis for subsequent optimization design.

[0309] Step S80: Performance evaluation

[0310] Finally, the actual parameters of the electromagnetic voltage transformer to be tested are collected as follows:

[0311] N1=2220;

[0312] N2=102;

[0313] A=0.043m 2 ;

[0314] l m =1.15m;

[0315] l g =2.8mm;

[0316] L σ =48mH;

[0317] R L =19Ω;

[0318] C=9.5μF;

[0319] V1=218kV;

[0320] I1=12A;

[0321] φ=21°;

[0322] θ c =37℃;

[0323] θ a =27℃;

[0324] THD = 2.9%;

[0325] These parameters are input into the SVM prediction model trained in step S70 to obtain the comprehensive performance evaluation function value of the device:

[0326] F=0.3·0.42%+0.2·8′+0.2·2.9%+0.1·0.76+0.1·35℃+0.1·

[0327] 21ms=0.126+0.032+0.058+0.076+0.035+0.010=0.337;

[0328] This shows that the transformer's performance indicators generally meet the requirements: the ratio error is within 0.5%, the angle error is within 10°, the harmonic distortion is within 3%, the core saturation is less than 0.8, the temperature rise is within 40°C, and the response time is less than 23ms. The comprehensive performance evaluation function value is 0.337, meeting the set goals.

[0329] Through the above steps, the effectiveness verification method of the electromagnetic voltage transformer current limiting and detuning device proposed in the present invention was successfully used to conduct a comprehensive performance analysis and optimized design of the device.

[0330] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for verifying the effectiveness of a current limiting and harmonic elimination device of an electromagnetic voltage transformer, characterized in that: The following steps are involved: S10. Establishing a mathematical model of an electromagnetic voltage transformer current limiting and harmonic elimination device taking into account multiple key parameters, wherein the multiple key parameters include transformer parameters, a current limiting resistor, a harmonic elimination capacitor, and electrical parameters, and the mathematical model includes an electromagnetic induction equation group, a voltage balance equation group, an impedance equation group, an error equation group, a harmonic analysis equation group, an iron core saturation equation group, a temperature rise equation group, and a dynamic response equation group; S20, setting the initial parameter range of the mathematical model, including the value ranges of the mutual inductor parameters, the current limiting resistor, the detuning capacitor, and the variation range of the electrical parameters; S30. Use the orthogonal test method to design a parameter combination scheme to ensure that the influence of each parameter is fully considered in a limited number of tests, and conduct an electromagnetic voltage transformer current limiting and detuning experiment based on the designed parameter combination scheme to obtain the experimental electrical parameters; S40, optimizing the mathematical model using the electrical parameters of the experiment to obtain an optimized mathematical model; S50, constructing an effectiveness evaluation function, including a plurality of effectiveness evaluation indicators and the weight of each effectiveness evaluation indicator, the effectiveness evaluation indicators include ratio difference, angle difference, harmonic distortion, core saturation, temperature rise, response time; S60, using the optimization mathematical model, within the set initial parameter range, generating multiple sets of sample data through a Monte Carlo simulation method, and calculating the effectiveness evaluation function value of each set of sample data; S70, using a support vector machine algorithm and using the effectiveness evaluation function value corresponding to the generated sample data machine as a training set, to establish an effectiveness prediction model for the electromagnetic voltage transformer current limiting and detuning device; S80. Collect the transformer parameters, current limiting resistor, and detuning capacitor of the electromagnetic voltage transformer current limiting and detuning device to be tested, input them into the electromagnetic voltage transformer current limiting and detuning device effectiveness prediction model, obtain the effectiveness evaluation function value of the electromagnetic voltage transformer current limiting and detuning device to be tested, and output it.

2. The method for verifying the effectiveness of a current limiting and harmonic elimination device for an electromagnetic voltage transformer according to claim 1, characterized in that: The transformer parameters include the number of primary winding turns, the number of secondary winding turns, the core cross-sectional area, the average magnetic path length, the air gap length, and the leakage inductance; the electrical parameters include the input voltage, the output voltage, the input current, the output current, the phase angle, the core temperature, the ambient temperature, and the harmonic content.

3. The method for verifying the effectiveness of a current limiting and harmonic elimination device of an electromagnetic voltage transformer according to claim 1, characterized in that: The electromagnetic induction equations are used to calculate the induced electromotive force and magnetization characteristics of the mutual inductor, including: a primary induced electromotive force equation, a secondary induced electromotive force equation and a magnetization curve equation.

4. The method for verifying the effectiveness of a current limiting and harmonic elimination device for an electromagnetic voltage transformer according to claim 1, characterized in that: The voltage balance equations are used to describe the voltage relationship between various parts of the transformer, including the primary winding voltage balance equation, the secondary winding voltage balance equation, the current limiting resistor voltage drop equation, and the detuning capacitor voltage equation.

5. The method for verifying the effectiveness of a current limiting and harmonic elimination device of an electromagnetic voltage transformer according to claim 1, characterized in that: The impedance equations are used to calculate the impedance characteristics of each part of the mutual inductor, including the primary winding impedance equation, the secondary winding impedance equation, the current limiting resistor impedance equation, the detuning capacitor impedance equation and the equivalent impedance equation.

6. The method for verifying the effectiveness of a current limiting and harmonic elimination device of an electromagnetic voltage transformer according to claim 1, characterized in that: The error equation group is used to calculate the measurement error of the mutual inductor, including a ratio difference calculation equation and an angle difference calculation equation.

7. The method for verifying the effectiveness of a current limiting and harmonic elimination device of an electromagnetic voltage transformer according to claim 1, characterized in that: The harmonic analysis equation group is used to analyze the harmonic characteristics of the transformer output, including the harmonic voltage equation and the total harmonic distortion calculation equation.

8. The method for verifying the effectiveness of a current limiting and harmonic elimination device of an electromagnetic voltage transformer according to claim 1, characterized in that: The core saturation equation group is used to evaluate the saturation degree of the transformer core, including a magnetic flux density calculation equation and a saturation judgment equation.

9. The method for verifying the effectiveness of a current limiting and harmonic elimination device of an electromagnetic voltage transformer according to claim 1, characterized in that: The temperature rise equations are used to calculate the temperature changes of various parts of the transformer, including the winding temperature rise equation, the core temperature rise equation and the ambient temperature influence equation.

10. The method for verifying the effectiveness of a current limiting and harmonic elimination device of an electromagnetic voltage transformer according to claim 1, characterized in that: The dynamic response equation group is used to describe the response characteristics of the mutual inductor under transient conditions, including a transient response equation and a response time calculation equation.

Images