A method, apparatus, medium and device for charged correction of a capacitive device

Abstract

The application discloses a kind of charged correction method, device, medium and equipment of capacitive equipment, belong to correction field.First, the end screen ground current signal of measured capacitive equipment and bus reference voltage signal are acquired, and multi-frequency domain dynamic model is constructed, then multi-frequency domain dynamic model and capacitive equipment equivalent circuit model are integrated into coupled system simulation model;Then with measured signal as target, the initial phase and amplitude of fundamental current and fundamental voltage in coupled system simulation model are iteratively optimized, when the error of simulation output and measured signal meets preset condition, the fundamental voltage phase, fundamental current phase and corresponding amplitude after sensor multi-frequency phase-frequency characteristic compensation are obtained, finally, according to the compensated fundamental phase difference, the corrected dielectric loss factor is calculated, and according to the compensated fundamental amplitude, the corrected capacitance is calculated.The application effectively solves the problem that the prior art cannot accurately and efficiently correct the capacitive equipment under voltage.

Description

Technical Field

[0001] This invention relates to the field of calibration, and more particularly to a method, apparatus, medium, and equipment for calibrating capacitive devices. Background Technology

[0002] In power systems, capacitive equipment (such as capacitive voltage transformers (CVTs), coupling capacitors, bushings, etc.) is widely used, and its insulation condition directly affects the safe and stable operation of the power grid. To ensure reliable equipment operation, its insulation condition needs to be monitored regularly. Currently, live-line testing technology is commonly used in the field, which assesses the degree of insulation degradation by measuring the dielectric loss factor (tanδ) and capacitance of the equipment without interrupting power.

[0003] However, due to the complex electromagnetic environment at substation sites, the detection signals are easily affected by harmonic interference, leading to significant errors in the measured tanδ and capacitance. Especially in substation environments with high levels of higher harmonics, traditional methods for calculating tanδ based on the fundamental phase difference become severely inaccurate due to harmonic distortion. Existing technologies sometimes employ digital filtering or Fast Fourier Transform (FFT) algorithms to suppress harmonics, but these methods often rely on fixed parameter models, making them difficult to adapt to varying field conditions. Furthermore, existing solutions do not fully consider the dynamic characteristics of the sensor-equipment coupling system—the frequency responses of current and voltage sensors are not ideally linear, introducing varying degrees of amplitude attenuation and phase shift at different frequencies. The DC drift of the sensor and the 3rd, 5th, and 7th harmonics of the power grid directly affect the measurement accuracy of the dielectric loss angle. In addition, while some studies attempt to correct measurement errors, they often rely on empirical coefficients or single threshold judgments, lacking a systematic multi-frequency dynamic compensation mechanism.

[0004] Traditional tanδ calculations are typically based on three idealized assumptions: the current / voltage signal contains only the fundamental component; the sensor is an ideally linear element with no phase shift or amplitude distortion; and there is no mutual influence between the device under test and the sensor. However, in reality, harmonics generate additional phase shifts through the coupling system, which are misinterpreted as fundamental phase differences, leading to tanδ calculation errors; the sensor's frequency response at harmonic frequencies is not corrected, causing distortion in the fundamental amplitude / phase extracted by FFT; and if the coupling system changes dynamically (e.g., temperature changes alter sensor parasitic parameters), fixed-parameter digital filters or calibration models struggle to cope effectively. These shortcomings prevent existing technologies from accurately and efficiently performing charged correction on capacitive devices. Summary of the Invention

[0005] This invention provides a method, apparatus, medium, and device for the live correction of capacitive devices, in order to solve the problem that existing technologies cannot accurately and efficiently perform live correction of capacitive devices.

[0006] In a first aspect, this application provides a method for calibrating a capacitive device, comprising: Acquire the grounding current signal of the last screen of the capacitive device under test and the corresponding bus reference voltage signal; Based on the preset sensor frequency response data, a multi-frequency domain dynamic model is constructed; The multi-frequency domain dynamic model is integrated with the equivalent circuit model of the capacitive device under test to form a coupled system simulation model; Using the grounding current signal of the last screen and the reference voltage signal of the bus as targets, the initial phase and amplitude of the fundamental current and the initial phase and amplitude of the fundamental voltage in the simulation model of the coupled system are iteratively optimized. When the error between the output signal of the simulation model of the coupled system and the grounding current signal of the last screen and the reference voltage signal of the bus meets the preset conditions, the compensated fundamental voltage phase, the compensated fundamental current phase and the corresponding amplitude are obtained. The corrected dielectric loss factor is calculated based on the phase difference between the compensated fundamental voltage phase and the compensated fundamental current phase, and the corrected capacitance is calculated based on the compensated fundamental voltage amplitude and the compensated fundamental current amplitude.

[0007] This application constructs a multi-frequency domain dynamic model of the sensor based on pre-calibrated data. This model accurately reflects the amplitude attenuation and phase shift characteristics of the current and voltage sensors at the fundamental frequency and various harmonic frequencies, thus solving the measurement deviation introduced by treating the sensor as an ideal linear element and ignoring its frequency-related distortion in traditional schemes. Furthermore, this application integrates the multi-frequency domain dynamic model of the sensor with the equivalent circuit model of the capacitive device under test into a coupled system simulation model. This simulation model restores the mutual influence between the sensor and the capacitive device in actual measurement, avoiding modeling errors caused by analyzing them separately. Based on this, this application uses the measured grounding current signal of the end screen and the bus reference voltage signal as targets. By iteratively optimizing the initial phase and amplitude of the fundamental voltage and fundamental current in the coupled system simulation model, the error between the simulation output signal and the measured signal converges to a minimum. Thus, the true fundamental voltage phase, fundamental current phase, and amplitude after multi-frequency phase-frequency characteristic compensation of the sensor are retrieved from the coupled simulation model. This process essentially reverse-engineers the true state of the measured signal within a high-fidelity digital twin environment, removing the additional phase shift generated by the sensor at harmonic frequencies and the contamination of fundamental phase estimation by harmonic leakage from the measurement results. Finally, the dielectric loss factor is calculated based on the compensated true fundamental phase difference, and the capacitance is calculated based on the compensated fundamental amplitude, ensuring that the calculation results accurately reflect the current insulation state of the capacitive device. Therefore, this application effectively eliminates the influence of sensor frequency response distortion and on-site harmonic interference on live-line detection results, significantly improving the measurement accuracy of dielectric loss factor and capacitance, and providing an accurate and reliable data foundation for assessing the insulation state of capacitive devices. This application effectively solves the problem that existing technologies cannot accurately and efficiently perform live-line correction on capacitive devices.

[0008] Furthermore, the step of constructing a multi-frequency domain dynamic model based on preset sensor frequency response data specifically involves: A sinusoidal excitation signal with a frequency range of 50 Hz to 500 Hz is applied to a preset current sensor and a preset voltage sensor, and the output signals of the current sensor and the voltage sensor at each frequency point are measured simultaneously. The ratio of the output amplitude to the input amplitude at each frequency point is obtained as amplitude-frequency response data, and the phase offset of the output signal relative to the input signal at each frequency point is obtained as phase-frequency response data. Based on the amplitude-frequency response data and phase-frequency response data, a linear time-invariant system model is constructed in the simulation platform as a multi-frequency domain dynamic model; the input of the multi-frequency domain dynamic model is a preset ideal voltage signal or a preset ideal current signal, and the output is a signal modulated by the actual frequency response of the sensor.

[0009] This application obtains amplitude-frequency response data and phase-frequency response data covering the fundamental frequency and major harmonic frequencies by applying sinusoidal excitation signals with a frequency range of 50 Hz to 500 Hz to current and voltage sensors and simultaneously measuring the output signals at each frequency point. Based on these calibration data, a linear time-invariant system model is constructed in the simulation platform as a multi-frequency domain dynamic model. This model can accurately characterize the amplitude attenuation characteristics and phase shift characteristics of the sensor at the fundamental frequency and each harmonic frequency, providing a high-fidelity sensor simulation model foundation for subsequent iterative correction.

[0010] Furthermore, the step of constructing a linear time-invariant system model as a multi-frequency domain dynamic model in the simulation platform based on the amplitude-frequency response data and phase-frequency response data specifically involves: The amplitude frequency response data and the phase frequency response data are identified using a preset rational function fitting method to obtain the numerator polynomial coefficients and the denominator polynomial coefficients, and a transfer function model is constructed based on the numerator polynomial coefficients and the denominator polynomial coefficients. The transfer function model is deployed to the simulation platform to obtain a multi-frequency domain dynamic model. The deviations of the amplitude frequency response and the phase frequency response of the multi-frequency domain dynamic model from the amplitude frequency response data at 50 Hz, 150 Hz, 250 Hz, 350 Hz and 450 Hz are verified in the simulation platform to be within the preset range.

[0011] This application identifies the amplitude-frequency response data and phase-frequency response data using a rational function fitting method, obtaining the numerator and denominator polynomial coefficients of the transfer function model. This quantifies the frequency response characteristics of the sensor into a computable transfer function model. Furthermore, this transfer function model is deployed in a simulation platform as a multi-frequency domain dynamic model, realizing the digital reproduction of the sensor's dynamic characteristics. Simultaneously, by verifying at frequency points of 50 Hz, 150 Hz, 250 Hz, 350 Hz, and 450 Hz, the deviations of the model's amplitude-frequency response and phase-frequency response from the calibration data are verified to be within a preset range. This ensures that the accuracy of the constructed multi-frequency domain dynamic model at each key frequency point meets the calibration requirements, providing a reliable model accuracy guarantee for subsequent iterative optimization.

[0012] Furthermore, the integration of the multi-frequency domain dynamic model with the equivalent circuit model of the capacitive device under test to form a coupled system simulation model specifically involves: Construct an equivalent circuit model of the capacitive device under test; the equivalent circuit model includes the main capacitance, equivalent series resistance, parasitic inductance, and stray capacitance; The multi-frequency domain dynamic model includes a current sensor sub-model and a voltage sensor sub-model; The current sensor sub-model is connected in series to the ground current output terminal of the last screen of the equivalent circuit model, and the voltage sensor sub-model is connected in parallel to the input terminal of the bus reference voltage signal of the equivalent circuit model, forming a coupled system simulation model in which the sensor and capacitive device influence each other.

[0013] This application constructs an equivalent circuit model of a capacitive device that includes the main capacitor, equivalent series resistance, parasitic inductance, and stray capacitance. A current sensor sub-model is connected in series to the ground current output terminal of the final screen of the equivalent circuit model, and a voltage sensor sub-model is connected in parallel to the bus reference voltage signal input terminal of the equivalent circuit model. This forms a coupled system simulation model in which the sensor and the capacitive device interact. This simulation model can completely reproduce the electrical coupling relationship between the sensor and the capacitive device in actual measurement, avoiding the modeling errors introduced by analyzing the two separately. It provides a high-fidelity digital twin environment for iterative correction based on this simulation model.

[0014] Furthermore, the iterative optimization of the initial phase and amplitude of the fundamental current and the initial phase and amplitude of the fundamental voltage in the coupled system simulation model, using the final screen grounding current signal and the bus reference voltage signal as targets, specifically involves: Using the initial phase of the fundamental voltage, the initial phase of the fundamental current, the amplitude of the fundamental voltage, and the amplitude of the fundamental current as variables to be optimized, an objective function is constructed; the objective function is the sum of the squared error between the simulated voltage signal output by the coupled system simulation model and the bus reference voltage signal, and the squared error between the simulated current signal output by the coupled system simulation model and the end screen grounding current signal. The nonlinear least squares algorithm is used to iteratively optimize the variable to be optimized. In each iteration, the value of the variable to be optimized is updated and the value of the objective function is recalculated. When the preset iteration termination condition is met, the iteration stops and the initial phase of the fundamental voltage, the initial phase of the fundamental current, the amplitude of the fundamental voltage, and the amplitude of the fundamental current obtained in the current iteration are output as the compensated fundamental voltage phase, the compensated fundamental current phase, the compensated fundamental voltage amplitude, and the compensated fundamental current amplitude, respectively.

[0015] This application uses the initial phase of the fundamental voltage, the initial phase of the fundamental current, the amplitude of the fundamental voltage, and the amplitude of the fundamental current as the variables to be optimized, and the sum of squared errors between the simulated signal and the measured signal as the objective function. It employs a nonlinear least squares algorithm to iteratively optimize the variables to be optimized, so that the optimization process can adaptively adjust the fundamental parameters until the simulation output and the measured signal achieve the best match. In this way, the true fundamental voltage phase and fundamental current phase after multi-frequency phase frequency characteristic compensation of the sensor are inverted from the coupled simulation model, and the additional phase shift generated by the sensor at harmonic frequencies is separated from the measurement results, thereby improving the accuracy of fundamental phase extraction.

[0016] Furthermore, the step of stopping iteration when a preset iteration termination condition is met specifically means: After each iteration, the voltage phase increment between the initial phase of the fundamental voltage obtained in the current iteration and the initial phase of the fundamental voltage obtained in the previous iteration, as well as the current phase increment between the initial phase of the fundamental current obtained in the current iteration and the initial phase of the fundamental current obtained in the previous iteration, are calculated. At the same time, the voltage amplitude increment between the amplitude of the fundamental voltage obtained in the current iteration and the amplitude of the fundamental voltage obtained in the previous iteration, as well as the current amplitude increment between the amplitude of the fundamental current obtained in the current iteration and the amplitude of the fundamental current obtained in the previous iteration are calculated. When the voltage phase increment and current phase increment are both less than the preset phase convergence threshold and the voltage amplitude increment and current amplitude increment are both less than the preset amplitude convergence threshold in three consecutive iterations, it is determined that the preset iteration termination condition is met and the iteration is stopped. When the number of iterations reaches the preset maximum number of iterations, the preset iteration termination condition is met, and the iteration stops.

[0017] This application calculates the voltage phase increment, current phase increment, voltage amplitude increment, and current amplitude increment after each iteration, and stops iterating when the phase increment and amplitude increment are all less than the corresponding convergence threshold in three consecutive iterations. This ensures that the optimization result has converged to the vicinity of the optimal solution and avoids correction deviations caused by insufficient iteration. At the same time, by setting the maximum number of iterations as the termination condition, it prevents infinite loops caused by slow algorithm convergence, thus ensuring the real-time performance and computational efficiency of the correction process.

[0018] Secondly, this application provides a charge correction device for capacitive devices. The charge correction device for capacitive devices includes: The acquisition module is used to acquire the end-screen grounding current signal and the corresponding bus reference voltage signal of the capacitive device under test. The building module is used to construct a multi-frequency domain dynamic model based on preset sensor frequency response data; An integration module is used to integrate a multi-frequency domain dynamic model with the equivalent circuit model of the capacitive device under test to form a coupled system simulation model; The optimization module is used to iteratively optimize the initial phase and amplitude of the fundamental current and the initial phase and amplitude of the fundamental voltage in the simulation model of the coupled system, taking the grounding current signal of the end screen and the reference voltage signal of the bus as targets. When the error between the output signal of the simulation model of the coupled system and the grounding current signal of the end screen and the reference voltage signal of the bus meets the preset conditions, the compensated fundamental voltage phase, the compensated fundamental current phase and the corresponding amplitude are obtained. The correction module is used to calculate the corrected dielectric loss factor based on the phase difference between the compensated fundamental voltage phase and the compensated fundamental current phase, and to calculate the corrected capacitance based on the compensated fundamental voltage amplitude and the compensated fundamental current amplitude.

[0019] This application acquires the grounding current signal of the end screen of the capacitive device under test and the corresponding bus reference voltage signal through an acquisition module, providing a real field data foundation for subsequent calibration. Simultaneously, the construction module builds a multi-frequency domain dynamic model based on preset sensor frequency response data. This model can characterize the amplitude attenuation and phase shift characteristics of the sensor at the fundamental frequency and various harmonic frequencies, thus solving the problem of treating the sensor as an ideal linear element and ignoring its frequency-related distortion in traditional solutions. Furthermore, the integration module integrates this multi-frequency domain dynamic model with the equivalent circuit model of the capacitive device under test into a coupled system simulation model. This simulation model restores the mutual influence between the sensor and the capacitive device in actual measurement, avoiding modeling errors caused by analyzing them separately. Based on this, the optimization module targets the measured grounding current signal of the last screen and the bus reference voltage signal. Through iterative optimization of the initial phase and amplitude of the fundamental current and fundamental voltage in the coupled system simulation model, the error between the simulation output signal and the measured signal converges to a minimum. This allows the true fundamental voltage phase, fundamental current phase, and amplitude after multi-frequency phase frequency characteristic compensation by the sensor to be inverted from the coupled simulation model. Essentially, this process reverse-engineers the true state of the measured signal in a high-fidelity digital twin environment, removing the additional phase shift generated by the sensor at harmonic frequencies from the measurement results. Finally, the correction module calculates the dielectric loss factor based on the compensated true fundamental phase difference and the capacitance based on the compensated fundamental amplitude, ensuring that the calculation results accurately reflect the current insulation state of the capacitive equipment. The collaborative work of these modules effectively eliminates the influence of sensor frequency response distortion and on-site harmonic interference on the live detection results, significantly improving the measurement accuracy of the dielectric loss factor and capacitance, and providing an accurate and reliable data foundation for the insulation state assessment of capacitive equipment.

[0020] Thirdly, this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a charge calibration method for a capacitive device as described above. Its beneficial effects are the same as those of the charge calibration method for a capacitive device provided in the first aspect of this application.

[0021] Fourthly, this application provides a terminal device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a charge correction method for any of the capacitive devices described in the first aspect. Attached Figure Description

[0022] Figure 1 : A schematic flowchart of an embodiment of the live-line calibration method for capacitive devices provided in this application; Figure 2 : A schematic diagram of an embodiment of the charge correction device for capacitive equipment provided in this application. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1 Please refer to Figure 1 In order to solve the problem that existing technologies cannot accurately and efficiently perform live calibration of capacitive devices, this invention provides a live calibration method for capacitive devices, including steps S01-S05.

[0025] S01: Acquire the grounding current signal of the last screen of the capacitive device under test and the corresponding bus reference voltage signal; In a preferred embodiment of this invention, the acquisition of the final screen grounding current signal and the corresponding bus reference voltage signal of the capacitive device under test specifically involves: During live-line testing, a signal acquisition channel must first be configured for the capacitive device under test. A current sensor (e.g., a high-precision through-core zero-flux current transformer) is installed on the grounding lead of the device under test to sense and acquire the grounding current signal. Simultaneously, a voltage sensor is installed on the bus side or the secondary signal of the bus voltage transformer is connected to acquire the bus reference voltage signal. The current and voltage sensors are connected to the two analog input channels of a dual-channel data acquisition card via shielded cables. The data acquisition card uses a synchronous sampling rate of no less than 10 kS / s and a resolution of no less than 16 bits to simultaneously sample and convert the analog signals output by the current and voltage sensors, obtaining digitized current and voltage sampling sequences, respectively. To ensure that the phase relationship between the voltage and current signals is not distorted during acquisition, the data acquisition card synchronizes with other associated acquisition devices in the system via the IEEE 1588 precision time protocol. The synchronously acquired digital current sampling sequence and voltage sampling sequence are used as input data for the subsequent construction of multi-frequency domain dynamic model and coupled system simulation model. They are compared with the simulated current signal and simulated voltage signal output by the simulation model, respectively, to drive the iterative optimization process.

[0026] S02: Construct a multi-frequency domain dynamic model based on preset sensor frequency response data; In a preferred embodiment of this invention, the step of constructing a multi-frequency domain dynamic model based on preset sensor frequency response data specifically includes: In this embodiment, the sensor frequency response data upon which the multi-frequency domain dynamic model is constructed is obtained through offline calibration of the actual current and voltage sensors. This calibration process is completed in a controlled laboratory environment before system deployment. Specifically, the calibration experiment is conducted in an electromagnetically shielded room, with the ambient temperature controlled at 23±2℃ and the relative humidity maintained at 50±5%RH to eliminate the influence of temperature and humidity fluctuations on the sensor's parasitic parameters. During calibration, a wideband signal generator is used to output a sinusoidal excitation signal with a frequency range of 50Hz to 500Hz. This frequency range covers the fundamental frequency of 50Hz and the 3rd, 5th, 7th, and 9th harmonic frequencies commonly found in power systems. The frequency scanning step size is set to no more than 10Hz, meaning that the sinusoidal excitation signal is output point-by-point in 10Hz intervals within the 50Hz to 500Hz range. At each frequency point, a sinusoidal excitation signal is applied to the input terminals of the current sensor and voltage sensor, respectively. After the system reaches steady state, a high-precision synchronous data acquisition card (e.g., a 24-bit resolution acquisition card with a sampling rate of no less than 10 kS / s) is used to simultaneously acquire the actual signals input to the sensors and the signals output by the sensors. For each frequency point, the amplitude and phase of the input signal, and the amplitude and phase of the output signal are recorded. The ratio of the output amplitude to the input amplitude is used as the amplitude-frequency response data at that frequency point, and the difference between the output phase and the input phase is used as the phase-frequency response data at that frequency point. This yields amplitude-frequency response data sequences and phase-frequency response data sequences covering the fundamental frequency and major harmonic frequencies, serving as the calibration basis for subsequent modeling. For example, for a certain type of capacitive end-screen current sensor, its amplitude error is measured to be +0.2% and its phase shift to be +2.0° at a frequency of 50Hz; its amplitude attenuation is measured to be -3.5% and its phase shift to be +8.3° at a frequency of 150Hz; and its amplitude attenuation is measured to be -7.1% and its phase shift to be +15.6° at a frequency of 250Hz. These data points together constitute the frequency response characteristic curve of the sensor.

[0027] After obtaining the amplitude-frequency response and phase-frequency response data, a linear time-invariant system model is constructed in the simulation platform based on these calibration data as a multi-frequency domain dynamic model. This multi-frequency domain dynamic model is a simulation module that can run in the simulation platform. Its input is a preset ideal voltage or ideal current signal, and its output is a signal modulated by the actual frequency response of the sensor. Specifically, in modeling, the amplitude-frequency response and phase-frequency response data at each frequency point are first converted into complex frequency response data. The real part of each data point is determined by the product of the amplitude-frequency response value and the cosine of the phase-frequency response at that frequency point, and the imaginary part is determined by the product of the amplitude-frequency response value and the sine of the phase-frequency response at that frequency point, thus obtaining a complex frequency response data sequence. Then, using this complex frequency response data sequence as the fitting target, rational function fitting methods (such as vector fitting algorithms) are used to identify the numerator and denominator polynomial coefficients of the transfer function model. This transfer function model describes the mathematical relationship between the sensor output signal and the input signal in the complex frequency domain, and the orders of its numerator and denominator polynomials are determined according to the fitting accuracy requirements. After identification, a transfer function model that fully characterizes the sensor's amplitude gain and phase shift characteristics for signals of different frequencies is obtained. Using a vector fitting algorithm, a fourth-order rational transfer function of the following form can be identified: Molecular polynomial and denominator polynomial It is identified from pre-calibrated frequency response data using a rational function fitting method, where: It is the complex frequency variable in the Laplace transform; The numerator polynomial has an order of . ; The denominator is a polynomial of order 1. (generally );coefficient and It is a real constant, determined by the system's physical structure or identification algorithm; the transfer function fully characterizes the system's amplitude gain and phase shift characteristics for signals of different frequencies.

[0028] Based on the above calibration data, the rational transfer function is as follows: In this transfer function model, the numerator polynomial reflects the zero-point characteristics of the sensor, mainly affecting high-frequency gain and phase lead behavior; the denominator polynomial reflects the pole characteristics of the sensor, mainly determining its low-pass filtering characteristics and phase lag behavior. When this transfer function model receives an input signal containing fundamental and harmonic components in the simulation platform, it applies corresponding amplitude gain and phase shift to different frequency components, thereby outputting a simulated signal consistent with the actual sensor response.

[0029] The identified transfer function model is deployed to the simulation platform to form the multi-frequency domain dynamic model. To verify the accuracy of the constructed multi-frequency domain dynamic model, simulation verification is performed using the identified transfer function model as an example. In this transfer function model, the numerator polynomial N... s =0.98+0.002s reflects the zero-point characteristics of the sensor, mainly affecting high-frequency gain and phase lead behavior; the denominator polynomial D s =1+0.015s+4.2×10 -5 +s²+3.1×10 -8 +s³+6.7×10 -12 +s 4 This reflects the pole characteristics of the sensor, primarily determining its low-pass filtering characteristics and phase hysteresis behavior. Simulation tests were conducted by inputting current signals containing a 50Hz fundamental component and a current signal containing a superposition of the 50Hz fundamental and 150Hz third harmonics into the transfer function model. The results showed that when the 50Hz component was input, the model output amplitude was approximately 0.98 times the input amplitude, with a phase shift of approximately +2°. When the 150Hz component was input, the model output amplitude attenuation was more significant than with the 50Hz component, with a phase shift of approximately +8°. These simulation results are largely consistent with the amplitude error of +0.2% and phase shift of +2.0° at 50Hz, and the amplitude attenuation of -3.5% and phase shift of +8.3% at 150Hz obtained from laboratory calibration. This indicates that the constructed transfer function model can accurately reproduce the amplitude gain and phase shift characteristics of the sensor at different frequencies. This verification example intuitively illustrates the working principle of the multi-frequency domain dynamic model—when the input signal contains fundamental and harmonic components, the model independently applies corresponding amplitude gain and phase shift to each frequency component, thereby outputting a simulated signal consistent with the actual sensor response. To ensure that the accuracy of the constructed multi-frequency domain dynamic model at each key frequency point meets the calibration requirements, the model is verified in a simulation platform. Specifically, sinusoidal test signals with frequencies of 50Hz, 150Hz, 250Hz, 350Hz, and 450Hz are input to the multi-frequency domain dynamic model, and the amplitude and phase of the model's output signal at each frequency point are measured. The deviations between these deviations and the amplitude-frequency response and phase-frequency response data obtained from calibration are calculated. When the amplitude-frequency response deviation at each frequency point does not exceed ±2% and the phase-frequency response deviation does not exceed ±1°, the model accuracy is deemed to meet the preset requirements, and the multi-frequency domain dynamic model can then be used for subsequent coupled system simulations. Through the above calibration, identification and verification process, the constructed multi-frequency domain dynamic model can accurately characterize the amplitude attenuation characteristics and phase shift characteristics of current sensors and voltage sensors at the fundamental frequency and each harmonic frequency, providing a high-fidelity sensor simulation model basis for subsequent iterative correction.

[0030] S03: Integrate the multi-frequency domain dynamic model with the equivalent circuit model of the capacitive device under test to form a coupled system simulation model; In a preferred embodiment of this invention, the integration of the multi-frequency domain dynamic model with the equivalent circuit model of the capacitive device under test to form a coupled system simulation model specifically involves: In this embodiment, an equivalent circuit model of the capacitive device under test is constructed in the simulation platform. This equivalent circuit model is used to characterize the electrical behavior of the capacitive device under test in actual operation, and its topology is determined according to the type and parameters of the capacitive device under test. Specifically, the equivalent circuit model adopts a π-type topology, including the main capacitor C0, the equivalent series resistance R, the parasitic inductance L, and the stray capacitance C. Among them, the main capacitor C0 represents the main insulation capacitance of the capacitive device and is the core component that determines the device's capacity; the equivalent series resistance R represents the dielectric loss and conductor resistance inside the device, and its value directly affects the accuracy of the dielectric loss factor calculation; the parasitic inductance L and the stray capacitance C characterize the parasitic effects of the device under high-frequency conditions and have an important influence on the propagation characteristics of harmonic components in the device. Since the insulation state of the capacitive device is affected by the ambient temperature, the parameter values ​​of the main capacitor C0 and the equivalent series resistance R will shift with temperature changes. Therefore, when constructing the equivalent circuit model, a temperature compensation function is configured for the main capacitor C0 and the equivalent series resistance R. This temperature compensation function corrects the values ​​of C0 and R based on the real-time ambient temperature. Specifically, the temperature value of the environment in which the capacitive device under test is located is collected in real time by a temperature sensor. Based on the pre-determined temperature characteristic curves of C0 and R, the actual values ​​of C0 and R at the current temperature are substituted into the equivalent circuit model for simulation calculation, so that the equivalent circuit model can reflect the true state of the device under different ambient temperatures.

[0031] Based on the completed construction of the equivalent circuit model, the previously constructed multi-frequency domain dynamic model is integrated with it. The multi-frequency domain dynamic model includes two independent sub-modules: a current sensor sub-model and a voltage sensor sub-model. The current sensor sub-model corresponds to the frequency response characteristics of the current sensor used to acquire the final screen grounding current signal, and the voltage sensor sub-model corresponds to the frequency response characteristics of the voltage sensor used to acquire the bus reference voltage signal. During integration, the current sensor sub-model is connected in series to the final screen grounding current output terminal of the equivalent circuit model, so that the final screen grounding current signal output by the equivalent circuit model first passes through the current sensor sub-model, and after amplitude gain and phase shift modulation, the simulated current signal is output. The voltage sensor sub-model is connected in parallel to the input terminal of the bus reference voltage signal of the equivalent circuit model, so that the bus reference voltage signal first passes through the voltage sensor sub-model, and after amplitude gain and phase shift modulation, the simulated voltage signal is output. Through the above series and parallel integration methods, a coupled system simulation model of the mutual influence between sensors and capacitive devices is formed in the simulation platform. In this coupled system simulation model, the frequency response characteristics of the sensor and the equivalent circuit characteristics of the capacitive device under test are placed in the same simulation environment and operate collaboratively. Signals flow bidirectionally between the device and the sensor—in the voltage channel, the bus reference voltage signal is modulated by the voltage sensor sub-model and then input to the equivalent circuit model; in the current channel, the grounding current of the final screen output by the equivalent circuit model is modulated by the current sensor sub-model and then output. This integrated approach allows the sensor's influence on the signal to be coupled with the electrical behavior of the capacitive device itself for comprehensive simulation, thus restoring the mutual influence between the sensor and the capacitive device in actual measurements. After the coupled system simulation model is constructed, it can be used for subsequent iterative optimization processes: when preset ideal fundamental voltage and fundamental current signals are input to the coupled system simulation model, the simulation model outputs a simulation signal modulated by the sensor's frequency response. This simulation signal can be directly compared with the field measured signal, providing a high-fidelity simulation environment for iterative optimization.

[0032] After the coupled system simulation model is constructed, this embodiment also simulates the on-site electromagnetic interference conditions through a harmonic environment reproduction module. Specifically, it imports raw voltage waveform data or raw current waveform data collected by an on-site waveform recording device, or receives harmonic spectrum data from a power quality monitoring system, and automatically extracts the amplitudes and initial phases of the 3rd, 5th, 7th, and 9th harmonics from the above data. The extracted harmonics are then used to generate corresponding harmonic signals according to their respective amplitudes and initial phases. These generated harmonic signals are then superimposed and used as interference sources, injected into the input of the completed coupled system simulation model. Through this method, the input signal of the coupled system simulation model not only includes the fundamental component but also harmonic components consistent with the actual on-site conditions, thus realistically reproducing the complex electromagnetic interference environment of the substation and enabling the simulation model to undergo subsequent iterative optimization under conditions consistent with actual operation.

[0033] S04: Taking the grounding current signal of the last screen and the reference voltage signal of the bus as the target, the initial phase and amplitude of the fundamental current and the initial phase and amplitude of the fundamental voltage in the simulation model of the coupled system are iteratively optimized. When the error between the output signal of the simulation model of the coupled system and the grounding current signal of the last screen and the reference voltage signal of the bus meets the preset conditions, the compensated fundamental voltage phase, the compensated fundamental current phase and the corresponding amplitude are obtained. In a preferred embodiment of this invention, the initial phase and amplitude of the fundamental current and the initial phase and amplitude of the fundamental voltage in the coupled system simulation model are iteratively optimized using the final screen grounding current signal and the bus reference voltage signal as targets. When the error between the output signal of the coupled system simulation model and the final screen grounding current signal and the bus reference voltage signal meets a preset condition, the compensated fundamental voltage phase, the compensated fundamental current phase, and the corresponding amplitude are obtained. Specifically: In this embodiment, the fundamental wave parameters in the constructed coupled system simulation model are iteratively optimized using the grounding current signal of the last screen and the bus reference voltage signal obtained from on-site measurements. The aim is to retrieve the true fundamental wave component unaffected by distortion from the simulation output after modulation by the sensor frequency response. The specific optimization process is as follows: First, define the variables to be optimized. Let the initial phase of the fundamental voltage be denoted as θu, the initial phase of the fundamental current as θi, the amplitude of the fundamental voltage as Au, and the amplitude of the fundamental current as Ai. These four parameters are used as the variables to be optimized, forming the optimization variable vector X = [θu, θi, Au, Ai]. T .

[0034] Secondly, an objective function is constructed to measure the difference between the simulated output and the measured signal. Given the values ​​of the optimization variables, the coupled system simulation model outputs a simulated voltage signal us(k) and a simulated current signal is(k). Let the measured bus reference voltage signal be um(k) and the grounding current signal of the final screen be im(k), where k represents the index of the discrete sampling point. The objective function J(X) is defined as the sum of the squared errors between the simulated voltage signal and the bus reference voltage signal, and the squared errors between the simulated current signal and the grounding current signal of the final screen. In other words, the objective function is the sum of the squared differences between the simulated voltage and the measured voltage at all sampling points, and the squared differences between the simulated current and the measured current. The mathematical expression of the objective function is as follows: J(X) = Σk [(um(k) - us(k))² + (im(k) - is(k))²] The values ​​of us(k) and is(k) depend on the optimization variable vector X, meaning that the simulated signal output by the coupled system simulation model changes accordingly with the adjustment of the initial phase of the fundamental voltage, the initial phase of the fundamental current, the amplitude of the fundamental voltage, and the amplitude of the fundamental current. The smaller the value of this objective function, the higher the degree of agreement between the simulation output and the measured signal on site, and the closer the corresponding optimization variable value is to the true value.

[0035] After the objective function is constructed, a nonlinear least squares algorithm is used to iteratively optimize the variables to be optimized. In each iteration, the algorithm calculates the value of the objective function and its gradient information based on the current value of the optimization variable, thereby determining the update direction and step size of the optimization variable, generating updated optimization variable values, and then re-driving the coupled system simulation model to calculate the new simulation output signal and objective function value. The iterative process is repeated in the above manner, causing the objective function value to decrease successively.

[0036] During the iteration process, a preset iteration termination condition is used to determine whether to stop the iteration. This embodiment sets two iteration termination conditions. The first termination condition is based on the degree of iteration convergence: after each iteration, the difference between the initial phase of the fundamental voltage obtained in the current iteration and the initial phase of the fundamental voltage obtained in the previous iteration is calculated, and its absolute value is used as the voltage phase increment; the difference between the initial phase of the fundamental current obtained in the current iteration and the initial phase of the fundamental current obtained in the previous iteration is calculated, and its absolute value is used as the current phase increment; the difference between the amplitude of the fundamental voltage obtained in the current iteration and the amplitude of the fundamental voltage obtained in the previous iteration is calculated, and its absolute value is used as the voltage amplitude increment; the difference between the amplitude of the fundamental current obtained in the current iteration and the amplitude of the fundamental current obtained in the previous iteration is calculated, and its absolute value is used as the current amplitude increment. When the voltage phase increment and current phase increment are both less than the preset phase convergence threshold (e.g., no greater than 0.01°) in three consecutive iterations, and the voltage amplitude increment and current amplitude increment are both less than the preset amplitude convergence threshold (e.g., no greater than 0.01%), it indicates that the optimization variables have converged to near the optimal solution. At this point, the iteration termination condition is satisfied, and the iteration stops. The second termination condition is based on the number of iterations: when the number of iterations reaches the preset maximum number of iterations (e.g., 200), to avoid infinite loops caused by slow algorithm convergence, the iteration termination condition is satisfied, and the iteration stops. If either of the above two termination conditions is satisfied, the iteration stops.

[0037] After the iteration stops, the initial phase of the fundamental voltage obtained in the current iteration is output as the compensated fundamental voltage phase, the initial phase of the fundamental current obtained in the current iteration is output as the compensated fundamental current phase, the amplitude of the fundamental voltage obtained in the current iteration is output as the compensated fundamental voltage amplitude, and the amplitude of the fundamental current obtained in the current iteration is output as the compensated fundamental current amplitude. Thus, through the above iterative optimization process, the true fundamental voltage phase, fundamental current phase, and their corresponding amplitudes after multi-frequency phase characteristic compensation by the sensor are successfully retrieved from the coupled system simulation model, providing an accurate data foundation for subsequent calculations of the corrected dielectric loss factor and capacitance.

[0038] S05: Calculate the corrected dielectric loss factor based on the phase difference between the compensated fundamental voltage phase and the compensated fundamental current phase, and calculate the corrected capacitance based on the compensated fundamental voltage amplitude and the compensated fundamental current amplitude.

[0039] In a preferred embodiment of this invention, the step of calculating the corrected dielectric loss factor based on the phase difference between the compensated fundamental voltage phase and the compensated fundamental current phase, and calculating the corrected capacitance based on the compensated fundamental voltage amplitude and the compensated fundamental current amplitude, specifically involves: In this embodiment, after the above iterative optimization process, the compensated fundamental voltage phase, compensated fundamental current phase, compensated fundamental voltage amplitude, and compensated fundamental current amplitude have been obtained. The compensated fundamental voltage phase and compensated fundamental current phase are both angle values, while the compensated fundamental voltage amplitude and compensated fundamental current amplitude are both scalar values.

[0040] First, the dielectric loss factor is calculated for correction. The fundamental voltage phase is subtracted from the fundamental current phase after compensation to obtain the fundamental phase difference Δφ, i.e., Δφ = |θu - θi|, where θu is the compensated fundamental voltage phase and θi is the compensated fundamental current phase. Ideally, the current of a pure capacitor leads the voltage by 90°. However, in actual capacitive devices, due to dielectric losses, the current leads the voltage by a slightly less than 90°. This deviation reflects the degree of dielectric loss in the device. Therefore, the absolute value of the difference between the fundamental phase difference Δφ and 90° is taken to obtain the tangent of the dielectric loss angle, i.e., tanδ = |90° - Δφ|. This value is used as the corrected dielectric loss factor output. This corrected dielectric loss factor eliminates the influence of amplitude attenuation and phase shift introduced by the sensor at the fundamental frequency and harmonic frequencies on the phase difference measurement, and can truly reflect the current dielectric loss state of the capacitive device.

[0041] Simultaneously, capacitance correction calculations are performed. Based on the fundamental definition of capacitance, C = I / (ωU), where U is the effective voltage value, I is the effective current value, and ω is the fundamental angular frequency, the compensated fundamental voltage amplitude is substituted as U, and the compensated fundamental current amplitude is substituted as I to calculate the corrected capacitance C = I / (2πf · U). Here, f is the fundamental frequency of 50Hz. This corrected capacitance eliminates the influence of the sensor's amplitude-frequency response characteristics on amplitude measurement, accurately reflecting the current main capacitance value of the capacitive device.

[0042] Thus, through the above correction process, the corrected dielectric loss factor and corrected capacitance are obtained. These two parameters together constitute the quantitative indicators of the insulation status of capacitive equipment: the corrected dielectric loss factor is used to assess the degree of degradation of the equipment's insulating dielectric, and the corrected capacitance is used to assess the integrity of the equipment's main insulation. By comparing the corrected dielectric loss factor and corrected capacitance with the equipment's historical data or standard values, it is possible to determine whether the insulation status of the tested capacitive equipment is normal, providing a reliable data basis for subsequent insulation status assessment, degradation trend analysis, and fault early warning.

[0043] In summary, this application constructs a multi-frequency domain dynamic model of the sensor based on pre-calibrated data. This model accurately reflects the amplitude attenuation and phase shift characteristics of the current and voltage sensors at the fundamental frequency and various harmonic frequencies, thus solving the measurement deviation introduced by treating the sensor as an ideal linear element and ignoring its frequency-related distortion in traditional schemes. Furthermore, this application integrates the multi-frequency domain dynamic model of the sensor with the equivalent circuit model of the capacitive device under test into a coupled system simulation model. This simulation model restores the mutual influence between the sensor and the capacitive device in actual measurement, avoiding modeling errors caused by analyzing them separately. Based on this, this application uses the measured grounding current signal of the end screen and the bus reference voltage signal as targets, and iteratively optimizes the initial phase and amplitude of the fundamental voltage and fundamental current in the coupled system simulation model, so that the error between the simulation output signal and the measured signal converges to a minimum. Thus, the true fundamental voltage phase, fundamental current phase, and amplitude after multi-frequency phase-frequency characteristic compensation of the sensor are retrieved from the coupled simulation model. This process essentially reverse-engineers the true state of the measured signal within a high-fidelity digital twin environment, removing the additional phase shift generated by the sensor at harmonic frequencies and the contamination of fundamental phase estimation by harmonic leakage from the measurement results. Finally, the dielectric loss factor is calculated based on the compensated true fundamental phase difference, and the capacitance is calculated based on the compensated fundamental amplitude, ensuring that the calculation results accurately reflect the current insulation state of the capacitive device. Therefore, this application effectively eliminates the influence of sensor frequency response distortion and on-site harmonic interference on live-line detection results, significantly improving the measurement accuracy of dielectric loss factor and capacitance, and providing an accurate and reliable data foundation for assessing the insulation state of capacitive devices. This application effectively solves the problem that existing technologies cannot accurately and efficiently perform live-line correction on capacitive devices.

[0044] Example 2 Please refer to Figure 2 This is a charge correction device for capacitive devices provided in the embodiments of this application.

[0045] In this embodiment, the charge correction device for capacitive devices includes an acquisition module 10, a construction module 20, an integration module 30, an optimization module 40, and a correction module 50.

[0046] The acquisition module 10 is used to acquire the end screen grounding current signal and the corresponding bus reference voltage signal of the capacitive device under test; Module 20 is used to construct a multi-frequency domain dynamic model based on preset sensor frequency response data; The integration module 30 is used to integrate the multi-frequency domain dynamic model with the equivalent circuit model of the capacitive device under test to form a coupled system simulation model; The optimization module 40 is used to iteratively optimize the initial phase and amplitude of the fundamental current and the initial phase and amplitude of the fundamental voltage in the simulation model of the coupled system, with the end screen grounding current signal and the bus reference voltage signal as the target. When the error between the output signal of the coupled system simulation model and the end screen grounding current signal and the bus reference voltage signal meets the preset conditions, the compensated fundamental voltage phase, the compensated fundamental current phase and the corresponding amplitude are obtained. The correction module 50 is used to calculate the corrected dielectric loss factor based on the phase difference between the compensated fundamental voltage phase and the compensated fundamental current phase, and to calculate the corrected capacitance based on the compensated fundamental voltage amplitude and the compensated fundamental current amplitude.

[0047] For ease of description and brevity, the embodiments of the present invention include all the embodiments of the above-described capacitive device charge correction method, and will not be repeated here.

[0048] Example 3 This application provides a computer-readable storage medium including a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute a capacitive device charge correction method. The capacitive device's charge correction method, if implemented as a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments can also be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0049] Example 4 This embodiment provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements any of the capacitive device charge correction methods described in Embodiment 1.

[0050] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for calibrating a capacitive device, characterized in that, include: Acquire the grounding current signal of the last screen of the capacitive device under test and the corresponding bus reference voltage signal; Based on the preset sensor frequency response data, a multi-frequency domain dynamic model is constructed; The multi-frequency domain dynamic model is integrated with the equivalent circuit model of the capacitive device under test to form a coupled system simulation model; Using the grounding current signal of the last screen and the reference voltage signal of the bus as targets, the initial phase and amplitude of the fundamental current and the initial phase and amplitude of the fundamental voltage in the simulation model of the coupled system are iteratively optimized. When the error between the output signal of the simulation model of the coupled system and the grounding current signal of the last screen and the reference voltage signal of the bus meets the preset conditions, the compensated fundamental voltage phase, the compensated fundamental current phase and the corresponding amplitude are obtained. The corrected dielectric loss factor is calculated based on the phase difference between the compensated fundamental voltage phase and the compensated fundamental current phase, and the corrected capacitance is calculated based on the compensated fundamental voltage amplitude and the compensated fundamental current amplitude.

2. The method for calibrating capacitive devices according to claim 1, characterized in that, The step of constructing a multi-frequency domain dynamic model based on preset sensor frequency response data specifically involves: A sinusoidal excitation signal with a frequency range of 50 Hz to 500 Hz is applied to a preset current sensor and a preset voltage sensor, and the output signals of the current sensor and the voltage sensor at each frequency point are measured simultaneously. The ratio of the output amplitude to the input amplitude at each frequency point is obtained as amplitude-frequency response data, and the phase offset of the output signal relative to the input signal at each frequency point is obtained as phase-frequency response data. Based on the amplitude-frequency response data and phase-frequency response data, a linear time-invariant system model is constructed in the simulation platform as a multi-frequency domain dynamic model; the input of the multi-frequency domain dynamic model is a preset ideal voltage signal or a preset ideal current signal, and the output is a signal modulated by the actual frequency response of the sensor.

3. The method for calibrating capacitive devices according to claim 2, characterized in that, The step of constructing a linear time-invariant system model as a multi-frequency domain dynamic model in the simulation platform based on the amplitude-frequency response data and phase-frequency response data is as follows: The amplitude frequency response data and the phase frequency response data are identified using a preset rational function fitting method to obtain the numerator polynomial coefficients and the denominator polynomial coefficients, and a transfer function model is constructed based on the numerator polynomial coefficients and the denominator polynomial coefficients. The transfer function model is deployed to the simulation platform to obtain a multi-frequency domain dynamic model. The deviations of the amplitude frequency response and the phase frequency response of the multi-frequency domain dynamic model from the amplitude frequency response data at 50 Hz, 150 Hz, 250 Hz, 350 Hz and 450 Hz are verified in the simulation platform to be within the preset range.

4. The method for calibrating capacitive devices according to claim 1, characterized in that, The integration of the multi-frequency domain dynamic model with the equivalent circuit model of the capacitive device under test to form a coupled system simulation model is specifically as follows: Construct an equivalent circuit model of the capacitive device under test; the equivalent circuit model includes the main capacitance, equivalent series resistance, parasitic inductance, and stray capacitance; The multi-frequency domain dynamic model includes a current sensor sub-model and a voltage sensor sub-model; The current sensor sub-model is connected in series to the ground current output terminal of the last screen of the equivalent circuit model, and the voltage sensor sub-model is connected in parallel to the input terminal of the bus reference voltage signal of the equivalent circuit model, forming a coupled system simulation model in which the sensor and capacitive device influence each other.

5. The method for calibrating capacitive devices according to claim 1, characterized in that, The method involves iteratively optimizing the initial phase and amplitude of the fundamental current and the fundamental voltage in the coupled system simulation model, using the final screen grounding current signal and the bus reference voltage signal as targets. Specifically: Using the initial phase of the fundamental voltage, the initial phase of the fundamental current, the amplitude of the fundamental voltage, and the amplitude of the fundamental current as variables to be optimized, an objective function is constructed; the objective function is the sum of the squared error between the simulated voltage signal output by the coupled system simulation model and the bus reference voltage signal, and the squared error between the simulated current signal output by the coupled system simulation model and the end screen grounding current signal. The nonlinear least squares algorithm is used to iteratively optimize the variable to be optimized. In each iteration, the value of the variable to be optimized is updated and the value of the objective function is recalculated. When the preset iteration termination condition is met, the iteration stops and the initial phase of the fundamental voltage, the initial phase of the fundamental current, the amplitude of the fundamental voltage, and the amplitude of the fundamental current obtained in the current iteration are output as the compensated fundamental voltage phase, the compensated fundamental current phase, the compensated fundamental voltage amplitude, and the compensated fundamental current amplitude, respectively.

6. The method for calibrating capacitive devices according to claim 5, characterized in that, The step of stopping iteration when a preset iteration termination condition is met specifically means: After each iteration, the voltage phase increment between the initial phase of the fundamental voltage obtained in the current iteration and the initial phase of the fundamental voltage obtained in the previous iteration, as well as the current phase increment between the initial phase of the fundamental current obtained in the current iteration and the initial phase of the fundamental current obtained in the previous iteration, are calculated. At the same time, the voltage amplitude increment between the amplitude of the fundamental voltage obtained in the current iteration and the amplitude of the fundamental voltage obtained in the previous iteration, as well as the current amplitude increment between the amplitude of the fundamental current obtained in the current iteration and the amplitude of the fundamental current obtained in the previous iteration are calculated. When the voltage phase increment and current phase increment are both less than the preset phase convergence threshold and the voltage amplitude increment and current amplitude increment are both less than the preset amplitude convergence threshold in three consecutive iterations, it is determined that the preset iteration termination condition is met and the iteration is stopped. When the number of iterations reaches the preset maximum number of iterations, the preset iteration termination condition is met, and the iteration stops.

7. A charge correction device for capacitive equipment, characterized in that, include: The acquisition module is used to acquire the end-screen grounding current signal and the corresponding bus reference voltage signal of the capacitive device under test. The building module is used to construct a multi-frequency domain dynamic model based on preset sensor frequency response data; An integration module is used to integrate a multi-frequency domain dynamic model with the equivalent circuit model of the capacitive device under test to form a coupled system simulation model; The optimization module is used to iteratively optimize the initial phase and amplitude of the fundamental current and the initial phase and amplitude of the fundamental voltage in the simulation model of the coupled system, taking the grounding current signal of the end screen and the reference voltage signal of the bus as targets. When the error between the output signal of the simulation model of the coupled system and the grounding current signal of the end screen and the reference voltage signal of the bus meets the preset conditions, the compensated fundamental voltage phase, the compensated fundamental current phase and the corresponding amplitude are obtained. The correction module is used to calculate the corrected dielectric loss factor based on the phase difference between the compensated fundamental voltage phase and the compensated fundamental current phase, and to calculate the corrected capacitance based on the compensated fundamental voltage amplitude and the compensated fundamental current amplitude.

8. The live-line correction device for capacitive equipment according to claim 7, characterized in that, The step of constructing a multi-frequency domain dynamic model based on preset sensor frequency response data specifically involves: A sinusoidal excitation signal with a frequency range of 50 Hz to 500 Hz is applied to a preset current sensor and a preset voltage sensor, and the output signals of the current sensor and the voltage sensor at each frequency point are measured simultaneously. The ratio of the output amplitude to the input amplitude at each frequency point is obtained as amplitude-frequency response data, and the phase offset of the output signal relative to the input signal at each frequency point is obtained as phase-frequency response data. Based on the amplitude-frequency response data and phase-frequency response data, a linear time-invariant system model is constructed in the simulation platform as a multi-frequency domain dynamic model; the input of the multi-frequency domain dynamic model is a preset ideal voltage signal or a preset ideal current signal, and the output is a signal modulated by the actual frequency response of the sensor.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the charge correction method for a capacitive device as described in any one of claims 1 to 6.

10. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the charge correction method for a capacitive device as described in any one of claims 1 to 6.

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