Grounding system of high-voltage switch cabinet

Abstract

The invention discloses a grounding system of a high-voltage switch cabinet, relates to the technical field of power systems, and solves the problems of poor resistance stability and poor safety in a grounding system of an existing high-voltage switch cabinet. The system comprises a real-time detection module, a self-adaptive grounding resistance adjusting module, a high-frequency transient suppression module, a fault detection and isolation module, a grounding current control module and a remote measurement module. The multi-section controllable resistor is connected with the grounding path in series, the resistance value is automatically adjusted by combining the environment compensation model, it is ensured that the grounding resistor is kept stable under different environment conditions, arc light reignition is detected and rapidly responded through the high-frequency transient suppression module, and generation of overvoltage is effectively suppressed; grounding current is monitored in real time through the fault detection and isolation module, further diffusion of faults is prevented, and reliability and safety of the system are improved; according to the invention, the resistance stability and safety of the existing grounding system are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and more particularly to a grounding system for a high-voltage switch cabinet. Background Art

[0002] With the rapid development of the electric power industry, the safety and reliability of high-voltage switchgear, as an important equipment in the power system, are increasingly valued. In the process of power transmission and distribution, the grounding system of high-voltage switchgear is a key link to ensure the safety of equipment and personnel, and its technical level and performance requirements are constantly improving. In the construction of modern power grids, the optimization and improvement of the grounding system of high-voltage switchgear has become the focus of the power industry;

[0003] At present, the grounding system of high-voltage switchgear generally adopts a metal-enclosed structure, and the internal space uses air or insulating gas as the insulating medium. The grounding system is fixedly installed with a grounding switch and a mechanical locking device is set between the circuit breaker to ensure effective grounding during equipment maintenance or failure to prevent electric shock accidents. At the same time, the grounding system must also meet certain resistance and conductivity requirements to achieve rapid current extraction and dispersion. However, with the expansion of the scale of the power grid and the increase in power load, higher requirements are placed on the performance of the grounding system of the high-voltage switchgear.

[0004] In the grounding system of high-voltage switchgear, grounding resistance is one of the key indicators of grounding system performance. However, due to changes in the physical and chemical properties of the soil (such as humidity, temperature, salt content, etc.), the grounding resistance may change over time and environmental conditions. This change may cause the grounding system to fail to provide adequate protection in some cases; in addition, grounding protection is an important measure to prevent equipment failure and electric shock accidents. However, the limitations of the method of using a neutral point without grounding or grounding through an arc suppression coil are particularly obvious. Although this grounding method can effectively limit the large current during single-phase grounding, the resulting arc restrike overvoltage problem cannot be ignored. Arc restrike overvoltage may place higher requirements on the insulation level of the equipment, thereby increasing the cost of equipment and the difficulty of manufacturing. In addition, frequent overvoltages may also accelerate equipment aging, shorten the service life of the equipment, and further increase the cost of operation and maintenance. Secondly, when a single-phase grounding fault occurs, the grounding system needs to quickly and accurately control the grounding current to prevent equipment damage and power outages. However, some grounding systems perform poorly in controlling the grounding current. This may cause the protection device to malfunction due to excessive current, or be insensitive due to too small current, so that the fault circuit cannot be cut off in time, increasing the risk of the system.

[0005] Therefore, a grounding system for a high-voltage switch cabinet is needed to solve the above problems. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention discloses a grounding system for a high-voltage switch cabinet, aiming to improve the stability and safety of the grounding system of the existing high-voltage switch cabinet.

[0007] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0008] A grounding system for a high-voltage switch cabinet, comprising:

[0009] A real-time detection module is used to monitor the changes of grounding resistance and environmental factors around the grounding system in real time, and output the grounding resistance change value and environmental factors to the adaptive grounding resistance adjustment module and the telemetry module; the environmental factors include soil moisture and temperature;

[0010] The adaptive grounding resistance adjustment module is used to dynamically adjust the grounding resistance according to the grounding resistance change value and environmental factors; the adaptive grounding resistance adjustment module connects multiple sections of controllable resistance to the grounding path in series, and the adaptive grounding resistance adjustment module constructs a resistance compensation value mapping model based on the environmental sensitive period index and the mutual correlation coefficient, and controls the resistance value through a dynamic resistance control algorithm; the resistance compensation value mapping model includes an environmental factor quantification unit, a resistance influence analysis unit, a compensation value calculation unit and an output feedback unit;

[0011] High-frequency transient suppression module, used to suppress high-frequency transient overvoltage caused by arc restrike;

[0012] a fault detection and isolation module, for detecting ground current, isolating fault circuits, and transmitting fault signals to the telemetry module;

[0013] A ground current control module, used for controlling the ground current;

[0014] The telemetry module is used to provide remote monitoring of the grounding system status; the telemetry module transmits the grounding system indicators to the remote terminal through wireless communication; the grounding system indicators include grounding resistance, current, soil resistivity and temperature data;

[0015] The output end of the real-time detection module is connected to the input end of the adaptive grounding resistance adjustment module and the telemetry module; the output end of the high-frequency transient suppression module, the fault detection and isolation module and the grounding current control module are connected to the input end of the telemetry module; the output end of the fault detection and isolation module is connected to the input end of the grounding current control module.

[0016] As a further technical solution of the present invention, the real-time detection module monitors the grounding resistance in real time through a grounding resistance detection unit; the grounding resistance detection unit supplies power to the grounding resistance through a constant current source circuit configured by an operational amplifier and an LDO regulator, and detects the voltage drop across the grounding resistance through a differential amplifier, and the voltage signal is converted into a digital signal through an analog-to-digital converter; the constant current source circuit ensures that the current remains constant during the entire detection process through a negative feedback mechanism, eliminating the error of power supply fluctuations; the differential amplifier is composed of two symmetrical operational amplifiers, which respectively detect the voltage across the grounding resistance and eliminate noise interference through differential output; the grounding resistance detection module also sends the calculated grounding resistance change value to the adaptive grounding resistance adjustment module and the telemetry module through the serial communication protocol SPI.

[0017] As a further technical solution of the present invention, the adaptive grounding resistance adjustment module receives and processes the digital signal of the grounding resistance detection module through a microcontroller, and the microcontroller processes the digital signal output by the grounding resistance detection module through an FIR filter and a sliding average filter, and calculates the change value of the grounding resistance through a least squares method; according to the calculated change value of the grounding resistance, the adaptive grounding resistance adjustment module realizes PWM signal amplification and driving of the controllable resistor array through a PWM driving circuit; the controllable resistor array is composed of a plurality of controllable resistor units, each of which is connected in series in a grounding path, and the conduction state of each controllable resistor unit is adjusted through PWM signal control to realize dynamic adjustment of the grounding resistance value; the adaptive grounding resistance adjustment module also realizes real-time monitoring of the change value of the grounding resistance through a closed-loop feedback circuit, and performs real-time feedback adjustment of the change value of the grounding resistance through a dynamic resistance control algorithm.

[0018] As a further technical solution of the present invention, the PWM drive circuit includes an input unit, an amplifying unit, a driving stage unit, a protection unit and an isolation unit; the input unit locks the state of the input signal through an input buffer; the amplifying unit amplifies the amplitude of the PWM signal through a high-gain operational amplifier; the driving stage unit generates a current to drive a controllable resistor array through a power transistor; the protection unit protects the circuit from abnormal conditions through a current limiting resistor, a clamping diode and a transient voltage suppressor; the isolation unit electrically isolates the main control circuit from the driving circuit through an optical coupler.

[0019] As a further technical solution of the present invention, the working method of the dynamic resistance control algorithm includes the following steps:

[0020] Step 1: Construct a resistance mapping matrix Among them, T n , RH n , I n , V nand FT n Respectively represent the temperature, humidity, current, voltage and fault type code of the high-voltage switchgear at the nth sampling moment; and perform noise suppression through a weighted sliding average filter;

[0021] Step 2: Based on the resistance mapping matrix E, the dynamic resistance requirement is calculated by a multivariable nonlinear function; the multivariable nonlinear function is trained by a neural network to obtain a weight vector θ and a feature mapping function G n (E), mapping environmental parameters to a high-dimensional feature space to calculate the ground resistance value; the feature mapping function constructs a feature mapping through a polynomial kernel function and an activation function; the formula expression for calculating the dynamic resistance requirement of the multivariate nonlinear function is:

[0022]

[0023] In formula (1), M represents the highest degree of polynomial terms; P represents the number of cross terms, which is used to control the complexity of feature mapping function; G n (E) represents a feature mapping function, which is used to map the nth row vector of the resistance mapping matrix to a high-dimensional feature space; θ represents a weight vector, which is used to map the output of the feature mapping function to the dynamic resistance requirement; μ c Represents the coefficients in the polynomial kernel function and the cross term, which is used to adjust the shape and complexity of the feature mapping function; indexset l Represents the parameter index set involved in the lth cross term; E n Represents the nth row vector of the resistance mapping matrix;

[0024] Step 3: Construct the state matrix of the controllable resistor array Where S n Indicates the switch state of the nth controllable resistor unit; and based on the current state S n and dynamic resistance requirement phi(E n ), through the fitness function F ij (S n ) determine the optimal PWM duty cycle setting; the fitness function formula is:

[0025]

[0026] In formula (2), L represents a solution, i.e., a set of PWM duty cycle settings; h, γ, and ε represent the weight coefficients of the error term, safety penalty term, and stability penalty term, respectively, which are used to balance the importance of different optimization objectives; P ij (L) represents the security penalty term for solution L; R a represents the ground resistance prediction value of the nth sample by the deep learning model; R pUnder solution L, the actual ground resistance value of the nth sample; δ ij (L) represents the stability penalty term of solution L; N represents the number of samples in the evaluation cycle; phi(E n ) represents the dynamic resistance requirement; T represents the length of the evaluation period, which is used to measure the calculation range of the cumulative error;

[0027] Step 4: Control the conduction state of the controllable resistor array through the PWM signal to adjust the ground resistance value; during the control process, monitor the system state and environmental changes in real time, and compare the actual ground resistance value with the predicted value in step 2. If an error occurs, adjust the control strategy through the feedback mechanism; the adjustment includes modifying the PWM duty cycle setting, adjusting the search parameters of the genetic algorithm, or retraining the dynamic resistance demand model; the feedback mechanism generates and adjusts the duty cycle of the PWM signal according to the resistance error through the PID controller; the calculation formula is:

[0028]

[0029] In formula (3), A t is the control parameter of the step growth rate, which is used to limit the growth rate of the step length; μ j represents the adjustment threshold, which is used to determine whether adaptive adjustment is required; ΔS is the adjustment step, which represents the adjustment amount of the PWM duty cycle setting; w t represents the current error; sign represents the sign function, which is used to determine the direction of adjustment; K P , K i and K d They represent the proportional, integral, and differential gains of the PID controller respectively.

[0030] As a further technical solution of the present invention, the environmental factor quantification unit establishes a mathematical model between environmental parameters and quantitative indicators through a statistical quantification method, and the statistical quantification method describes the mapping relationship between environmental parameters and quantitative indicators through a nonlinear model, and minimizes the difference between the predicted value and the actual value through regression analysis to solve the model parameters; the resistance influence analysis unit fits the nonlinear mapping between environmental parameters and resistance changes through a support vector machine and a neural network; the support vector machine uses a kernel function to map the input data to a high-dimensional space, and finds the optimal hyperplane to maximize the classification interval; the neural network realizes the approximation and prediction of complex functions through a multi-layer network structure and a back propagation algorithm; based on the results of the resistance influence analysis, the compensation value calculation unit uses a Gaussian The process regression and gradient descent methods dynamically calculate and output the resistance compensation value; the Gaussian process regression is based on the prior assumption of the Gaussian process, describes the correlation between input variables through the kernel function, and uses Bayesian inference to calculate the predicted value; the gradient descent method is used to fine-tune the compensation value considering the system stability and real-time requirements; the output feedback unit transmits the calculated resistance compensation value to the adaptive grounding resistance adjustment module in real time through the communication protocol and API interface, and receives system status feedback; the output end of the environmental factor quantification unit is connected to the input end of the resistance influence analysis unit; the output end of the resistance influence analysis unit is connected to the input end of the compensation value calculation unit; the output end of the compensation value calculation unit is connected to the input end of the output feedback unit.

[0031] As a further technical solution of the present invention, the high-frequency transient suppression module connects a transient voltage suppression diode in parallel to the ground path. When an overvoltage occurs, the transient voltage suppression diode is turned on; the high-frequency transient suppression module includes a detection response unit, an energy absorption unit and a filtering stabilization unit; the detection response unit uses a high-bandwidth analog front end and a fast Fourier transform to monitor the voltage waveform of the power system in real time. Once a high-frequency transient is detected, the transient voltage suppression diode is switched on to direct the transient current to a preset safe path. The detection response unit also provides instant voltage clamping through a metal oxide varistor; when a high-frequency transient occurs, the energy absorption unit stores energy through a capacitor bank. The invention relates to a method for storing energy, and converting the stored electrical energy into heat energy through a positive temperature coefficient thermistor and dissipating it into the environment; the energy absorption unit also releases the stored energy through a gas discharge tube; the filtering stabilization unit connects a multi-stage LC filtering network in parallel in the circuit, and the multi-stage LC filtering network filters out redundant frequency components through a surface acoustic wave filter; the filtering stabilization unit also optimizes the energy release process through an adaptive tuning mechanism; the adaptive tuning mechanism evaluates the fitness of the cutoff frequency and bandwidth through a particle swarm optimization algorithm to tune the value of the objective function; in the process of parameter adjustment, the adaptive tuning mechanism also simulates the response of the filter to the transient voltage waveform through finite element analysis to evaluate the energy absorption and release effects.

[0032] As a further technical solution of the present invention, the fault detection and isolation module monitors the ground current through a current transformer. When a current exceeding a threshold is detected, the fault detection and isolation module transmits a signal to the isolation circuit through a high-speed optical coupler, triggers a solid-state relay to cut off the power supply, and encapsulates the fault information into a data packet and transmits it to the telemetry module through the communication interface circuit RS-485; the fault detection and isolation module includes a current detection unit, a signal processing unit, an isolation execution unit and a communication unit; the current detection unit outputs a voltage signal proportional to the current through a Rogowski coil, and adjusts the gain and offset of the transformer through a self-calibration circuit; the self-calibration circuit periodically generates a known current signal as a reference, compares the reference signal and the transformer output signal, detects and adjusts the gain and offset of the transformer; the signal processing unit identifies fault characteristic signal parameters through fast Fourier transform and wavelet transform; the fault characteristic signal parameters include harmonic distortion rate, amplitude mutation amount and phase jump parameters; once a fault signal is detected, the isolation execution unit cuts off the connection between the fault circuit and the main system through a silicon-controlled rectifier, and after the isolation operation is completed, transmits the fault information to the telemetry module through the communication unit.

[0033] As a further technical solution of the present invention, the ground current control module limits the ground current through a self-resetting fuse PPTC, and adjusts the current limiting value through a current feedback loop; the current feedback loop includes a current sensor, a comparator, a control logic unit and a drive circuit; the current sensor is used to capture the current signal in the grounding line in real time and convert the current signal into an electrical signal; the comparator is used to compare the detected current value with a preset safety threshold to determine whether the grounding current needs to be adjusted. If adjustment is required, the control logic unit generates an adjustment control signal through an adaptive PID controller based on the deviation between the current ground current and the target current, and adjusts the working state of the self-resetting fuse PPTC through the drive circuit.

[0034] The beneficial effects of the present invention are:

[0035] 1. The present invention monitors the grounding resistance and environmental factors (such as soil moisture and temperature) in real time through a real-time monitoring module and an adaptive grounding resistance adjustment module, and transmits the data to the adaptive grounding resistance adjustment module. The adaptive grounding resistance adjustment module dynamically adjusts the grounding resistance according to these data. By connecting multiple sections of controllable resistors in series with the grounding path and combining the environmental compensation model, the resistance value is automatically adjusted to ensure that the grounding resistance remains stable under different environmental conditions. The problem of grounding resistance fluctuating with environmental changes is solved, and the adaptability and reliability of the system are improved.

[0036] 2. The present invention detects and quickly responds to arc restrike through a high-frequency transient suppression module, and effectively suppresses the generation of overvoltage by quickly cutting off and restoring the circuit. It protects the insulation level of the equipment, reduces equipment cost and manufacturing difficulty, extends the service life of the equipment, and reduces operation and maintenance costs. It solves the overvoltage problem caused by arc restrike and improves the safety and stability of the system.

[0037] 3. The present invention uses a fault detection and isolation module to monitor the grounding current in real time. Once a single-phase grounding fault is detected, the isolation mechanism is immediately activated to quickly cut off the fault circuit. This prevents the further spread of the fault and protects other circuits that are operating normally. This solves the problem of untimely fault detection and isolation and improves the reliability and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0039] Figure 1This is an overall architecture diagram of a grounding system of a high-voltage switch cabinet of the present invention;

[0040] Figure 2 A working process framework diagram of a grounding system of a high-voltage switch cabinet of the present invention;

[0041] Figure 3 It is a workflow framework diagram of the high-frequency transient suppression module of the present invention;

[0042] Figure 4 It is a working flow chart of the resistance compensation value mapping model of the present invention;

[0043] Figure 5 It is a workflow framework diagram of the fault detection and isolation module of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] like Figure 1-Figure 5 As shown, a grounding system of a high-voltage switch cabinet includes:

[0046] A real-time detection module is used to monitor the changes of environmental factors around the grounding resistance and the grounding system in real time, and output the grounding resistance change value and environmental factors to the adaptive grounding resistance adjustment module and the telemetry module; the environmental factors include soil moisture and temperature; the real-time detection module monitors the grounding resistance in real time through a grounding resistance detection unit; the grounding resistance detection unit supplies power to the grounding resistance through a constant current source circuit configured by an operational amplifier and an LDO regulator, and detects the voltage drop across the grounding resistance through a differential amplifier, and the voltage signal is converted into a digital signal through an analog-to-digital converter; the constant current source circuit ensures that the current remains constant throughout the detection process through a negative feedback mechanism to eliminate the error of power supply fluctuations; the differential amplifier is composed of two symmetrical operational amplifiers, which respectively detect the voltage across the grounding resistance and eliminate noise interference through differential output; the grounding resistance detection module also sends the calculated grounding resistance change value to the adaptive grounding resistance adjustment module and the telemetry module through the serial communication protocol SPI.

[0047] In the specific implementation, the operational amplifier is used to provide high gain and low output impedance, and the LDO regulator is used to provide a stable power supply voltage. The constant current source circuit ensures that the current remains constant throughout the detection process through a negative feedback mechanism, eliminating the error of power supply fluctuations. The inverting input of the operational amplifier is connected to a reference voltage source, and the non-inverting input is connected to one end of the grounding resistor. The LDO regulator provides a stable power supply voltage to ensure the stable operation of the circuit under different environmental conditions. The negative feedback mechanism forms a feedback loop through a resistor and a grounding resistor to keep the output current constant. The constant current source circuit ensures the stability of the current during the detection process, eliminates the influence of power supply fluctuations on the measurement results, and improves the accuracy and reliability of the measurement.

[0048] The differential amplifier consists of two symmetrical op amps, which detect the voltage at both ends of the grounding resistor respectively and eliminate noise interference through differential output. The differential amplifier can amplify the difference between the two input signals while suppressing common-mode noise. The two symmetrical op amps are connected to the two ends of the grounding resistor respectively, and the output signal is connected to the analog-to-digital converter through the differential output. The input of the differential amplifier is matched by precision resistors to ensure the balance of the two input signals, thereby effectively eliminating noise interference. The differential amplifier improves the signal-to-noise ratio of the signal, ensures the measurement accuracy in a complex electromagnetic environment, and reduces the impact of external interference on the measurement results. The analog-to-digital converter ADC converts analog signals into digital signals through sampling and quantization processes. The sampling process discretizes the continuous analog signal, and the quantization process converts the discrete analog signal into a digital code. The resolution of the ADC determines the accuracy of the converted digital signal. Finally, the grounding resistance detection module sends the calculated grounding resistance change value to the adaptive grounding resistance adjustment module and the telemetry module through the SPI interface. The master device sends the clock signal through the SCLK line, the MOSI line sends data, the MISO line receives data, and the SS line controls the selection of the slave device. Under actual working conditions, the working process of the ground resistance detection unit is as follows: the constant current source circuit and the differential amplifier are initialized to ensure that the circuit is in a stable state. The LDO regulator provides a stable power supply voltage, the operational amplifier enters the working state, and the input end of the differential amplifier is matched by a precision resistor to ensure the balance of the two input signals. The constant current source circuit provides a constant current for the ground resistance, and the differential amplifier detects the voltage drop across the ground resistance. The differential amplifier transmits the voltage signal to the analog-to-digital converter through the differential output end. The analog-to-digital converter converts the analog voltage signal into a digital signal and obtains the value of the ground resistance through internal calculation. The ground resistance detection module sends the calculated ground resistance change value to the adaptive ground resistance adjustment module and the telemetry module through the SPI interface. The master device sends the clock signal through the SCLK line, the MOSI line sends data, the MISO line receives data, and the SS line controls the selection of the slave device. During the data transmission process, the system monitors the change of the ground resistance in real time and transmits the data to the central processing unit for further processing and analysis. The central processing unit dynamically adjusts the ground resistance through the adaptive ground resistance adjustment module according to the received ground resistance change value. The adaptive grounding resistance adjustment module connects multiple controllable resistors in series with the grounding path and automatically adjusts the resistance value in combination with the environmental compensation model to ensure that the grounding resistance remains stable under different environmental conditions.

[0049] Compared with the same type of hardware, the constant current source circuit ensures that the current remains constant throughout the detection process through a negative feedback mechanism, eliminating the error of power supply fluctuations. The differential amplifier eliminates noise interference through differential output and improves the signal-to-noise ratio of the signal. These designs ensure the high accuracy and stability of ground resistance detection. The symmetrical design and differential output mode of the differential amplifier effectively eliminate common-mode noise and improve the anti-interference ability of the system. In a complex electromagnetic environment, the system can still maintain high-precision measurement results. Through the SPI protocol, the ground resistance detection module can transmit data to the adaptive ground resistance adjustment module and the telemetry module at high speed and reliably. The full-duplex communication mode of the SPI protocol simplifies the data transmission process and improves the communication efficiency and reliability of the system. In addition, the LDO regulator provides a stable power supply voltage to ensure the stable operation of the circuit under different environmental conditions. The low power design extends the service life of the system and improves the reliability of the system. The high gain and low output impedance characteristics of the operational amplifier and differential amplifier further improve the performance of the system. Among them, operational amplifier parameters: bandwidth: 10MHz, input bias current: 1pA, supply voltage range: 2.7V to36V; LDO regulator parameters: output voltage: 3.3V, maximum output current: 800mA, quiescent current: 5μA; differential amplifier parameters: gain range: 1 to 10000, bandwidth: 1MHz, supply voltage range: 2.7V to 36V; analog-to-digital converter parameters: resolution: 16 bits, sampling rate: 860SPS, supply voltage range: 2.0V to 5.5V; serial communication protocol SPI1 clock frequency is 10MHz, and data transmission rate is 10Mbps.

[0050] The adaptive grounding resistance adjustment module is used to dynamically adjust the grounding resistance according to the grounding resistance change value and environmental factors; the adaptive grounding resistance adjustment module connects multiple sections of controllable resistance in series with the grounding path, and the adaptive grounding resistance adjustment module constructs a resistance compensation value mapping model based on the environmental sensitive period index and the mutual correlation coefficient, and controls the resistance value through a dynamic resistance control algorithm; the resistance compensation value mapping model includes an environmental factor quantification unit, a resistance influence analysis unit, a compensation value calculation unit and an output feedback unit; the adaptive grounding resistance adjustment module receives and processes the digital signal of the grounding resistance detection module through a microcontroller, and the microcontroller performs FIR filtering and sliding average filtering on the grounding resistance. The digital signal output by the resistance detection module is processed, and the change value of the grounding resistance is calculated by the least square method; according to the calculated change value of the grounding resistance, the adaptive grounding resistance adjustment module realizes PWM signal amplification and driving of the controllable resistance array through the PWM driving circuit; the controllable resistance array is composed of a plurality of controllable resistance units, each of which is connected in series in the grounding path, and the conduction state of each controllable resistance unit is adjusted by PWM signal control to realize dynamic adjustment of the grounding resistance value; the adaptive grounding resistance adjustment module also realizes real-time monitoring of the change value of the grounding resistance through a closed-loop feedback circuit, and performs real-time feedback adjustment on the change value of the grounding resistance through a dynamic resistance control algorithm.

[0051] In the specific implementation, the microcontroller receives the digital signal of the ground resistance detection module through the SPI or I2C interface. The internally integrated FIR filter and sliding average filter preprocess the received digital signal to remove noise and interference. Subsequently, the change value of the ground resistance is calculated by the least square method. The FIR filter is a digital filter that performs signal filtering by performing convolution operation on the input signal. The FIR filter performs weighted summation on the input signal through a set of predefined coefficients to filter out high-frequency noise and interference. The FIR filter has a linear phase characteristic and can maintain the integrity of the signal. The sliding average filter smoothes the fluctuation of the signal by averaging the data in a certain window. The sliding average filter averages the data in the window through a window of fixed length. Each time new data arrives, the data in the window is updated and a new average value is calculated. The sliding average filter can smooth the short-term fluctuation of the signal, improve the stability of the signal, and reduce the influence of instantaneous interference. The least square method is a mathematical optimization method that fits the best linear relationship by minimizing the sum of squares of errors. The least squares method fits a series of data points to find a straight line so that the sum of the squares of the distances from all data points to this straight line is minimized. In this module, the least squares method is used to calculate the change value of the grounding resistance. The PWM drive circuit controls the amplitude of the signal by changing the pulse width to drive the controllable resistor array. The PWM drive circuit generates a PWM signal through a microcontroller, and drives the controllable resistor array after amplification by a power amplifier. The conduction state of each controllable resistor unit is controlled by the PWM signal. By changing the duty cycle of the PWM signal, the conduction time of each controllable resistor unit is adjusted, thereby realizing the dynamic adjustment of the grounding resistance. The PWM drive circuit can accurately control the conduction state of each controllable resistor unit to achieve high-precision and real-time adjustment of the grounding resistance. The controllable resistor array consists of multiple controllable resistor units, each of which is connected in series in the grounding path. The conduction state of each controllable resistor unit is adjusted by PWM signal control to realize the dynamic adjustment of the grounding resistance value. Each controllable resistor unit consists of a fixed resistor and a controllable switch (such as MOSFET). The PWM signal adjusts the equivalent resistance value of the resistance unit by controlling the on and off of the switch. Multiple resistance units are connected in series to form an adjustable resistance array. The controllable resistance array can achieve high-precision and high-flexibility resistance adjustment to ensure the stability of the grounding resistance under different environmental conditions. The closed-loop feedback circuit is used to monitor the change value of the grounding resistance in real time, and to make real-time feedback adjustment to the change value of the grounding resistance through a dynamic resistance control algorithm. The closed-loop feedback circuit monitors the change value of the grounding resistance in real time through a feedback sensor, and feeds back the monitored value to the microcontroller. The microcontroller adjusts the duty cycle of the PWM signal through the dynamic resistance control algorithm to achieve real-time adjustment of the grounding resistance.The closed-loop feedback circuit can monitor and adjust the ground resistance in real time, ensuring the stability and reliability of the system and improving the response speed and control accuracy of the system.

[0052] In actual working state, the workflow of the adaptive ground resistance adjustment module is as follows: the microcontroller continuously receives and processes the digital signal of the ground resistance detection module, and the FIR filter and the sliding average filter continuously perform signal preprocessing. The PWM drive circuit and the controllable resistor array adjust the conduction state of each controllable resistor unit according to the PWM signal generated by the microcontroller to maintain the stability of the ground resistance. The closed-loop feedback circuit monitors the change value of the ground resistance in real time to ensure the stability and reliability of the system. When the microcontroller detects that the change value of the ground resistance exceeds the preset range, the duty cycle of the PWM signal is adjusted through the dynamic resistance control algorithm to drive the controllable resistor array to adjust the ground resistance. The closed-loop feedback circuit monitors the adjusted ground resistance value in real time to ensure that the adjusted resistance value meets the expectation. At this time, the system enters the resistance adjustment and feedback state to ensure the real-time adjustment and stability of the ground resistance. Under different environmental conditions, the microcontroller adjusts the ground resistance in real time according to the changes in environmental factors through the dynamic resistance control algorithm. The closed-loop feedback circuit ensures the adaptability and stability of the system and improves the adaptability and reliability of the system. The system enters the environmental adaptation and adaptive adjustment state to ensure the stability of the ground resistance under different environmental conditions.

[0053] Compared with the same type of hardware, the present invention removes noise and interference through FIR filter and sliding average filtering to ensure high precision and stability of the signal. The least square method can accurately calculate the change value of the grounding resistance and improve the precision and reliability of the system. In addition, the PWM drive circuit can accurately control the conduction state of each controllable resistance unit to achieve high precision and real-time adjustment of the grounding resistance. The closed-loop feedback circuit ensures the rapid response and high sensitivity of the system and improves the control accuracy and response speed of the system. Secondly, through the dynamic resistance control algorithm, the system can adjust the grounding resistance in real time according to the changes in environmental factors to ensure the adaptability and environmental adaptability of the system. The closed-loop feedback circuit ensures the stability and reliability of the system and improves the adaptability and reliability of the system. At the same time, through cooperation with the telemetry module, the operation and maintenance personnel can monitor the state of the grounding resistance in real time through the remote terminal, take maintenance measures in time, reduce the fault response time, and improve the reliability and safety of the system.

[0054] Furthermore, the environmental factor quantification unit establishes a mathematical model between environmental parameters and quantitative indicators through a statistical quantification method, and the statistical quantification method describes the mapping relationship between environmental parameters and quantitative indicators through a nonlinear model, and minimizes the difference between the predicted value and the actual value through regression analysis to solve the model parameters; the resistance impact analysis unit fits the nonlinear mapping between environmental parameters and resistance changes through a support vector machine and a neural network; the support vector machine uses a kernel function to map the input data to a high-dimensional space, and finds the optimal hyperplane to maximize the classification interval; the neural network realizes the approximation and prediction of complex functions through a multi-layer network structure and a back propagation algorithm; based on the results of the resistance impact analysis, the compensation value calculation unit adopts a Gaussian process regression The resistance compensation value is dynamically calculated and output by the gradient descent method; the Gaussian process regression is based on the prior assumption of the Gaussian process, describes the correlation between the input variables by the kernel function, and calculates the predicted value by Bayesian inference; the gradient descent method is used to fine-tune the compensation value considering the system stability and real-time requirements; the output feedback unit transmits the calculated resistance compensation value to the adaptive grounding resistance adjustment module in real time through the communication protocol and API interface, and receives system status feedback; the output end of the environmental factor quantification unit is connected to the input end of the resistance influence analysis unit; the output end of the resistance influence analysis unit is connected to the input end of the compensation value calculation unit; the output end of the compensation value calculation unit is connected to the input end of the output feedback unit.

[0055] In the grounding system of the high-voltage switchgear, the environmental factor quantification unit establishes a mathematical model between environmental parameters and quantitative indicators through statistical quantification methods. The statistical quantification method describes the mapping relationship between environmental parameters (such as temperature, humidity, current, voltage, etc.) and quantitative indicators through nonlinear models, and minimizes the difference between the predicted value and the actual value through regression analysis to solve the model parameters. The resistance influence analysis unit fits the nonlinear mapping between environmental parameters and resistance changes through support vector machines (SVM) and neural networks. SVM uses kernel functions to map input data to high-dimensional space and find the optimal hyperplane to maximize the classification interval. Commonly used kernel functions include linear kernels, polynomial kernels, and Gaussian kernels. Neural networks achieve approximation and prediction of complex functions through multi-layer network structures and back propagation algorithms. The neural network includes an input layer, a hidden layer, and an output layer. The weights are adjusted through the back propagation algorithm to minimize the loss function. The resistance influence analysis unit can accurately fit the nonlinear relationship between environmental parameters and resistance changes, providing a reliable basis for compensation value calculation. The compensation value calculation unit uses Gaussian process regression (GPR) and gradient descent method to dynamically calculate and output the resistance compensation value. GPR is based on the prior assumption of Gaussian process, describes the correlation between input variables through kernel function, and uses Bayesian inference to calculate the predicted value. Common kernel functions include square exponential kernel and Markov kernel. Gradient descent method is used to fine-tune the compensation value considering the system stability and real-time requirements. The loss function is minimized by iteratively updating the weights. The compensation value calculation unit can dynamically calculate and output the resistance compensation value to ensure the stability of the grounding resistance under different environmental conditions. The output feedback unit transmits the calculated resistance compensation value to the adaptive grounding resistance adjustment module in real time through the communication protocol and API interface, and receives system status feedback. The output feedback unit exchanges data with the adaptive grounding resistance adjustment module through standard communication protocols (such as Modbus or CAN) and API interfaces to ensure real-time data transmission and feedback. The output feedback unit can realize real-time data transmission and system status feedback to ensure the stability and reliability of the system.

[0056] In actual working conditions, Figure 4As shown in the figure, the working process of the resistance compensation value mapping model is as follows: Environmental factor quantification: A mathematical model between environmental parameters and quantitative indicators is established using a statistical quantification method. Next, the model parameters are solved by minimizing the difference between the predicted value and the actual value through regression analysis. Then, the nonlinear mapping between environmental parameters and resistance changes is fitted using support vector machines and neural networks. Based on the results of the resistance impact analysis, the resistance compensation value is dynamically calculated using Gaussian process regression and gradient descent methods. Finally, the calculated resistance compensation value is transmitted to the adaptive grounding resistance adjustment module in real time, and system state feedback is received. Specifically, first, the environmental factor quantification unit continuously collects environmental parameters, establishes a mathematical model between environmental parameters and quantitative indicators through a statistical quantification method, and outputs quantitative indicators. The resistance impact analysis unit fits the nonlinear mapping between environmental parameters and resistance changes through support vector machines and neural networks, and outputs the fitting results. The compensation value calculation unit uses Gaussian process regression and gradient descent methods to dynamically calculate and output the resistance compensation value to ensure the stability of the grounding resistance. The output feedback unit transmits the resistance compensation value to the adaptive grounding resistance adjustment module in real time through the communication protocol and API interface, and receives system state feedback to ensure the stability and reliability of the system. When environmental parameters change, the environmental factor quantification unit updates the quantitative index in real time, the resistance impact analysis unit refits the nonlinear mapping between environmental parameters and resistance changes, the compensation value calculation unit recalculates the resistance compensation value, and the output feedback unit transmits the new resistance compensation value in real time to ensure real-time adjustment of the grounding resistance. The system enters the environmental change and adjustment state to ensure the stability of the grounding resistance under different environmental conditions. When the system detects a fault, the environmental factor quantification unit incorporates the fault type code into the quantitative index, the resistance impact analysis unit refits the nonlinear mapping between environmental parameters and resistance changes, the compensation value calculation unit recalculates the resistance compensation value, and the output feedback unit transmits the new resistance compensation value in real time to ensure real-time adjustment of the grounding resistance. The system enters the fault detection and response state to ensure the stability of the grounding resistance under fault conditions.

[0057] Compared with the hardware of the same type, the environmental factor quantification unit of the present invention can convert complex environmental parameters into easy-to-process quantitative indicators through statistical quantification methods and nonlinear models, providing accurate data support for subsequent resistance impact analysis. Through support vector machines and neural networks, the resistance impact analysis unit can accurately fit the nonlinear relationship between environmental parameters and resistance changes, providing a reliable basis for compensation value calculation. Through Gaussian process regression and gradient descent method, the compensation value calculation unit can dynamically calculate and output the resistance compensation value to ensure the stability of grounding resistance under different environmental conditions. Secondly, through real-time data transmission and system state feedback, the output feedback unit can ensure the rapid response and high sensitivity of the system. Through Gaussian process regression and gradient descent method, the compensation value calculation unit can adjust the resistance compensation value in real time to ensure the rapid response and high sensitivity of the system. Through the environmental factor quantification unit and the resistance impact analysis unit, the system can comprehensively consider multiple environmental factors and system parameters to ensure the stability of grounding resistance under different environmental conditions. Through the compensation value calculation unit and the output feedback unit, the system can adjust the resistance compensation value in real time to ensure the adaptability and environmental adaptability of the system. At the same time, through standard communication protocols and API interfaces, the output feedback unit can realize real-time data transmission and system state feedback to ensure efficient communication and remote monitoring of the system. Operation and maintenance personnel can monitor the status of grounding resistance in real time through remote terminals, take maintenance measures in a timely manner, reduce fault response time, and improve system reliability and safety.

[0058] In a specific embodiment, the hardware working environment of the resistance compensation value mapping model in the high-voltage switch cabinet grounding system needs to meet the following requirements: the operating temperature range should be -20°C to 70°C to adapt to different environmental conditions. The storage temperature range should be -40°C to 85°C to ensure the storage safety of the equipment at extreme temperatures. The relative humidity should be between 10% and 90% without condensation to prevent damage to the equipment due to excessive humidity. The input voltage range should be 220V±10% and the frequency should be 50 / 60Hz to ensure a stable power supply. The power supply fluctuation should not exceed ±5% to avoid system failures caused by unstable power supply. The equipment should comply with EMC (Electromagnetic Compatibility) standards to ensure normal operation in an electromagnetic environment without interference. The equipment should be able to withstand vibrations with a frequency of 10Hz to 500Hz and an amplitude of 0.075mm to ensure stable operation in an industrial environment. The experimental equipment includes high-voltage switchgear model HSK-1000; microcontroller: model STM32F407; environmental sensors: temperature sensor DS18B20, humidity sensor DHT22, current sensor LEM LAH 50-P, voltage sensor ACS712; controllable resistor array: 10 controllable resistor units, each unit consists of MOSFET (IRF540N) and 10Ω fixed resistor; PWM drive circuit: power amplifier LM2902, switch circuit IRF540N; data acquisition system: NI LabVIEW data acquisition card; the experimental platform is Windows 10 operating system, MATLAB R2021a;

[0059] Based on the above hardware operating environment, the experimental group and the control group were tested under the same environmental conditions. The experimental group used the resistance compensation value mapping model to dynamically adjust the grounding resistance. The control group used the traditional fixed resistance adjustment method without dynamic adjustment. Collect environmental parameters (temperature, humidity, current, voltage) and grounding resistance values. And record the grounding resistance value and system stability of each group under different environmental conditions; the experimental data record is shown in Table 1:

[0060] Table 1 Experimental data comparison table

[0061]

[0062] It can be seen from data table 2 that under different environmental conditions, the error of grounding resistance in the experimental group is always kept within 0.2Ω, while the error of the control group gradually increases, reaching a maximum of 2.2Ω. This shows that the resistance compensation value mapping model can significantly improve the accuracy of grounding resistance. In addition, the system stability score of the experimental group is always kept above 8.6, while the system stability score of the control group gradually decreases, and the lowest drops to 1.0. This shows that the resistance compensation value mapping model can significantly improve the stability of the system. Secondly, the experimental group can maintain good performance under different environmental conditions, while the performance of the control group decreases significantly when the environment changes greatly. This shows that the resistance compensation value mapping model has strong environmental adaptability.

[0063] A high-frequency transient suppression module is used to suppress high-frequency transient overvoltage caused by arc restrike; the high-frequency transient suppression module connects a transient voltage suppression diode in parallel to the ground path, and when an overvoltage occurs, the transient voltage suppression diode is turned on; the high-frequency transient suppression module includes a detection response unit, an energy absorption unit and a filtering stabilization unit; the detection response unit uses a high-bandwidth analog front end and a fast Fourier transform to monitor the voltage waveform of the power system in real time. Once a high-frequency transient is detected, the transient voltage suppression diode is switched on to direct the transient current to a preset safe path. The detection response unit also provides instant voltage clamping through a metal oxide varistor; when a high-frequency transient occurs, the energy absorption unit The element stores energy through a capacitor group, and converts the stored electrical energy into heat energy through a positive temperature coefficient thermistor and dissipates it into the environment; the energy absorption unit also releases the stored energy through a gas discharge tube; the filtering stabilization unit connects a multi-stage LC filtering network in parallel in the circuit, and the multi-stage LC filtering network filters out redundant frequency components through a surface acoustic wave filter; the filtering stabilization unit also optimizes the energy release process through an adaptive tuning mechanism; the adaptive tuning mechanism evaluates the fitness of the cutoff frequency and bandwidth through a particle swarm optimization algorithm to tune the value of the objective function; in the parameter adjustment process, the adaptive tuning mechanism also simulates the response of the filter to a transient voltage waveform through finite element analysis to evaluate the energy absorption and release effects.

[0064] In a specific implementation, a transient voltage suppressor diode (TVS) is a fast-response semiconductor device that can quickly turn on when an overvoltage is detected, directing the transient current to a preset safe path, thereby protecting the circuit from damage caused by overvoltage. The TVS diode is connected in parallel to the ground path. When an overvoltage is detected, the TVS diode quickly turns on and directs the transient current to the ground wire, thereby clamping the voltage within a safe range. The TVS diode has a low clamping voltage and a high response speed, and can turn on within a few nanoseconds. The detection response unit uses a high-bandwidth analog front end and a fast Fourier transform (FFT) to monitor the voltage waveform of the power system in real time. Once a high-frequency transient is detected, the conduction state of the TVS diode is immediately switched. Specifically, the high-bandwidth analog front end can capture high-frequency transient signals, and the fast Fourier transform algorithm performs frequency domain analysis on the captured signal to identify the transient component. The detection response unit controls the conduction state of the TVS diode through a high-speed comparator and a drive circuit. The metal oxide varistor (MOV) provides instant voltage clamping, further enhancing the protection effect. The detection response unit can monitor and respond to transient overvoltage in real time, ensuring that the TVS diode is turned on in the shortest time and protecting the circuit from damage caused by overvoltage. The energy absorption unit stores energy through a capacitor bank and converts the stored electrical energy into heat energy and dissipates it into the environment through a positive temperature coefficient thermistor (PTC). In addition, the energy absorption unit also releases the stored energy through a gas discharge tube (GDT). The capacitor bank is connected in parallel in the circuit to absorb transient energy. The PTC thermistor increases its resistance at high temperatures, converts the stored electrical energy into heat energy and dissipates it into the environment. The GDT breaks down under high voltage and releases the stored energy. These components work together to ensure effective absorption and release of energy. The energy absorption unit can effectively absorb and release transient energy, protect the circuit from overvoltage shock, and extend the service life of the equipment. The filtering and stabilization unit connects a multi-stage LC filtering network in parallel in the circuit and filters out redundant frequency components through a surface acoustic wave filter (SAW). The filtering and stabilization unit also optimizes the energy release process through an adaptive tuning mechanism. The multi-stage LC filtering network is used to filter out high-frequency noise, and the SAW filter is used to further filter out redundant frequency components. The adaptive tuning mechanism evaluates the fitness of the cutoff frequency and bandwidth through the particle swarm optimization algorithm (PSO) and tunes the value of the objective function. The finite element analysis (FEA) simulates the response of the filter to the transient voltage waveform and evaluates the energy absorption and release effect. The filter stabilization unit can effectively filter out high-frequency noise and optimize the energy release process.

[0065] In actual work, the high-frequency transient suppression module works as follows: Figure 3As shown: The high-frequency transient suppression module first uses a high-bandwidth analog front end and fast Fourier transform (FFT) to monitor the voltage waveform of the power system in real time. Once a high-frequency transient is detected, the transient current is directed to a safe path by switching the conduction state of the transient voltage suppression diode, and the voltage is clamped using a metal oxide varistor. When a high-frequency transient occurs, energy is stored through a capacitor bank, and the electrical energy is converted into heat energy for dissipation through a positive temperature coefficient thermistor, or released through a gas discharge tube. A multi-stage LC filter network and a surface acoustic wave filter are connected in parallel in the circuit to filter out redundant frequency components. Finally, the fitness of the filter parameters (cutoff frequency and bandwidth) is evaluated by a particle swarm optimization algorithm, and the response of the filter to the transient voltage waveform is simulated using finite element analysis to optimize the energy release process. Specifically, the detection response unit is initialized first, and the high-bandwidth analog front end begins to monitor the voltage waveform of the power system. The TVS diode and MOV are in a standby state, ready to respond to transient overvoltages. The capacitor bank and PTC thermistor of the energy absorption unit are in a charging state, and the GDT is in a non-breakdown state. The multi-stage LC filter network and SAW filter of the filter stabilization unit are in operation, and the adaptive tuning mechanism is initialized. Next, the detection response unit monitors the voltage waveform in real time through a high-bandwidth analog front end. Once a high-frequency transient is detected, the fast Fourier transform algorithm performs frequency domain analysis on the captured signal to identify the transient component. The high-speed comparator and drive circuit immediately switch the conduction state of the TVS diode to direct the transient current to the ground wire. The MOV provides instant voltage clamping to further enhance the protection effect. The capacitor bank of the energy absorption unit absorbs the transient energy, and the PTC thermistor converts the stored electrical energy into heat energy and dissipates it into the environment. The GDT breaks down under high voltage and releases the stored energy. These components work together to ensure effective absorption and release of energy and protect the circuit from overvoltage shocks. Finally, the multi-stage LC filter network and SAW filter of the filter stabilization unit filter out high-frequency noise and optimize signal quality. The adaptive tuning mechanism evaluates the fitness of the cutoff frequency and bandwidth through the particle swarm optimization algorithm and tunes the value of the objective function. Finite element analysis simulates the response of the filter to the transient voltage waveform and evaluates the energy absorption and release effect.

[0066] Compared with similar hardware, the detection response unit uses a high-bandwidth analog front end and fast Fourier transform, which can monitor and respond to transient overvoltages in real time, ensure that the TVS diode is turned on in the shortest time, and protect the circuit from overvoltage damage. MOV provides instant voltage clamping, further enhancing the protection effect. The energy absorption unit effectively absorbs and releases transient energy through the synergy of capacitor banks, PTC thermistors and GDTs, protects the circuit from overvoltage shocks, and extends the service life of the equipment. These components work together to ensure effective management and release of energy. In addition, the filtering and stabilization unit uses a multi-stage LC filter network and SAW filter to effectively filter out high-frequency noise and optimize signal quality. The adaptive tuning mechanism evaluates the fitness of the cutoff frequency and bandwidth through the particle swarm optimization algorithm and tunes the value of the objective function. Finite element analysis simulates the response of the filter to the transient voltage waveform, evaluates the energy absorption and release effect, and ensures the stability and reliability of the system. Through efficient energy management and filtering technology, the system can maintain high reliability in complex electromagnetic environments. The adaptive tuning mechanism ensures continuous optimization of the system, reduces maintenance costs, and extends the service life of the equipment. Among them, transient voltage suppression diode (TVS) parameters: maximum clamping voltage: 220V, maximum peak pulse power: 600W, response time: 1ns; metal oxide varistor (MOV) parameters: rated voltage: 220V, maximum energy absorption: 20J, response time: 25ns; capacitor bank parameters: capacitance: 10μF, withstand voltage: 500V, temperature range: -55℃ to +125℃; positive temperature coefficient thermistor (PTC) parameters: rated voltage: 200V, rated current: 1A, temperature range: -40℃ to +125℃; gas discharge tube (GDT) parameters: rated voltage: 220V, maximum discharge current: 10kA, response time: 1μs; surface acoustic wave filter (SAW) parameters: center frequency: 2.5GHz, bandwidth: 100MHz, insertion loss: 3dB; multi-stage LC filter network parameters: cut-off frequency: 500kHz, bandwidth: 100kHz, insertion loss: 1dB.

[0067] The fault detection and isolation module is used to detect the grounding current, isolate the fault circuit, and transmit the fault signal to the telemetry module; the fault detection and isolation module monitors the grounding current through a current transformer. When a current exceeding a threshold is detected, the fault detection and isolation module transmits the signal to the isolation circuit through a high-speed optical coupler, triggers the solid-state relay to cut off the power supply, and encapsulates the fault information into a data packet and transmits it to the telemetry module through the communication interface circuit RS-485; the fault detection and isolation module includes a current detection unit, a signal processing unit, an isolation execution unit and a communication unit; the current detection unit outputs a signal that is in phase with the current through a Rogowski coil. The self-calibration circuit generates a voltage signal proportional to the current of the transformer, and adjusts the gain and offset of the transformer through a self-calibration circuit; the self-calibration circuit periodically generates a known current signal as a reference, compares the reference signal and the transformer output signal, detects and adjusts the gain and offset of the transformer; the signal processing unit identifies the fault characteristic signal parameters through fast Fourier transform and wavelet transform; the fault characteristic signal parameters include harmonic distortion rate, amplitude mutation amount and phase jump parameters; once a fault signal is detected, the isolation execution unit cuts off the connection between the fault circuit and the main system through a silicon-controlled rectifier, and after the isolation operation is completed, the fault information is transmitted to the telemetry module through the communication unit.

[0068] In a specific implementation, the self-calibration circuit periodically generates a known current signal as a reference, compares the reference signal with the transformer output signal, and detects and adjusts the gain and offset of the transformer. The self-calibration circuit includes a reference signal generator, a comparator, and an adjustment circuit. The signal processing unit identifies the fault characteristic signal parameters, including harmonic distortion rate, amplitude mutation amount, and phase jump parameters, through fast Fourier transform (FFT) and wavelet transform. The FFT algorithm converts the time domain signal into a frequency domain signal and identifies the harmonic components in the signal. The wavelet transform identifies the mutation and transient components in the signal through multi-resolution analysis. The signal processing unit implements these algorithms through a high-speed processor (such as DSP or FPGA) to ensure real-time processing and analysis. FFT and wavelet transform can accurately identify fault characteristic signals, improve the accuracy and sensitivity of fault detection, ensure that faults are discovered at an early stage, and reduce the damage of faults to the system. When the signal processing unit detects a fault signal, the isolation execution unit transmits the signal to the solid-state relay (SSR) through a high-speed optical coupler, triggering the SCR to cut off the power supply. The combination of SCR and SSR ensures the rapid isolation of the fault circuit, protects the main system from the fault, and improves the safety and reliability of the system. The communication unit includes RS-485 transceiver and communication protocol processor.

[0069] In actual working conditions, Figure 5As shown in the figure, the working process of the fault detection and isolation module is as follows: first, the ground current is monitored through the current transformer. When the current exceeds the preset threshold, it is judged as a fault. Then the fault signal is transmitted to the isolation circuit through the high-speed optical coupler. The solid-state relay is triggered to cut off the power supply. And the fault information is encapsulated into a data packet through the communication interface circuit. Finally, the fault information data packet is transmitted to the telemetry module through RS-485. Specifically, the current detection unit is initialized, and the Rogowski coil starts to detect the ground current. The self-calibration circuit generates a reference signal and adjusts the gain and offset of the transformer. The signal processing unit is initialized and ready for signal processing. The isolation execution unit and the communication unit are initialized and ready to respond to the fault signal. The current detection unit outputs a voltage signal proportional to the current through the Rogowski coil, and the self-calibration circuit regularly generates a reference signal to adjust the gain and offset of the transformer. The signal processing unit analyzes the voltage signal in real time through the FFT and wavelet transform algorithms to identify the fault characteristic signal parameters, including harmonic distortion rate, amplitude mutation amount and phase jump parameters. Once the signal processing unit detects a fault signal that exceeds the threshold, the isolation execution unit transmits the signal to the solid-state relay through a high-speed optical coupler, triggering the SCR to cut off the connection between the fault circuit and the main system. After the isolation operation is completed, the communication unit encapsulates the fault information into a data packet through the RS-485 interface and transmits it to the telemetry module. The communication unit communicates with the telemetry module through the RS-485 interface and transmits the fault information to the remote monitoring center. Operation and maintenance personnel can view fault information in real time through the remote terminal, take maintenance measures in time, reduce fault response time, and improve system reliability and safety.

[0070] Compared with similar hardware, the current detection unit uses Rogowski coils and self-calibration circuits to ensure high accuracy and long-term stability of current detection. The self-calibration circuit improves the reliability of fault detection by regularly generating reference signals and adjusting the gain and offset of the transformer. The signal processing unit analyzes the voltage signal in real time through FFT and wavelet transform algorithms to identify fault characteristic signal parameters. These algorithms can accurately identify fault characteristics and improve the sensitivity and accuracy of fault detection. The isolation execution unit ensures the rapid isolation of the fault circuit through high-speed optical couplers and solid-state relays to protect the main system from the impact of the fault. The communication unit encapsulates the fault information into data packets through the RS-485 interface and transmits it to the telemetry module. The RS-485 interface has the ability of long-distance transmission and multi-point communication, ensuring that the fault information can be transmitted to the remote monitoring center in time, which is convenient for operation and maintenance personnel to remotely monitor and manage. This not only improves the communication efficiency of the system, but also reduces the operation and maintenance costs. In addition, through efficient fault detection and isolation technology, the system can maintain high reliability in complex electromagnetic environments. The optimized design of the self-calibration circuit and communication unit ensures the long-term stability and reliability of the system, reduces maintenance costs, and extends the service life of the equipment. Among them, the Rogowski coil adopts Rogowski-1000, parameters: coil diameter: 100mm, frequency range: 10Hz to1MHz, output voltage: 1mV / A; in the self-calibration circuit, the reference signal generator parameters: temperature coefficient: 0.075% / ℃, output current: 1μA to 1mA; comparator parameters: working voltage: 2V to 36V, response time: 1μs; the signal processing unit adopts TMS320F28335, parameters: main frequency: 150MHz, memory: 128KB Flash, 32KB RAM; solid-state relay (SSR) parameters: rated voltage: 240VAC, rated current: 12A, response time: 10μs; silicon-controlled rectifier (SCR) parameters: rated voltage: 1200V, rated current: 100A, response time: 5μs; high-speed optical coupler parameters: working voltage: 5V, response time: 100ns; RS-485 transceiver parameters: working voltage: 3.3V to 5.5V, data transmission rate: 2.5Mbps; communication protocol processor parameters: main frequency: 72MHz, memory: 64KB Flash, 20KB RAM.

[0071] A ground current control module, used to control the ground current; the ground current control module limits the ground current through a self-resetting fuse PPTC, and adjusts the current limit value through a current feedback loop; the current feedback loop includes a current sensor, a comparator, a control logic unit and a drive circuit; the current sensor is used to capture the current signal in the ground line in real time and convert the current signal into an electrical signal; the comparator is used to compare the detected current value with a preset safety threshold to determine whether the ground current needs to be adjusted. If adjustment is required, the control logic unit generates an adjustment control signal through an adaptive PID controller based on the deviation between the current ground current and the target current, and adjusts the working state of the self-resetting fuse PPTC through the drive circuit;

[0072] In specific implementation, the self-resetting fuse (PPTC) is a positive temperature coefficient thermistor that can quickly heat up and increase the resistance value when the current exceeds a predetermined value, thereby limiting the current. When the current returns to normal, the PPTC will automatically reset and return to a low resistance state. In practice, the PPTC is connected in parallel to the ground path. When the current exceeds the predetermined value, the PPTC quickly heats up, the resistance value increases, and the current is limited. When the current returns to normal, the PPTC cools down and the resistance value returns to a low resistance state. The PPTC can respond quickly when the current is overloaded, protecting the circuit from damage caused by overcurrent. At the same time, it has a self-resetting function, and there is no need to replace the fuse, which improves the reliability and maintenance convenience of the system. The current sensor in the current feedback loop captures the current signal in the ground line in real time and converts the current signal into an electrical signal. The comparator compares the detected current value with the preset safety threshold to determine whether the ground current needs to be adjusted. If adjustment is required, the control logic unit generates an adjustment control signal through an adaptive PID controller based on the deviation between the current ground current and the target current, and adjusts the working state of the PPTC through the drive circuit. The current feedback loop ensures precise control of the ground current and improves the stability and safety of the system. Under actual working conditions, the working process of the ground current control module is as follows: the current sensor continuously monitors the current signal in the ground line, and the comparator compares the detected current value with the preset safety threshold. If the current value is within the safety range, the control logic unit and the drive circuit remain in standby state, the PPTC is in a low resistance state, and the ground current flows normally. When the current sensor detects that the ground current exceeds the preset safety threshold, the comparator outputs a high-level signal, indicating that the ground current needs to be adjusted. The control logic unit generates an adjustment control signal through an adaptive PID controller based on the deviation between the current ground current and the target current. The drive circuit controls the working state of the PPTC according to the generated adjustment control signal, and the PPTC quickly heats up, increases the resistance value, and limits the current. At this time, the system enters the current overload response state to ensure the rapid limitation of the ground current and protect the circuit from damage by overcurrent. When the current returns to normal, the PPTC cools down, the resistance value returns to the low resistance state, and the ground current resumes normal flow. The control logic unit and the drive circuit continue to monitor and adjust the ground current to ensure the stability and safety of the system. The system enters the current recovery and self-reset state to ensure the normal operation of the circuit.

[0073] Compared with similar hardware, the adaptive PID controller can dynamically adjust the control parameters according to the real-time status of the system, improve the accuracy and stability of the control, and ensure the precise control of the ground current. In addition, the PPTC can respond quickly when the current is overloaded to protect the circuit from damage caused by overcurrent. The control logic unit and the drive circuit ensure the effective transmission and execution of the control signal, improving the response speed and control accuracy of the system. Secondly, the PPTC has a self-reset function. When the current returns to normal, the PPTC will automatically reset and return to a low-resistance state without replacing the fuse, which improves the reliability and maintenance convenience of the system. At the same time, through cooperation with the telemetry module, the operation and maintenance personnel can monitor the status of the ground current in real time through the remote terminal, take maintenance measures in time, reduce fault response time, and improve the reliability and safety of the system.

[0074] The telemetry module is used to provide remote monitoring of the grounding system status; the telemetry module transmits the grounding system indicators to the remote terminal through wireless communication; the grounding system indicators include grounding resistance, current, soil resistivity and temperature data;

[0075] In specific implementation, Figure 2 As shown in the figure, the real-time detection module is responsible for monitoring the ground resistance and environmental factors, and sending the data to the adaptive ground resistance adjustment module and the telemetry module. The adaptive ground resistance adjustment module adjusts the ground path according to the received data. The telemetry module is responsible for transmitting the data to the remote terminal through wireless communication. The high-frequency transient suppression module directly sends a suppression signal to the ground path. When the fault detection and isolation module detects a fault, it sends a fault signal to the telemetry module and sends an isolation signal to the ground path. The ground current control module sends a control signal to the ground path as needed.

[0076] Furthermore, the PWM drive circuit includes an input unit, an amplifying unit, a driving stage unit, a protection unit and an isolation unit; the input unit locks the state of the input signal through an input buffer; the amplifying unit amplifies the amplitude of the PWM signal through a high-gain operational amplifier; the driving stage unit generates current through power transistors to drive the controllable resistor array; the protection unit protects the circuit from abnormal conditions through current limiting resistors, clamping diodes and transient voltage suppressors; the isolation unit electrically isolates the main control circuit from the driving circuit through an optical coupler. Furthermore, the working method of the dynamic resistance control algorithm includes the following steps: Step 1, construct a resistance mapping matrix Among them, T n , RH n , I n , V n and FT nRespectively represent the temperature, humidity, current, voltage and fault type code of the high-voltage switchgear at the nth sampling moment; and perform noise suppression through a weighted sliding average filter;

[0077] Step 2: Based on the resistance mapping matrix E, the dynamic resistance requirement is calculated by a multivariable nonlinear function; the multivariable nonlinear function is trained by a neural network to obtain a weight vector θ and a feature mapping function G n (E), mapping environmental parameters to a high-dimensional feature space to calculate the ground resistance value; the feature mapping function constructs a feature mapping through a polynomial kernel function and an activation function; the formula expression for calculating the dynamic resistance requirement of the multivariate nonlinear function is:

[0078]

[0079] In formula (1), M represents the highest degree of polynomial terms; P represents the number of cross terms, which is used to control the complexity of feature mapping function; G n (E) represents a feature mapping function, which is used to map the nth row vector of the resistance mapping matrix to a high-dimensional feature space; θ represents a weight vector, which is used to map the output of the feature mapping function to the dynamic resistance requirement; μ c Represents the coefficients in the polynomial kernel function and the cross term, which is used to adjust the shape and complexity of the feature mapping function; indexset l Represents the parameter index set involved in the lth cross term; E n Represents the nth row vector of the resistance mapping matrix;

[0080] Step 3: Construct the state matrix of the controllable resistor array Where S n Indicates the switch state of the nth controllable resistor unit; and based on the current state S n and dynamic resistance requirement phi(E n ), through the fitness function F ij (S n ) determine the optimal PWM duty cycle setting; the fitness function formula is:

[0081]

[0082] In formula (2), L represents a solution, i.e., a set of PWM duty cycle settings; h, γ, and ε represent the weight coefficients of the error term, safety penalty term, and stability penalty term, respectively, which are used to balance the importance of different optimization objectives; P ij (L) represents the security penalty term for solution L; R a represents the ground resistance prediction value of the nth sample by the deep learning model; R p Under solution L, the actual ground resistance value of the nth sample; δ ij(L) represents the stability penalty term of solution L; N represents the number of samples in the evaluation cycle; phi(E n ) represents the dynamic resistance requirement; T represents the length of the evaluation period, which is used to measure the calculation range of the cumulative error;

[0083] Step 4: Control the conduction state of the controllable resistor array through the PWM signal to adjust the ground resistance value; during the control process, monitor the system state and environmental changes in real time, and compare the actual ground resistance value with the predicted value in step 2. If an error occurs, adjust the control strategy through the feedback mechanism; the adjustment includes modifying the PWM duty cycle setting, adjusting the search parameters of the genetic algorithm, or retraining the dynamic resistance demand model; the feedback mechanism generates and adjusts the duty cycle of the PWM signal according to the resistance error through the PID controller; the calculation formula is:

[0084]

[0085] In formula (3), A t is the control parameter of the step growth rate, which is used to limit the growth rate of the step length; μ j represents the adjustment threshold, which is used to determine whether adaptive adjustment is required; ΔS is the adjustment step, which represents the adjustment amount of the PWM duty cycle setting; w t represents the current error; sign represents the sign function, which is used to determine the direction of adjustment; K P , K i and K d They represent the proportional, integral, and differential gains of the PID controller respectively.

[0086] In the specific implementation, the workflow of the dynamic resistance control algorithm is as follows: the environmental factor quantification unit continuously collects environmental parameters to form a resistance mapping matrix E, and the weighted sliding average filter suppresses noise in the resistance mapping matrix to improve the stability and accuracy of the data. In the dynamic resistance demand calculation stage, the dynamic resistance demand phi (E n ). The fitness function determines the optimal PWM duty cycle setting, and the PWM drive circuit controls the conduction state of the controllable resistor array to maintain the stability of the grounding resistance. The closed-loop feedback circuit monitors the change value of the grounding resistance in real time to ensure the stability and reliability of the system. When the environmental factors change, the environmental factor quantification unit updates the resistance mapping matrix E in real time. In the dynamic resistance demand calculation stage, the dynamic resistance demand phi (E n). The fitness function redefines the optimal PWM duty cycle setting, and the PWM drive circuit adjusts the conduction state of the controllable resistor array to ensure real-time adjustment of the grounding resistance. The closed-loop feedback circuit monitors the adjusted grounding resistance value in real time to ensure that the adjusted resistance value meets expectations. At this point, the system enters the environmental change and adjustment state to ensure real-time adjustment and stability of the grounding resistance. When the system detects a fault, the environmental factor quantification unit incorporates the fault type code into the resistance mapping matrix E. In the dynamic resistance demand calculation phase, the dynamic resistance demand phi (E n ). The fitness function re-determines the optimal PWM duty cycle setting, and the PWM drive circuit adjusts the conduction state of the controllable resistor array to ensure real-time adjustment of the ground resistance. The closed-loop feedback circuit monitors the adjusted ground resistance value in real time to ensure that the adjusted resistance value meets the expected value. At this point, the system enters the fault detection and response state to ensure real-time adjustment and stability of the ground resistance.

[0087] Compared with the same type of hardware, the present invention uses multivariable nonlinear functions and neural network training, and the dynamic resistance control algorithm can accurately calculate the dynamic resistance demand, thereby improving the accuracy and stability of the system. Through the PID controller and the closed-loop feedback mechanism, the system can adjust the grounding resistance in real time to ensure the high accuracy and stability of the system. Through the weighted sliding average filter and the fitness function, the system can quickly respond to environmental changes and fault detection, thereby improving the response speed and sensitivity of the system. Through the PID controller and the closed-loop feedback mechanism, the system can adjust the grounding resistance in real time to ensure the rapid response and high sensitivity of the system. Through the environmental factor quantification unit and the resistance impact analysis unit, the system can comprehensively consider a variety of environmental factors and system parameters to ensure the stability of the grounding resistance under different environmental conditions. Through the dynamic resistance control algorithm and the closed-loop feedback mechanism, the system can adjust the grounding resistance in real time to ensure the adaptability and environmental adaptability of the system. Through cooperation with the telemetry module, the operation and maintenance personnel can monitor the state of the grounding resistance in real time through the remote terminal, take maintenance measures in time, reduce the fault response time, and improve the reliability and safety of the system.

[0088] In the specific implementation, the hardware working environment of the dynamic resistance control algorithm includes: high-voltage switch cabinet: model HSK-1000; microcontroller: model STM32F407; environmental sensors: temperature sensor DS18B20, humidity sensor DHT22, current sensor LEM LAH 50-P, voltage sensor ACS712; controllable resistor array: 10 controllable resistor units, each unit consists of MOSFET (IRF540N) and 10Ω fixed resistor; PWM drive circuit: power amplifier LM2902, switch circuit IRF540N; data acquisition system: NI LabVIEW data acquisition card; experimental platform: Windows 10 operating system, MATLABR2021a;

[0089] Based on the above hardware environment, environmental sensors are used to collect environmental parameters such as temperature, humidity, current, and voltage in real time, and grounding current and voltage are collected through current sensors and voltage sensors. The collected data is transmitted to the microcontroller and preprocessed through a weighted sliding average filter. Based on the resistance mapping matrix, the dynamic resistance demand is calculated through a multivariable nonlinear function. The optimal PWM duty cycle setting is determined by the fitness function to control the conduction state of the controllable resistor array. The PID controller generates and adjusts the duty cycle of the PWM signal according to the resistance error, monitors the system status and environmental changes in real time, verifies the effectiveness of the dynamic resistance control algorithm in the high-voltage switchgear grounding system, and evaluates its performance and stability under different environmental conditions. The experimental data records are shown in Table 2:

[0090] Table 2 Experimental data record table

[0091]

[0092] As can be seen from the table, as the environmental parameters change, the error between the predicted resistance and the actual resistance is always kept within 0.2Ω, indicating that the dynamic resistance control algorithm has high accuracy. When the fault type is coded as 1, the error is still kept within 0.2Ω, indicating that the algorithm can maintain good performance under fault conditions. The output of the PID controller is gradually adjusted with the change of environmental parameters, ensuring the real-time adjustment of the grounding resistance. In the case of a fault, the PID output increases slightly, indicating that the algorithm can respond to the fault in time and adjust the grounding resistance. The total fitness value of the fitness function is always low, indicating that the algorithm can maintain good performance under different environmental conditions. In the case of a fault, the safety penalty item increases slightly, but the total fitness value remains at a low level, indicating that the algorithm can remain stable under fault conditions. Therefore, the application of the dynamic resistance control algorithm in the grounding system of high-voltage switchgear has achieved remarkable results. The experimental results show that under different environmental conditions, the error between the predicted resistance and the actual resistance of the algorithm is always kept within 0.2Ω. In addition, the PID controller can adjust the duty cycle of the PWM signal in real time to ensure the real-time adjustment of the grounding resistance. Secondly, the total fitness value of the fitness function is always low, indicating that the algorithm can maintain good performance under different environmental conditions. At the same time, in the event of a fault, the algorithm can respond to the fault in a timely manner and adjust the grounding resistance to ensure the stability and safety of the system.

[0093] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A grounding system for a high voltage switch cabinet, characterized in that: The system comprises: A real-time detection module is used to monitor the changes of grounding resistance and environmental factors around the grounding system in real time, and output the grounding resistance change value and environmental factors to the adaptive grounding resistance adjustment module and the telemetry module; the environmental factors include soil moisture and temperature; The adaptive grounding resistance adjustment module is used to dynamically adjust the grounding resistance according to the grounding resistance change value and environmental factors; the adaptive grounding resistance adjustment module connects multiple sections of controllable resistance to the grounding path in series, and the adaptive grounding resistance adjustment module constructs a resistance compensation value mapping model based on the environmental sensitive period index and the mutual correlation coefficient, and controls the resistance value through a dynamic resistance control algorithm; the resistance compensation value mapping model includes an environmental factor quantification unit, a resistance influence analysis unit, a compensation value calculation unit and an output feedback unit; High-frequency transient suppression module, used to suppress high-frequency transient overvoltage caused by arc restrike; a fault detection and isolation module, for detecting ground current, isolating fault circuits, and transmitting fault signals to the telemetry module; A ground current control module, used for controlling the ground current; The telemetry module is used to provide remote monitoring of the grounding system status; the telemetry module transmits the grounding system indicators to the remote terminal through wireless communication; the grounding system indicators include grounding resistance, current, soil resistivity and temperature data; The output end of the real-time detection module is connected to the input end of the adaptive grounding resistance adjustment module and the telemetry module; the output end of the high-frequency transient suppression module, the fault detection and isolation module and the grounding current control module are connected to the input end of the telemetry module; the output end of the fault detection and isolation module is connected to the input end of the grounding current control module.

2. A grounding system for a high-voltage switch cabinet according to claim 1, characterized in that: The real-time detection module monitors the grounding resistance in real time through the grounding resistance detection unit; the grounding resistance detection unit supplies power to the grounding resistance through a constant current source circuit configured by an operational amplifier and an LDO regulator, and detects the voltage drop across the grounding resistance through a differential amplifier, and the voltage signal is converted into a digital signal through an analog-to-digital converter; the constant current source circuit ensures that the current remains constant during the entire detection process through a negative feedback mechanism, eliminating the error of power supply fluctuation; the differential amplifier is composed of two symmetrical operational amplifiers, which respectively detect the voltage across the grounding resistance and eliminate noise interference through differential output; the grounding resistance detection module also sends the calculated grounding resistance change value to the adaptive grounding resistance adjustment module and the telemetry module through the serial communication protocol SPI.

3. The grounding system of a high-voltage switch cabinet according to claim 1, characterized in that: The adaptive grounding resistance adjustment module receives and processes the digital signal of the grounding resistance detection module through a microcontroller, and the microcontroller processes the digital signal output by the grounding resistance detection module through an FIR filter and a sliding average filter, and calculates the change value of the grounding resistance through a least squares method; According to the calculated grounding resistance change value, the adaptive grounding resistance adjustment module realizes PWM signal amplification and driving of the controllable resistor array through the PWM driving circuit; the controllable resistor array is composed of a plurality of controllable resistor units, each of which is connected in series in the grounding path, and the conduction state of each controllable resistor unit is adjusted through PWM signal control to realize dynamic adjustment of the grounding resistance value; the adaptive grounding resistance adjustment module also realizes real-time monitoring of the grounding resistance change value through a closed-loop feedback circuit, and performs real-time feedback adjustment of the grounding resistance change value through a dynamic resistance control algorithm.

4. A grounding system for a high-voltage switch cabinet according to claim 3, characterized in that: The PWM drive circuit includes an input unit, an amplifying unit, a driving stage unit, a protection unit and an isolation unit; the input unit locks the state of the input signal through an input buffer; the amplifying unit amplifies the amplitude of the PWM signal through a high-gain operational amplifier; the driving stage unit generates a current through a power transistor to drive a controllable resistor array; the protection unit protects the circuit from abnormal conditions through a current limiting resistor, a clamping diode and a transient voltage suppressor; The isolation unit electrically isolates the main control circuit from the drive circuit through an optical coupler.

5. The grounding system of a high-voltage switch cabinet according to claim 3, characterized in that: The working method of the dynamic resistance control algorithm comprises the following steps: Step 1: Construct a resistance mapping matrix Among them, T n , RH n , I n , V n and FT n Respectively represent the temperature, humidity, current, voltage and fault type code of the high-voltage switchgear at the nth sampling moment; and perform noise suppression through a weighted sliding average filter; Step 2: Based on the resistance mapping matrix E, the dynamic resistance requirement is calculated by a multivariable nonlinear function; the multivariable nonlinear function is trained by a neural network to obtain a weight vector θ and a feature mapping function G n (E), mapping environmental parameters to a high-dimensional feature space to calculate the ground resistance value; the feature mapping function constructs a feature mapping through a polynomial kernel function and an activation function; the formula expression for calculating the dynamic resistance requirement of the multivariate nonlinear function is: In formula (1), M represents the highest degree of polynomial terms; P represents the number of cross terms, which is used to control the complexity of feature mapping function; G n (E) represents a feature mapping function, which is used to map the nth row vector of the resistance mapping matrix to a high-dimensional feature space; θ represents a weight vector, which is used to map the output of the feature mapping function to the dynamic resistance requirement; μ c Represents the coefficients in the polynomial kernel function and the cross term, which is used to adjust the shape and complexity of the feature mapping function; indexset l Represents the parameter index set involved in the lth cross term; E n Represents the nth row vector of the resistance mapping matrix; Step 3: Construct the state matrix of the controllable resistor array Where S n Indicates the switch state of the nth controllable resistor unit; and based on the current state S n and dynamic resistance requirement phi(E n ), through the fitness function F ij (S n ) determine the optimal PWM duty cycle setting; the fitness function formula is: In formula (2), L represents a solution, i.e., a set of PWM duty cycle settings; h, γ, and ε represent the weight coefficients of the error term, safety penalty term, and stability penalty term, respectively, which are used to balance the importance of different optimization objectives; P ij (L) represents the security penalty term for solution L; R a represents the ground resistance prediction value of the nth sample by the deep learning model; R p Under solution L, the actual ground resistance value of the nth sample; δ ij (L) represents the stability penalty term of solution L; N represents the number of samples in the evaluation cycle; phi(E n ) represents the dynamic resistance requirement; T represents the length of the evaluation period, which is used to measure the calculation range of the cumulative error; Step 4: Control the conduction state of the controllable resistor array through the PWM signal to adjust the ground resistance value. During the control process, monitor the system state and environmental changes in real time, and compare the actual ground resistance value with the predicted value in step 2. If an error occurs, adjust the control strategy through the feedback mechanism; the adjustment includes modifying the PWM duty cycle setting, adjusting the search parameters of the genetic algorithm, or retraining the dynamic resistance demand model. The feedback mechanism generates and adjusts the duty cycle of the PWM signal according to the resistance error through the PID controller; the calculation formula is: In formula (3), A t is the control parameter of the step growth rate, which is used to limit the growth rate of the step length; μ j represents the adjustment threshold, which is used to determine whether adaptive adjustment is required; ΔS is the adjustment step, which represents the adjustment amount of the PWM duty cycle setting; w t represents the current error; sign represents the sign function, which is used to determine the direction of adjustment; K P , K i and K d They represent the proportional, integral, and differential gains of the PID controller respectively.

6. A grounding system for a high-voltage switch cabinet according to claim 1, characterized in that: The environmental factor quantification unit establishes a mathematical model between environmental parameters and quantitative indicators through a statistical quantification method. The statistical quantification method describes the mapping relationship between environmental parameters and quantitative indicators through a nonlinear model, and minimizes the difference between the predicted value and the actual value through regression analysis to solve the model parameters; the resistance impact analysis unit fits the nonlinear mapping between environmental parameters and resistance changes through a support vector machine and a neural network; the support vector machine maps input data to a high-dimensional space using a kernel function to find an optimal hyperplane to maximize the classification interval; the neural network achieves approximation and prediction of complex functions through a multi-layer network structure and a back-propagation algorithm; Based on the results of the resistance impact analysis, the compensation value calculation unit dynamically calculates and outputs the resistance compensation value by using Gaussian process regression and gradient descent method; the Gaussian process regression is based on the prior assumption of the Gaussian process, describes the correlation between input variables by kernel function, and calculates the predicted value by Bayesian inference; the gradient descent method is used to fine-tune the compensation value considering the system stability and real-time requirements; The output feedback unit transmits the calculated resistance compensation value to the adaptive ground resistance adjustment module in real time through the communication protocol and API interface, and receives system status feedback; the output end of the environmental factor quantification unit is connected to the input end of the resistance impact analysis unit; The output end of the resistance influence analysis unit is connected to the input end of the compensation value calculation unit; the output end of the compensation value calculation unit is connected to the input end of the output feedback unit.

7. A grounding system for a high voltage switch cabinet according to claim 1, characterized in that: The high-frequency transient suppression module connects a transient voltage suppression diode in parallel to a ground path, and when an overvoltage occurs, the transient voltage suppression diode is turned on; the high-frequency transient suppression module includes a detection response unit, an energy absorption unit and a filtering stabilization unit; The detection response unit uses a high-bandwidth analog front end and a fast Fourier transform to monitor the voltage waveform of the power system in real time. Once a high-frequency transient is detected, the transient voltage suppression diode is switched to a conducting state to direct the transient current to a preset safe path. The detection response unit also provides instant voltage clamping through a metal oxide varistor. When a high-frequency transient occurs, the energy absorption unit stores energy through a capacitor bank, and converts the stored electrical energy into heat energy through a positive temperature coefficient thermistor and dissipates it into the environment. The energy absorption unit also releases the stored energy through a gas discharge tube. The filtering and stabilizing unit connects a multi-stage LC filtering network in parallel in the circuit, and the multi-stage LC filtering network filters out redundant frequency components through a surface acoustic wave filter. The filtering and stabilizing unit also optimizes the energy release process through an adaptive tuning mechanism. The adaptive tuning mechanism evaluates the fitness of the cutoff frequency and bandwidth through a particle swarm optimization algorithm to tune the value of the objective function. During the parameter adjustment process, the adaptive tuning mechanism also simulates the filter's response to transient voltage waveforms through finite element analysis to evaluate the energy absorption and release effects.

8. The grounding system of a high-voltage switch cabinet according to claim 1, characterized in that: The fault detection and isolation module monitors the ground current through a current transformer. When a current exceeding a threshold is detected, the fault detection and isolation module transmits a signal to an isolation circuit through a high-speed optical coupler, triggers a solid-state relay to cut off the power supply, and encapsulates the fault information into a data packet and transmits it to the telemetry module through the communication interface circuit RS-485; the fault detection and isolation module includes a current detection unit, a signal processing unit, an isolation execution unit and a communication unit; the current detection unit outputs a voltage signal proportional to the current through a Rogowski coil, and adjusts the gain and offset of the transformer through a self-calibration circuit; The self-calibration circuit periodically generates a known current signal as a reference, compares the reference signal with the transformer output signal, detects and adjusts the gain and offset of the transformer; the signal processing unit identifies the fault characteristic signal parameters through fast Fourier transform and wavelet transform; The fault characteristic signal parameters include harmonic distortion rate, amplitude mutation and phase jump parameters; once a fault signal is detected, the isolation execution unit cuts off the connection between the fault circuit and the main system through a silicon-controlled rectifier, and after the isolation operation is completed, the fault information is transmitted to the telemetry module through the communication unit.

9. A grounding system for a high voltage switch cabinet according to claim 1, characterized in that: The ground current control module limits the ground current through the self-resetting fuse PPTC, and adjusts the current limit value through the current feedback loop; the current feedback loop includes a current sensor, a comparator, a control logic unit and a drive circuit; the current sensor is used to capture the current signal in the ground line in real time and convert the current signal into an electrical signal; the comparator is used to compare the detected current value with the preset safety threshold to determine whether the ground current needs to be adjusted. If adjustment is required, the control logic unit generates an adjustment control signal through an adaptive PID controller based on the deviation between the current ground current and the target current, and adjusts the working state of the self-resetting fuse PPTC through the drive circuit.

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