35kV line tower grounding on-line monitoring method and system

Abstract

The invention discloses a 35kV line tower grounding online monitoring method and system, and relates to the technical field of tower grounding monitoring, and the method comprises the steps: collecting on-site initial parameters, and constructing a digital twin grounding model; determining a closed-loop injection current sequence based on the multi-step prediction model and a controller; current injection testing is carried out, grounding voltage and current are measured in real time, actual grounding resistance is calculated, a predicted value and an actually measured value are compared, and digital twinborn parameters are fed back and updated; when a preset period or an error threshold is triggered, executing self-calibration current injection, and calculating and correcting a model coefficient; and circularly executing and outputting a monitoring evaluation result. According to the method, low-energy-consumption and anti-disturbance closed-loop injection control is realized by adopting multi-objective optimization and a TiDE time sequence prediction model; an error feedback and self-adaptive calibration mechanism is introduced to ensure the continuous precision and stability of the prediction model; collection, prediction, control and calibration processes are executed periodically, and intelligent support is provided for ground resistance monitoring, fault early warning and operation and maintenance decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of pole tower grounding monitoring, in particular to a 35kV line tower grounding online monitoring method and system. Background Art

[0002] With the continuous expansion of power systems and the increasing complexity of transmission line operating environments, traditional tower ground resistance testing relies primarily on manual on-site four-pole testing with power outages or local isolation. This method is not only time-consuming and labor-intensive, but also often fails to reflect the dynamic changes of the tower grounding network under varying soil moisture, temperature, and meteorological conditions due to low sampling frequency and delayed data updates. When soil resistivity changes significantly, traditional methods are often unable to quickly detect and adjust detection strategies, posing potential safety risks.

[0003] While some online monitoring systems are capable of real-time acquisition of ground voltage and current, these systems generally fail to seamlessly integrate field-collected data with high-fidelity physical simulation models. They lack a closed-loop "reality-to-simulation" mapping mechanism, making it impossible to ensure that the monitoring model can dynamically update with environmental changes. This lack of a digital twin architecture limits the constructed simulation model to a static reference, making it difficult to reliably support subsequent predictive control and error correction. Furthermore, existing technologies generally fail to incorporate multi-step predictive control strategies, making it difficult to proactively assess and proactively adjust the tower grounding state over time. The lack of closed-loop control methods based on model predictive control (MPC) or deep time-series prediction models means that the injected current is often set empirically. This can lead to excessive current causing soil thermal disturbances and insufficient control to accurately correct the ground resistance. Furthermore, due to the continuous deviation of model parameters from actual field conditions and the cumulative errors caused by environmental disturbances during long-term operation, existing online monitoring systems often lack self-calibration capabilities. They are unable to dynamically adjust model parameters by injecting known currents and comparing predicted values with measured values, making it difficult to maintain long-term monitoring accuracy and reliability. The above shortcomings make it difficult for traditional tower grounding online monitoring systems to meet the high-precision, real-time and intelligent operation and maintenance requirements when facing complex and changeable on-site environments. Summary of the Invention

[0004] In view of the fact that the existing 35kV line tower grounding monitoring method relies on manual four-pole testing or distributed sensors, it is not only unable to capture the temporal and spatial changes of soil resistivity in real time, but also lacks closed-loop mapping and multi-step predictive control strategies with high-fidelity physical simulation models, resulting in empirical injection current and inability to self-calibrate model deviations, making it difficult to meet the requirements of high-precision, real-time and intelligent operation and maintenance. Therefore, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to provide a 35kV line tower grounding online monitoring method and system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides an online monitoring method for 35kV line tower grounding, including: collecting initial parameters on site and constructing a digital twin grounding model; determining a closed-loop injection current sequence based on a multi-step prediction model and a controller; implementing a current injection test and measuring the grounding voltage and current in real time, calculating the actual grounding resistance, comparing the predicted value with the measured value and providing feedback to update the digital twin model; performing self-calibration current injection, calculating and correcting the model coefficients, and recalculating the control quantity when a preset cycle or error threshold is triggered; and looping and outputting the monitoring and evaluation results.

[0008] As a preferred solution of the 35kV line tower grounding online monitoring method of the present invention, the construction of the digital twin grounding model includes: 3 Grid modeling of the area surrounding the tower is performed for the smallest unit, and environmental parameters such as topography, resistivity distribution, and meteorological factors are collected. A grounding physical field simulation model is established based on the finite element analysis platform to obtain the initial grounding resistance prediction function. The resistivity of each grid node is measured using the quadrupole method and a state vector is constructed. A simulation interface is created to receive observation data in real time and update the model status. A digital twin platform is deployed to provide high-fidelity support for subsequent model prediction and calibration.

[0009] As a preferred solution of the 35kV line tower grounding online monitoring method described in the present invention, the multi-step prediction model and controller include: based on the time series depth model TiDE, inputting the current state and the initial value of the control sequence, and outputting the future multi-step conductivity field and grounding resistance estimation; establishing a control objective function and adding injection current amplitude and change rate constraints; using the OSQP solver to online calculate the optimal current sequence and perform the first-step control.

[0010] As a preferred solution of the 35kV line tower grounding online monitoring method of the present invention, the TiDE model is obtained through offline training of no less than 10,000 sets of historical data, and the prediction error is less than 0.02Ω.

[0011] As a preferred solution of the 35kV line tower grounding online monitoring method described in the present invention, the construction of the control objective function includes: setting the error between the predicted grounding resistance and the reference value as the main optimization target to achieve accurate tracking of the grounding status; adding a penalty for the energy cost or amplitude of the injected current to take into account both system energy efficiency and equipment stability; setting boundary conditions such as current amplitude and change rate as constraints, and finally constructing the control objective function and calling the solver for real-time solution.

[0012] As a preferred solution of the 35kV line tower grounding online monitoring method described in the present invention, updating the digital twin model includes: real-time collection of actual grounding resistance values, comparing them with the predicted values of the digital twin model, and calculating the current error value; calling the sensitivity analysis module of the digital twin simulation platform to obtain the degree of influence of each conductivity parameter on the grounding resistance, that is, the sensitivity coefficient; according to the error value and sensitivity, adjusting the conductivity parameters of the corresponding area to enhance the consistency between the model and the actual situation on site, and realizing dynamic correction of the digital twin model.

[0013] As a preferred solution of the 35kV line tower grounding online monitoring method described in the present invention, the calculation of the correction model coefficient includes: the system automatically triggers a calibration process at fixed intervals or when it detects that the cumulative estimation error exceeds a threshold; injects a test current of known magnitude into the grounding system, and simultaneously collects the corresponding grounding voltage in real time to measure the actual grounding resistance; compares the measured grounding resistance with the simulation model prediction value, calculates a calibration coefficient, and uses it to correct the current model parameters and subsequent prediction results.

[0014] In a second aspect, in order to further solve the problems existing in tower grounding monitoring, the present invention provides an embodiment of a 35kV line tower grounding online monitoring system, which includes: an electrode module, arranged around the tower grounding device, for collecting electrical signals required for grounding resistance measurement; a resistance measurement module, for processing the signal collected by the electrode module based on the three-pole grounding resistance measurement principle to obtain the grounding resistance value of the line tower; a main control module, for controlling the resistance measurement process, storing measurement data, analyzing the grounding status, and determining whether the grounding resistance exceeds a set threshold; a communication module, for uploading grounding resistance data and alarm information to a remote monitoring platform via a wireless network, and the communication module supports at least one of communication modes such as 4G, NB-IoT or LoRa.

[0015] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the 35kV line tower grounding online monitoring method as described in the first aspect of the present invention is implemented.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the 35kV line tower grounding online monitoring method as described in the first aspect of the present invention is implemented.

[0017] The beneficial effects of the present invention are:

[0018] 1. This invention builds a "reality-simulation" mapping closed loop by gridding the geographical and soil environmental parameters around the tower and combining finite element simulation with equivalent circuit modeling. This enables the simulation system to seamlessly update the real-time data collected on site, thereby providing highly consistent and high-precision digital twin support for subsequent ground resistance prediction.

[0019] 2. This invention is based on a multivariable control objective function (including injection current, conductivity field, target resistance, etc.), introduces the TiDE deep time series model to estimate future conductivity field changes, and then uses the OSQP fast solver to calculate the optimal injection sequence online. This invention can quickly respond to field changes while minimizing energy consumption, effectively suppress overshoot and harmonics, and achieve precise closed-loop injection control.

[0020] 3. This method compares the measured ground resistance with the digital twin prediction value, constructs an error feedback adjustment function based on the sensitivity partial derivative, and iteratively corrects the grid conductivity estimate. This ensures that the digital twin model can continuously adapt to environmental changes, dynamically corrects the prediction error, and significantly improves the accuracy and stability of the model in long-term operation.

[0021] 4. The present invention is designed to automatically inject a known current to complete calibration periodically or when the cumulative error exceeds the limit. The calibration coefficient is calculated by comparing the predicted value and the measured value under the same conditions, and the subsequent prediction and control strategies are multiplied by the coefficient to correct them. This realizes multiple rounds of "prediction → execution → calibration → update" closed-loop iterations, ensuring the long-term reliability and accuracy of the system in complex environments.

[0022] 5. The present invention periodically and continuously performs data acquisition, digital twin update, MPC solution, injection control and error correction, and triggers the calibration mechanism in real time; at the same time, it records and outputs key parameters such as ground resistance estimation curve, control current sequence, error change trend and calibration coefficient, providing detailed data support for fault warning, performance evaluation and operation and maintenance decision-making, greatly reducing manual inspection costs and improving operation management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0024] Figure 1 This is a flow chart for implementing the present invention in Example 1. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0028] Example 1

[0029] Reference Figure 1 , which is the first embodiment of the present invention, provides a 35kV line tower grounding online monitoring method, comprising the following steps:

[0030] Step S1: Collect initial parameters on site and build a digital twin grounding model, which includes the following sub-steps:

[0031] S1-1: Collect geographical and soil environmental parameters around the tower, including topography, resistivity distribution, meteorological factors, etc., with a 1m 3 Mesh the target area to the smallest element.

[0032] S1-2: Use a finite element analysis platform (such as COMSOL Multiphysics) to establish a tower grounding physical field model, simulate the potential distribution and current path under current injection, and generate an initial grounding state prediction model. As the initial state of the digital twin;

[0033] It should be noted that f twinIt is represented as a digital twin prediction function, that is, a simulation-physics integrated model constructed to predict the mathematical function or mapping relationship of ground resistance after inputting real environmental parameters; X0 is represented as the initial input variable vector, which contains a set of known and measurable parameters describing the actual working state of the tower grounding system (including the three-dimensional distribution of soil resistivity, the geometric structure parameters of the tower grounding body, the historical grounding resistance value, the ambient humidity or soil moisture content, the surface hardness and compaction parameters, the surface electric field disturbance factors (such as lightning strikes, interference sources), and the current soil temperature); It is represented by the predicted initial value of the ground resistance, which is used as the benchmark for subsequent MPC control and error analysis.

[0034] S1-3: Acquisition of initial measured resistivity field The grid nodes are measured by quadrupole method. And define the initial conductivity of each grid cell Construct the digital twin state vector and use it as the initial input of the digital twin system;

[0035] Among them, the digital twin state vector can be expressed as:

[0036]

[0037] It should be noted that M is the total number of grids, the sampling period Δt = 1s; the MPC control step size N = 10 steps, corresponding to a 10s prediction; the self-calibration period K = 60 steps (i.e., 60s) or the cumulative error threshold εth = 0.05Ω.

[0038] S1-4: Build a twin simulation interface to ensure that the simulation system can be updated and solved based on real-time data collected on-site, forming a closed loop of "reality-simulation" mapping;

[0039] Specifically, at each sampling time t, the following data are obtained in real time:

[0040]

[0041] Further, synchronous update: Send to the simulation platform via API;

[0042] simulation:

[0043] X(t)←f sim (X(t - ),y(t));

[0044] Where, f sim is the state update function based on the joint modeling of finite element and equivalent circuit, which can be automatically completed by COMSOL solver; y(t) is the field observation data vector at time t; V g (t) is the voltage observation value at time t; Ig (t) is the current observation value at time t; T(t) is the temperature value; H(t) is the relative humidity.

[0045] S1-5: Complete the deployment of the digital twin platform to provide high-fidelity prediction support for subsequent MPC control strategies and error calibration.

[0046] Step S2: Design a multi-step prediction model and controller, which includes the following sub-steps:

[0047] S2-1: A time series prediction-based depth model (TiDE) is introduced to estimate the conductivity field changes and corresponding ground resistance values under future multi-step control;

[0048] Specifically:

[0049] Input: current state X(t) and initial value of control sequence;

[0050] Output: N-step state in the future Including conductivity field and corresponding impedance estimation R est (t+k|t);

[0051] Among them, the TiDE model is obtained through offline training of historical collected data (no less than 10,000 groups), with an error of less than 0.02Ω;

[0052] Specifically, the TiDE model loss function uses MSE (mean square error), which can be expressed as:

[0053]

[0054] Where, is the mean square error loss (used for optimizing the model); N is the number of prediction steps; M is the number of regional grids; is the model prediction value; σ i (t+k) is the actual conductivity value (from the test set or actual measurement)

[0055] S2-2: Determine the predictive control step length N (e.g., N = 10 steps, corresponding to a 10-second prediction), and establish a control objective function containing multiple variables such as the controlled quantity, conductivity field, and target resistance;

[0056] Specifically, the control objective function can be expressed by the following formula:

[0057]

[0058] Where J is the optimization target value (cost function); is the k-th step control quantity (i.e., injection current); R est (t+k|t) represents the estimated value of the ground resistance at the future time t+k based on the current time t; R refis the expected reference value, usually the maximum grounding resistance allowed by the standard; λ is the energy consumption penalty coefficient, which adjusts the weight of the control current.

[0059] S2-3: Add control quantity constraints (such as maximum injection current and rate of change limit) to prevent abnormal phenomena such as system overshoot or harmonic excitation;

[0060] Specifically: Indicates the upper limit of current amplitude; It indicates that the changes between adjacent steps are smooth, preventing sudden changes from impacting the equipment.

[0061] S2-4: Use the fast optimization solver (OSQP) to calculate the optimal control sequence online and send the first control variable to the actuator in real time to achieve closed-loop injection control;

[0062] Specifically:

[0063] Use the open source library OSQP to solve quadratic programming and obtain the optimal sequence

[0064] Execute the first step control: send to the injection device

[0065] Step S3: Perform a self-calibration current injection test, including the following sub-steps:

[0066] S3-1: According to the control quantity output by MPC in step S2 Apply current to the tower grounding system through the field control module. The recommended injection duration is 100 to 200 ms to avoid soil thermal disturbance.

[0067] S3-2: Real-time measurement of injected current I through voltage and current sensors t and ground voltage V t , and calculate the current actual grounding resistance R meas (t) = V t / I t .

[0068] S3-3: Compare the measured value with the predicted value of the digital twin model Compare and get the error term

[0069] It should be noted that the digital twin model prediction value The initial grounding state prediction model is established in step S1 Provided, X tThe system state vector for the current control cycle includes the current injection value, soil resistivity distribution, moisture content, ambient temperature, and grounding structure parameters. The prediction process can be solved using a finite element simulation platform or rapidly calculated using a data-driven recurrent neural network, ensuring physical consistency and real-time responsiveness of the predicted value.

[0070] S3-4: Construct an error feedback adjustment function, which can be expressed by the following formula:

[0071]

[0072] Where σ i (t) is the conductivity estimate of the i-th grid, η is the learning rate, is the sensitivity partial derivative, which is actually obtained by linearizing the twin model;

[0073] S3-5: According to the modified σ i (t+1) Update the digital twin model to make it more in line with on-site reality.

[0074] Step S4: Design and execute a self-calibration injection test, including the following sub-steps:

[0075] S4-1: The self-calibration injection process is started at fixed intervals (e.g., 60 seconds) or when the accumulated error exceeds a threshold (the threshold is set by the staff) to improve long-term stability and reliability;

[0076] S4-2: Inject a known current (e.g. 2A) into the system, measure the response voltage, and calculate the actual resistance on site

[0077] S4-3: By comparing the predicted values under the same conditions With actual value Calculation coefficient

[0078] S4-4: All subsequent prediction values, control judgment basis and state estimation are multiplied and corrected to ensure that the subsequent control strategy is more in line with the actual environmental changes;

[0079] Specifically:

[0080] All subsequent estimates: R est ←kR est ;

[0081] State vector: X←kX;

[0082] Return to step S2 immediately and recalculate the control amount;

[0083] It should be noted that this step implements multiple rounds of "prediction → execution → calibration → update" closed-loop iterations to always maintain system accuracy and reliability.

[0084] Step S5: cyclically execute and output monitoring and evaluation results, including the following sub-steps:

[0085] S5-1: With a 1s cycle, it sequentially completes data acquisition, digital twin simulation update, MPC solution, control execution, and error correction, realizing the periodic execution of the entire process of acquisition-prediction-control-feedback.

[0086] S5-2: Real-time determination of whether the calibration trigger conditions are met, timely insertion of the self-calibration injection process, and maintenance of the long-term accuracy of the system;

[0087] S5-3: Record and output all key indicators and control trajectories, including the current ground resistance estimation curve, control current sequence, error change trend, calibration coefficient change and other key parameters, to facilitate subsequent tracing and analysis.

[0088] In summary, the present invention achieves high-precision linkage between field data and simulation systems by constructing a digital twin model that integrates geographic and soil parameters; adopts multi-objective optimization and the TiDE time series prediction model, combined with the OSQP fast solver, to achieve low-energy, anti-disturbance closed-loop injection control; introduces an error feedback and adaptive calibration mechanism based on sensitivity analysis to ensure the continuous accuracy and stability of the prediction model; periodically executes the acquisition, prediction, control, and calibration processes, and outputs key parameters to provide intelligent support for ground resistance monitoring, fault warning, and operation and maintenance decision-making.

[0089] Example 2

[0090] Embodiment 2 is the second embodiment of the present invention. This embodiment differs from the first embodiment in that it further provides a 35kV line tower grounding online monitoring system, including:

[0091] The electrode module is arranged around the tower grounding device and is used to collect the electrical signals required for ground resistance measurement;

[0092] A resistance measurement module is used to process the signal collected by the electrode module based on the three-pole ground resistance measurement principle to obtain the ground resistance value of the line tower;

[0093] The main control module is used to control the resistance measurement process, store the measurement data, analyze the grounding status, and determine whether the grounding resistance exceeds the set threshold;

[0094] A communication module, used to upload ground resistance data and alarm information to a remote monitoring platform via a wireless network. The communication module supports at least one of 4G, NB-IoT, or LoRa communication methods;

[0095] A power supply module, which is used to supply power to the main control module, resistance measurement module and communication module. The power supply module includes a solar panel, a battery and a voltage stabilizing circuit;

[0096] The installation shell is used to encapsulate the above modules and has waterproof, dustproof, lightning-proof and electromagnetic interference-resistant capabilities to adapt to the operation requirements of harsh outdoor environments;

[0097] The main control module further includes:

[0098] A data processing unit is used to filter, remove noise and smooth the raw data of ground resistance;

[0099] A threshold judgment unit is used to compare the current ground resistance value with a set threshold value, and generate an abnormal alarm message if it exceeds the set threshold value;

[0100] The configuration management unit is used to receive configuration parameter instructions sent by the remote monitoring platform to remotely modify the sampling interval, threshold parameters and communication frequency.

[0101] This embodiment also provides a computer device, which is applicable to a 35kV line tower grounding online monitoring method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a 35kV line tower grounding online monitoring method proposed in the above embodiment.

[0102] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0103] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a 35kV line tower grounding online monitoring method as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A 35kV line tower grounding online monitoring method, characterized by: include: S1, collect initial parameters on site and build a digital twin grounding model; S2, determines the closed-loop injection current sequence based on the multi-step prediction model and controller; S3: Conduct current injection testing and measure ground voltage and current in real time, calculate actual ground resistance, compare predicted values with measured values, and provide feedback to update the digital twin model. S4, when a preset period or an error threshold is triggered, a self-calibration current injection is performed, the model coefficients are calculated and corrected, and the control quantity is recalculated; S5, looping through steps S1 to S4 and outputting monitoring and evaluation results.

2. The 35kV line tower grounding online monitoring method according to claim 1, characterized in that: Building a digital twin grounding model includes: 3 Grid modeling of the area surrounding the tower is performed for the smallest unit, and environmental parameters such as topography, resistivity distribution, and meteorological factors are collected. A grounding physical field simulation model is established based on the finite element analysis platform to obtain the initial grounding resistance prediction function. The resistivity of each grid node is measured using the quadrupole method and a state vector is constructed. A simulation interface is created to receive observation data in real time and update the model status. A digital twin platform is deployed to provide high-fidelity support for subsequent model prediction and calibration.

3. The 35kV line tower grounding online monitoring method according to claim 1, characterized in that: The multi-step prediction model and controller include: based on the time series depth model TiDE, inputting the current state and initial values of the control sequence, and outputting future multi-step conductivity field and ground resistance estimates; establishing a control objective function and adding injection current amplitude and change rate constraints; and using the OSQP solver to online calculate the optimal current sequence and execute the first-step control.

4. The 35kV line tower grounding online monitoring method according to claim 3, characterized in that: The TiDE model is obtained through offline training on no less than 10,000 sets of historical data, with a prediction error of less than 0.02Ω.

5. The 35kV line tower grounding online monitoring method according to claim 3, characterized in that: The construction of the control objective function includes: setting the error between the predicted grounding resistance and the reference value as the main optimization target to achieve accurate tracking of the grounding status; adding a penalty for the energy cost or amplitude of the injected current to take into account both system energy efficiency and equipment stability; setting boundary conditions such as current amplitude and change rate as constraints, and finally constructing the control objective function and calling the solver for real-time solution.

6. The 35kV line tower grounding online monitoring method according to claim 1, characterized in that: Updating the digital twin model includes: collecting the actual grounding resistance value in real time, comparing it with the predicted value of the digital twin model, and calculating the current error value; calling the sensitivity analysis module of the digital twin simulation platform to obtain the influence of each conductivity parameter on the grounding resistance, that is, the sensitivity coefficient; according to the error value and sensitivity, the conductivity parameters of the corresponding area are adjusted to enhance the consistency between the model and the actual situation on site, and realize the dynamic correction of the digital twin model.

7. The 35kV line tower grounding online monitoring method according to claim 1, characterized in that: Calculating the corrected model coefficients includes: the system automatically triggers a calibration process at fixed intervals or when it detects that the cumulative estimated error exceeds a threshold; injecting a test current of known magnitude into the grounding system, while simultaneously collecting the corresponding grounding voltage in real time to measure the actual grounding resistance; comparing the measured grounding resistance with the predicted value of the simulation model, calculating a calibration coefficient, and using it to correct the current model parameters and subsequent prediction results.

8. A 35kV line tower grounding online monitoring system, based on the 35kV line tower grounding online monitoring method according to any one of claims 1 to 7, characterized in that: include, The electrode module is arranged around the tower grounding device and is used to collect the electrical signals required for ground resistance measurement; A resistance measurement module is used to process the signal collected by the electrode module based on the three-pole ground resistance measurement principle to obtain the ground resistance value of the line tower; The main control module is used to control the resistance measurement process, store the measurement data, analyze the grounding status, and determine whether the grounding resistance exceeds the set threshold; The communication module is used to upload ground resistance data and alarm information to the remote monitoring platform via a wireless network. The communication module supports at least one of the communication modes such as 4G, NB-IoT or LoRa.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the 35kV line tower grounding online monitoring method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the 35kV line tower grounding online monitoring method according to any one of claims 1 to 7 are implemented.

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