Intelligent cable branch box adaptive load control method and system

Abstract

The invention belongs to the technical field of power distribution automation of a power system, and particularly relates to a self-adaptive load control method and system for an intelligent cable branch box, and the method comprises the steps: collecting and preprocessing the current data of a branch loop and the temperature data of a cable joint in real time, obtaining a data confidence factor, and extracting a current change rate and a temperature rise rate; obtaining a load prediction value in a future set time period through a load trend prediction model; calculating the residual heat tolerance time for reaching the limit tolerance temperature; calculating an output load control adjustment coefficient based on the adaptive load control decision model and a particle swarm optimization algorithm; and judging a current state interval according to the load control adjustment coefficient, and controlling an execution end to execute a grading response strategy of through-flow maintenance, short-time overload monitoring and early warning and breaking current limiting in combination with the residual heat tolerance time. According to the method, the residual heat tolerance time is calculated in combination with load prediction and a transient thermal circuit model, and hierarchical response of load control is realized through a particle swarm optimized radial basis function neural network decision.

Description

Technical Field

[0001] This invention belongs to the field of power system distribution automation technology, specifically relating to an adaptive load control method and system for intelligent cable branch boxes. Background Technology

[0002] Cable branch boxes are intermediate connection devices in power distribution network systems, mainly used for the transfer, branching, and power distribution of cable lines. In the construction of urban power distribution networks, cable branch boxes solve the problem of multi-branch connection in long-distance cable transmission. Intelligent cable branch boxes, based on the traditional metal box structure, integrate electronic sensing technology, microcomputer processing technology, and network communication technology. They are equipped with high-voltage load switches, current and voltage transformers, and intelligent monitoring terminals. Through fiber optic networks and wireless communication modules, they establish data connections with the distribution automation master station, enabling remote monitoring of the operating status of distribution lines, fault location, and load data uploading. This improves the observability of the distribution network and is a basic support unit for building intelligent distribution networks and realizing distribution automation.

[0003] To address the issues of insufficient accuracy in load monitoring and limited power supply reliability in power distribution lines, existing technologies typically employ fixed current protection thresholds for load control. However, zero-point drift caused by sensors in harsh environments can lead to distorted data acquisition. Fixed threshold logic cannot verify the authenticity of the data, resulting in circuit breaker malfunctions. Furthermore, fixed thresholds ignore the impact of ambient temperature differences on cable heat dissipation and the thermal inertia of the cable itself. This can lead to forced disconnection of the power supply circuit when the load briefly exceeds the limit but the cable insulation temperature has not reached its limit, resulting in power outages in cable lines and wasted dynamic transmission potential of the cable. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive load control method and system for intelligent cable branch boxes. By eliminating distorted data through electrothermal coupling verification, calculating the remaining thermal withstand time by combining load prediction and transient thermal circuit model, and making decisions through radial basis function neural network optimized by particle swarm optimization, a graded response of load control is achieved, taking into account both power supply continuity and equipment safety.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: an intelligent cable branch box adaptive load control method, comprising the following steps: Real-time acquisition and preprocessing of current data in branch circuits and temperature data at cable joints; electrothermal coupling reliability verification of preprocessed current and temperature data; acquisition of data confidence factor; and extraction of current change rate and temperature rise rate. The preprocessed current data is input into the load trend prediction model to obtain the load prediction value for a future set time period. Combining load forecast values, using the transient thermal circuit equivalent model, and based on the thermal characteristic parameters and ultimate withstand temperature of the cable insulation material, the remaining thermal withstand time to reach the ultimate withstand temperature is calculated using the finite difference iterative algorithm. An adaptive load control decision model with a radial basis function neural network architecture is adopted as the input of data confidence factor, current change rate, temperature rise rate, load forecast value and remaining heat tolerance time. The model is trained and optimized using particle swarm optimization algorithm, and the output load control regulation coefficient is calculated. The current state range is determined based on the load control adjustment coefficient, and the remaining heat tolerance time is combined with the control actuator to implement a graded response strategy of maintaining flow, short-term overload monitoring and early warning, and disconnection and current limiting.

[0006] Preferably, the process of real-time acquisition and preprocessing of current data from branch circuits and temperature data at cable joints includes: A Hall current sensor is placed at the wire of the branch circuit to sense the magnetic field generated when the wire of the branch circuit is energized, outputs a Hall voltage signal corresponding to the magnetic induction intensity, and obtains the current data at each discrete moment according to the ratio of the Hall voltage signal to the current. A fiber Bragg grating temperature sensor is arranged on the surface of the cable joint insulation layer to receive broadband optical signals and reflect narrowband light with a center wavelength. The center wavelength drifts due to changes in the grating period and effective refractive index caused by changes in the cable joint temperature. The amount of center wavelength drift is detected, and the temperature data at each discrete moment is calculated based on the amount of drift. A discrete linear dynamic system model containing system state equations and observation equations is constructed. The process noise covariance matrix and the measurement noise covariance matrix are defined. The Kalman filter algorithm is used to perform iterative calculations on the collected current data and temperature data for time update and measurement update, respectively. The optimal estimates of current and temperature after filtering out noise interference are output as preprocessed current data and temperature data, respectively.

[0007] Preferably, the process of performing electrothermal coupling confidence verification on the preprocessed current and temperature data to obtain the data confidence factor includes: Obtain the equivalent resistance parameters, heat dissipation coefficient of the connector, and ambient temperature parameters of the branch circuit. Construct an electrothermal coupling physical benchmark model based on Joule's law. Input the optimal current estimate into the electrothermal coupling physical benchmark model to calculate the theoretical temperature rise at the current moment. Calculate the absolute error between the optimal temperature estimate and the theoretical temperature rise, and use the absolute error as the residual index; Based on the residual index, the confidence factor of the data is calculated using the Gaussian membership function.

[0008] Preferably, the process of extracting the rate of change of current and the rate of temperature rise includes: Obtain the optimal current and optimal temperature estimates at the current sampling time, as well as the optimal current and optimal temperature estimates at the previous sampling time; Calculate the current difference between the current at the current sampling time and the optimal current estimate at the previous sampling time, and divide the current difference by the sampling time interval to obtain the current change rate; Calculate the temperature difference between the current sampling time and the optimal temperature estimate of the previous sampling time, and divide the temperature difference by the sampling time interval to obtain the temperature rise rate.

[0009] Preferably, the process of inputting the preprocessed current data into the load trend prediction model to obtain the load forecast value for a future set time period includes: Set the length of the historical time window, use a sliding window mechanism to extract the best historical current estimate, and construct the input time series vector; A load trend prediction model is constructed, which includes an input layer, a long short-term memory network layer, and an output layer. The input time series vector is input into the long short-term memory network layer. The long short-term memory network layer includes a preset number of long short-term memory network units. Each long short-term memory network unit includes a forget gate, an input gate, an output gate, and a cell state. The forget gate is used to receive the input data at the current time step and the hidden layer state at the previous time step. The forget gate coefficient is calculated through the activation function to determine the degree of retention of the cell state at the previous time step. The input gate is used to receive the input data at the current time step and the hidden layer state at the previous time step. The input gating coefficient and the candidate cell state are calculated to determine the degree of update of the new information at the current time step. The current cell state is calculated by multiplying the cell state of the previous time step by the forgetting gating coefficient, and then adding the product of the candidate cell state and the input gating coefficient. The current input data and the hidden layer state of the previous time step are received by the output gate. The output gating coefficient is calculated, and the current cell state is processed by the activation function and multiplied by the output gating coefficient to obtain the current hidden layer state. The current hidden layer state is input into the output layer, and a linear transformation is performed through the weight matrix and bias term of the fully connected layer to calculate and output the current prediction sequence for a future set time period, which is used as the load prediction value.

[0010] Preferably, the process of calculating the remaining thermal tolerance time to reach the ultimate tolerance temperature using a finite difference iterative algorithm includes: Thermal characteristic parameters include equivalent thermal resistance parameters and equivalent thermal capacity parameters. The equivalent thermal resistance parameter is calculated by combining the thermal conductivity of the cable insulation material with the geometric dimensions of the cable insulation layer. The equivalent thermal capacity parameter is calculated by combining the specific heat capacity parameter of the cable insulation material with the density and volume of the cable insulation layer. A transient thermal circuit equivalent model containing equivalent thermal resistance parameters and equivalent thermal capacity parameters is constructed. The optimal temperature estimate at the current moment is used as the initial temperature state for iterative calculation; Based on the current prediction sequence and transient thermal circuit equivalent model within a future time period, the finite difference iterative algorithm is used to deduce the cable temperature value at each discrete moment in the future point by point according to the time step. The calculated cable temperature values ​​at each discrete future moment are compared one by one with the ultimate tolerance temperature of the cable insulation material. When it is determined that the cable temperature value at a discrete moment exceeds the ultimate tolerance temperature for the first time, the time difference between the discrete moment and the current moment is calculated, and the time difference is used as the remaining thermal tolerance time.

[0011] Preferably, the process of training and optimizing using the particle swarm optimization algorithm and calculating the output load control adjustment coefficient includes: The data confidence factor, current change rate, temperature rise rate, load prediction value, and remaining heat tolerance time are normalized and combined to construct the input feature vector. An adaptive load control decision model is constructed, which includes a decision input layer, a decision hidden layer, and a decision output layer. The decision hidden layer uses the Gaussian radial basis function as the activation function and obtains a historical operation dataset containing the input feature vectors of historical moments and the corresponding ideal load control adjustment coefficients as model training samples. The particle swarm optimization algorithm is used to optimize the network parameters of the adaptive load control decision model, resulting in an optimized adaptive load control decision model. The network parameters include the basis function center vector of the decision hidden layer, the basis function width vector of the decision hidden layer, and the output weights from the decision hidden layer to the decision output layer. Initialize the particle swarm, set the particle swarm size, learning factor, inertia weight and maximum number of iterations, and encode the network parameters as particle position vectors; Perform particle fitness evaluation, input historical operation dataset into adaptive load control decision model, calculate model output value using network parameters corresponding to the current particle position vector, calculate mean square error between model output value and ideal load control adjustment coefficient, and use the reciprocal of mean square error as fitness function value; Update the optimal extreme value by comparing the fitness function value of the current particle with the individual historical best fitness function value of the current particle and the global historical best fitness function value of the population. If the fitness function value of the current particle is larger, then use the position of the current particle to replace and update the corresponding individual historical best position and the global historical best position respectively. Perform iterative optimization, adjust the particle's flight speed and spatial position based on the individual historical best position and the global historical best position, and repeatedly execute the above particle fitness evaluation and update optimization extreme value process until the maximum number of iterations or the fitness function value satisfies one of the convergence conditions. The final global historical best position is decoded into the optimized basis function center vector, basis function width vector and output weight. The input feature vector at the current moment is input into the optimized adaptive load control decision model. The output of the Gaussian radial basis function is calculated through the decision hidden layer, and then linearly weighted and summed through the decision output layer to calculate the load control adjustment coefficient.

[0012] Preferably, the process of the control execution terminal implementing the hierarchical response strategy of maintaining current flow, short-term overload monitoring and early warning, and interrupting current limiting includes: Based on the calculated load control adjustment coefficient and remaining heat tolerance time, a preset yellow warning threshold and a preset red action threshold are set, and the load control adjustment coefficient is numerically compared with the yellow warning threshold and the red action threshold respectively. When the load control adjustment coefficient is less than or equal to the yellow warning threshold, the current state is determined to be in the normal range, and the control execution end maintains the current flow state. When the load control adjustment coefficient is greater than the yellow warning threshold and less than the red action threshold, it is determined that the current state is in the short-term overload range, a warning signal is generated, the control execution end maintains the current flow state and enters the short-term overload monitoring warning state, and uses the remaining heat tolerance time as the initial duration to start countdown monitoring. When the load control adjustment coefficient is greater than or equal to the red action threshold, and when the countdown monitoring returns to zero in the short-term overload monitoring and early warning state, it is determined that the current state is in the danger zone, and the control execution terminal executes the disconnection and current limiting command to cut off and limit the current of the branch circuit accordingly. The control response feedback data from the execution end is collected in real time. The control response feedback data, along with the optimal current estimate, optimal temperature estimate, load forecast, remaining heat tolerance time, and load control adjustment coefficient, are packaged and sent to the monitoring terminal for storage and recording.

[0013] Preferably, it also includes generating a visualized load state map based on the load control adjustment coefficient: On the display interface of the monitoring terminal, a load status monitoring coordinate system for visualizing the load status map is constructed, with the horizontal axis set as the time dimension and the vertical axis as the load control adjustment coefficient dimension. In the load status monitoring coordinate system, draw the first boundary line corresponding to the yellow warning threshold and the second boundary line corresponding to the red action threshold. Use the first boundary line and the second boundary line to divide the load status monitoring coordinate system into the normal operation area, the short-term overload warning area and the dangerous action area. The historical sequence and current value of the load control adjustment coefficient are mapped to the load status monitoring coordinate system, and a continuous load status change trajectory curve is generated by spline interpolation. Determine the region where each data point on the load state change trajectory curve is located, and configure the corresponding color display attribute for the load state change trajectory curve according to the region it is located in; When the load control adjustment coefficient is in the short-term overload warning area, a countdown dynamic icon is generated in the load status monitoring coordinate system, and the real-time value of the countdown monitoring is mapped to the countdown dynamic icon for superimposed display.

[0014] An intelligent cable branch box adaptive load control system, used to implement the above method, includes: The sensing and verification module collects and preprocesses the current data of the branch circuit and the temperature data at the cable joint in real time. It performs electrothermal coupling reliability verification on the preprocessed current and temperature data, obtains the data confidence factor, and extracts the current change rate and temperature rise rate. The trend prediction module inputs the preprocessed current data into the load trend prediction model to obtain the load prediction value for a future set time period. The thermal simulation module combines load prediction values ​​and uses a transient thermal circuit equivalent model. Based on the thermal characteristic parameters and ultimate withstand temperature of the cable insulation material, it solves the remaining thermal withstand time to reach the ultimate withstand temperature using a finite difference iterative algorithm. The intelligent control decision module uses an adaptive load control decision model with a radial basis function neural network architecture to input data confidence factor, current change rate, temperature rise rate, load forecast value and remaining heat tolerance time, and uses particle swarm optimization algorithm for training and optimization to calculate the output load control regulation coefficient. The graded response module determines the current state range based on the load control adjustment coefficient and, in conjunction with the remaining heat tolerance time, controls the actuator to execute a graded response strategy that includes maintaining flow, short-term overload monitoring and early warning, and disconnection and current limiting.

[0015] The beneficial effects of this invention are as follows: This invention introduces an electrothermal coupling reliability verification mechanism during the data acquisition stage, uses the Joule law physical model to verify the logical consistency between current and temperature data, calculates the data confidence factor as a weight parameter for subsequent control decisions, effectively identifies and eliminates data distortion caused by sensor zero-point drift and damage, avoids circuit breaker erroneous tripping caused by abnormal data from a single sensor, and significantly improves the accuracy and operational reliability of intelligent cable branch boxes in harsh operating environments.

[0016] This invention uses a transient thermal circuit equivalent model, combined with load prediction values ​​and thermal characteristic parameters of cable insulation materials, to deduce the temperature rise trajectory of the cable insulation layer and calculate the remaining thermal withstand time. It breaks through the limitation of the traditional fixed current threshold protection mode on the short-term overload capacity of cables. Under the premise of ensuring that the temperature of the cable insulation layer does not exceed the limit, it allows branch lines to operate under short-term overload, fully explores the dynamic transmission potential of cable lines, and maintains the continuity of power supply to the greatest extent while ensuring equipment safety.

[0017] This invention employs a radial basis function neural network optimized by particle swarm optimization algorithm to construct an adaptive load control decision model. It comprehensively processes multi-dimensional feature vectors of data confidence, load change trends, and remaining heat withstand time, establishing a nonlinear mapping relationship between input features and load control regulation coefficients. This realizes the transformation from a passive post-trip strategy to an active hierarchical response strategy. It can accurately determine the current operating status based on the load control regulation coefficients and remaining heat withstand time, and execute operations such as maintaining current flow, short-term overload monitoring and early warning, and disconnection and current limiting, thus achieving refined and intelligent load management of the distribution network. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 These are simulation test diagrams used in the verification process of this invention; Figure 3 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0020] Example 1: As Figure 1 As shown, the intelligent cable branch box adaptive load control method includes the following steps: S1. Real-time acquisition and preprocessing of current data of branch circuits and temperature data at cable joints, performing electrothermal coupling reliability verification on the preprocessed current and temperature data, obtaining data confidence factor, and extracting current change rate and temperature rise rate.

[0021] A Hall current sensor is placed at the conductor of the branch circuit to sense the magnetic field generated when the conductor of the branch circuit is energized, outputs a Hall voltage signal corresponding to the magnetic induction intensity, and obtains the current data at each discrete moment based on the ratio of the Hall voltage signal to the current.

[0022] A fiber Bragg grating temperature sensor is arranged on the surface of the cable joint insulation layer to receive broadband optical signals and reflect narrowband light with a center wavelength. The center wavelength drifts due to changes in the grating period and effective refractive index caused by changes in the cable joint temperature. The amount of center wavelength drift is detected, and the temperature data at each discrete moment is calculated based on the amount of drift.

[0023] A discrete linear dynamic system model containing system state equations and observation equations is constructed. The process noise covariance matrix and the measurement noise covariance matrix are defined. Based on the discrete linear dynamic system model, the process noise covariance matrix and the measurement noise covariance matrix, the Kalman filter algorithm is used to perform iterative calculations of time update and measurement update on the collected current data and temperature data, respectively. The optimal estimates of current and temperature after filtering out noise interference are output as the preprocessed current data and temperature data, respectively.

[0024] Obtain the equivalent resistance parameters, junction heat dissipation coefficient, and ambient temperature parameters of the branch circuit. Construct an electrothermal coupling physical benchmark model based on Joule's law. Input the optimal current estimate into the electrothermal coupling physical benchmark model to calculate the theoretical temperature rise at the current moment. .

[0025] The heat generated by the current flowing through the cable is calculated using Joule's law. Then, combining the cable's thermal characteristics (such as equivalent thermal resistance and equivalent heat capacity) with the ambient temperature, a dynamic model of the cable's temperature change is established. By integrating the heat generated by the current with the cable's heat dissipation process (including convection and radiation), a differential equation for the cable's temperature change over time is derived. Finally, this equation is used to calculate the cable's temperature change under different current conditions, thus constructing an electrothermal coupling physical benchmark model for evaluating the cable's thermal state during actual operation.

[0026] Calculate the optimal temperature estimate Compared with theoretical temperature rise The absolute error between the two is used as the residual index.

[0027] Based on the residual index, the data confidence factor is calculated using the Gaussian membership function. ,in, This represents the preset residual standard deviation threshold. This represents an exponential function with the natural constant as its base.

[0028] Obtain the optimal current and optimal temperature estimates at the current sampling time, as well as the optimal current and optimal temperature estimates at the previous sampling time.

[0029] Calculate the current difference between the current at the current sampling time and the optimal current estimate at the previous sampling time, and divide the current difference by the sampling time interval to obtain the current change rate.

[0030] Calculate the temperature difference between the current sampling time and the optimal temperature estimate of the previous sampling time, and divide the temperature difference by the sampling time interval to obtain the temperature rise rate.

[0031] S2. Input the preprocessed current data into the load trend prediction model to obtain the load prediction value for the future set time period.

[0032] The historical time window length is set, and a sliding window mechanism is used to extract the optimal historical current estimate and construct the input time series vector.

[0033] A load trend prediction model is constructed, which includes an input layer, a long short-term memory network layer, and an output layer. The input time series vector is input into the long short-term memory network layer. The long short-term memory network layer includes a preset number of long short-term memory network units. Each long short-term memory network unit includes a forget gate, an input gate, an output gate, and a cell state.

[0034] The forget gate is used to receive the input data at the current time step and the hidden layer state at the previous time step. The forget gate coefficient (the control coefficient of the forget gate) is calculated through the activation function to determine the degree of retention of the cell state at the previous time step.

[0035] The input gate is used to receive the input data at the current time step and the hidden layer state at the previous time step. The input gating coefficient and the candidate cell state are calculated to determine the degree of update of the new information at the current time step.

[0036] The current cell state is calculated by multiplying the cell state of the previous time step by the forgetting gating coefficient, and then adding the product of the candidate cell state and the input gating coefficient.

[0037] The input data at the current time step and the hidden layer state at the previous time step are received by the output gate. The output gating coefficient is calculated, and the cell state at the current time step is processed by the activation function and multiplied by the output gating coefficient to obtain the hidden layer state at the current time step.

[0038] The current hidden layer state is input into the output layer, and a linear transformation is performed through the weight matrix and bias term of the fully connected layer to calculate and output the current prediction sequence for a future set time period, which is used as the load prediction value.

[0039] S3. Combining the load prediction value, using the transient thermal circuit equivalent model, and based on the thermal characteristic parameters and ultimate tolerance temperature of the cable insulation material, the remaining thermal tolerance time to reach the ultimate tolerance temperature is calculated using the finite difference iterative algorithm.

[0040] Thermal characteristic parameters include equivalent thermal resistance parameters. and equivalent heat capacity parameters The equivalent thermal resistance parameter is calculated by combining the thermal conductivity of the cable insulation material with the geometric dimensions of the cable insulation layer. The equivalent heat capacity parameter is calculated by combining the specific heat capacity parameter of the cable insulation material with the density and volume of the cable insulation layer. Construct a system that includes equivalent thermal resistance parameters. and equivalent heat capacity parameters The transient thermal circuit equivalent model.

[0041] The optimal temperature estimate at the current moment is used as the initial temperature state for iterative calculation.

[0042] Based on the predicted current sequence and transient thermal circuit equivalent model within a future time period, a finite difference iterative algorithm is used to predict the cable temperature value at each discrete time step, point by point. The cable temperature value at the (k+1)th time step is... The calculation formula is as follows: ; In the formula, This represents the cable temperature value at the k-th time step. Indicates the time step. This represents the predicted current value at the k-th time step in the current prediction sequence. This represents the resistance of the wires in the branch circuit. This indicates the ambient temperature parameter.

[0043] The calculated cable temperature values ​​at each discrete future moment are compared one by one with the ultimate tolerance temperature of the cable insulation material. When it is determined that the cable temperature value at a discrete moment exceeds the ultimate tolerance temperature for the first time, the time difference between the discrete moment and the current moment is calculated, and the time difference is used as the remaining thermal tolerance time.

[0044] S4. The data confidence factor, current change rate, temperature rise rate, load forecast value and remaining heat tolerance time are input into an adaptive load control decision model with a radial basis function neural network architecture, and the particle swarm optimization algorithm is used for training and optimization to calculate the output load control regulation coefficient.

[0045] The data confidence factor, current change rate, temperature rise rate, load prediction value, and remaining heat tolerance time are normalized and combined to construct the input feature vector X.

[0046] An adaptive load control decision model is constructed, which includes a decision input layer, a decision hidden layer, and a decision output layer. The decision hidden layer uses the Gaussian radial basis function as the activation function and obtains the historical operation dataset containing the input feature vector X and the corresponding ideal load control adjustment coefficient at historical moments as the model training samples.

[0047] The particle swarm optimization algorithm is used to optimize the network parameters of the adaptive load control decision model, resulting in an optimized adaptive load control decision model. The network parameters include the basis function center vector of the decision hidden layer, the basis function width vector of the decision hidden layer, and the output weights from the decision hidden layer to the decision output layer.

[0048] Initialize the particle swarm, setting the particle population size, learning factor, inertia weight, and maximum number of iterations, and encode the network parameters as particle position vectors.

[0049] Perform particle fitness evaluation, input historical operation dataset into adaptive load control decision model, calculate model output value using network parameters corresponding to the current particle position vector, calculate mean square error between model output value and ideal load control adjustment coefficient, and use the reciprocal of mean square error as fitness function value.

[0050] Update the optimal extreme value by comparing the current particle's fitness function value with the current particle's individual historical best fitness function value and the population's global historical best fitness function value. If the current particle's fitness function value is larger, then use the current particle's position to replace and update the corresponding individual historical best position and global historical best position, respectively.

[0051] Perform iterative optimization, adjust the particle's flight speed and spatial position based on the individual historical best position and the global historical best position, and repeatedly execute the above particle fitness evaluation and update optimization extreme value process until the maximum number of iterations or the fitness function value satisfies one of the convergence conditions. The final global historical best position is decoded into the optimized basis function center vector, basis function width vector and output weight.

[0052] The input feature vector at the current time will be constructed. The optimized adaptive load control decision model is input, and the output of the Gaussian radial basis function is calculated through the decision hidden layer. Then, the load control regulation coefficient is calculated by linear weighted summation through the decision output layer. Where m represents the number of nodes in the hidden layer neurons of the decision-making layer. This represents the output weight from the j-th hidden layer neuron to the output layer. This represents the center vector of the basis functions of the j-th decision hidden layer neuron. Describes the Euclidean norm. Let represent the basis function width vector of the j-th decision hidden layer neuron.

[0053] S5. Determine the current state range based on the load control adjustment coefficient, and combine it with the remaining heat tolerance time to control the actuator to implement a graded response strategy of maintaining flow, short-term overload monitoring and early warning, and disconnection and current limiting.

[0054] Based on the calculated load control adjustment coefficient and remaining heat tolerance time, preset yellow warning threshold and preset red action threshold are set, and the load control adjustment coefficient is numerically compared with the yellow warning threshold and the red action threshold respectively.

[0055] When the load control adjustment coefficient is less than or equal to the yellow warning threshold, the current state is determined to be in the normal range, and the control execution end maintains the current flow state.

[0056] When the load control adjustment coefficient is greater than the yellow warning threshold and less than the red action threshold, it is determined that the current state is in the short-term overload range, a warning signal is generated, the control execution end maintains the current flow state and enters the short-term overload monitoring and warning state, and starts countdown monitoring using the remaining heat tolerance time as the initial duration.

[0057] When the load control adjustment coefficient is greater than or equal to the red action threshold, and when the countdown monitoring reaches zero in the short-term overload monitoring and early warning state, it is determined that the current state is in the danger zone, and the control execution terminal executes the disconnection and current limiting command to cut off and limit the current of the branch circuit accordingly.

[0058] The control response feedback data from the execution end is collected in real time. The control response feedback data, along with the optimal current estimate, optimal temperature estimate, load forecast, remaining heat tolerance time, and load control adjustment coefficient, are packaged and sent to the monitoring terminal for storage and recording.

[0059] In this embodiment, the method further includes: S6, generating a visual load status map based on the load control adjustment coefficient.

[0060] A load status monitoring coordinate system with a visual load status map is constructed on the display interface of the monitoring terminal, with the horizontal axis set as the time dimension and the vertical axis as the load control adjustment coefficient dimension.

[0061] In the load status monitoring coordinate system, draw the first boundary line corresponding to the yellow warning threshold and the second boundary line corresponding to the red action threshold. Use the first boundary line and the second boundary line to divide the load status monitoring coordinate system into the normal operation area, the short-term overload warning area and the dangerous action area.

[0062] The historical sequence and current value of the load control adjustment coefficient are mapped to the load status monitoring coordinate system, and a continuous load status change trajectory curve is generated by spline interpolation.

[0063] The region where each data point on the load state change trajectory curve is located is determined, and the corresponding color display attribute is configured for the load state change trajectory curve according to the region. The curve segment located in the normal operation region is configured with the first color, the curve segment located in the short-term overload warning region is configured with the second color, and the curve segment located in the dangerous action region is configured with the third color.

[0064] When the load control adjustment coefficient is in the short-term overload warning area, a countdown dynamic icon is generated in the load status monitoring coordinate system, and the real-time value of the countdown monitoring is mapped to the countdown dynamic icon for superimposed display.

[0065] like Figure 2 As shown, through simulation experiments, the ability of the method in this embodiment to perform adaptive graded response and tap the dynamic transmission potential of the cable under dynamic time-varying load and sudden overload conditions is verified.

[0066] With the resistance of the wires in the branch circuit Equivalent thermal resistance parameters and equivalent heat capacity parameters The physical model parameters are used, with current data containing sinusoidal fluctuations and step disturbances for 600 seconds as input test excitation. The simulation test diagram is shown below. Figure 2 As shown.

[0067] Figure 2 In the diagram, the horizontal axis represents the simulation time t (in seconds), and the vertical axis represents the load control adjustment coefficient. Figure 2 The lower dashed line is the first boundary line, and the upper dashed line is the second boundary line. The first boundary line corresponds to the yellow warning threshold (0.6), and the second boundary line corresponds to the red action threshold (0.85). Using these two boundary lines, the load status monitoring coordinate system is divided into the normal operation area at the bottom, the short-term overload warning area in the middle, and the dangerous action area at the top. The area below the first boundary line is the normal operation area, the area between the first and second boundary lines is the short-term overload warning area, and the area above the second boundary line is the dangerous action area.

[0068] Figure 2The 0s-300s interval shows the adaptive monitoring phase of the method in this embodiment under dynamic load. During this period, the input current data exhibits periodic fluctuations, and the load control adjustment coefficient adaptively switches between approximately 0.4 (normal operation range) and approximately 0.7 (short-term overload warning range). When the load control adjustment coefficient jumps to 0.7 and enters the short-term overload warning range, although it exceeds the first boundary line, the remaining thermal withstand time calculated by combining the transient thermal circuit equivalent model indicates that the cable insulation temperature has not yet reached the limit withstand temperature. Therefore, only short-term overload monitoring and warning and countdown monitoring are executed, and the disconnection and current limiting command is not executed. This process fully verifies that the method in this embodiment can utilize the thermal inertia of the cable to tap its dynamic transmission potential and avoid the frequent malfunctions of traditional fixed threshold protection during load fluctuations.

[0069] Figure 2 The 300s-600s interval illustrates the active defense phase of the method in this embodiment under sudden overload. At 300s, a sudden strong load interference causes the load control adjustment coefficient to rapidly climb to around 0.9, exceeding the second boundary line and remaining in the dangerous operation zone. It is determined that the cable insulation faces irreversible thermal damage risk, and a disconnection and current limiting command is immediately triggered. Subsequently, at around 450s, with the execution of the protection action, the current in the branch circuit is forcibly limited, and the load control adjustment coefficient drops sharply and returns to the normal operating range of around 0.4. This process verifies that the method in this embodiment can achieve accurate state identification and decisive graded response when facing real overload faults, effectively ensuring equipment safety.

[0070] The verification process shows that the method in this embodiment effectively solves the problems of malfunction caused by sensor data distortion and insufficient flexibility of traditional fixed threshold protection by combining electrothermal coupling reliability verification and transient thermal circuit equivalent model. By adopting an adaptive load control decision model optimized by particle swarm optimization algorithm, the maximum transmission efficiency of the power distribution line within the safety boundary is guaranteed.

[0071] In summary, the method in this embodiment demonstrates accurate state identification and reliable proactive defense capabilities in complex power distribution network load fluctuation environments. Furthermore, through multi-dimensional feature fusion and hierarchical response mechanisms, it constructs an intelligent management and control system for the distribution terminal that balances power supply reliability and equipment safety, exhibiting significant technical advantages and application value.

[0072] Example 2: As Figure 3 As shown, the intelligent cable branch box adaptive load control system is used to implement the method in Example 1. It includes a sensing and verification module, a trend prediction module, a thermal simulation module, an intelligent control decision module, and a hierarchical response module, wherein the modules are connected by electrical signals.

[0073] The sensing and verification module collects and preprocesses the current data of the branch circuit and the temperature data at the cable joint in real time. It performs electrothermal coupling reliability verification on the preprocessed current and temperature data, obtains the data confidence factor, and extracts the current change rate and temperature rise rate.

[0074] The trend prediction module inputs the preprocessed current data into the load trend prediction model to obtain the load prediction value for a future set time period.

[0075] The thermal simulation module combines load prediction values ​​and utilizes a transient thermal circuit equivalent model. Based on the thermal characteristic parameters and ultimate withstand temperature of the cable insulation material, it solves the remaining thermal withstand time to reach the ultimate withstand temperature using a finite difference iterative algorithm.

[0076] The intelligent control decision module uses an adaptive load control decision model with a radial basis function neural network architecture to input data confidence factor, current change rate, temperature rise rate, load forecast value and remaining heat tolerance time, and then uses particle swarm optimization algorithm for training and optimization to calculate the output load control regulation coefficient.

[0077] The graded response module determines the current state range based on the load control adjustment coefficient and, in conjunction with the remaining heat tolerance time, controls the actuator to execute a graded response strategy that includes maintaining flow, short-term overload monitoring and early warning, and disconnection and current limiting.

Claims

1. A method for adaptive load control of a smart cable branch box, characterized in that, The method comprises the following steps: real-time acquisition and preprocessing of current data of the branch circuit and temperature data at the cable joint, performing electrical-thermal coupling credibility verification on the preprocessed current data and temperature data, obtaining a data confidence factor, and extracting a current rate of change and a temperature rise rate; inputting the preprocessed current data into a load trend prediction model to obtain a load prediction value in a future set time period; combining the load prediction value, using a transient thermal circuit equivalent model, and solving by a finite difference iteration algorithm according to thermal characteristic parameters and a limit tolerance temperature of cable insulation material to calculate a residual thermal tolerance time to the limit tolerance temperature; inputting the data confidence factor, the current rate of change, the temperature rise rate, the load prediction value and the residual thermal tolerance time into an adaptive load control decision model adopting a radial basis function neural network architecture, and training and optimizing by a particle swarm optimization algorithm to calculate and output a load control adjustment coefficient; determining a current state interval according to the load control adjustment coefficient, and controlling an execution end to execute a hierarchical response strategy of maintaining current flow, short-time overload monitoring and early warning and branch limiting current in combination with the residual thermal tolerance time.

2. The intelligent cable junction box adaptive load control method of claim 1, wherein, The process of real-time acquisition and preprocessing of current data of the branch circuit and temperature data at the cable joint comprises: arranging a Hall current sensor at a conductor of the branch circuit to sense a magnetic field generated when the conductor is electrified, output a Hall voltage signal corresponding to the magnetic induction intensity, and obtain current data at each discrete time according to the proportional relationship between the Hall voltage signal and the current; arranging a fiber Bragg grating temperature sensor on the surface of the insulation layer of the cable joint to receive a broadband light signal and reflect a narrowband light with a center wavelength, wherein the center wavelength drifts with the changes of the grating period and the effective refractive index caused by the temperature change of the cable joint, the drift amount of the center wavelength is detected, and the temperature data at each discrete time is calculated according to the drift amount; constructing a discrete linear dynamic system model containing system state equations and observation equations, setting a process noise covariance matrix and a measurement noise covariance matrix, and performing iterative calculation of time update and measurement update on the collected current data and temperature data by a Kalman filtering algorithm to output current optimal estimation values and temperature optimal estimation values filtered from noise interference as the preprocessed current data and temperature data.

3. The method of claim 2, wherein, The process of performing electrical-thermal coupling credibility verification on the preprocessed current data and temperature data to obtain a data confidence factor comprises: obtaining equivalent resistance parameters of the branch circuit, joint heat dissipation coefficients and environmental temperature parameters, constructing an electrical-thermal coupling physical benchmark model based on Joule's law, inputting the current optimal estimation value into the electrical-thermal coupling physical benchmark model, and calculating a theoretical temperature rise value at the current time; calculating the absolute error between the temperature optimal estimation value and the theoretical temperature rise value, and taking the absolute error as a residual index; based on the residual index, calculating the data confidence factor by using a Gaussian membership function.

4. The method of claim 2, wherein, The process of extracting the current rate of change and the temperature rise rate comprises: obtaining the current optimal estimation value and the temperature optimal estimation value at the current sampling time, and the current optimal estimation value and the temperature optimal estimation value at the last sampling time; The current difference of the current optimal estimation value between the current sampling time and the last sampling time is calculated, and the current difference is divided by the sampling time interval to obtain the current change rate; The temperature difference of the temperature optimal estimation value between the current sampling time and the last sampling time is calculated, and the temperature difference is divided by the sampling time interval to obtain the temperature rise rate.

5. The intelligent cable cabinet adaptive load control method of claim 2, wherein, The process of inputting the preprocessed current data into the load trend prediction model to obtain the load prediction value in the future set time period includes: The length of the historical time window is set, and the sliding window mechanism is adopted to extract the historical current optimal estimation value to construct an input time sequence vector; A load trend prediction model including an input layer, a long short-term memory network layer and an output layer is constructed, the input time sequence vector is input into the long short-term memory network layer, the long short-term memory network layer includes a preset number of long short-term memory network units, and each long short-term memory network unit includes a forgetting gate, an input gate, an output gate and a cell state; The forgetting gate receives the input data at the current time and the hidden layer state at the last time, and a forgetting gate coefficient is calculated through an activation function to determine the retention degree of the cell state at the last time; The input gate receives the input data at the current time and the hidden layer state at the last time, and an input gate coefficient and a candidate cell state are calculated to determine the update degree of the new information at the current time; The product of the cell state at the last time and the forgetting gate coefficient is added to the product of the candidate cell state and the input gate coefficient to calculate the cell state at the current time; The output gate receives the input data at the current time and the hidden layer state at the last time, and an output gate coefficient is calculated, and the cell state at the current time is multiplied by the output gate coefficient after being processed by an activation function to obtain the hidden layer state at the current time; The hidden layer state at the current time is input into the output layer, and the output future current prediction sequence in the set time period is calculated through linear transformation of the weight matrix and the bias term of the full connection layer, which is taken as the load prediction value.

6. The intelligent cable cabinet adaptive load control method of claim 5, wherein, The process of calculating the residual thermal tolerance time to reach the limit tolerance temperature by the finite difference iteration algorithm includes: The thermal characteristic parameters include the equivalent thermal resistance parameter and the equivalent thermal capacity parameter, the equivalent thermal resistance parameter is calculated by using the thermal conductivity of the cable insulation material combined with the geometric size of the cable insulation layer, the equivalent thermal capacity parameter is calculated by using the specific heat capacity of the cable insulation material combined with the density and volume of the cable insulation layer, and the transient thermal circuit equivalent model including the equivalent thermal resistance parameter and the equivalent thermal capacity parameter is constructed; The temperature optimal estimation value at the current time is taken as the initial temperature state of the iterative calculation; Based on the current prediction sequence in the future set time period and the transient thermal circuit equivalent model, the finite difference iteration algorithm is adopted to deduce the cable temperature value at each discrete time in the future step by step; The calculated cable temperature value at each discrete time in the future is compared with the limit tolerance temperature of the cable insulation material one by one, and when it is determined that the cable temperature value at a discrete time first exceeds the limit tolerance temperature, the time difference between the discrete time and the current time is calculated, and the time difference is taken as the residual thermal tolerance time.

7. The intelligent cable cabinet adaptive load control method of claim 1, wherein, The process of calculating the load control adjustment coefficient includes: The data confidence factor, current rate of change, temperature rise rate, load prediction value, and residual thermal tolerance time are normalized and combined to form an input feature vector; An adaptive load control decision model is constructed, which includes a decision input layer, a decision hidden layer, and a decision output layer. The decision hidden layer uses a Gaussian radial basis function as the activation function. The input feature vector containing historical time and the corresponding ideal load control adjustment coefficient historical operation data set are obtained as the model training sample; The particle swarm optimization algorithm is used to optimize the network parameters of the adaptive load control decision model, and the optimized adaptive load control decision model is obtained. The network parameters include the basis function center vector of the decision hidden layer, the basis function width vector of the decision hidden layer, and the output weight from the decision hidden layer to the decision output layer; The particle swarm is initialized, the particle swarm size, learning factor, inertia weight, and maximum iteration number are set, and the network parameters are encoded as the position vector of the particle; The particle fitness evaluation is performed, the historical operation data set is input into the adaptive load control decision model, the network parameters corresponding to the current particle position vector are used to calculate the model output value, the mean square error between the model output value and the ideal load control adjustment coefficient is calculated, and the reciprocal of the mean square error is used as the fitness function value; The optimization extreme value is updated, and the fitness function value of the current particle is compared with the individual historical optimal fitness function value and the global historical optimal fitness function value of the population. If the fitness function value of the current particle is larger, the position of the current particle is used to replace the corresponding individual historical optimal position and global historical optimal position; Iterative optimization is performed, and the flight speed and spatial position of the particle are adjusted according to the individual historical optimal position and the global historical optimal position. The particle fitness evaluation and optimization extreme value updating process are repeatedly executed until one of the maximum iteration number and the fitness function value meets the convergence condition. The final global historical optimal position is decoded into the optimized basis function center vector, basis function width vector, and output weight; The input feature vector of the current time is input into the optimized adaptive load control decision model, the output of the Gaussian radial basis function is calculated through the decision hidden layer, and the load control adjustment coefficient is calculated through the linear weighted sum of the decision output layer.

8. The intelligent cable cabinet adaptive load control method of claim 2, wherein, The process of executing the hierarchical response strategy of maintaining through-flow, short-time overload monitoring and early warning, and breaking limit current includes: Based on the calculated load control adjustment coefficient and residual thermal tolerance time, the preset yellow warning threshold and the preset red action threshold are set, and the load control adjustment coefficient is compared with the yellow warning threshold and the red action threshold; When the load control adjustment coefficient is less than or equal to the yellow warning threshold, it is determined that the current state is in the normal interval, and the control execution end maintains the through-flow state at the current time. When the load control adjustment coefficient is greater than the yellow pre-warning threshold and less than the red action threshold, it is determined that the current state is in the short-time overload interval, a pre-warning signal is generated, the control execution end maintains the current time flow state and enters the short-time overload monitoring and pre-warning state, and the residual heat tolerance time is used as an initial time length to start the countdown monitoring; When the load control adjustment coefficient is greater than or equal to the red action threshold, and the countdown monitoring is zero in the short-time overload monitoring and pre-warning state, it is determined that the current state is in the dangerous interval, and the control execution end executes a breaking and current-limiting instruction to cut off and limit the current of the branch circuit; Real-time control response feedback data of the execution end is collected, and the control response feedback data, the current optimal estimation value, the temperature optimal estimation value, the load prediction value, the residual heat tolerance time and the load control adjustment coefficient are packaged and sent to the monitoring terminal for storage and recording.

9. The method of claim 8, wherein, A visual load state atlas is generated according to the load control adjustment coefficient: A load state monitoring coordinate system of the visual load state atlas is constructed on a display interface of the monitoring terminal, the horizontal axis is set as the time dimension, and the vertical axis is set as the load control adjustment coefficient dimension; A first boundary line corresponding to the yellow pre-warning threshold and a second boundary line corresponding to the red action threshold are drawn in the load state monitoring coordinate system, and the load state monitoring coordinate system is divided into a normal operation region, a short-time overload warning region and a dangerous action region by the first boundary line and the second boundary line; A historical sequence and a current value of the load control adjustment coefficient are mapped into the load state monitoring coordinate system, and a continuous load state change trajectory curve is generated by spline interpolation; Each data point on the load state change trajectory curve is determined to be in which region, and the load state change trajectory curve is configured with a corresponding color display attribute according to the region; When the load control adjustment coefficient is in the short-time overload warning region, a countdown dynamic icon is generated in the load state monitoring coordinate system, and a real-time value of the countdown monitoring is mapped to the countdown dynamic icon for superimposed display.

10. A smart cable branch box adaptive load control system for implementing the smart cable branch box adaptive load control method of any of claims 1-9, characterized by, It includes: A perception verification module that collects and pre-processes current data and temperature data at the cable joint of the branch circuit in real time, performs electrical-thermal coupling credibility verification on the pre-processed current data and temperature data, obtains a data confidence factor, and extracts a current change rate and a temperature rise rate; A trend prediction module that inputs the pre-processed current data into a load trend prediction model to obtain a load prediction value in a future set time period; A thermal state deduction module that, in combination with the load prediction value, uses a transient thermal circuit equivalent model, according to thermal characteristic parameters and a limit tolerance temperature of cable insulation material, solves by a finite difference iteration algorithm to calculate a residual heat tolerance time to the limit tolerance temperature; A smart control decision module that inputs the data confidence factor, the current change rate, the temperature rise rate, the load prediction value and the residual heat tolerance time into an adaptive load control decision model using a radial basis function neural network architecture, and trains and optimizes using a particle swarm optimization algorithm to calculate and output a load control adjustment coefficient. The grading response module determines the current state interval according to the load control adjustment coefficient, and controls the execution end to execute the grading response strategies of maintaining through-flow, short-time overload monitoring and early warning, and disconnection current limiting in combination with the residual heat tolerance time.

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