Fault automatic switching line system

Abstract

The invention relates to an automatic fault switching line system, which is characterized in that a fault tripping signal of a working power supply is monitored, and the position of a power-off line is determined according to the fault tripping signal; the bearing capacity of the standby line to be switched is evaluated, and the incoming line load capacity after switching is predicted; according to the spare line bearing capacity and the incoming line load capacity, spare power automatic switching action is carried out; and after the spare power automatic switching action, the access mode and the load distribution strategy of the line are automatically adjusted according to the line parameters monitored in real time and the load condition of the whole station. According to the method, the problem that the spare power automatic switching action accuracy and safety are insufficient due to the fact that the spare line bearing capacity is difficult to evaluate and the incoming line load capacity is difficult to predict in the prior art is solved. The random forest model is trained by using the basic information of the standby line and the environmental factors to evaluate the bearing capacity of the standby line, and the accuracy of spare power automatic switching control is improved. An ARIMA model is established according to power station load distribution and time law characteristics, the incoming line load capacity is predicted, and the safety of spare power automatic switching is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of transformer substations and relates to a fault automatic switching line system. Background Art

[0002] In modern society, power supply and various data communications are extremely dependent on line transmission. Whether it is household electricity, corporate production, Internet communications, financial transactions and other activities, once a line fails, it will cause serious impact. For example, power line failures may cause power outages, affect residents' lives, corporate shutdowns, and even endanger the normal operation of important places such as hospitals and transportation hubs. Communication line failures will cause network interruptions, hinder online business development, and affect people's information exchange. However, in actual operation, lines often fail due to natural factors (such as bad weather, earthquakes, etc.), human factors (such as construction damage, accidental touch, etc.) and aging. In order to quickly restore transmission when a line fails, ensure the normal progress of various activities, and reduce losses caused by failures, there is an urgent need for a fault automatic switching line system that can automatically and quickly switch transmission to a backup line after detecting a line failure, maintaining normal power supply and data communication functions.

[0003] However, when a section of the busbar in the substation loses pressure due to an incoming line fault, a busbar fault, or a fault in the upper power supply, the backup power supply automatic input device (backup automatic input) will immediately start and perform the corresponding action. The specific operation is to close the section switch connecting the two sections of the busbar. In this way, the power supply line that originally carried part of the load will instantly bear all the load of the entire station. This situation often leads to a series of adverse consequences. Because the power supply line was originally operated according to a certain load configuration, it suddenly has to bear the load of the entire station, which causes the incoming line load to increase sharply, far exceeding the range that it can carry during normal operation. This sudden increase in the incoming line load is very likely to trigger the action of the incoming line protection device, which will cause the incoming line to trip. Once the incoming line trips, the entire substation loses the power supply from the incoming line, which eventually causes a serious failure situation of the entire station losing pressure. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a fault automatic switching line system, which aims to construct a load prediction algorithm to evaluate the carrying capacity of the standby line to be switched, and predict the incoming line load after the switching; establish an adaptive control mechanism to automatically adjust the line access mode and load distribution strategy according to the line parameters and the load situation of the entire station monitored in real time; reduce the incoming line load by adjusting the transformer tap, switching on and off the reactive compensation device, etc., so that it is kept within a safe range to prevent tripping and loss of pressure in the entire station.

[0005] The purpose of the present invention can be achieved through the following technical solutions: The present application provides a fault automatic switching line system, including a fault monitoring module, a load analysis module, a standby automatic switching module and an adaptive control module, wherein the fault monitoring module, the load analysis module, the standby automatic switching module and the adaptive control module are communicatively connected, wherein: The fault monitoring module is used to monitor the fault tripping signal of the working power supply and determine the position of the power-off line according to the fault tripping signal; The load analysis module is used to evaluate the carrying capacity of the standby line to be switched and predict the incoming line load after switching; The standby automatic switching module is used to perform standby automatic switching action according to the standby line carrying capacity and incoming line load; The adaptive control module is used to automatically adjust the line access mode and load distribution strategy according to the line parameters and the load situation of the entire station monitored in real time after the standby automatic switching action.

[0006] Further, in the fault monitoring module, the monitoring of the fault tripping signal of the working power supply and determining the power-off line position according to the fault tripping signal include the following steps: Establish monitoring connection: Make correct electrical and communication connections between the fault monitoring module and the working power supply and its related protection devices; Configure a sensor network: install current transformers, voltage transformers and temperature sensors at key line nodes of the working power supply to collect line data in real time, including current, voltage and temperature data; Real-time signal monitoring: Receive line data from sensors and protection devices in real time. When a signal change in line data that meets the preset fault tripping conditions is found, it is determined that a fault tripping signal has been detected. Signal verification and confirmation: After a fault trip signal is initially monitored, the fault trip signal is further verified by referring to the changes in other line data at the same time; Collect line parameter change data: after detecting the fault trip signal, collect the change data of the relevant line data of each line; Comparative analysis and judgment: Compare and analyze the change data of the line data with the pre-stored normal operation data, and determine the location of the power outage line in combination with the power system topology; Generate fault report: Generate fault report based on the monitored fault trip signal and the process of determining the location of the power-off line.

[0007] Furthermore, in the load analysis module, the evaluation of the carrying capacity of the standby line to be switched includes the following steps: S1. Obtain basic information of the standby line, wherein the basic information includes basic parameters and rated parameters; the basic parameters include line material, cross-sectional area and line length; the rated parameters include rated voltage, rated current and rated power; S2. Identify environmental factors of the backup line, where the environmental factors include temperature, humidity and electromagnetic interference; S3, using the acquired basic information of the backup line and environmental factors as independent variables and the carrying capacity of the backup line as the dependent variable, training the carrying capacity analysis model; S4. Input the basic information of the standby line to be switched and the environmental factors thereof into the carrying capacity analysis model, and output the carrying capacity of the standby line to be switched.

[0008] Furthermore, the carrying capacity analysis model is configured as a random forest model, including the following construction steps: Divide the preprocessed sample data into training set, validation set and test set. The training set is used to train the random forest model; the validation set is used to monitor and adjust the performance of the model during the training process, and optimize the parameters of the model according to the performance indicators on the validation set; the test set is used to evaluate the final performance of the model after the model training is completed, so as to verify whether the model can accurately predict new data; Training the random forest model: Use the divided training set to train the random forest model. During the training process, each decision tree grows independently based on the data in the training set, analyzes and judges the input features according to the set parameters, gradually forms their own decision paths, and finally constructs a random forest model composed of multiple decision trees; Evaluate model performance: Use the test set to evaluate the performance of the trained random forest model. The evaluation indicators include mean square error, mean absolute error, and determination coefficient.

[0009] Furthermore, in the load analysis module, the predicting of the incoming line load after switching includes the following steps: Obtain load distribution based on the switching conditions of all load types in the power station; Acquire current time regularity characteristics, wherein the time regularity characteristics include production activity characteristics and resident life characteristics; Input the load distribution and time regularity characteristics into a preset time series model to predict the incoming load sequence data of the standby line in a future period of time; Obtain statistical information of the incoming line load sequence data, wherein the statistical information includes a maximum value, a minimum value, an average value, and a standard deviation.

[0010] Furthermore, the time series model is configured as an ARIMA model, comprising the following steps: Collect historical data: collect historical data samples from relevant data sources of the power station, wherein the historical data samples include incoming line load, load distribution and time regularity characteristics; Test data stability: Use ADF test to test the stability of the pre-processed historical data samples of incoming line load; Model order determination: Determine the autoregressive order and moving average order by observing the autocorrelation function graph and partial autocorrelation function graph; Training ARIMA model: The ARIMA model is trained according to the determined autoregressive order and moving average order. During the training process, the model adjusts the autoregressive coefficient and the moving average coefficient to minimize the error between the predicted value and the true value. Evaluate model performance: Use the test set to evaluate the performance of the trained ARIMA model.

[0011] Furthermore, in the standby automatic switching module, the standby automatic switching action is performed according to the standby line carrying capacity and the incoming line load, including the following steps: Compare the load difference between the backup line carrying capacity and the incoming line load; When the load difference exceeds a preset load difference threshold, the standby automatic switching action is suspended; When the load difference does not exceed the preset load difference threshold, the corresponding section switch is closed to connect the backup power supply from the failed working power supply line to the backup line. Further, the load difference is calculated as follows: , In the formula, D e Indicates load difference; C e Indicates the carrying capacity of the backup line; J e Indicates the incoming line load.

[0012] Furthermore, when the line load exceeds a preset load threshold, the adaptive control module reduces the incoming line load by adjusting the transformer tap or switching on the reactive compensation device.

[0013] Beneficial effects of the present invention: (1) By monitoring the fault tripping signal of the working power supply, and determining the location of the power-off line according to the fault tripping signal; evaluating the carrying capacity of the standby line to be switched, and predicting the incoming line load after switching; performing the standby automatic switching action according to the standby line carrying capacity and the incoming line load; after the standby automatic switching action, automatically adjusting the line access mode and load distribution strategy according to the line parameters and the load situation of the entire station monitored in real time. The present invention solves the problem that the existing technology is difficult to evaluate the standby line carrying capacity and predict the incoming line load, resulting in insufficient accuracy and safety of the standby automatic switching action.

[0014] (2) The existing basic information and environmental factors of the backup line are used as independent variables, and the carrying capacity of the backup line is used as the dependent variable to train the random forest model. This enables the prediction of the carrying capacity of the backup line based on the existing information, thereby improving the accuracy of the backup automatic switching control.

[0015] (3) An ARIMA model is established based on the load distribution and time regularity characteristics of the power station to predict the incoming load sequence data of the backup line in the future, so as to predict the incoming load according to the actual situation and improve the safety of the backup line. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 The figure is a structural diagram of a fault automatic switching line system in the present invention.

[0018] Figure 2 A flowchart of a random forest model constructed in one embodiment of the present invention.

[0019] Figure 3 A flowchart of ARIMA model construction in one embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0021] See also Figure 1-Figure 3 The present application provides a fault automatic switching line system, including a fault monitoring module, a load analysis module, a standby automatic switching module and an adaptive control module, wherein the fault monitoring module, the load analysis module, the standby automatic switching module and the adaptive control module are communicatively connected, wherein: The fault monitoring module is used to monitor the fault tripping signal of the working power supply and determine the position of the power-off line according to the fault tripping signal; In this embodiment, the fault monitoring module is a critical part of the fault automatic switching line system. It mainly undertakes two important tasks. The first is to closely monitor the fault tripping signal of the working power supply. During the normal operation of the working power supply, various sensors and monitoring instruments connected to the power supply and its protection device are used to always pay attention to whether there is a fault tripping signal caused by the action of the protection device due to line short circuit, overload, equipment failure, etc. Once such a signal is captured, the next step is immediately entered. The second is to accurately determine the location of the power-off line based on the monitored situation. In view of the fact that there may be multiple lines derived from the working power supply in the power system, it relies on a clear understanding of the system topology, and combines the information such as the changes in the relevant parameters of each line at the moment of the fault. Through analysis and comparison, it accurately locks which line is out of power due to the fault tripping, thereby providing a key basis for the subsequent system to carry out targeted standby automatic action and other processing, and ensure that the power supply of the corresponding load can be quickly restored.

[0022] Further, in the fault monitoring module, the monitoring of the fault tripping signal of the working power supply and determining the power-off line position according to the fault tripping signal include the following steps: Establish monitoring connection: First, make correct electrical and communication connections between the fault monitoring module and the working power supply and its related protection devices. Through appropriate signal lines, data lines, etc., ensure that various signals from the working power supply output and the protection device can be obtained, laying the foundation for subsequent monitoring work.

[0023] Configure sensor network: Install various types of sensors, including current transformers, voltage transformers, temperature sensors, etc., at key line nodes of the working power supply, such as the incoming and outgoing terminals. These sensors are used to collect parameter information such as current, voltage, temperature, etc. on the line in real time, so as to monitor the operating status of the working power supply from multiple angles, because the occurrence of faults is often accompanied by abnormal changes in these parameters, and these changes may trigger fault trip signals.

[0024] Real-time signal monitoring: Start the monitoring program, and the fault monitoring module begins to receive various signal data from sensors and protection devices in real time. The data is continuously analyzed and processed, focusing on signal characteristics related to fault tripping, such as sudden increase in current (which may indicate a short circuit fault) and sudden drop in voltage (which may be caused by line disconnection or overload). Once a signal change that meets the preset fault tripping conditions is found, it is determined that a fault tripping signal has been detected.

[0025] Signal verification and confirmation: After initially monitoring a possible fault trip signal, the fault monitoring module will further verify the signal to avoid misjudgment. It may refer to the changes in other related parameters at the same time, or compare and analyze with historical normal operation data. For example, if the current only increases momentarily, but then quickly returns to normal, and other parameters do not show obvious abnormalities, then it may not be a real fault trip signal, but a temporary interference. Only after multiple verifications and confirmations are made, it is finally determined that a valid fault trip signal has been monitored.

[0026] Collect line parameter change data: At the moment of detecting the fault trip signal, the fault monitoring module will immediately collect the change data of the relevant parameters of each line (all lines derived from the working power supply) at this time, including but not limited to current, voltage, power factor, etc. Because different lines will have different changes in their own parameters when the working power trips due to a fault, these change data will become an important basis for determining the location of the power-off line.

[0027] Comparative analysis and judgment: The collected parameter change data of each line at the moment of fault are compared and analyzed with the parameter templates of each line during normal operation and the fault feature templates pre-stored in the module. For example, if the current of a certain line drops to zero and the voltage also drops sharply at the moment of fault tripping, and the parameter changes of other lines do not meet this characteristic, then it is very likely that this line is the power-off line. Through this detailed comparative analysis and logical judgment, combined with the understanding of the topological structure of the entire power system (the connection relationship and direction of each line, etc.), the location of the power-off line can be accurately determined.

[0028] Generate fault report: Once the location of the power outage line is determined, the fault monitoring module will generate a detailed fault report based on the detected fault tripping signal and the entire process of determining the location of the power outage line. The report includes key information such as the time when the fault occurred, the specific characteristics of the fault tripping signal, and the number or identification of the power outage line.

[0029] Transmit fault information: The generated fault report is transmitted to other related modules in the system in a timely manner, such as the load analysis module, the standby automatic switching module, the adaptive control module, etc., so that these modules can quickly carry out their respective work according to the received fault information. For example, the load analysis module can evaluate the carrying capacity of the standby line based on this, and the standby automatic switching module can decide whether and how to perform the standby automatic switching action, etc., so as to ensure that the entire fault automatic switching line system can operate efficiently and harmoniously.

[0030] The load analysis module is used to evaluate the carrying capacity of the standby line to be switched and predict the incoming line load after switching; In this embodiment, the load analysis module is mainly responsible for two important tasks. The first is to evaluate the carrying capacity of the backup line to be switched. To this end, it will comprehensively collect the design parameters of the backup line, such as material, cross-sectional area, length, rated voltage, current, etc., while considering the operating environment factors of the line, such as temperature, electromagnetic interference, special area conditions, etc. And combined with the current, voltage and other operating parameters obtained in real time by sensors, professional calculation and analysis models are used for dynamic evaluation to accurately determine the actual carrying capacity of the backup line at the moment.

[0031] The second is to predict the incoming load after the switch. This requires a detailed analysis of the existing load distribution of the entire station, and the specific usage and overall structure of different types of loads such as power, lighting, and electric heating. It is also necessary to combine the specific strategy of the backup automatic investment module and make an estimate based on the different line switching methods it may take. At the same time, fully consider the trend of the entire station load changing over time, production activities, and residents' living habits, and use statistical analysis methods and prediction models to accurately predict the incoming load entering the backup line after the switch, thereby providing strong support for the reasonable operation of the system and effective decision-making.

[0032] Furthermore, in the load analysis module, the evaluation of the carrying capacity of the standby line to be switched includes the following steps: S1. Obtain basic information of the backup line; This is the first step in the assessment, which aims to comprehensively collect various key parameters of the backup line, which lay the foundation for the subsequent accurate assessment of its carrying capacity.

[0033] Basic parameters: The line material (such as copper, aluminum, etc.) determines the conductivity of the line. Different materials have different resistivities, which will affect the resistance when current passes through. The cross-sectional area is directly related to the amount of current that the line can allow to pass through. Generally, the larger the cross-sectional area, the larger the current that can be carried. The line length cannot be ignored. Longer lines will cause more loss of electricity during transmission due to the cumulative effect of resistance, which will in turn affect the carrying capacity.

[0034] Rated parameters: The rated voltage specifies the normal working voltage value adapted during line design, and is the voltage standard to ensure the normal operation of the line and connected equipment; the rated current limits the maximum current value that can pass through the line under a safe state. Exceeding this value may cause problems such as line overheating and damage; the rated power comprehensively reflects the maximum power that the line can transmit under the conditions of rated voltage and rated current, and is one of the important indicators for measuring the line's carrying capacity.

[0035] S2. Identify the environmental factors of the backup line; In addition to the basic information of the line itself, the environmental factors in which it is located also have an important impact on the carrying capacity and need to be accurately identified and included in the evaluation.

[0036] Temperature: The ambient temperature will change the resistance characteristics of the circuit material. For example, when the temperature rises, the resistance of the metal material will usually increase, which means that at the same voltage, the current that can pass through the circuit will decrease, thereby reducing the carrying capacity of the circuit.

[0037] Humidity: A high humidity environment may affect the insulation performance of the line, making the line more susceptible to leakage, which will not only cause power loss, but may also cause safety hazards and indirectly affect the line's ability to carry loads normally.

[0038] Electromagnetic interference: If there is strong electromagnetic interference around the backup line, it may interfere with the transmission stability of the electrical signal in the line, resulting in signal distortion, reduced transmission efficiency and other problems, which will adversely affect the line's ability to carry loads.

[0039] S3. Training carrying capacity analysis model In this step, the acquired basic information of the backup line and the identified environmental factors are used as independent variables, and the carrying capacity of the backup line (including the maximum carrying current and the maximum carrying voltage) is used as the dependent variable to train a special carrying capacity analysis model.

[0040] Model construction basis: Through a large amount of experimental data, actual operation data and theoretical analysis results, the inherent logical relationship model between basic line information, environmental factors and carrying capacity is established. For example, based on the actual carrying capacity test data of lines of different materials, different cross-sectional areas and different lengths under various temperature, humidity and electromagnetic interference environments, it is determined how these factors interact with each other and ultimately affect the maximum carrying current and maximum carrying voltage of the line.

[0041] Purpose of model training: By continuously inputting different combinations of independent variable data (i.e., the combination of various basic line information and environmental factors) and corresponding dependent variable data (actual carrying capacity data), the model can learn and master the rules between these data, so that it can accurately predict the corresponding backup line carrying capacity based on the new input basic line information and environmental factors.

[0042] S4. Input the basic information of the standby line to be switched and the environmental factors thereof into the carrying capacity analysis model, and output the carrying capacity of the standby line to be switched.

[0043] This is the last step of the evaluation, which is to accurately input the specific basic information of the backup line to be switched and the actual environmental factors data into the trained carrying capacity analysis model.

[0044] Data input requirements: Ensure that the input data is complete, accurate, and matches the data format and type used during model training. For example, if the temperature data during model training is in degrees Celsius, the temperature data input for the backup line to be switched should also be in the same unit.

[0045] Interpretation of output results: Based on the input data and the rules it has learned, the model will quickly output the carrying capacity of the backup line to be switched, specifically the values ​​of the maximum carrying current and the maximum carrying voltage. These output results will directly provide a key basis for subsequent judgments on whether the backup line can meet the load demand after switching, and help the system reasonably arrange line switching and load distribution operations to ensure the stable operation of the entire power supply system.

[0046] Furthermore, the carrying capacity analysis model is configured as a random forest model, including the following construction steps: 1. Data collection and preprocessing stage Collect sample data: Collect a large amount of sample data about backup lines from multiple existing backup line-related data sets and actual operation records. These data should cover basic information such as different line materials, cross-sectional areas, lengths, rated voltages, rated currents, and rated powers, as well as actual carrying capacity data under corresponding environmental factors such as temperature, humidity, and electromagnetic interference, including specific values ​​of maximum carrying current and maximum carrying voltage. Ensure that the collected data is representative and diverse, and can reflect various possible line conditions and environmental conditions, so as to provide sufficient basic data for building an accurate random forest model.

[0047] Data cleaning: Check the integrity of the collected sample data and remove data records with missing values, erroneous values, or data that are obviously inconsistent with the actual situation. For example, if the cross-sectional area value of a line in a record is negative, this is obviously inconsistent with physical reality and such data should be deleted. In the case of a small number of missing values, it can be processed according to other relevant data features or using appropriate filling methods (such as mean filling, median filling, etc.) to ensure the integrity and availability of the data.

[0048] Data standardization: Since the data value ranges of different features may vary greatly, for example, the route length may be in kilometers, while the temperature is in degrees Celsius, in order to avoid some features having a dominant influence on the model due to their large value range during the subsequent model training process, all data need to be standardized. Commonly used standardization methods include Z-score standardization, which is to subtract the mean of each feature from the value of the feature, and then divide it by the standard deviation of the feature, so that the data of all features are converted into a standard normal distribution with a mean of 0 and a standard deviation of 1, so that different features can be treated equally in model training.

[0049] 2. Feature Selection and Extraction Stage Determine relevant features: Based on the prior knowledge of the impact on the backup line's carrying capacity and the results of data analysis, determine which features to select from the collected data as input features for the random forest model. In this case, line material, cross-sectional area, length, rated voltage, rated current, rated power, temperature, humidity, electromagnetic interference, etc. are clearly selected as relevant features because these features have been proven to have a significant impact on the backup line's carrying capacity.

[0050] Feature encoding: For some non-numerical features, such as line material (which may be copper, aluminum, etc.), encoding is required to convert it into numerical form so that the model can process it. For example, one-hot encoding can be used to encode line material copper as [1, 0], line material aluminum as [0, 1], etc., so that different material categories can participate in model training in the form of appropriate numerical values.

[0051] 3. Model parameter setting stage Determine the number of decision trees: The random forest model is an integrated model composed of multiple decision trees. First, you need to determine the number of decision trees included in the model. Too few decision trees may lead to insufficient generalization of the model and fail to accurately capture the complex relationships in the data; too many decision trees may lead to overfitting, making the model perform well on the training data but poorly on new data.

[0052] Usually, the appropriate number of decision trees can be determined through methods such as cross-validation. For example, starting from a small initial value (such as 10 trees), gradually increase the number of decision trees, and observe the performance indicators of the model on the validation data set (such as mean square error, accuracy, etc.). When the performance indicators no longer show significant improvement, the corresponding number of decision trees is a more appropriate value.

[0053] Set other parameters: In addition to the number of decision trees, you also need to set some other related parameters, such as the maximum depth of each decision tree, the minimum number of samples based on which nodes are split, etc. The maximum depth of each decision tree determines the growth of the decision tree. Too deep a decision tree may lead to overfitting. Generally, you can try different depth values ​​(such as 3, 5, 7, etc.) and determine the appropriate depth based on the performance of the model on the validation data set. The minimum number of samples based on which nodes are split controls the growth speed and complexity of the decision tree. A smaller minimum number of samples may cause the decision tree to be too complex, while a larger minimum number of samples may cause the decision tree to grow slowly. It is also necessary to determine the appropriate value based on the actual situation and the performance on the validation data set.

[0054] 4. Model training phase Divide the training set, validation set, and test set: Divide the preprocessed sample data into training set, validation set, and test set according to a certain ratio (such as the common 7:2:1 or 8:1:1, etc.). The training set is used to train the random forest model so that the model can learn the rules and feature relationships in the data; the validation set is used to monitor and adjust the performance of the model during the training process, and optimize the parameters of the model according to the performance indicators on the validation set; the test set is used to evaluate the final performance of the model after the model training is completed to verify whether the model can accurately predict new data.

[0055] Training the random forest model: Use the divided training set to train the random forest model. During the training process, each decision tree will grow independently based on the data in the training set, analyze and judge the input features according to the set parameters (such as node splitting basis, maximum depth, etc.), gradually form their own decision paths, and finally build a random forest model composed of multiple decision trees.

[0056] During the training process, we should pay close attention to the performance indicators of the model on the validation set, such as mean square error, accuracy, etc., and adjust the model parameters (such as the number of decision trees, maximum depth, etc.) in time according to the changes in these indicators to improve the performance and generalization ability of the model.

[0057] 5. Model evaluation and optimization stage Evaluate model performance: Use the test set to evaluate the performance of the trained random forest model. Calculate various performance indicators of the model on the test set, such as mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R²). The mean square error measures the average of the squared difference between the model's predicted value and the true value. The smaller the value, the more accurate the model prediction; the mean absolute error measures the average of the absolute value between the model's predicted value and the true value. The smaller the value, the better; the coefficient of determination reflects the proportion of the model's prediction results that can explain the variation in the real data. The value range is between 0 and 1. The closer it is to 1, the better the model's prediction effect.

[0058] Optimize the model: If the performance indicators of the model on the test set are not ideal, the model needs to be optimized. Possible optimization measures include: increasing the amount of training data, re-adjusting model parameters (such as the number of decision trees, maximum depth, etc.), trying different feature combinations or extracting new features, etc. Through continuous evaluation and optimization, the random forest model can achieve better performance, accurately predict its carrying capacity based on the input basic information and environmental factors of the backup line, and provide a reliable basis for line switching decisions in the fault automatic switching line system.

[0059] Furthermore, in the load analysis module, the predicting of the incoming line load after switching includes the following steps: Obtain load distribution based on the switching conditions of all load types in the power station; Load type analysis: The area served by the power station usually covers many different types of loads, such as industrial loads (various production equipment), commercial loads (power equipment in shopping malls, office buildings, etc.), residential loads (household appliances, etc.), etc. The power consumption characteristics and patterns of each load type are different. Industrial loads may consume a large amount of electricity during the daytime production period on weekdays, and the commercial loads have a relatively stable power demand during business hours. The residential loads will show morning and evening peaks and other patterns according to the daily life of residents.

[0060] Switch status monitoring: By monitoring the switch status (on or off) of each load type, you can know in real time which loads are using electricity and which are idle. For example, in a factory, by monitoring the switch status of each production workshop equipment, you can clearly know the number and type of equipment currently in operation, and then infer the scale of power consumption of industrial loads; for residential communities, monitoring the opening and closing status of the switches in each household's meter box can roughly grasp the power consumption of residents' loads.

[0061] Determine load distribution: By combining the monitoring results of the switching conditions of all load types, the current load distribution of the power station can be accurately obtained. That is, it is clear how much electricity different areas and different types of loads consume at the current moment, and their proportion in the total load, thus providing a basic data based on the current actual power consumption for subsequent prediction of incoming line load.

[0062] Acquire current time regularity characteristics, wherein the time regularity characteristics include production activity characteristics and resident life characteristics; Characteristics of production activities: For loads involving production and business activities such as industry and commerce, it is necessary to have a deep understanding of the time patterns of their production activities. For example, a manufacturing plant may implement a shift system of day shift, mid-shift, and night shift. The production intensity and equipment opening conditions of different shifts are different. The day shift is usually the busiest period of production activities and the equipment is fully open, and the power consumption will reach a peak; while some service-oriented enterprises may only operate during specific time periods on weekdays, and their power demand also shows corresponding time patterns. By analyzing the time arrangements, shift rotations, seasonal production adjustments and other characteristics of these production activities, it is possible to grasp the influence of production activities on the load of power stations.

[0063] Residents’ life characteristics: The electricity consumption patterns of residents’ lives also have an important impact on the load of power stations. Generally speaking, there is a small peak in residents’ electricity consumption after getting up in the morning, which is used for preparing breakfast, washing and other activities; there is a large peak in electricity consumption from dusk to night, when people go home from get off work and turn on various electrical appliances such as lighting, televisions, air conditioners, and water heaters; the electricity consumption on weekends and weekdays may also be different. On weekends, because people spend more time at home, electrical appliances are used more frequently, and the load will change relative to weekdays. Analyzing the daily routines of these residents’ lives, the differences between weekends and weekdays, and other characteristics will help to fully understand the impact of residents’ lives on the load of power stations.

[0064] Integration of overall time regularity characteristics: Integrate various time regularity characteristics such as production activity characteristics and residents' life characteristics to form a complete description of the current time regularity. This description can reflect the power consumption change pattern of different types of loads in the service area of ​​the entire power station based on time factors during the current time period, providing important input information related to the time dimension for subsequent prediction using time series models.

[0065] Input the load distribution and time regularity characteristics into a preset time series model to predict the incoming load sequence data of the standby line in a future period of time; Principle of time series model: The time series model is a statistical model that predicts future data based on historical data and time sequence. It assumes that data has certain regularity and correlation in time, and infers future development trends by analyzing past data patterns. In this scenario, using the acquired load distribution (based on the current actual power consumption) and time regularity characteristics (reflecting the time-based power consumption changes of different loads) as input, the time series model can dig out the time series relationship hidden behind these data, and then predict the incoming load of the backup line in the future.

[0066] Prediction process: The integrated load distribution and time law characteristic data are accurately input into the preset time series model. The model will analyze and process the input data according to the algorithm and parameters set internally. For example, it may identify the changing trend of incoming line load in the future under similar load distribution and time law in the past, and make predictions based on this. The model will output the incoming line load sequence data of the backup line in the future (such as the next hour, day or week, etc., the specific time span can be set according to actual needs). This sequence data presents the expected incoming line load of the backup line at different times in the future.

[0067] Obtain statistical information of the incoming line load sequence data, wherein the statistical information includes a maximum value, a minimum value, an average value, and a standard deviation.

[0068] Maximum and minimum values: Obtaining the maximum and minimum values ​​in the incoming load sequence data can intuitively understand the extreme conditions that may occur in the incoming load of the backup line in the future. The maximum value represents the highest load that may occur in the forecast period, which is crucial for judging whether the backup line can withstand such a high load; the minimum value reflects the lowest possible load, which helps to understand the operation of the backup line during the period of light load, and can also be used to analyze the fluctuation range of the load.

[0069] Average value: By calculating the average value of the incoming load sequence data, we can get the average level of the incoming load of the backup line in the future. The average value is an indicator that comprehensively reflects the overall situation of future load. It can give us a more balanced understanding of the load bearing pressure of the future backup line, and help us consider the average load situation in system planning and decision-making, so as to reasonably arrange line switching and other operations.

[0070] Standard Deviation: Standard deviation measures the degree of dispersion of incoming load sequence data relative to the mean value. A larger standard deviation indicates that the load will fluctuate greatly in the future, and there may be some sudden high or low load conditions, which requires attention during system design and operation. For example, additional measures may be needed to cope with large load fluctuations to ensure the stable operation of the backup line. By obtaining these statistical information, we can have a more comprehensive and in-depth understanding of the future incoming load of the backup line, and provide a more adequate decision-making basis for the effective operation of the fault automatic switching line system.

[0071] Furthermore, the time series model is configured as an ARIMA (autoregressive moving average) model, comprising the following steps: 1. Data Collection and Preparation Collecting historical data: Collect historical data on incoming load and related load distribution, time regularity characteristics, etc. from the power station monitoring system, meter records and other relevant data sources. These data should cover a sufficiently long period of time to reflect the load changes in different seasons, working days and rest days, different production activity cycles, etc. For example, collect hourly or daily incoming load data for the past year or even several years, as well as the load distribution at the corresponding time (such as the proportion of electricity consumption of various types of loads) and time regularity characteristics (production activity arrangements, residents' daily routines, etc.).

[0072] Data cleaning and preprocessing: Perform integrity checks on the collected historical data and remove data records with missing values, erroneous values, or data that are obviously inconsistent with the actual situation. For a small number of missing values, appropriate filling methods can be used, such as mean filling, median filling, or interpolation filling based on previous and next data, to ensure data integrity.

[0073] Standardize the data to make the data value ranges of different features comparable. Common standardization methods include Z-score standardization, which subtracts the mean of each feature from the value of the feature and then divides it by the standard deviation of the feature to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1, so that all data can be treated equally in subsequent model training to avoid affecting model performance due to large differences in data magnitude.

[0074] 2. Data Stationarity Test Phase Check data stationarity: Use statistical test methods, such as ADF (Augmented Dickey-Fuller) test, to test the stability of the pre-processed historical data of incoming line load. Stability refers to the property that the statistical characteristics of the data (such as mean, variance, etc.) do not change over time. If the data is not stable, directly applying the ARIMA model may lead to inaccurate prediction results.

[0075] The ADF test determines whether the data is stationary by calculating the test statistic and comparing it with the corresponding critical value. If the test statistic is less than the critical value, the null hypothesis is rejected, that is, the data is considered to be stationary; conversely, if the test statistic is greater than the critical value, the null hypothesis is accepted, indicating that the data is non-stationary.

[0076] Data differential processing: If the ADF test finds that the incoming load data is non-stationary, the data needs to be differentiated to make it stable. Differentiation is to subtract the data of the previous moment from the current data, and perform multiple differential operations (first-order difference, second-order difference, etc.) until the data passes the stationarity test.

[0077] 3. Model Ordering Stage Determine the autoregressive order (p): The autoregressive order can be preliminarily determined by observing the autocorrelation function (ACF) and partial autocorrelation function (PACF) plots. The autocorrelation function describes the correlation between a time series and its own lagged values, while the partial autocorrelation function describes the correlation between a time series and its own lagged values ​​after controlling for the influence of the intermediate lagged values.

[0078] Generally speaking, the autocorrelation function gradually decays to zero after a certain number of lags, while the partial autocorrelation function suddenly cuts off or decays to zero quickly after a certain number of lags. Based on these characteristics, the possible range of values ​​of the autoregressive order can be preliminarily determined. For example, if the ACF graph shows a trend of slow decay, and the PACF graph suddenly cuts off after lag 3, then you can preliminarily consider setting it to around 3.

[0079] Determine the moving average order (q): The ACF and PACF graphs are also used to determine the moving average order. The moving average model fits time series data by moving the error term, and the method for determining its order is similar to that of the autoregressive order.

[0080] Observe the decay law of the data in the ACF and PACF diagrams, and combine the understanding of the characteristics of the incoming load data to determine the possible range of values ​​of the moving average order. For example, if the ACF diagram shows a trend of rapid decay after lag 2, and the PACF diagram also changes significantly after lag 2, then you can consider setting it to around 2.

[0081] Comprehensively determine the model order: After preliminarily determining the possible range of values ​​for the autoregressive order and the moving average order, we try different combinations and use the validation data set to evaluate the ARIMA model under different combinations.

[0082] 4. Model training phase Divide into training set and test set: The historical data of incoming load that has been preprocessed and whose stability and model order have been determined are divided into a training set and a test set according to a certain ratio (such as the common 7:3 or 8:2, etc.). The training set is used to train the ARIMA model so that the model can learn the laws and characteristic relationships in the data; the test set is used to evaluate the final performance of the model after the model training is completed to verify whether the model can accurately predict new data.

[0083] Train the ARIMA model: Use the divided training set to train the ARIMA model. According to the determined autoregressive order, moving average order, and difference order (if difference processing is performed), the data in the training set is input into the ARIMA model and trained according to the model algorithm and parameter settings. During the training process, the model will adjust the parameters such as the autoregressive coefficient and the moving average coefficient to minimize the error between the predicted value and the true value, thereby learning the inherent laws and changing trends of the incoming line load data.

[0084] 5. Model evaluation and optimization stage Evaluate model performance: Use the test set to evaluate the performance of the trained ARIMA model. Calculate various performance indicators of the model on the test set, such as mean square error (MSE), mean absolute error (MAE), coefficient of determination (R²), etc.

[0085] Optimizing the model: If the performance of the model on the test set is not ideal, the model needs to be optimized. Possible optimization measures include: increasing the amount of training data, re-adjusting the model order (such as re-determining the autoregressive order and the moving average order), trying different difference processing methods (if the data requires difference), or further feature extraction and processing of the data.

[0086] Through continuous evaluation and optimization, the ARIMA model can achieve better performance and accurately predict the incoming load of the standby line in the future based on the input load distribution and time law characteristics, providing a reliable basis for the effective operation of the fault automatic switching line system.

[0087] The standby automatic switching module is used to perform standby automatic switching action according to the standby line carrying capacity and incoming line load; In this embodiment, the standby automatic switching module plays a key role in the fault automatic switching line system. It mainly works based on the standby line carrying capacity provided by the load analysis module (covering indicators such as maximum carrying current and maximum carrying voltage, and clarifying the limit of the electric load that the line can bear) and the predicted incoming line load.

[0088] First, the backup automatic switching module fully grasps the load carrying capacity of the backup line and determines the load range it can accept based on this. At the same time, it receives and understands the incoming line load, which is affected by multiple factors such as the load distribution of the entire station and the law of power consumption, and is a dynamic value.

[0089] Next, compare and analyze the incoming load and the backup line carrying capacity. If the incoming load is within the acceptable range, the backup automatic switching action is feasible from the load bearing perspective; otherwise, the switching strategy needs to be reconsidered, such as adjusting the load distribution, to ensure that the carrying capacity requirements are met.

[0090] Finally, when the conditions for the backup automatic switching action are met, that is, the incoming load is appropriate and the line is fault-free and connected normally, the module will trigger the action to execute operations such as closing the section switch and disconnecting the connection with the faulty working power supply, so as to achieve fast and accurate switching of the load from the faulty working power supply line to the backup line, ensure the continuity and stability of the power supply, and reduce the impact of power outages.

[0091] Furthermore, in the standby automatic switching module, the standby automatic switching action is performed according to the standby line carrying capacity and the incoming line load, including the following steps: Compare the load difference between the backup line carrying capacity and the incoming line load (taking the average value); In the operation of the backup automatic switching module, the first key step is to compare the load difference between the backup line carrying capacity and the incoming line load. Here, the average value of the incoming line load is mentioned because the incoming line load may be a value that changes dynamically over time. Taking its average value can comprehensively reflect the approximate load situation that will be switched to the backup line to a certain extent.

[0092] By comparing the load capacity indicators of the backup line (such as the maximum load current, the maximum load voltage corresponding to the maximum load power, etc.) with the average value of the incoming line load, the specific load difference between the two is calculated. This difference value can intuitively show the margin of the backup line when taking on the incoming line load or whether there is an overload risk. For example, if the maximum power that the backup line can carry is 1,000 kilowatts, and the average incoming line load is 800 kilowatts, then the load difference is 200 kilowatts, which shows that the backup line has a certain load margin in theory.

[0093] When the load difference exceeds a preset load difference threshold, the standby automatic switching action is suspended; After the load difference value is obtained, it will be compared with the preset load difference threshold. This preset load difference threshold is a key indicator set based on factors such as the system's safe operation requirements, line performance characteristics, and past experience.

[0094] When the load difference exceeds the threshold, it means that the incoming load is either too high relative to the carrying capacity of the backup line, which may cause the backup line to be overloaded, or too low, which may cause some power quality problems such as power factor (for example, the power factor may decrease when lightly loaded, affecting the efficiency of the power grid). In order to avoid these potential risks from causing adverse effects on the system, such as damage to the backup line and unstable power supply, the backup automatic transfer module will decisively suspend the backup automatic transfer action at this time. After the action is suspended, the system may further analyze the reasons, such as re-evaluating the prediction accuracy of the incoming load, checking the actual status of the backup line, etc., so as to take appropriate measures to adjust this unbalanced situation in the future.

[0095] When the load difference does not exceed a preset load difference threshold, the corresponding section switch is closed to connect the backup power supply from the failed working power supply line to the backup line.

[0096] On the contrary, when it is found through comparison that the load difference does not exceed the preset load difference threshold, it means that from the perspective of load bearing, the current backup line has sufficient capacity to undertake the incoming load that is about to be switched, and this acceptance is within the reasonable range set by the system and will not cause the various risks mentioned above.

[0097] In this case, the standby automatic transfer module will close the corresponding section switches according to the established procedures. The operation of these section switches is the key link to accurately and quickly connect the backup power supply from the faulty working power supply line to the backup line. By closing these switches, the load originally powered by the faulty working power supply can be successfully transferred to the backup line, and the backup power supply continues to provide power to it, thereby ensuring the continuity of power supply and minimizing the power outage time and impact on electrical equipment caused by the working power failure. Furthermore, the load difference is calculated as follows: , In the formula, D e Indicates load difference; C e Indicates the carrying capacity of the backup line; J e Indicates the incoming line load.

[0098] The adaptive control module is used to automatically adjust the line access mode and load distribution strategy according to the line parameters and the load situation of the entire station monitored in real time after the standby automatic switching action.

[0099] In this embodiment, the adaptive control module works according to the real-time monitoring information after the standby automatic switching action is completed. On the one hand, it pays close attention to the line parameters, such as voltage, current, resistance, reactance, power factor, etc., to understand the actual operating status of each line, such as voltage can reflect the power supply level, current reflects the load level, etc. On the other hand, it comprehensively grasps the load situation of the whole station, covering the total load, distribution, nature and change trend, etc., and clearly understands the scale of electricity consumption, whether it is overloaded or unbalanced, the characteristics of different types of loads, and the dynamic change law of loads.

[0100] Based on these real-time monitoring conditions, the module will automatically adjust the line access method and load distribution strategy. In terms of access method, if a backup line has abnormalities such as large voltage fluctuations, its access point will be changed or the connection order will be adjusted to ensure stable power supply. In terms of load distribution strategy, when the total load of the entire station increases or a local overload occurs, part of the load will be transferred to achieve balanced distribution; if the power supply quality of sensitive load lines is poor, the load distribution of other lines will be adjusted to give priority to its power supply, thereby ensuring the stable operation of the power system, improving the efficiency of power utilization and meeting the power supply needs of different loads.

[0101] Furthermore, when the line load exceeds a preset load threshold, the adaptive control module reduces the incoming line load by adjusting the transformer tap or switching on the reactive compensation device.

[0102] In this embodiment, during the operation of the adaptive control module, when it is monitored that the line load exceeds the preset load threshold, it indicates that the current line load has reached a critical state that requires intervention and regulation. The preset load threshold is a key indicator set based on factors such as the design carrying capacity of the line, past operating experience, and the requirements for ensuring the stable operation of the power system. Once the line load exceeds the threshold, it may cause a series of problems such as line overload, excessive voltage fluctuations, and increased power loss, thereby affecting the normal power supply of the power system and the safe operation of electrical equipment.

[0103] (1) Adjust the transformer tap to reduce the incoming line load: Transformer tap principle: Transformer taps refer to adjustable connection points set on the transformer windings. By changing the position of the tap, the transformer transformation ratio can be changed, thereby affecting the output voltage and current. When the line load is too high, the adaptive control module can use this feature to make adjustments.

[0104] Adjustment process and effect: Specifically, when it is found that the line load exceeds the threshold, the module will issue a command to adjust the transformer tap. For example, if the transformer tap is adjusted in the direction of voltage reduction, the output voltage of the transformer will decrease. When the load resistance is relatively fixed, the voltage reduction will cause the current in the line to decrease, thereby reducing the incoming line load. This adjustment method can alleviate the overload pressure of the line to a certain extent, return the line load to a more reasonable range, and ensure the normal operation of the line.

[0105] (2) Switching on the reactive power compensation device reduces the incoming line load: Function of reactive power compensation device: Reactive power compensation device is mainly used to compensate reactive power in the power grid. In the power system, the existence of reactive power will lead to increased current, which will increase line loss and reduce power factor. Switching on and off reactive power compensation device can change the reactive power distribution in the power grid, thus affecting the load of the line.

[0106] Switching operation and impact: When the line load exceeds the load threshold, the adaptive control module will decide whether to switch the reactive compensation device and how to switch it according to the reactive power status of the current power grid. If the reactive power of the current power grid is insufficient, the module may switch on the reactive compensation device to increase the reactive power supply in the power grid, so that the power factor in the power grid is improved. When the voltage and active power are relatively fixed, the increase in power factor will lead to a decrease in current, thereby reducing the incoming load. Conversely, if there is an excess of reactive power in the power grid, the module may cut off some reactive compensation devices to optimize the reactive power distribution, thereby also achieving the purpose of reducing the incoming load.

[0107] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A fault automatic switching line system, characterized in that: It includes a fault monitoring module, a load analysis module, a standby automatic transfer module and an adaptive control module, wherein the fault monitoring module, the load analysis module, the standby automatic transfer module and the adaptive control module are communicatively connected, wherein: The fault monitoring module is used to monitor the fault tripping signal of the working power supply and determine the position of the power-off line according to the fault tripping signal; The load analysis module is used to evaluate the carrying capacity of the standby line to be switched and predict the incoming line load after switching, and includes the following steps: Obtain load distribution based on the switching conditions of all load types in the power station; Acquire current time regularity characteristics, wherein the time regularity characteristics include production activity characteristics and resident life characteristics; Input the load distribution and time regularity characteristics into a preset time series model to predict the incoming load sequence data of the standby line in a future period of time; Obtaining statistical information of the incoming line load sequence data, wherein the statistical information includes a maximum value, a minimum value, an average value, and a standard deviation; The standby automatic switching module is used to perform standby automatic switching actions according to the standby line carrying capacity and incoming line load; The adaptive control module is used to automatically adjust the line access mode and load distribution strategy according to the line parameters and the load situation of the entire station monitored in real time after the standby automatic switching action.

2. A fault automatic switching line system according to claim 1, characterized in that: In the fault monitoring module, the fault tripping signal of the monitoring working power supply is monitored, and the position of the power-off line is determined according to the fault tripping signal, including the following steps: Establish monitoring connection: Make correct electrical and communication connections between the fault monitoring module and the working power supply and its related protection devices; Configure a sensor network: install current transformers, voltage transformers and temperature sensors at key line nodes of the working power supply to collect line data in real time, including current, voltage and temperature data; Real-time signal monitoring: Receive line data from sensors and protection devices in real time. When a signal change in line data that meets the preset fault tripping conditions is found, it is determined that a fault tripping signal has been detected. Signal verification and confirmation: After a fault trip signal is initially monitored, the fault trip signal is further verified by referring to the changes in other line data at the same time; Collect line parameter change data: after detecting the fault trip signal, collect the change data of the relevant line data of each line; Comparative analysis and judgment: Compare and analyze the line data changes with the pre-stored normal operation data, and determine the location of the power outage line in combination with the power system topology; Generate fault report: Generate fault report based on the monitored fault trip signal and the process of determining the location of the power-off line.

3. A fault automatic switching line system according to claim 1, characterized in that: In the load analysis module, the evaluation of the carrying capacity of the standby line to be switched includes the following steps: S1. Obtain basic information of the standby line, wherein the basic information includes basic parameters and rated parameters; the basic parameters include line material, cross-sectional area and line length; the rated parameters include rated voltage, rated current and rated power; S2. Identify environmental factors of the backup line, where the environmental factors include temperature, humidity and electromagnetic interference; S3, using the acquired basic information of the backup line and environmental factors as independent variables and the carrying capacity of the backup line as the dependent variable, training the carrying capacity analysis model; S4. Input the basic information of the standby line to be switched and the environmental factors thereof into the carrying capacity analysis model, and output the carrying capacity of the standby line to be switched.

4. A fault automatic switching line system according to claim 3, characterized in that: The carrying capacity analysis model is configured as a random forest model and includes the following construction steps: Divide the preprocessed sample data into training set, validation set and test set. The training set is used to train the random forest model; the validation set is used to monitor and adjust the performance of the model during the training process, and optimize the parameters of the model according to the performance indicators on the validation set; the test set is used to evaluate the final performance of the model after the model training is completed, so as to verify whether the model can accurately predict new data; Training the random forest model: Use the divided training set to train the random forest model. During the training process, each decision tree grows independently based on the data in the training set, analyzes and judges the input features according to the set parameters, gradually forms their own decision paths, and finally constructs a random forest model composed of multiple decision trees; Evaluate model performance: Use the test set to evaluate the performance of the trained random forest model. The evaluation indicators include mean square error, mean absolute error, and determination coefficient.

5. A fault automatic switching line system according to claim 1, characterized in that: The time series model is configured as an ARIMA model, comprising the following steps: Collect historical data: collect historical data samples from relevant data sources of the power station, the historical data samples including incoming line load, load distribution and time regularity characteristics; Test data stability: Use ADF test to test the stability of the pre-processed historical data samples of incoming line load; Model order determination: Determine the autoregressive order and moving average order by observing the autocorrelation function graph and partial autocorrelation function graph; Training ARIMA model: The ARIMA model is trained according to the determined autoregressive order and moving average order. During the training process, the model adjusts the autoregressive coefficient and the moving average coefficient to minimize the error between the predicted value and the true value. Evaluate model performance: Use the test set to evaluate the performance of the trained ARIMA model.

6. A fault automatic switching line system according to claim 1, characterized in that: In the standby automatic switching module, the standby automatic switching action is performed according to the standby line carrying capacity and the incoming line load, including the following steps: Compare the load difference between the backup line carrying capacity and the incoming line load; When the load difference exceeds a preset load difference threshold, the standby automatic switching action is suspended; When the load difference does not exceed a preset load difference threshold, the corresponding section switch is closed to connect the backup power supply from the failed working power supply line to the backup line.

7. A fault automatic switching line system according to claim 6, characterized in that: The load difference is calculated as follows: ， In the formula, D e Indicates load difference; C e Indicates the carrying capacity of the backup line; J e Indicates the incoming line load.

8. The automatic fault switching line system according to claim 1, characterized in that: The adaptive control module reduces the incoming line load by adjusting the transformer tap or switching on and off the reactive compensation device when the line load exceeds a preset load threshold.

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