Bypass shelter and main power line intelligent switching method and system

Abstract

The invention relates to an intelligent switching method and system for a bypass shelter and a main power line, and the method comprises the steps: deploying a plurality of sensors to monitor the state of the main power line, enabling the plurality of sensors to carry out the periodic collection through the regular transmission of data, and obtaining the detection data of the main power line in a plurality of periods; taking the main power line detection data of the adjacent three periods as a group of real-time detection data, comparing the detection data of the middle period with the detection data of the previous period and the detection data of the next period in the group of real-time detection data, and performing detection data change analysis to obtain a real-time detection change analysis result; based on a time sequence analysis method, predicting virtual detection data of a next period according to a real-time detection change analysis result; and evaluating the state of the main power line according to the virtual detection data, and judging whether to execute the switching action of the main power line and the bypass shelter. According to the method, prediction can be carried out in advance before the power line is abnormal, a bypass switching decision is made, and the stability and safety of a power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power transportation, and in particular to a method and system for intelligently switching a bypass shelter and a main power line. Background Art

[0002] As the scale and complexity of power systems continue to expand, the stability and reliability of power lines are becoming increasingly important issues in power supply systems. Especially in large-scale power grids, main power lines often face risks such as failures, overloads, and short circuits. If these problems are not handled in a timely manner, they may cause large-scale power outages and even endanger the safety of the entire power grid.

[0003] Traditional power system monitoring methods usually rely on manual inspections, regular inspections or simple fault detection. These methods often fail to reflect the state changes of power lines in real time, and it is difficult to predict potential fault risks in a timely manner. Although modern power systems have gradually introduced sensors and intelligent monitoring equipment to monitor the state of power lines, many existing technologies still remain at the level of single data collection and fault alarm, making it difficult to achieve in-depth analysis and prediction of power line status.

[0004] At present, line switching control in power systems mainly relies on pre-set thresholds or simple fault judgment rules. When an abnormality such as overload or short circuit occurs in the line, the system will perform bypass switching according to the set rules. However, this method usually has the following problems: 1. Lack of accurate fault prediction: The existing system mainly relies on real-time detection data and lacks the ability to predict the future status of the line. It can only respond after a fault occurs, but cannot predict the potential fault risks of the power line.

[0005] 2. Over-reliance on manual operation: Many power systems still rely on humans to judge line status and perform switching manually, which increases the complexity of operation and the risk of human error.

[0006] 3. Lack of intelligent data analysis: Existing power system monitoring and management usually lacks comprehensive analysis and intelligent decision-making of multi-cycle data, and cannot efficiently discover trend changes in power lines. Summary of the invention

[0007] To this end, the technical problem to be solved by the present invention is to overcome the defects in the switching control of the main power line and the bypass cabin in the power system in the prior art. The present invention provides a method and system for intelligent switching of the bypass cabin and the main power line, which combines data collection by multiple sensors, time series analysis, and real-time data change detection and prediction technology. It can make advance predictions before abnormalities occur in the power line and automatically make bypass switching decisions, thereby significantly improving the stability and safety of the power system.

[0008] In order to solve the above technical problems, the present invention provides a method for intelligently switching between a bypass shelter and a main power line, comprising: Deploy multiple sensors to monitor the status of the main power line. The multiple sensors periodically collect data by sending data at regular intervals to obtain multiple cycles of main power line detection data; including: calculating a first change state, that is, the data change amount between an intermediate period and a previous period in the real-time detection data, and calculating a second change state, that is, the data change amount between an intermediate period and a subsequent period in the real-time detection data; determining a change trend according to the first change state and the second change state, including: a linear change trend, a fluctuation change trend, and a rate change trend; The detection data of the main power line of three adjacent cycles are taken as a group of real-time detection data, and the detection data of the middle cycle in the group of real-time detection data are compared with the detection data of the previous cycle and the next cycle, and the detection data change analysis is performed to obtain the real-time detection change analysis result; including: extracting the change trend from the real-time detection change analysis result; establishing an information database of the main power line, managing and storing the operation data information of the main power line from use to the occurrence of abnormalities; constructing a main power line prediction model based on the change trend and the information database of the main power line, which is used to predict the virtual detection data of the next cycle of the main power line; Based on the time series analysis method, the virtual detection data of the next cycle is predicted according to the real-time detection change analysis results; The status of the main power line is evaluated based on the virtual detection data, and it is determined whether to perform the switching action between the main power line and the bypass cabin.

[0009] In one embodiment of the present invention, establishing an information database of a main power line includes: Determine the data types that need to be entered into the information database, including: electrical parameters and environmental parameters of the main power line operation and abnormal events that affect the operation of the main power line; Select database technology based on data volume and query requirements, collect historical data information on the operation of the main power lines, and design corresponding table structures for different types of operation data, including field names, types, and constraints; Formulate data extraction strategies and support the extraction of data from the information database of the main power lines by setting filtering conditions for the prediction of the main power lines.

[0010] In one embodiment of the present invention, a main power line prediction model is constructed based on the change trend and the information database of the main power line, including: Extracting historical operation data matching the current detection cycle from the information database of the main power line; Perform deep learning analysis on the extracted historical performance data to identify typical trend patterns of performance changes in main power lines; Combine the deep learning analysis results with the current power line periodic detection change analysis results to establish a prediction model; The established prediction model is used to predict the future status of the main power lines, and the model is continuously optimized based on actual test results.

[0011] In one embodiment of the present invention, the sensor comprises: Current sensors for real-time monitoring of the current load of the main power line; Voltage sensor, used to monitor the voltage fluctuation of the main power line in real time; A temperature sensor for monitoring the temperature of the main power line and the temperature of the power equipment connected to the main power line; Insulation resistance sensor, used to detect the insulation resistance of the main power line; Ground resistance sensors are used to monitor the resistance of the grounding system in the main power line.

[0012] In one embodiment of the present invention, an abnormal threshold of the main power line is set, and when data from multiple sensors monitoring the main power line exceeds the set abnormal threshold, a switching action between the main power line and the bypass cabin is executed.

[0013] In one embodiment of the present invention, after switching to the bypass shelter, the main power line is repaired or replaced, and after determining that the main power line is restored, the switching action between the bypass shelter and the main power line is performed.

[0014] In one embodiment of the present invention, after switching to the bypass shelter, the method further includes: Deploy a variety of sensors to monitor the status of the bypass shelter. The sensors periodically collect data by sending data at regular intervals to obtain bypass shelter detection data for multiple periods. The bypass shelter detection data of three adjacent cycles are taken as a group of real-time detection data, and the detection data of the middle cycle in the group of real-time detection data are compared with the detection data of the previous cycle and the next cycle, and the detection data change analysis is performed to obtain the real-time detection change analysis results; Based on the time series analysis method, the virtual detection data of the next cycle is predicted according to the real-time detection change analysis results; The status of the bypass shelter is evaluated based on the virtual detection data, and it is determined whether to perform the switching action between the bypass shelter and the main power line.

[0015] In order to solve the above technical problems, the present invention also provides a bypass shelter and main power line intelligent switching system, comprising: Multi-sensor monitoring module: deploys multiple sensors to monitor the status of the main power line in real time. The sensors send data regularly to achieve periodic collection, thereby obtaining main power line detection data for multiple periods; Data processing module: responsible for receiving data from the multi-sensor monitoring module, and processing the main power line detection data of three adjacent cycles as a group of real-time detection data. The data processing module compares the detection data of the middle cycle with the detection data of the previous cycle and the next cycle in this group of real-time detection data, performs detection data change analysis, and obtains real-time detection change analysis results; Prediction model module: Based on the time series analysis method, the real-time detection change analysis results are used to predict the virtual detection data of the next cycle. The prediction model module has a built-in main power line prediction model, which can make accurate predictions based on historical data and current change trends; State evaluation and decision support module: using the virtual detection data provided by the prediction model module to evaluate the state of the main power line, and judging whether it is necessary to perform a switching action between the main power line and the bypass shelter according to the evaluation result; Switching control module: According to the decision of the state evaluation and decision support module, the switching control module will automatically perform corresponding operations to ensure the safe switching process from the main power line to the bypass cabin.

[0016] The above technical solution of the present invention has the following advantages compared with the prior art: Compared with the prior art, the intelligent switching method between the bypass shelter and the main power line of the present invention has the following advantages: 1. The existing system mainly relies on real-time detection data and lacks the ability to predict the future state of the line. It can often only react after a fault occurs, but cannot predict the potential fault risk of the power line. This application uses a time series analysis method to predict the virtual detection data of the next cycle based on the real-time detection change analysis results, thereby realizing the prediction of the future state of the power line.

[0017] 2. Many power systems still rely on manual judgment of line status and manual switching, which increases the complexity of operation and the risk of human error. This application realizes automated status assessment and switching decisions through intelligent sensor monitoring and data analysis, reducing reliance on manual operation.

[0018] 3. Existing power system monitoring and management usually lacks comprehensive analysis and intelligent decision-making of multi-cycle data, and cannot efficiently discover trend changes in power lines; this application analyzes detection data of multiple cycles to obtain real-time detection change analysis results, and predicts virtual detection data of the next cycle based on time series analysis methods, thereby realizing in-depth analysis and intelligent decision-making of power line status. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein: Figure 1 It is a flow chart of the method for intelligently switching the bypass shelter and the main power line of the present invention; Figure 2 It is a structural block diagram of the bypass shelter and main power line intelligent switching system of the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0021] Existing power system monitoring methods mainly rely on manual inspections, regular inspections or simple fault detection. These methods often cannot reflect the status changes of power lines in real time, and it is difficult to predict potential fault risks in a timely manner. Although modern power systems have gradually introduced sensors and intelligent monitoring equipment to monitor the status of power lines, many existing technologies still remain at the single data collection and fault alarm level, making it difficult to achieve in-depth analysis and prediction of the power line status.

[0022] Existing line switching control mainly relies on pre-set thresholds or simple fault judgment rules. When the line is overloaded, short-circuited or other abnormalities occur, the system will perform bypass switching according to the set rules. However, this method lacks accurate fault prediction, is too dependent on manual operation, and lacks intelligent data analysis. Embodiment 1

[0023] In order to overcome the above limitations, refer to Figure 1 As shown, the present application proposes a method for intelligently switching between a bypass shelter and a main power line, comprising: When solving the problem of main power line status monitoring and intelligent switching, it is first necessary to deploy multiple sensors to monitor the status of the main power line. These sensors include current sensors, voltage sensors, temperature sensors, insulation resistance sensors, and ground resistance sensors. Multiple sensors send data regularly for periodic collection to obtain detection data for multiple cycles. The detection data of three adjacent cycles are taken as a set of real-time detection data. By comparing the detection data of the middle cycle with the detection data of the previous cycle and the next cycle, the detection data change analysis is performed to obtain the real-time detection change analysis results. Based on the time series analysis method, the virtual detection data of the next cycle is predicted according to the real-time detection change analysis results. The time series analysis method is a statistical method used to analyze and predict time series data. By analyzing historical data, future data change trends can be predicted; The status of the main power line is evaluated based on the virtual detection data to determine whether to perform the switching action between the main power line and the bypass cabin.

[0024] Through this method, the status of the main power line can be accurately evaluated and predicted, and it can be determined in time whether it is necessary to switch to the bypass cabin to ensure the stability and reliability of the power supply.

[0025] The method of the present application realizes accurate assessment and prediction of the status of the main power line through real-time monitoring and data analysis, and can timely determine whether it is necessary to switch to the bypass cabin to ensure the stability and reliability of power supply. Compared with the existing technology, the method of the present application has the following advantages: The present invention compares the detection data of adjacent cycles, analyzes the changes in the detection data, and predicts the virtual data of the next cycle in combination with the time series analysis; this method combining multi-cycle data comparison with time series prediction can provide more accurate judgment on the trend of changes in the state of the main power line.

[0026] In addition, the future state of the power line is predicted through the results of real-time change analysis, and it is judged whether a bypass cabin switching operation is needed. Different from the existing technology, this prediction method can respond to potential fault risks in advance, rather than waiting until the actual fault occurs before switching.

[0027] Furthermore, the present invention proposes a comprehensive evaluation mechanism from data collection to change analysis, then to state prediction, and finally to switching judgment; this complete closed-loop system not only improves the degree of automation of power line switching, but also improves its prediction accuracy and system stability.

[0028] Specifically, the present application also discloses a process of "comparing the detection data of the intermediate period in the group of real-time detection data with the detection data of the previous period and the next period, and performing detection data change analysis", including: calculating the first change state, that is, the data change amount between the intermediate period in the real-time detection data and the previous period, calculating the second change state, that is, the data change amount between the intermediate period in the real-time detection data and the next period; determining the change trend according to the first change state and the second change state, including: linear change trend, fluctuation change trend and rate change trend.

[0029] The present application compares the data of the intermediate cycle of real-time detection data with the data of the previous cycle and the next cycle, calculates the data change amount between the intermediate cycle and the previous cycle and the data change amount between the intermediate cycle and the next cycle, which are respectively called the first change state and the second change state; through these two change states, the change of data can be effectively captured, and the change trend can be determined according to the change state; the change trend includes linear change trend, fluctuating change trend and rate change trend; in this way, the state of the power line can be better predicted, thereby improving the reliability and stability of the power system.

[0030] Specifically, the first change state refers to the data change between the middle period and the previous period in the real-time detection data, which can be obtained by a simple subtraction operation. Similarly, the second change state refers to the data change between the middle period and the next period in the real-time detection data, which can also be obtained by a subtraction operation. These two change states can be expressed as Δ1 and Δ2 respectively. Based on these two change states, the change trend of the data can be determined.

[0031] For example: if Δ1 and Δ2 are both positive and close in value, it can be considered that the data presents a linear change trend; if Δ1 and Δ2 have different signs, there may be a fluctuating change trend; if the absolute value difference between Δ1 and Δ2 is large, there may be a rate change trend.

[0032] In actual operation, in order to better understand and explain these changing trends, you can use chart tools to visualize the data, for example: draw a line graph to show the data points of each cycle and their changing trends, or use a bar chart or area chart to intuitively show the size of the change between different cycles.

[0033] Therefore, the present application can accurately capture the changes in data by comparing the data changes in the intermediate cycle with the previous and next cycles, and by determining the change trend, it can better predict the status of the power line; this method not only improves the accuracy of data analysis, but also can provide strong support for the maintenance and management of the power system, and solves the problem of lack of accurate fault prediction and intelligent data analysis in the prior art.

[0034] Apply these analysis results to the constructed prediction model: input the change trend as one of the features into the prediction model to help the model more accurately understand the changes that may occur in the future under the current state. The specific process is: based on the time series analysis method, predict the virtual detection data of the next cycle according to the real-time detection change analysis results, including: extracting the change trend from the real-time detection change analysis results; establishing an information database for the main power lines to manage and store the operating data information of the main power lines from use to the occurrence of abnormalities; constructing a main power line prediction model based on the change trend and the information database of the main power lines to predict the virtual detection data of the next cycle of the main power lines.

[0035] The present application can identify the change pattern of the main power line status by extracting the change trend from the real-time detection change analysis results; establish an information database of the main power line to manage and store the operating data information from use to the occurrence of an abnormality, providing rich historical data support for the prediction model; and construct a main power line prediction model based on the extracted change trend and information database to predict the virtual detection data of the next cycle.

[0036] Through these technical means, the problem of how to predict the virtual detection data of the next cycle based on the real-time detection change analysis results can be effectively solved. Compared with the existing technology, this application not only relies on real-time detection data, but also combines historical operation data and change trend analysis to improve the accuracy and reliability of the prediction model, thereby effectively reducing the probability of power line failures and improving the stability and reliability of the power system.

[0037] Furthermore, in order to realize the above process, the present invention also proposes a process of "establishing an information database of the main power line", including: determining the data type that needs to be entered into the information database, including electrical parameters, environmental parameters of the main power line operation, and abnormal events that affect the operation of the main power line; selecting appropriate database technology according to the data volume and query requirements, collecting historical data information of the main power line operation, and designing corresponding table structures for different types of operating data, including field names, types and constraints; formulating data extraction strategies, and supporting the extraction of data from the information database of the main power line by setting filtering conditions for the prediction of the main power line.

[0038] Among them, the data type is determined to ensure that the information database covers all necessary operating data, including the electrical parameters of the main power line (such as current, voltage, etc.), environmental parameters (such as temperature, humidity, etc.) and abnormal events that affect the operation of the main power line (such as faults, overloads, etc.). Appropriate data interfaces are developed or configured to realize automatic data collection and import, ensuring that all relevant sensors can be correctly connected to the information database and can transmit data in real time.

[0039] In actual operation, when choosing database technology, you can choose a relational database or a non-relational database based on the amount of data and query requirements; and use table structure design to design corresponding table structures for different types of operating data. The design of the table structure includes field names, types and constraints to ensure data standardization and integrity.

[0040] Formulate data extraction strategies and set filtering conditions to extract useful data from the information database. These data will be used to predict the status of the main power lines, further improving the accuracy and reliability of the prediction.

[0041] By establishing an information database of the main power lines, efficient management and storage of the main power line operation data can be achieved, and accurate prediction of the main power line status can be supported; compared with the prior art, the technical solution of the present application has the following advantages: first, determining the data type ensures that all necessary operation data are contained in the database, avoiding data omissions; second, selecting appropriate database technology and designing table structures ensure efficient management and query of data, and improves system performance; finally, formulating a data extraction strategy enables the extraction of required data from the database for status prediction, thereby improving the speed of extracting predicted data.

[0042] Furthermore, the present application also uses deep learning technology and historical data to build a model that can accurately predict the future status of the main power lines.

[0043] Specifically, historical operating data matching the current detection cycle are extracted from the information database of the main power line. These data include electrical parameters, environmental parameters, etc. of the power line. The time period of historical data that needs to be compared is determined according to the real-time detection cycle, and the extracted data under specific conditions is filtered: for example, only historical data under the same environmental conditions (such as similar temperature, humidity, etc.) or specific operating states (such as load level) are extracted.

[0044] Then, deep learning analysis is performed on these historical performance data to identify typical trend patterns of changes in the performance of the main power lines. As a preferred implementation method, deep learning analysis can adopt advanced machine learning algorithms such as convolutional neural networks (CNN) or long short-term memory networks (LSTM); these algorithms can process large amounts of historical data and extract key trend patterns from them.

[0045] By combining the results of deep learning analysis with the results of periodic detection changes in the current power lines, a prediction model is established. The prediction model can determine the state changes of the main power lines based on the results of historical data analysis, and adjust the model parameters to more accurately reflect the state changes of the power lines under various conditions, taking into account different usage conditions (such as ambient temperature, load conditions, etc.).

[0046] Use this prediction model to predict the future status of the main power lines. Input the latest real-time detection data into the prediction model to predict the operating status of the power lines within a certain period of time in the future. Continuously optimize the model based on actual test results, regularly collect new detection data and compare them with the results predicted by the model, adjust model parameters based on the differences, and improve prediction accuracy.

[0047] In order to further improve the accuracy of the model, more auxiliary data can be introduced, such as load operation data, load forecast data, etc. These data can provide more contextual information and help to more accurately predict the future status of power lines.

[0048] In this way, the present application can effectively solve the problem of inaccurate prediction of the future state of the main power line in the prior art; compared with the traditional method that relies on real-time detection data and simple fault judgment rules, the prediction model of the present application can predict the potential failure risks of the power line in advance, thereby improving the stability and reliability of the power system.

[0049] Specifically, in order to realize the detection of the main power line, the sensors provided include: a current sensor for real-time monitoring of the current load of the main power line; a voltage sensor for real-time monitoring of the voltage fluctuation of the main power line; a temperature sensor for monitoring the temperature of the main power line and the temperature of the power equipment connected to the main power line; an insulation resistance sensor for detecting the insulation resistance of the main power line; and a grounding resistance sensor for monitoring the resistance of the grounding system in the main power line.

[0050] By deploying a variety of sensors, the electrical parameters, environmental parameters and safety status of the main power lines can be fully and real-time acquired, thereby achieving accurate monitoring of the status of the main power lines. This provides basic data support for subsequent power line status assessment and intelligent switching, and solves the technical problems of real-time monitoring and accurate acquisition of power line status.

[0051] Specifically, the current sensor can adopt a Hall effect sensor or an electromagnetic sensor to ensure accurate monitoring within different current ranges; the voltage sensor can be a capacitive sensor or a resistive voltage divider sensor to accurately detect voltage fluctuations; the temperature sensor can adopt a thermocouple or thermistor to ensure sensitive response to temperature changes; the insulation resistance sensor can use a megohmmeter or a high resistance meter to ensure accurate detection of the insulation status; the ground resistance sensor can adopt a ground resistance tester to ensure the safety of the grounding system.

[0052] Taking into account the safety of use, the present invention also provides an emergency switching method, which is similar to the traditional line abnormality detection method, namely: setting an abnormal threshold of the main power line. When the data of the main power line monitored by multiple sensors exceeds the set abnormal threshold, it means that the main power line is already in an abnormal operating state. At this time, the circuit will not be periodically analyzed and predicted, but the switching action between the main power line and the bypass cabin will be directly executed. This method ensures that when an abnormality occurs in the main power line, it can be switched to the bypass cabin in time, thereby avoiding power interruption and further damage.

[0053] In actual use, the bypass shelter can be used as a short-term replacement for the backup circuit, or as a long-term circuit to replace the main power line: Specifically, when the bypass cabin is used as a backup circuit for a short period of time, after switching to the bypass cabin, the main power line is repaired or replaced to ensure that the main power line returns to normal; then, the switching action between the bypass cabin and the main power line is performed again to restore the normal operation of the main power line.

[0054] Specifically, when the bypass cabin is used as a long-term circuit to replace the main power line, after switching to the bypass cabin, multiple sensors are deployed to monitor the status of the bypass cabin. The multiple sensors periodically collect data by sending data at regular intervals to obtain multiple cycles of bypass cabin detection data; the bypass cabin detection data of three adjacent cycles are used as a group of real-time detection data, and the detection data of the middle cycle in the group of real-time detection data are compared with the detection data of the previous cycle and the next cycle, and the detection data change analysis is performed to obtain the real-time detection change analysis results; based on the time series analysis method, the virtual detection data of the next cycle is predicted according to the real-time detection change analysis results; the state of the bypass cabin is evaluated according to the virtual detection data, and it is determined whether to perform the switching action between the bypass cabin and the main power line; the above process is the same as the process of switching the main power line to the bypass cabin, and will not be repeated here. Embodiment 2

[0055] Reference Figure 2 As shown, based on the first embodiment, the present invention further provides a bypass shelter and main power line intelligent switching system for implementing the above method, including: Multi-sensor monitoring module: deploys multiple sensors to monitor the status of the main power line in real time. The sensors send data regularly to achieve periodic collection, thereby obtaining main power line detection data for multiple cycles; Data processing module: responsible for receiving data from the multi-sensor monitoring module, and processing the main power line detection data of three adjacent cycles as a set of real-time detection data. The data processing module compares the detection data of the middle cycle with the detection data of the previous cycle and the next cycle in this set of real-time detection data, performs detection data change analysis, and obtains real-time detection change analysis results; Prediction model module: Based on the time series analysis method, the real-time detection change analysis results are used to predict the virtual detection data of the next cycle. The prediction model module has a built-in main power line prediction model, which can make accurate predictions based on historical data and current change trends; State assessment and decision support module: Use the virtual detection data provided by the prediction model module to assess the state of the main power line, and determine whether it is necessary to perform the switching action between the main power line and the bypass cabin based on the assessment results; Switching control module: According to the decision of the status assessment and decision support module, the switching control module will automatically perform the corresponding operations to ensure a safe and efficient switching process from the main power line to the bypass cabin.

[0056] The system monitors the main power lines in real time through a multi-sensor monitoring module to ensure periodic data collection. The data processing module receives and processes the data, and performs change analysis by comparing the detection data of three adjacent cycles to obtain real-time detection change analysis results. The prediction model module uses the change analysis results based on the time series analysis method to predict the virtual detection data of the next cycle to ensure the accuracy of the prediction. The state assessment and decision support module uses virtual detection data to evaluate the state of the main power line and determine whether switching is required. The switching control module executes switching according to the evaluation results to ensure the safety and efficiency of the switching process.

[0057] The intelligent switching system between the bypass cabin and the main power line proposed in this application realizes the intelligent switching between the main power line and the bypass cabin through the collaborative work of modules such as multi-sensor monitoring, data processing, prediction model, state evaluation and decision support, and switching control. Compared with the existing technology, the present application can monitor the state of the main power line in real time, and based on the time series analysis method, accurately predict the virtual detection data of the next cycle, so as to predict the potential risks of the power line before a fault occurs, execute the switching action in time, and ensure the stability and reliability of the power supply.

[0058] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0059] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. The method for intelligently switching the bypass shelter and the main power line is characterized by: include: Deploy a variety of sensors to monitor the status of the main power line. The sensors periodically collect data by sending data at regular intervals to obtain multiple cycles of main power line detection data. The main power line detection data of three adjacent cycles are taken as a group of real-time detection data, and the detection data of the middle cycle in the group of real-time detection data are compared with the detection data of the previous cycle and the next cycle, and the detection data change analysis is performed to obtain the real-time detection change analysis result; including: calculating the first change state, that is, the data change amount between the middle cycle in the real-time detection data and the previous cycle, and calculating the second change state, that is, the data change amount between the middle cycle in the real-time detection data and the next cycle; determining the change trend according to the first change state and the second change state, including: linear change trend, fluctuation change trend and rate change trend; Based on the time series analysis method, the virtual detection data of the next cycle is predicted according to the real-time detection change analysis results; including: extracting the change trend from the real-time detection change analysis results; establishing an information database of the main power line, managing and storing the operation data information of the main power line from use to the occurrence of an abnormality; constructing a main power line prediction model based on the change trend and the information database of the main power line, for predicting the virtual detection data of the next cycle of the main power line; The status of the main power line is evaluated based on the virtual detection data, and it is determined whether to perform the switching action between the main power line and the bypass cabin.

2. The method for intelligently switching the bypass shelter and the main power line according to claim 1 is characterized in that: Establish information database of main power lines, including: Determine the data types that need to be entered into the information database, including: electrical parameters and environmental parameters of the main power line operation and abnormal events that affect the operation of the main power line; Select database technology based on data volume and query requirements, collect historical data information on the operation of the main power lines, and design corresponding table structures for different types of operation data, including field names, types, and constraints; Formulate data extraction strategies and support the extraction of data from the information database of the main power lines by setting filtering conditions for the prediction of the main power lines.

3. The method for intelligently switching the bypass shelter and the main power line according to claim 1 is characterized in that: Building a main power line prediction model based on the change trend and the information database of the main power line includes: Extracting historical operation data matching the current detection cycle from the information database of the main power line; Perform deep learning analysis on the extracted historical performance data to identify typical trend patterns of performance changes in main power lines; Combine the deep learning analysis results with the current power line periodic detection change analysis results to establish a prediction model; The established prediction model is used to predict the future status of the main power lines, and the model is continuously optimized based on actual test results.

4. The method for intelligently switching the bypass shelter and the main power line according to claim 1 is characterized in that: The sensor comprises: Current sensors for real-time monitoring of the current load of the main power line; Voltage sensor, used to monitor the voltage fluctuation of the main power line in real time; A temperature sensor for monitoring the temperature of the main power line and the temperature of the power equipment connected to the main power line; Insulation resistance sensor, used to detect the insulation resistance of the main power line; Ground resistance sensors are used to monitor the resistance of the grounding system in the main power line.

5. The method for intelligently switching between a bypass shelter and a main power line according to claim 1, characterized in that: An abnormal threshold of the main power line is set. When the data of the main power line monitored by multiple sensors exceeds the set abnormal threshold, the switching action between the main power line and the bypass cabin is executed.

6. The method for intelligently switching the bypass shelter and the main power line according to claim 1 is characterized in that: After switching to the bypass cabin, the main power line is repaired or replaced. After confirming that the main power line has been restored, the switching operation between the bypass cabin and the main power line is performed.

7. The method for intelligently switching the bypass shelter and the main power line according to claim 6 is characterized in that: After switching to the bypass shelter, it also includes: Deploy a variety of sensors to monitor the status of the bypass shelter. The sensors periodically collect data by sending data at regular intervals to obtain bypass shelter detection data for multiple periods. The bypass shelter detection data of three adjacent cycles are taken as a group of real-time detection data, and the detection data of the middle cycle in the group of real-time detection data are compared with the detection data of the previous cycle and the next cycle, and the detection data change analysis is performed to obtain the real-time detection change analysis results; Based on the time series analysis method, the virtual detection data of the next cycle is predicted according to the real-time detection change analysis results; The status of the bypass shelter is evaluated based on the virtual detection data, and it is determined whether to perform the switching action between the bypass shelter and the main power line.

8. An intelligent switching system between a bypass shelter and a main power line, characterized in that: include: Multi-sensor monitoring module: deploys multiple sensors to monitor the status of the main power line in real time. The sensors send data regularly to achieve periodic collection, thereby obtaining main power line detection data for multiple periods; Data processing module: responsible for receiving data from the multi-sensor monitoring module, and processing the main power line detection data of three adjacent cycles as a group of real-time detection data. The data processing module compares the detection data of the middle cycle with the detection data of the previous cycle and the next cycle in this group of real-time detection data, performs detection data change analysis, and obtains real-time detection change analysis results; Prediction model module: Based on the time series analysis method, the real-time detection change analysis results are used to predict the virtual detection data of the next cycle. The prediction model module has a built-in main power line prediction model, which can make accurate predictions based on historical data and current change trends; State evaluation and decision support module: using the virtual detection data provided by the prediction model module to evaluate the state of the main power line, and judging whether it is necessary to perform a switching action between the main power line and the bypass shelter according to the evaluation result; Switching control module: According to the decision of the state evaluation and decision support module, the switching control module will automatically perform corresponding operations to ensure the safe switching process from the main power line to the bypass cabin.

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