An intelligent dispatching system for power distribution network circuit breaker groups based on internet of things

Abstract

This invention discloses an intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things (IoT), belonging to the field of intelligent dispatching and control technology. It includes an intelligent dispatching platform, which is communicatively connected to the following modules: The data acquisition and sensing module is used to install various sensors on the distribution network circuit breakers to collect and preprocess the circuit breaker operating status data. This invention, through the intelligent dispatching system, monitors the operating status of the circuit breaker group in real time, enabling rapid fault identification and automatic isolation and recovery operations. Furthermore, through multi-dimensional evaluation indicators and risk warning mechanisms, it can detect potential fault hazards in advance, take timely preventative measures, and reduce power outage time. In addition, by optimizing load distribution and rationally controlling the operation of circuit breakers, it avoids power outages caused by equipment overload or misoperation, significantly improving the power supply reliability of the distribution network and ensuring a continuous and stable power supply for users.

Description

Technical Field

[0001] This invention relates to the field of intelligent dispatching and control technology, and specifically to an intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things. Background Technology

[0002] With the acceleration of urbanization and the large-scale integration of new energy sources, the scale of power distribution networks is constantly expanding and the structure is becoming increasingly complex. Modern society has higher and higher requirements for the reliability of power supply. Any short-term power outage may cause huge losses to enterprises and residents. Circuit breakers in the power distribution network are key equipment to ensure the reliability of power supply. By monitoring the status of circuit breakers, faults can be detected and dealt with in a timely manner, reducing the time and scope of power outages.

[0003] In existing technologies, the analysis of abnormal conditions of circuit breaker groups in distribution networks often focuses on a single indicator, which is difficult to comprehensively reflect the overall operating status of the circuit breaker group. This results in the inability to capture the dynamic changes and potential problems of the circuit breaker group in a timely manner. Therefore, how to use digital twin technology to construct a virtual model of the circuit breaker group in the distribution network, simulate the physical characteristics, electrical characteristics and mutual interactions of the circuit breakers, and define multi-dimensional evaluation indicators in the digital twin model to comprehensively evaluate the operating status and potential risks of the circuit breaker group is the problem to be solved by this invention. To this end, an intelligent scheduling system for distribution network circuit breaker groups based on the Internet of Things is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A smart dispatching system for distribution network circuit breaker groups based on the Internet of Things (IoT) includes a smart dispatching platform, which is communicatively connected to the following modules, wherein:

[0007] The data acquisition and sensing module is used to install various sensors on the circuit breakers of the distribution network, collect and preprocess the operating status data of the circuit breakers, and then transmit them to the intelligent dispatching platform through Internet of Things communication technology.

[0008] The digital twin model building module is used to build digital twin models of distribution network circuit breaker groups to simulate their physical characteristics, electrical characteristics, and interactions between them.

[0009] The anomaly assessment and analysis module is used to define multi-dimensional assessment indicators, use a digital twin model to comprehensively assess the operating status of the circuit breaker group, identify abnormal situations of the circuit breaker group, and issue early warning signals.

[0010] The intelligent dispatch management module is used to generate intelligent dispatch strategies based on the operation requirements of the distribution network and the operating status of the circuit breaker group, and then transform the intelligent dispatch strategies into specific control commands.

[0011] A further improvement to the technical solution of the present invention is that the data acquisition and sensing module specifically includes:

[0012] Multiple sensors, including current transformers, voltage sensors, temperature sensors, humidity sensors, and mechanical vibration sensors, are installed in key parts of the distribution network circuit breakers to ensure that the sensors are installed in precise positions and can accurately capture the operating status of the circuit breakers. At the same time, the sensors are calibrated and debugged to ensure their measurement accuracy and stability.

[0013] The sensors collect the circuit breaker's operating status data according to the set sampling frequency. Among them, the current transformer continuously monitors the current value flowing through the circuit breaker and captures the current fluctuation, including the load current during normal operation and abnormal current changes such as overload current and short-circuit current. The voltage sensor synchronously collects the voltage data at both ends of the circuit breaker to reflect the stability of the voltage and whether there are abnormal phenomena such as voltage dips and rises. The mechanical vibration sensor monitors the vibration of the circuit breaker during operation and judges the operating status of mechanical components by the changes in vibration frequency and amplitude. The temperature sensor monitors the temperature of key internal components of the circuit breaker.

[0014] The collected operational status data is preprocessed, including data cleaning, filtering, and format conversion, and then transmitted to the intelligent scheduling platform via IoT communication technology.

[0015] A further improvement of the technical solution of the present invention is that: the digital twin model construction module includes a physical characteristic modeling unit and an electrical characteristic modeling unit;

[0016] The physical characteristic modeling unit, based on the physical structure and working principle of the circuit breaker, uses 3D modeling software and physical simulation technology to construct a virtual model of the circuit breaker's physical characteristics, simulates the mechanical structure of the circuit breaker, the mechanical characteristics of moving parts, and the interaction between parts, reflects the physical state and operation process of the circuit breaker, and provides a basis for analyzing the mechanical faults and performance degradation of the circuit breaker.

[0017] The electrical characteristic modeling unit is used to establish a virtual model of electrical characteristics based on the electrical parameters and circuit topology of the circuit breaker, simulate the electrical performance of the circuit breaker under different load conditions, simulate the electrical response of the circuit breaker under fault conditions, and help analyze the electrical faults of the circuit breaker.

[0018] A further improvement to the technical solution of the present invention is that the physical characteristic modeling unit specifically includes:

[0019] Based on the actual physical structure of the circuit breaker, the geometric shape and internal structure of the circuit breaker are drawn using 3D modeling software. After completing the 3D model, the model is topologically optimized to obtain the 3D physical structure model of the circuit breaker.

[0020] Based on the working principle and material properties of circuit breakers, corresponding mechanical parameters are assigned to each component in the three-dimensional physical structure model of the circuit breaker, including the elastic modulus, yield strength and density of the material, to describe the deformation and stress distribution of the component under stress. The mechanical parameters are set by consulting material handbooks, experimental tests or referring to data provided by equipment manufacturers. After the parameter setting is completed, a preliminary static mechanical analysis is performed on the three-dimensional physical structure model of the circuit breaker to verify the rationality of the mechanical parameters and ensure that the model can accurately reflect the mechanical behavior of the circuit breaker.

[0021] For the moving parts in the circuit breaker, kinematic and dynamic models are established using physical simulation software (ANSYS Mechanical), defining the motion constraints and driving methods of each moving part, and setting initial and boundary conditions to simulate the dynamic behavior of the circuit breaker during the opening and closing process. The simulation analysis includes the displacement, velocity, and acceleration motion parameters of the moving parts, as well as the interaction forces between the parts during the motion process.

[0022] The simulation results are compared with the circuit breaker's design standards and normal operating data to assess whether the circuit breaker's physical state meets the requirements, analyze potential mechanical faults, including contact wear, spring fatigue, and component loosening, and combine the abnormal data in the simulation results to determine the location and cause of the fault.

[0023] A further improvement to the technical solution of the present invention is that the electrical characteristic modeling unit specifically includes:

[0024] Collect the electrical parameters of the circuit breaker from the technical manuals, product manuals or equipment nameplates provided by the equipment manufacturer, including rated voltage, rated current, short-circuit breaking current, making current and operating time (opening time, closing time). At the same time, clarify the circuit topology of the circuit breaker in the distribution network, including its connection method, load type and connection relationship with other electrical equipment, draw a detailed circuit topology diagram and mark the electrical characteristics of each node and branch.

[0025] Using circuit simulation software (MATLAB / Simulink), a virtual model of the electrical characteristics of the circuit breaker is established based on the collected electrical parameters and circuit topology. In the virtual model of the electrical characteristics of the circuit breaker, the parameter values ​​of each electrical component are set to simulate its electrical characteristics.

[0026] In the established virtual electrical characteristic model, normal operating conditions are set, the simulation program is run, the electrical performance of the circuit breaker under different load conditions is simulated, the changes in electrical parameters are recorded, the simulation results are analyzed, and the accuracy and reliability of the model are verified. Through multiple simulations, detailed electrical characteristic data of the circuit breaker under normal operating conditions are obtained as a benchmark for subsequent fault analysis.

[0027] In the virtual model of the electrical characteristics of the circuit breaker, various fault scenarios are set up, including short-circuit faults, overload faults, voltage dips or swells. Then, circuit simulation software is run to simulate the electrical response of the circuit breaker under fault conditions, observe and record the operating characteristics of the circuit breaker, analyze the impact of faults on the electrical performance of the circuit breaker, and the circuit breaker's response capability to faults. By comparing the simulation results under fault conditions with normal operating data, potential electrical faults in the circuit breaker can be identified.

[0028] A further improvement of the technical solution of the present invention is that: the anomaly assessment and analysis module includes a state assessment unit and a risk trend early warning unit;

[0029] The status assessment unit is used to define multi-dimensional assessment indicators in the digital twin model, including current, voltage, temperature, number of operations, failure rate and energy efficiency, to comprehensively assess the operating status of the circuit breaker group and determine whether the circuit breaker is in normal operating condition.

[0030] The risk trend early warning unit is used to analyze the historical operating data and current status of the circuit breaker group using digital twin models and machine learning algorithms, analyze the fault risk trend of the circuit breaker group, issue early warning signals based on the fault risk trend, and provide anomaly cause analysis and handling suggestions.

[0031] A further improvement to the technical solution of the present invention is that the state evaluation unit specifically includes:

[0032] In the digital twin model, multi-dimensional evaluation indicators are defined by combining the operating characteristics and actual needs of the circuit breaker group, including current, voltage, temperature, number of operations, failure rate and energy efficiency.

[0033] By using IoT communication technology, the operating status data of the circuit breaker group, as well as the number of times the equipment is operated and historical fault records, are obtained from the data acquisition and sensing module. The multi-source data is cleaned and fused to remove noise and outliers, ensuring the accuracy and consistency of the data. The processed multi-source data is then mapped into a digital twin model.

[0034] Based on historical operating status data and the monitoring requirements of circuit breakers, allowable values ​​for each evaluation indicator are preset, and the deviation between the value of each evaluation indicator and its respective allowable value is analyzed, thereby enabling real-time evaluation of the operating status of the circuit breaker group.

[0035] Based on the evaluation results of the circuit breaker group's operating status, a detailed evaluation report is generated, including the evaluation results of each evaluation index for each circuit breaker and a description of abnormal states. The evaluation report is then fed back to the intelligent scheduling platform.

[0036] A further improvement to the technical solution of the present invention is that the risk tendency early warning unit specifically includes:

[0037] Historical and current operating status data of circuit breaker groups are collected, and evaluation index analysis is performed on the collected operating status data to extract evaluation indexes including current, voltage, temperature, number of operations, failure rate and energy efficiency. Then, the evaluation indexes based on historical operating status data are integrated to obtain a dataset, which is divided into training set and test set. Abnormal data are marked according to the pre-set allowable values ​​of each evaluation index.

[0038] We selected a machine learning algorithm based on a neural network model to construct a fault prediction model for circuit breaker groups. We trained the neural network model using a training set and evaluated the model using a test set. We repeated the training and evaluation process until we obtained a fault prediction model that was trained and had excellent performance.

[0039] Deploy a fault prediction model, input the evaluation indicators extracted from the current operating status data into the fault prediction model, output the evaluation indicators that deviate from the allowable values, and then calculate the risk trend index by combining the current values ​​of each evaluation indicator with their allowable values, and analyze the fault risk trend of the circuit breaker group.

[0040] Based on the calculated risk tendency index and historical operating status data, the fault risk of the circuit breaker group is divided into different risk levels, namely low risk level, medium risk level and high risk level, and corresponding risk thresholds are assigned to each risk level in combination with the risk tendency index.

[0041] The risk level of the circuit breaker group is determined, an early warning signal is issued, and the assessment indicators that lead to an increased fault risk are marked for the circuit breakers that issue the early warning. Then, based on the analysis results and the preset fault risk knowledge base, the abnormal cause analysis and specific handling suggestions are provided. The handling suggestions include, but are not limited to, adjusting operating parameters, performing equipment maintenance or replacing parts. The analysis results and handling suggestions are fed back to the intelligent scheduling platform to provide decision support for operation and maintenance personnel.

[0042] A further improvement to the technical solution of this invention lies in the following: the calculation process of the risk tendency index is as follows:

[0043] Define the evaluation indicators for the circuit breaker group, including current, voltage, temperature, number of operations, failure rate and energy efficiency. For each evaluation indicator, determine its allowable value, which is the rated value provided by the equipment manufacturer. Then, through the data acquisition and sensing module, obtain the current operating status data of the circuit breaker group, including the current value of each evaluation indicator.

[0044] For each evaluation indicator, calculate the ratio of its current value to the allowable value, and use the logarithmic function to calculate the relative deviation value to measure the relative deviation between the current value and the allowable value of the evaluation indicator. For each evaluation indicator, calculate the square of the difference between its current value and the allowable value, and calculate the ratio of the square of the difference to its allowable value to obtain the absolute deviation value to measure the absolute deviation between the current value and the allowable value of the evaluation indicator.

[0045] The relative and absolute deviation values ​​are summed to obtain the comprehensive deviation value of each evaluation indicator. Then, the deviation values ​​of all evaluation indicators are added together to obtain the risk trend index.

[0046] A further improvement to the technical solution of the present invention is that the intelligent scheduling management module specifically includes:

[0047] Through IoT communication technology, real-time data on the operational needs of the power distribution network and the operational status of circuit breaker groups are collected, covering the current, voltage, temperature, number of operations, and fault status information of each circuit breaker.

[0048] Based on the collected and analyzed information on the distribution network operation requirements and circuit breaker group operation status, an intelligent scheduling strategy is generated in combination with preset scheduling rules.

[0049] The generated intelligent scheduling strategy is transformed into specific control commands, specifying the operation actions and parameter adjustment values ​​of each circuit breaker. Then, through IoT communication technology, the control commands are sent to the circuit breaker group of the distribution network. At the same time, a command feedback mechanism is established to monitor in real time whether the circuit breakers are executing the commands correctly. If the execution is abnormal, the operation is resent. After the circuit breaker group executes the control commands, the operating status of the distribution network and the operation of the circuit breakers are continuously monitored to analyze whether the intelligent scheduling strategy has achieved the expected goals.

[0050] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows:

[0051] This invention provides an intelligent dispatching system for circuit breaker groups in a distribution network based on the Internet of Things (IoT). Through this intelligent dispatching system, the operating status of the circuit breaker group is monitored in real time, enabling rapid fault identification and automatic isolation and recovery operations. Furthermore, by employing multi-dimensional evaluation indicators and risk warning mechanisms, potential fault hazards are detected in advance, allowing for timely preventative measures and reducing power outage time. In addition, by optimizing load distribution and rationally controlling circuit breaker operation, power outages caused by equipment overload or misoperation are avoided, significantly improving the power supply reliability of the distribution network and ensuring a continuous and stable power supply for users.

[0052] This invention provides an intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things. With the help of digital twin models and machine learning algorithms, it can comprehensively analyze multi-dimensional data such as the electrical performance, mechanical performance, and operating environment of the circuit breaker group to fully assess its operating status. When a fault occurs, the system can quickly and accurately determine the fault type, location, and cause, providing maintenance personnel with detailed anomaly cause analysis and handling suggestions. Compared with traditional analysis methods based on a single indicator, it can more accurately capture dynamic changes and potential problems, improving the accuracy of fault diagnosis. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0054] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0055] Figure 2 This is a schematic diagram of the workflow of the risk tendency early warning unit of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] Example 1, as Figure 1 , Figure 2 As shown, this invention provides an intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things, including an intelligent dispatching platform. The intelligent dispatching platform is communicatively connected to the following modules, wherein:

[0058] The data acquisition and sensing module is used to install various sensors on the distribution network circuit breakers to collect and preprocess the circuit breaker's operating status data. This data is then transmitted to the intelligent dispatching platform via IoT communication technology. Multiple sensors, including current transformers, voltage sensors, temperature sensors, humidity sensors, and mechanical vibration sensors, are installed at key locations on the distribution network circuit breakers. The installation of these sensors ensures precise placement and accurate capture of the circuit breaker's operating status. Specifically, current transformers are installed at the circuit breaker's current input and output ports to monitor current magnitude in real time. Temperature sensors are installed near heat-prone contacts and coil components to promptly acquire temperature change data. Simultaneously, the sensors are calibrated and adjusted to ensure measurement accuracy and stability. The sensors collect the circuit breaker's operating status data according to a set sampling frequency. The current transformer continuously monitors the current flowing through the circuit breaker, capturing current fluctuations. The system monitors various conditions, including load current during normal operation and abnormal current changes such as overload current and short-circuit current. Voltage sensors synchronously collect voltage data across the circuit breaker to reflect voltage stability and the presence of abnormal voltage dips or rises. Mechanical vibration sensors monitor the vibration of the circuit breaker during operation, judging the operating status of mechanical components by changes in vibration frequency and amplitude. Temperature sensors monitor the temperature of key internal components of the circuit breaker. The collected operating status data is preprocessed, including data cleaning, filtering, and format conversion. Data cleaning removes obvious errors and abnormal data points, filtering reduces noise interference, and improving data accuracy. Different types of data are initially fused and format converted to unify data formats and encoding methods, ensuring data consistency and compatibility. Finally, the preprocessed data is transmitted to the intelligent scheduling platform via IoT communication technology.

[0059] The digital twin model building module is used to build a digital twin model of a distribution network circuit breaker group to simulate its physical characteristics, electrical characteristics and the interaction between them. The digital twin model building module includes a physical characteristic modeling unit and an electrical characteristic modeling unit.

[0060] The physical characteristic modeling unit, based on the physical structure and working principle of the circuit breaker, utilizes 3D modeling software and physical simulation technology to construct a virtual model of the circuit breaker's physical characteristics. This model simulates the circuit breaker's mechanical structure, the mechanical characteristics of moving parts, and the interactions between components, reflecting the circuit breaker's physical state and operating process. This provides a basis for analyzing mechanical faults and performance degradation in the circuit breaker. Based on the actual physical structure of the circuit breaker, design drawings, technical manuals, and other materials are consulted to clarify the shape, size, material, and assembly relationships of each component (contacts, arc-extinguishing chamber, operating mechanism, springs, etc.). 3D modeling software is used to draw the circuit breaker's geometric shape and internal structure. After completing the 3D model, topology optimization is performed on the model. Unnecessary details were removed to improve the efficiency of subsequent simulations, resulting in a three-dimensional physical structure model of the circuit breaker. Based on the circuit breaker's working principle and material properties, corresponding mechanical parameters were assigned to each component in the three-dimensional physical structure model, including the material's elastic modulus, yield strength, and density, describing the deformation and stress distribution of the components under load. These mechanical parameters were set by consulting material handbooks, experimental tests, or referring to data provided by the equipment manufacturer. After parameter setting, a preliminary static mechanical analysis was performed on the three-dimensional physical structure model of the circuit breaker to verify the rationality of the mechanical parameters and ensure that the model accurately reflects the mechanical behavior of the circuit breaker. For the moving parts in the circuit breaker, physical simulation software (ANSYS) was used... (Mechanical) Establish kinematic and dynamic models, define the motion constraints and driving methods of each moving component, and set initial and boundary conditions to simulate the dynamic behavior of the circuit breaker during the opening and closing process. The simulation analysis includes the displacement, velocity, and acceleration motion parameters of the moving components, as well as the interaction forces between the components during the motion. The simulation results are compared with the circuit breaker's design standards and normal operation data to evaluate whether the circuit breaker's physical state meets the requirements, analyze potential mechanical faults, including contact wear, spring fatigue, and component loosening, and determine the location and cause of the fault by combining the abnormal data in the simulation results.

[0061] The electrical characteristic modeling unit is used to establish a virtual model of the electrical characteristics of the circuit breaker based on its electrical parameters and circuit topology. This model simulates the electrical performance of the circuit breaker under different load conditions and its electrical response under fault conditions, aiding in the analysis of electrical faults. Electrical parameters of the circuit breaker, including rated voltage, rated current, short-circuit breaking current, making current, and operating time (opening time, closing time), are collected from technical manuals, product specifications, or equipment nameplates provided by the equipment manufacturer. Simultaneously, the circuit topology of the circuit breaker in the distribution network is defined, including its connection method, load type, and connection relationships with other electrical equipment. A detailed circuit topology diagram is drawn, and the electrical characteristics of each node and branch are labeled. Using circuit simulation software (MATLAB / Simulink), a virtual model of the circuit breaker's electrical characteristics is established based on the collected electrical parameters and circuit topology. In the virtual model, parameter values ​​for each electrical component are set to simulate its electrical characteristics. Normal operating conditions are set within the established virtual electrical characteristic model, and the simulation program is run to simulate the electrical performance of the circuit breaker under different load conditions. Changes in electrical parameters are recorded, and simulation results are analyzed to verify the model's accuracy and reliability. Through multiple simulations, detailed electrical characteristic data of the circuit breaker under normal operating conditions are obtained as a benchmark for subsequent fault analysis. Various fault scenarios are set in the virtual electrical characteristic model of the circuit breaker, including short-circuit faults, overload faults, and voltage dips or swells. Circuit simulation software is then run to simulate the electrical response of the circuit breaker under fault conditions. The circuit breaker's operating characteristics are observed and recorded, and the impact of faults on the circuit breaker's electrical performance and its response capability are analyzed. By comparing the simulation results under fault conditions with normal operating data, potential electrical faults in the circuit breaker are identified.

[0062] The anomaly assessment and analysis module is used to define multi-dimensional assessment indicators, use a digital twin model to comprehensively assess the operating status of the circuit breaker group, identify abnormal situations of the circuit breaker group, and issue early warning signals.

[0063] The intelligent dispatch management module is used to generate intelligent dispatch strategies based on the operation requirements of the distribution network and the operating status of the circuit breaker group. The intelligent dispatch strategies are then transformed into specific control commands and sent to the circuit breaker group of the distribution network through Internet of Things (IoT) communication technology. This enables control functions for the opening and closing operations and parameter adjustments of the circuit breakers. The intelligent dispatch strategies include decisions on circuit breaker opening and closing operations, load allocation, fault isolation, and recovery.

[0064] Example 2, as Figure 1 , Figure 2 As shown, based on Embodiment 1, the present invention provides a technical solution: preferably, the anomaly assessment and analysis module includes a state assessment unit and a risk trend early warning unit;

[0065] The status assessment unit defines multi-dimensional assessment indicators in the digital twin model, including current, voltage, temperature, number of operations, failure rate, and energy efficiency. It comprehensively assesses the operating status of the circuit breaker group to determine whether the circuit breakers are in normal operating condition. In the digital twin model, combining the operating characteristics and actual needs of the circuit breaker group, it defines multi-dimensional assessment indicators, including current, voltage, temperature, number of operations, failure rate, and energy efficiency. Specifically, the current indicator monitors whether the circuit breaker's load current is within the rated range; the voltage indicator assesses the voltage stability across the circuit breaker; the temperature indicator detects temperature changes in key components to prevent overheating; the number of operations indicator counts the number of opening and closing operations of the circuit breaker to assess mechanical life; the failure rate indicator records the failure frequency of the circuit breaker; and the energy efficiency indicator evaluates the circuit breaker's performance. Energy utilization efficiency comprehensively covers the electrical performance, mechanical performance, and operational efficiency of circuit breakers. Through IoT communication technology, it acquires operational status data of the circuit breaker group, as well as the number of equipment operations and historical fault records from the data acquisition and sensing module. The multi-source data is cleaned and fused to remove noise and outliers, ensuring the accuracy and consistency of the data. The processed multi-source data is then mapped into a digital twin model. Based on historical operational status data and the monitoring requirements of the circuit breakers, allowable values ​​for each evaluation indicator are preset. The deviation between the value of each evaluation indicator and its respective allowable value is analyzed, thereby conducting real-time evaluation of the operational status of the circuit breaker group. Based on the evaluation results of the operational status of the circuit breaker group, a detailed evaluation report is generated, including the evaluation results of each evaluation indicator for each circuit breaker and a description of abnormal states. The evaluation report is then fed back to the intelligent dispatching platform.

[0066] The risk trend early warning unit utilizes digital twin models and machine learning algorithms to analyze the historical operating data and current status of circuit breaker groups, analyze the fault risk trends of the circuit breaker groups, and issue early warning signals based on these trends. It also provides anomaly cause analysis and handling suggestions. The unit collects historical and current operating status data of the circuit breaker groups and analyzes the collected data for evaluation indicators, extracting indicators including current, voltage, temperature, number of operations, failure rate, and energy efficiency. These evaluation indicators based on historical operating status data are then integrated to obtain a dataset, which is divided into training and testing sets. Abnormal data is marked according to pre-defined allowable values ​​for each evaluation indicator. A machine learning algorithm based on a neural network model is selected to construct a fault prediction model for the circuit breaker groups. The neural network model is trained using the training set. During training, the backpropagation algorithm is used to continuously adjust the model parameters to optimize prediction performance. Multiple experiments are conducted with different hyperparameters to compare the model's prediction accuracy, recall, and other indicators, selecting the optimal parameter combination. After training, the model is evaluated using the testing set. If the evaluation results are unsatisfactory, the model structure or parameters are further adjusted, and the training and evaluation process is repeated until a well-trained and high-performance fault prediction model is obtained. The fault prediction model is then deployed, and the evaluation indicators extracted from the current operating status data are input into the fault prediction model. The model outputs evaluation indicators that deviate from the allowable values. Then, the deviations between the current values ​​and the allowable values ​​of each evaluation indicator are combined to calculate the risk trend index. The fault risk trend of the circuit breaker group is analyzed. Based on the calculated risk trend index and historical operating status data, the fault risk of the circuit breaker group is divided into different risk levels: low risk, medium risk, and high risk. The risk trend index is combined to assign corresponding risk thresholds to each risk level, determine the risk level of the circuit breaker group, issue early warning signals, and mark the evaluation indicators that lead to increased fault risk for the circuit breakers that issue early warnings. Then, based on the analysis results and the preset fault risk knowledge base, the abnormal cause analysis and specific handling suggestions are provided. The handling suggestions include, but are not limited to, adjusting operating parameters, performing equipment maintenance, or replacing parts. The analysis results and handling suggestions are fed back to the intelligent scheduling platform to provide decision support for operation and maintenance personnel.

[0067] The calculation process for the risk tendency index is as follows:

[0068] The evaluation indicators for the circuit breaker group are clearly defined, including current, voltage, temperature, number of operations, failure rate, and energy efficiency. For each evaluation indicator, its allowable value is determined. The allowable value is the rated value provided by the equipment manufacturer. Then, the current operating status data of the circuit breaker group is obtained through the data acquisition and sensing module, including the current value of each evaluation indicator. For each evaluation indicator, the ratio of its current value to the allowable value is calculated. The relative deviation value is calculated by combining the logarithmic function to measure the relative deviation between the current value and the allowable value of the evaluation indicator. For each evaluation indicator, the square of the difference between its current value and the allowable value is calculated, and the ratio of the square of the difference to its allowable value is calculated to obtain the absolute deviation value. The relative deviation value and the absolute deviation value are summed to obtain the comprehensive deviation value of each evaluation indicator. Finally, the deviation values ​​of all evaluation indicators are added together to obtain the risk tendency index.

[0069] The formula for calculating the risk tendency index is:

[0070] ;

[0071] In the formula, This is a risk directional index, representing the failure risk trend of a circuit breaker group. To assess the number of indicators, The current running state of the first The current value of the evaluation indicator is the first one. The permissible value for each evaluation indicator, when near hour, Smaller indicates lower risk. Significant deviation hour, An increase indicates a rise in risk;

[0072] Multiple risk levels correspond one-to-one with multiple risk thresholds, as follows:

[0073] The risk threshold for low-risk levels is: This indicates that the circuit breaker group is in a relatively stable state.

[0074] The risk threshold for medium-risk level is: This indicates that the circuit breaker group poses a certain risk and requires attention.

[0075] The risk threshold for high-risk levels is: This indicates that there is a significant risk to the circuit breaker group, and immediate action is required.

[0076] in, As a risk orientation index, These are the upper threshold for low-risk levels and the lower threshold for medium-risk levels. These are the upper threshold for medium-risk levels and the lower threshold for high-risk levels;

[0077] The intelligent scheduling and management module specifically includes:

[0078] Through IoT communication technology, real-time data on the operational demand of the power distribution network and the operational status of the circuit breaker group are collected, covering current, voltage, temperature, number of operations, and fault status information of each circuit breaker. Based on the collected and analyzed data on the operational demand of the power distribution network and the operational status of the circuit breaker group, and combined with preset scheduling rules, an intelligent scheduling strategy is generated. Specifically, in the decision-making regarding the opening and closing of circuit breakers, based on load distribution and power supply reliability requirements, it determines which circuit breakers need to be closed to ensure power supply and which need to be opened to isolate faults or adjust loads. In terms of load distribution, the load carrying capacity of each circuit breaker is optimized to avoid overload. For fault isolation and recovery, the system formulates fault location and isolation plans, as well as subsequent power restoration path planning. The generated intelligent dispatch strategy is transformed into specific control commands, specifying the operation actions and parameter adjustment values ​​of each circuit breaker. Then, through IoT communication technology, the control commands are sent to the distribution network circuit breaker group. At the same time, a command feedback mechanism is established to monitor in real time whether the circuit breakers are executing the commands correctly. If the execution is abnormal, the operation is resent. After the circuit breaker group executes the control commands, the operating status of the distribution network and the operation of the circuit breakers are continuously monitored to analyze whether the intelligent dispatch strategy has achieved the expected goals.

[0079] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things, comprising an intelligent dispatching platform, characterized in that, The intelligent scheduling platform has the following communication connections: The data acquisition and sensing module is used to install various sensors on the circuit breakers of the distribution network, collect and preprocess the operating status data of the circuit breakers, and then transmit them to the intelligent dispatching platform through Internet of Things communication technology. The digital twin model building module is used to build digital twin models of distribution network circuit breaker groups to simulate their physical characteristics, electrical characteristics, and interactions between them. The anomaly assessment and analysis module is used to define multi-dimensional assessment indicators, use a digital twin model to comprehensively assess the operating status of the circuit breaker group, identify abnormal situations of the circuit breaker group and issue early warning signals. The anomaly assessment and analysis module includes a status assessment unit and a risk trend early warning unit. The status assessment unit is used to define multi-dimensional assessment indicators in the digital twin model, including current, voltage, temperature, number of operations, failure rate, and energy efficiency, to comprehensively assess the operating status of the circuit breaker group and determine whether the circuit breakers are in normal operating condition. Specifically, it includes: In the digital twin model, multi-dimensional evaluation indicators are defined by combining the operating characteristics and actual needs of the circuit breaker group, including current, voltage, temperature, number of operations, failure rate and energy efficiency. Using IoT communication technology, the system acquires the operating status data of the circuit breaker group, as well as the number of times the equipment is operated and historical fault records from the data acquisition and sensing module. The multi-source data is then cleaned and fused, and the processed multi-source data is mapped into the digital twin model. Based on historical operating status data and the monitoring requirements of circuit breakers, allowable values ​​for each evaluation indicator are preset, and the deviation between the value of each evaluation indicator and its respective allowable value is analyzed, thereby enabling real-time evaluation of the operating status of the circuit breaker group. Based on the evaluation results of the circuit breaker group's operating status, a detailed evaluation report is generated, including the evaluation results of each evaluation indicator for each circuit breaker and a description of abnormal states. The evaluation report is then fed back to the intelligent scheduling platform. The risk trend early warning unit is used to analyze the historical operating data and current status of the circuit breaker group using digital twin models and machine learning algorithms, analyze the fault risk trend of the circuit breaker group, issue early warning signals based on the fault risk trend, and provide abnormal cause analysis and handling suggestions. The intelligent dispatch management module is used to generate intelligent dispatch strategies based on the operation requirements of the distribution network and the operating status of the circuit breaker group, and then transform the intelligent dispatch strategies into specific control commands.

2. The intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things as described in claim 1, characterized in that: The data acquisition and sensing module specifically includes: Multiple sensors are installed in key parts of the distribution network circuit breaker, including current transformers, voltage sensors, temperature sensors, humidity sensors, and mechanical vibration sensors. At the same time, the sensors are calibrated and debugged. The sensors collect the circuit breaker's operating status data according to the set sampling frequency. Among them, the current transformer continuously monitors the current value flowing through the circuit breaker, the voltage sensor synchronously collects the voltage data across the circuit breaker, the mechanical vibration sensor monitors the vibration of the circuit breaker during operation, and the temperature sensor monitors the temperature of key internal components of the circuit breaker. The collected operational status data is preprocessed, including data cleaning, filtering, and format conversion, and then transmitted to the intelligent scheduling platform via IoT communication technology.

3. The intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things according to claim 1, characterized in that: The digital twin model building module includes a physical characteristic modeling unit and an electrical characteristic modeling unit; The physical characteristic modeling unit, based on the physical structure and working principle of the circuit breaker, uses 3D modeling software and physical simulation technology to construct a virtual model of the physical characteristics of the circuit breaker, simulating the mechanical structure of the circuit breaker, the mechanical characteristics of moving parts, and the interaction between parts. The electrical characteristic modeling unit is used to establish a virtual model of electrical characteristics based on the electrical parameters and circuit topology of the circuit breaker, and to simulate the electrical performance of the circuit breaker under different load conditions.

4. The intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things according to claim 3, characterized in that: The physical property modeling unit specifically includes: Based on the actual physical structure of the circuit breaker, the geometric shape and internal structure of the circuit breaker are drawn using 3D modeling software. After completing the 3D model, the model is topologically optimized to obtain the 3D physical structure model of the circuit breaker. Based on the working principle and material properties of circuit breakers, corresponding mechanical parameters are assigned to each component in the three-dimensional physical structure model of the circuit breaker, including the elastic modulus, yield strength and density of the material, to describe the deformation and stress distribution of the component under stress. After the parameter setting is completed, a preliminary static mechanical analysis is performed on the three-dimensional physical structure model of the circuit breaker to verify the rationality of the mechanical parameters. For the moving parts in the circuit breaker, kinematic and dynamic models are established using physical simulation software, the motion constraints and driving methods of each moving part are defined, and initial and boundary conditions are set to simulate the dynamic behavior of the circuit breaker during the opening and closing process. The simulation analysis includes the displacement, velocity, and acceleration motion parameters of the moving parts, as well as the interaction forces between the parts during the motion process. The simulation results are compared with the circuit breaker's design standards and normal operating data to assess whether the circuit breaker's physical state meets the requirements, analyze potential mechanical faults, and determine the location and cause of the fault by combining the abnormal data in the simulation results.

5. The intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things according to claim 3, characterized in that: The electrical characteristic modeling unit specifically includes: Collect the electrical parameters of the circuit breaker, including rated voltage, rated current, short-circuit breaking current, making current and operating time. At the same time, clarify the circuit topology of the circuit breaker in the distribution network, including its connection method, load type and connection relationship with other electrical equipment, draw the circuit topology diagram and label the electrical characteristics of each node and branch. Using circuit simulation software, a virtual model of the electrical characteristics of the circuit breaker is established based on the collected electrical parameters and circuit topology. In the virtual model of the electrical characteristics of the circuit breaker, the parameter values ​​of each electrical component are set to simulate its electrical characteristics. In the established virtual model of electrical characteristics, normal operating conditions are set, the simulation program is run, the electrical performance of the circuit breaker under different load conditions is simulated, the changes in electrical parameters are recorded, and detailed electrical characteristic data of the circuit breaker under normal operating conditions are obtained through multiple simulations. In the virtual model of the electrical characteristics of the circuit breaker, various fault scenarios are set up, including short-circuit faults, overload faults, voltage dips or swells. Then, circuit simulation software is run to simulate the electrical response of the circuit breaker under fault conditions, observe and record the operating characteristics of the circuit breaker, analyze the impact of faults on the electrical performance of the circuit breaker, and the circuit breaker's response capability to faults. By comparing the simulation results under fault conditions with normal operating data, potential electrical faults in the circuit breaker can be identified.

6. The intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things according to claim 1, characterized in that: The risk trend early warning unit specifically includes: Historical and current operating status data of circuit breaker groups are collected, and evaluation index analysis is performed on the collected operating status data to extract evaluation indexes including current, voltage, temperature, number of operations, failure rate and energy efficiency. Then, the evaluation indexes based on historical operating status data are integrated to obtain a dataset, which is divided into training set and test set. Abnormal data are marked according to the pre-set allowable values ​​of each evaluation index. We selected a machine learning algorithm based on a neural network model to construct a fault prediction model for circuit breaker groups. We trained the neural network model using a training set and evaluated the model using a test set. We repeated the training and evaluation process until we obtained a fault prediction model that was trained and had excellent performance. Deploy a fault prediction model, input the evaluation indicators extracted from the current operating status data into the fault prediction model, output the evaluation indicators that deviate from the allowable values, and then calculate the risk trend index by combining the current values ​​of each evaluation indicator with their allowable values, and analyze the fault risk trend of the circuit breaker group. Based on the calculated risk tendency index and historical operating status data, the fault risk of the circuit breaker group is divided into different risk levels, namely low risk level, medium risk level and high risk level, and corresponding risk thresholds are assigned to each risk level in combination with the risk tendency index. The risk level of the circuit breaker group is determined, an early warning signal is issued, and the assessment indicators that lead to an increased fault risk are marked for the circuit breakers that issue the early warning. Then, based on the analysis results and the preset fault risk knowledge base, the abnormal cause analysis and specific handling suggestions are provided, and the analysis results and handling suggestions are fed back to the intelligent dispatching platform.

7. The intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things according to claim 6, characterized in that: The calculation process for the risk tendency index is as follows: Define the evaluation indicators for the circuit breaker group, including current, voltage, temperature, number of operations, failure rate and energy efficiency. For each evaluation indicator, determine its allowable value, and then obtain the current operating status data of the circuit breaker group, including the current value of each evaluation indicator, through the data acquisition and sensing module. For each evaluation indicator, calculate the ratio of its current value to the allowable value, and use the logarithmic function to calculate the relative deviation value. For each evaluation indicator, calculate the square of the difference between its current value and the allowable value, and calculate the ratio of the square of the difference to its allowable value to obtain the absolute deviation value. The relative and absolute deviation values ​​are summed to obtain the comprehensive deviation value of each evaluation indicator. Then, the deviation values ​​of all evaluation indicators are added together to obtain the risk trend index.

8. The intelligent dispatching system for distribution network circuit breaker groups based on the Internet of Things according to claim 1, characterized in that: The intelligent scheduling and management module specifically includes: Through IoT communication technology, real-time data on the operational needs of the power distribution network and the operational status of the circuit breaker group are collected, covering the current, voltage, temperature, number of operations, and fault status information of each circuit breaker. Based on the collected and analyzed information on the distribution network operation requirements and circuit breaker group operation status, an intelligent scheduling strategy is generated in combination with preset scheduling rules. The generated intelligent scheduling strategy is transformed into specific control commands, specifying the operation actions and parameter adjustment values ​​of each circuit breaker. Then, through IoT communication technology, the control commands are sent to the circuit breaker group of the distribution network. At the same time, a command feedback mechanism is established to monitor in real time whether the circuit breakers are executing the commands correctly. If the execution is abnormal, the operation is resent. After the circuit breaker group executes the control commands, the operating status of the distribution network and the operation of the circuit breakers are continuously monitored to analyze whether the intelligent scheduling strategy has achieved the expected goals.

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