Primary and secondary fuse column circuit breaker intelligent control system and method

Abstract

The present application relates to power distribution network fault information technical field, especially to a kind of primary and secondary fusion pole-mounted circuit breaker intelligent control system and method, including data perception module, data processing module, control scheme generation module, fault control module and control feedback module, the present application is by collecting node equipment operating data, carries out intelligent data processing, constructs distributed consensus mechanism and generates control scheme, adjusts control strategy by multiple optimization verification, accurately executes fault control instruction, and continuously optimizes control process by real-time feedback mechanism, can realize the rapid isolation and recovery of power distribution network fault, significantly improves power distribution network fault processing efficiency and system reliability.

Description

Technical Field

[0001] This invention relates to the field of power distribution network fault information technology, and in particular to an intelligent control system and method for a primary and secondary integrated pole-mounted circuit breaker. Background Technology

[0002] With the increasing automation of distribution networks, pole-mounted circuit breakers, which integrate primary and secondary circuits, have become key equipment, making their intelligent control a research focus. Traditional control methods mainly rely on centralized decision-making at the master station, resulting in slow response speed and high risk of single-point failures. Existing distributed control schemes mostly employ fixed master nodes or simple polling mechanisms, lacking the ability to dynamically evaluate node states and struggling to adapt to complex power grid environments. During fault handling, existing systems often rely on single electrical quantity criteria, failing to comprehensively consider multi-dimensional factors such as equipment health status, communication quality, and workload, leading to insufficient accuracy and reliability of control decisions. Furthermore, traditional methods lack effective feedback calibration mechanisms, making it impossible to dynamically optimize control strategies based on the real-time operating status of the power grid. Therefore, there is an urgent need to develop a circuit breaker control system capable of intelligent node election, multi-dimensional state evaluation, and adaptive optimization to improve the speed, reliability, and intelligence of distribution network fault handling.

[0003] Chinese patent application CN106355357A discloses a method and information processing system for handling power distribution network faults. The method includes: a mobile device that can detect power distribution network faults in a timely manner and distinguish whether the fault occurs on the high-voltage side or the low-voltage side, reminding the holder to take appropriate action; locating the device on a map embedded in the mobile device; the mobile device identifying possible affected transformer areas on the low-voltage side based on the fault description through text analysis and locating the area on the map; the mobile device collecting its current geographical coordinates, comparing them with the fault location, and generating a navigation path; the mobile device generating a single-line diagram through a single-line diagram processing module, and the mobile device holder simulating operations through the single-line diagram to analyze the accurate location of the fault and feasible power transfer schemes; the system includes a mobile device, a mobile security network, and a back-end business system; however, this solution still suffers from slow fault response speed and low system reliability due to reliance on centralized decision-making at the main station. Summary of the Invention

[0004] To address this, the present invention provides an intelligent control system and method for a primary and secondary integrated pole-mounted circuit breaker, which overcomes the problems of slow fault response speed and low system reliability caused by reliance on centralized decision-making at the master station in the prior art.

[0005] To achieve the above objectives, on the one hand, the present invention provides an intelligent control system for a primary and secondary integrated pole-mounted circuit breaker, comprising:

[0006] The data sensing module is used to collect operational data from node devices;

[0007] The data processing module is used to process the node device's operating data to obtain the processed node device operating data.

[0008] The control scheme generation module is used to filter commander nodes, generate fault control schemes based on the operation data of commander nodes and processed node equipment, judge the term of command and re-filter commander nodes based on the term of command, and adjust the communication during the generation process of fault control schemes.

[0009] The fault control module is used to perform fault control according to the fault control scheme;

[0010] The control feedback module is used to monitor the bus voltage drop based on the processed node equipment operation data, perform voltage calibration in the judgment process of the commander's term of office based on the bus voltage drop, and revise the voltage in the communication adjustment process based on the bus voltage drop.

[0011] Furthermore, the data processing module processes the node device's operating data to obtain processed node device operating data, specifically as follows:

[0012] The data processing module performs data cleaning on the node device operation data to obtain cleaned node device operation data, then fills the cleaned node device operation data to obtain filled node device operation data, and further deletes invalid data from the filled node device operation data to obtain processed node device operation data.

[0013] Furthermore, the control scheme generation module includes:

[0014] The dynamic decision-making unit is used to filter commander nodes through a consensus mechanism to obtain commander nodes. It is also used to judge the term of command and re-filter commander nodes based on the term of command. Furthermore, it is used to judge the faults of commander nodes based on the processed node equipment operation data and optimize the judgment process of commander term status based on the judgment results. It is also used to judge the task saturation of commanders based on the processed node equipment operation data and perform saturation correction in the fault optimization process based on the judgment results. Finally, it is used to judge the communication status of equipment based on the processed node equipment operation data and adjust the communication in the generation process of fault control scheme based on the judgment results.

[0015] The scheme generation unit is used to generate fault control schemes based on the operational data of the commander node and the processed node equipment.

[0016] Furthermore, the dynamic decision-making unit filters commander nodes through a consensus mechanism to obtain commander nodes, specifically as follows:

[0017] The dynamic decision-making unit calculates the node priority score q based on the node's historical control success rate A1, communication quality score A2, equipment health status coefficient A3, first priority weight coefficient α1, second priority weight coefficient α2, and third priority weight coefficient α3. The calculation is set as q = α1 × A1 + α2 × A2 + α3 × A3. This yields the node priority score q for all nodes, resulting in a node priority score set Q{q1, q2, q3, ..., qn-1, qn}. The node with the highest reputation score is selected as the commander node from this set Q. Here, q1 refers to the node priority score of the first node, q2 refers to the node priority score of the second node, q3 refers to the node priority score of the third node, qn-1 refers to the node priority score of the (n-1)th node, and qn refers to the node priority score of the nth node, where n is the node's order.

[0018] Furthermore, the dynamic decision-making unit assesses the commander's tenure and re-selects commander nodes based on the tenure status, specifically as follows:

[0019] The term of office T of the commander node in the processed node device operation data is compared with the preset term of office T0. Based on the comparison result, the term of office of the commander is judged, and the commander nodes are re-selected based on the judgment result.

[0020] When T < T0, the dynamic decision-making unit determines that the commander's term of office has not ended and does not re-screen the commander node;

[0021] When T≥T0, the dynamic decision-making unit determines that the commander's term has ended and re-screens the commander nodes.

[0022] Furthermore, the dynamic decision-making unit performs fault assessment on the commander node based on the processed node device operating data, and optimizes the assessment process for the commander's term status based on the assessment results, specifically as follows:

[0023] A commander node fault diagnosis model is constructed, and the commander node operation data from the processed node device operation data is input into the commander node fault diagnosis model. The commander node fault status output by the commander node fault diagnosis model is obtained. The commander node fault status includes whether the commander node has a fault or not. The fault diagnosis process for the commander's term status is optimized based on the commander node fault status, wherein:

[0024] When the commander node failure condition is that the commander node does not have a failure, the dynamic decision-making unit does not perform fault optimization in the judgment process of the commander's term of office.

[0025] When the commander node is faulty, the dynamic decision-making unit optimizes the judgment process of the commander's term status. The optimization includes: ignoring the remaining term of the current commander node, determining its term status as ended, triggering the control scheme generation module to immediately re-screen the commander node, not including the commander node in the node priority score set Q, and notifying staff to repair the commander node.

[0026] Furthermore, the dynamic decision-making unit judges the saturation status of the commander node based on the processed node device operating data, and performs saturation correction on the fault optimization process based on the judgment result, specifically as follows:

[0027] Based on the commander node's CPU utilization (B1), memory usage (B2), communication bandwidth usage (B3), first saturation weighting coefficient (γ1), second saturation weighting coefficient (γ2), and third saturation weighting coefficient (γ3) from the processed node device operation data, the task saturation index W is calculated, set as W = B1 × γ1 + B2 × γ2 + B3 × γ3. The task saturation index W is compared with the preset task saturation index W0. Based on the comparison result, the commander's task saturation status is judged, and the fault optimization process is saturated and corrected according to the judgment result.

[0028] When W≤W0, the dynamic decision-making unit determines that the commander's task saturation is unsaturated and does not perform saturation correction on the fault optimization process;

[0029] When W > W0, the dynamic decision-making unit determines that the commander's task saturation is saturated and performs saturation correction on the fault optimization process. The saturation correction includes: if the commander node fault condition is that the commander node does not have a fault, the commander nodes are also re-screened, and a fault correction condition is added when re-screening the commander nodes. The fault correction condition is: the task saturation index of the commander node is the minimum value in the node priority score set, and the commander node whose task saturation is saturated is not included in the node priority score set Q.

[0030] Furthermore, the scheme generation unit generates a fault control scheme based on the commander node, specifically as follows:

[0031] The fault control scheme generation model is constructed using the fault control scheme generation model construction method. The electrical data of each node is obtained through the commander node to obtain the electrical dataset. The electrical dataset is then input into the fault control scheme generation model to obtain the fault control scheme output by the fault control scheme generation model.

[0032] Furthermore, the communication adjustment unit judges the device communication status based on the processed node device operating data, and adjusts the communication in the fault control scheme generation process according to the judgment result, specifically as follows:

[0033] A historical fault control scheme database is constructed, and the signal strength M in the processed node device operation data is compared with the preset signal strength M0. Based on the comparison results, the device communication status is judged, and the communication adjustment process of the fault control scheme generation process is adjusted according to the judgment results.

[0034] When M > M0, the communication adjustment unit determines that the device communication status is qualified and does not adjust the communication during the generation process of the fault control scheme.

[0035] When M≤M0, the communication adjustment unit determines that the device communication is unqualified, adjusts the communication process of the fault control scheme generation process, cancels the generation of the fault control scheme based on the commander node, inputs the current node electrical parameters in the processed node device operation data into the historical fault scheme control database, obtains the historical fault control scheme in the historical fault scheme control database, and outputs the historical fault control scheme as the fault control scheme.

[0036] The control feedback module monitors the bus voltage drop based on the processed node equipment operating data, and performs voltage calibration in the process of judging the commander's term of office based on the bus voltage drop. Specifically:

[0037] The instantaneous bus voltage drop rate Sj is calculated based on the current bus voltage value V(t), the bus voltage value V(t-Δt) of the previous calculation cycle, and the calculation cycle Δt in the processed node equipment operation data. Sj is set to [V(t) - V(t-Δt)] / Δt. The instantaneous bus voltage drop rate Sj is compared with the preset instantaneous bus voltage drop rate Sj0. Based on the comparison result, the bus voltage drop situation is judged. Based on the judgment result, voltage calibration is performed during the judgment process for the commander's term of office. Wherein:

[0038] When Sj≤Sj0, the control feedback module determines that the bus voltage drop is a normal drop and does not perform voltage calibration in the judgment process of the commander's term of office.

[0039] When Sj > Sj0, the control feedback module determines that the bus voltage drop is abnormal and performs voltage calibration on the judgment process of the commander's term status. The specific content of the voltage calibration is: ignoring the remaining term time of the current commander node, determining that its term status has ended, and triggering the control scheme generation module to immediately re-select the commander nodes.

[0040] The control feedback module revises the communication adjustment process based on the bus voltage drop, specifically as follows:

[0041] When the bus voltage drop is a normal drop, the control feedback module does not perform voltage correction during the communication adjustment process;

[0042] When the bus voltage drops abnormally, the control feedback module performs voltage revision during the communication adjustment process. The voltage revision includes: acquiring the fault status of the adjacent nodes of the node, inputting the fault status into a preset rule judgment library, acquiring the preset control scheme output by the preset rule judgment library, and outputting the preset control scheme as the fault control scheme.

[0043] On the other hand, the present invention also provides a method for a primary and secondary integrated pole-mounted circuit breaker intelligent control system, comprising:

[0044] Step S1: Collect the operating data of the node devices;

[0045] Step S2: Process the node device operation data to obtain processed node device operation data;

[0046] Step S3 involves filtering commander nodes, generating a fault control scheme based on the commander nodes and the processed node equipment operation data, judging the commander's term of office, re-filtering commander nodes based on the commander's term of office, and adjusting the communication in the fault control scheme generation process.

[0047] Step S4: Perform fault control according to the fault control plan;

[0048] Step S5: Monitor the bus voltage drop based on the processed node equipment operation data, perform voltage calibration on the commander's term assessment process based on the bus voltage drop, and revise the communication adjustment process based on the bus voltage drop.

[0049] Compared with existing technologies, the beneficial effects of this invention are as follows: the system collects node device operation data through a data sensing module, providing a complete and reliable data foundation for subsequent processing; the system also processes the node device operation data through a data processing module to obtain processed node device operation data, ensuring that the data quality meets the requirements of intelligent control; the system also dynamically elects a commander node through a control scheme generation module, and adjusts communication during the fault control scheme generation process, generating emergency fault control schemes based on historical data when device communication is poor, controlling the fault, thereby improving system reliability and fault response speed; the system also controls the fault according to the fault control scheme through a fault control module, thereby improving the system's fault reliability and fault response speed; the system also dynamically calibrates the commander node selection process and communication adjustment strategy based on bus voltage drop through a control feedback module, further improving the commander node's adaptability, thereby improving fault response speed and system reliability.

[0050] In particular, the system also optimizes and corrects the faults and saturation of the commander's term of office by using a dynamic decision-making unit, combined with node fault judgment and task saturation assessment, and selects the most suitable equipment node as the commander node so as to generate a fault control scheme based on the most suitable commander node, thereby improving fault response speed and system reliability. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the structure of the intelligent control system for the first and second stage integrated pole-mounted circuit breaker in this embodiment;

[0052] Figure 2 This is a schematic diagram of the control scheme generation module in this embodiment;

[0053] Figure 3 This is a flowchart illustrating the method of the intelligent control system for a secondary integrated pole-mounted circuit breaker in this embodiment. Detailed Implementation

[0054] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0055] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0056] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0057] Please see Figure 1 As shown, this is a schematic diagram of the structure of the intelligent control system for the secondary integrated pole-mounted circuit breaker in this embodiment. The system includes:

[0058] The data sensing module is used to collect operational data from node devices;

[0059] The data processing module is used to process the node device operation data to obtain the processed node device operation data. The data processing module is connected to the data sensing module.

[0060] The control scheme generation module is used to filter commander nodes, generate fault control schemes based on the operation data of commander nodes and processed node equipment, judge the term of command and re-filter commander nodes based on the term of command, and adjust the communication during the generation process of fault control schemes. The control scheme generation module is connected to the data processing module.

[0061] A fault control module is used to perform fault control according to a fault control scheme, and the fault control module is connected to the control scheme generation module;

[0062] The control feedback module is used to monitor the bus voltage drop based on the processed node equipment operation data, and to perform voltage calibration in the judgment process of the commander's term of office based on the bus voltage drop. It is also used to revise the voltage in the communication adjustment process based on the bus voltage drop. The control feedback module is connected to the fault control module.

[0063] Specifically, the intelligent control system and method for primary and secondary integrated pole-mounted circuit breakers are applied to the control terminal of primary and secondary integrated pole-mounted circuit breakers in distribution networks. The system collects operational data from node devices, performs intelligent data processing, constructs a distributed consensus mechanism to generate control schemes, adjusts control strategies through multiple optimization verifications, accurately executes fault control commands, and continuously optimizes the control process through a real-time feedback mechanism. This enables rapid isolation and recovery of distribution network faults, significantly improving the efficiency of distribution network fault handling and system reliability. The system collects operational data from node devices through a data sensing module, providing a complete and reliable data foundation for subsequent processing. The system also processes the operational data from node devices through a data processing module to obtain processed node device data. The system operates by generating data to ensure data quality meets intelligent control requirements. It also dynamically elects commander nodes through a control scheme generation module and adjusts communication during the fault control scheme generation process. When equipment communication is poor, it generates emergency fault control schemes based on historical data to control faults, thereby improving system reliability and fault response speed. Furthermore, the system uses a fault control module to control faults according to the fault control scheme, further enhancing system reliability and fault response speed. Finally, the system uses a control feedback module to dynamically calibrate the commander node selection process and communication adjustment strategy based on bus voltage drops, further improving the commander node's adaptability and thus increasing fault response speed and system reliability.

[0064] Specifically, the node device operation data includes the commander node's term of office, commander node operation data, commander node CPU utilization, commander node memory occupancy, communication bandwidth occupancy, signal strength, current bus voltage value, and bus voltage value of the previous calculation cycle. The data sensing module collects the commander node's term of office through a high-precision clock source. The data sensing module collects the commander node's operation data, including the opening and closing coil current waveform and the energy storage motor operating current, through the operating system kernel's performance monitoring module. The data sensing module collects the commander node's CPU utilization (CPU stands for Central Processing Unit). The data sensing module collects the commander node's memory occupancy through the memory manager's statistics interface. The data sensing module collects the communication bandwidth occupancy through the network interface controller's traffic statistics register. The data sensing module collects the signal strength through a communication signal strength sensor. The data sensing module collects the current bus voltage value and the bus voltage value of the previous calculation cycle through a voltage transformer.

[0065] Specifically, the data processing module processes the node device's operating data to obtain processed node device operating data, specifically:

[0066] The data processing module performs data cleaning on the node device operation data to obtain cleaned node device operation data, then fills the cleaned node device operation data to obtain filled node device operation data, and further deletes invalid data from the filled node device operation data to obtain processed node device operation data.

[0067] Specifically, this embodiment does not limit the specific method of data cleaning for the node device operation data. Those skilled in the art can set it according to the actual situation, such as setting reasonable range thresholds for each data item in the node device operation data, identifying and marking data exceeding the reasonable range thresholds to obtain abnormal data points, and using the sliding window mean method to smooth the abnormal data points. The size of the sliding window in the sliding window mean method is set according to the sampling frequency of the node device operation data. This embodiment does not limit the specific implementation method of data filling for the cleaned node device operation data. Those skilled in the art can set it according to the actual situation, such as using linear interpolation to smooth the cleaned node device data. The running data is used for data filling. The linear interpolation method is a curve fitting method widely used in numerical analysis, mathematics, and computer science. It estimates the function value of any unknown point between two known data points by constructing a straight line connecting the two known data points. This embodiment does not limit the specific implementation method of deleting invalid data from the running data of the node device after filling. Those skilled in the art can set it according to the actual situation. For example, M uses Z-score anomaly detection to detect outliers in the running data of the node device after filling and deletes the detected outliers. Z-score anomaly detection is a statistical method that identifies outliers by measuring how many times the distance between a data point and the mean is greater than the standard deviation.

[0068] Specifically, the data processing module processes the node device's operating data to obtain processed node device operating data, thereby providing highly accurate and reliable data.

[0069] Specifically, the fault control module performs fault control according to the fault control scheme using a PID control algorithm.

[0070] Specifically, the PID control algorithm refers to a control algorithm that uses a linear combination of the proportional, integral, and derivative components of the system error to form a control quantity to drive the controlled object.

[0071] Specifically, the control feedback module monitors the bus voltage drop based on the processed node equipment operating data, and performs voltage calibration in the process of determining the commander's term of office based on the bus voltage drop.

[0072] The instantaneous bus voltage drop rate Sj is calculated based on the current bus voltage value V(t), the bus voltage value V(t-Δt) of the previous calculation cycle, and the calculation cycle Δt in the processed node equipment operation data. Sj is set to [V(t) - V(t-Δt)] / Δt. The instantaneous bus voltage drop rate Sj is compared with the preset instantaneous bus voltage drop rate Sj0. Based on the comparison result, the bus voltage drop situation is judged. Based on the judgment result, voltage calibration is performed during the judgment process for the commander's term of office. Wherein:

[0073] When Sj≤Sj0, the control feedback module determines that the bus voltage drop is a normal drop and does not perform voltage calibration in the judgment process of the commander's term of office.

[0074] When Sj > Sj0, the control feedback module determines that the bus voltage drop is abnormal and performs voltage calibration during the judgment process of the commander's term status. The specific content of the voltage calibration is as follows: ignoring the remaining term time of the current commander node, the commander's term status is determined to be the end of the term, and the control scheme generation module is triggered to immediately re-screen the commander node, not including the commander node in the node priority score set Q, and notifying the staff to carry out maintenance on the commander node.

[0075] Specifically, the current bus voltage value refers to the bus voltage value collected at the current moment, and the bus voltage value of the previous calculation cycle refers to the bus voltage value collected during the last calculation of the instantaneous bus voltage drop rate. The calculation cycle refers to the minimum time interval for the system to continuously calculate and judge the bus voltage drop rate. Since the voltage sampling period of the mainstream equipment is 10 milliseconds, Δt = 10 milliseconds is set. The preset instantaneous bus voltage drop rate refers to a preset value for judging the bus voltage drop situation. Based on the analysis of a large amount of fault recording data from the 10kV distribution network, the voltage fluctuation rate caused by normal load switching is less than 0.5 pu / s within a 10ms calculation cycle, so Sj0 = 0.5 is set. pu / s, the bus voltage drop situation refers to the situation of whether the bus voltage drop is normal, which is judged by the instantaneous bus voltage drop rate and the preset instantaneous bus voltage drop rate. The bus voltage drop situation includes normal drop and abnormal drop. The current commander node refers to the node that is the commander node in the current time. The remaining term time refers to the time remaining before the term of 24 hours for the node that is the commander node in the current time.

[0076] Specifically, the control feedback module revises the communication adjustment process based on the bus voltage drop, as follows:

[0077] When the bus voltage drop is a normal drop, the control feedback module does not perform voltage correction during the communication adjustment process;

[0078] When the bus voltage drops abnormally, the control feedback module performs voltage revision during the communication adjustment process. The voltage revision includes: acquiring the fault status of the adjacent nodes of the node, inputting the fault status into a preset rule judgment library, acquiring the preset control scheme output by the preset rule judgment library, and outputting the preset control scheme as the fault control scheme.

[0079] Specifically, the adjacent fault status refers to the fault status of nodes adjacent to the faulty node. Fault status query requests are sent to electrically connected upstream and downstream adjacent nodes. The upstream or downstream adjacent nodes return response information, which includes: whether an abnormal voltage drop is detected, whether an overcurrent is detected, and the current direction (with power flow direction as positive), and the current bus voltage value. The preset rule judgment library refers to a pre-set rule judgment library used to locate the faulty section based on the adjacent fault status and output a corresponding preset control scheme. The rule judgment library includes judgment data, judgment results, and preset control schemes. The judgment data refers to data obtained from the response information, including: Case 1: Local node overcurrent, and upstream node voltage normal and no overcurrent, downstream node voltage abnormal and overcurrent; Case 2: Local node overcurrent, and upstream node voltage abnormal and also overcurrent, downstream node voltage abnormal but no overcurrent; Case 3: Local node communicates with all successfully connected adjacent nodes. All points were monitored for abnormal voltage drops and overcurrent. The judgment result refers to the result of logical judgment based on the judgment data, including Result 1 corresponding to Case 1: the fault point is located on the line between the local node and the downstream node; Result 2 corresponding to Case 2: the fault point is located on the line between the local node and the upstream node; Result 3 corresponding to Case 3: the fault point is located in the upstream common power supply area of ​​all nodes. The preset control scheme refers to the control scheme corresponding to the judgment result, including Scheme 1 corresponding to Result 1: output a trip command to the circuit breaker of the local node, isolate only the fault section between the local node and the downstream node, and notify the downstream node of the voltage loss; Scheme 2 corresponding to Result 2: keep the local node circuit breaker closed, and at the same time send an interlocking command to the upstream node requesting its tripping to isolate the upstream fault section; and Scheme 3 corresponding to Result 3: cancel the local control scheme generated by the communication adjustment unit or historical database, and instead execute the revision strategy of reporting to the master station system and waiting for the superior dispatch command.

[0080] Please see Figure 2As shown, this is a schematic diagram of the control scheme generation module in this embodiment. The control scheme generation module includes:

[0081] The dynamic decision-making unit is used to filter commander nodes through a consensus mechanism to obtain commander nodes. It is also used to judge the term of command and re-filter commander nodes based on the term of command. It is also used to judge the faults of commander nodes based on the processed node equipment operation data and optimize the judgment process of commander term of command based on the judgment results. It is also used to judge the task saturation of commanders based on the processed node equipment operation data and perform saturation correction in the fault optimization process based on the judgment results. It is also used to judge the communication status of equipment based on the processed node equipment operation data and adjust the communication in the generation process of fault control scheme based on the judgment results.

[0082] The scheme generation unit is used to generate fault control schemes based on the commander node, and the scheme generation unit is connected to the dynamic decision unit.

[0083] Specifically, the dynamic decision-making unit selects commander nodes through a consensus mechanism to obtain commander nodes, as follows:

[0084] The dynamic decision-making unit calculates the node priority score q based on the node's historical control success rate A1, communication quality score A2, equipment health status coefficient A3, first priority weight coefficient α1, second priority weight coefficient α2, and third priority weight coefficient α3. The calculation is set as q = α1 × A1 + α2 × A2 + α3 × A3. This yields the node priority score q for all nodes, resulting in a node priority score set Q{q1, q2, q3, ..., qn-1, qn}. The node with the highest reputation score is selected as the commander node from this set Q. Here, q1 refers to the node priority score of the first node, q2 refers to the node priority score of the second node, q3 refers to the node priority score of the third node, qn-1 refers to the node priority score of the (n-1)th node, and qn refers to the node priority score of the nth node, where n is the node's order.

[0085] Specifically, the node historical control success rate refers to the ratio of the number of fault control tasks successfully executed by the node within a specified time period to the total number of fault control tasks received. The calculation formula is: A1 = LC / LZ × 100%, where LC is the number of successfully executed fault control tasks and LZ is the total number of fault control tasks. The communication quality score is a multi-dimensional comprehensive evaluation index based on node communication performance. It is obtained by inputting the communication latency, packet loss rate, and bandwidth utilization from the processed node device operating data into the communication quality score model, and then obtaining the communication quality score output by the model. The communication quality score model is a machine learning model that takes communication latency, packet loss rate, and bandwidth utilization as input and outputs the communication quality score. This embodiment does not limit the construction method of the communication quality score model; those skilled in the art can set it according to actual conditions, such as... A communication quality scoring model is obtained by training a machine learning model on a communication quality training set. The communication quality training set refers to the dataset used to train the machine learning model to obtain the communication quality scoring model. Communication latency refers to the average round-trip latency between a node and other nodes in the network. In this embodiment, communication latency is obtained through a traffic monitoring coprocessor. Packet loss rate refers to the data packet transmission success rate. In this embodiment, packet loss rate is obtained through a traffic monitoring coprocessor. Bandwidth utilization rate refers to the proportion of bandwidth used for effective data transmission. In this embodiment, bandwidth utilization rate is obtained through a traffic monitoring coprocessor. The device health status coefficient comprehensively reflects the physical condition of the node device. The health status indicators are obtained by inputting CPU temperature, system runtime, and number of abnormal restarts into a health status assessment model, and then obtaining the device health status coefficient output by the health status assessment model. The health status assessment model is a machine learning model that takes CPU temperature, storage device lifespan, system runtime, and number of abnormal restarts as inputs and outputs the device health status coefficient. This embodiment does not limit the construction method of the health status assessment model; those skilled in the art can set it according to actual conditions. For example, the machine learning model can be trained using a health assessment training set. The health assessment training set refers to the dataset used to train the machine learning model to obtain the health status assessment model. CPU temperature refers to the real-time operating temperature of the central processing unit chip core, which is directly read from the CPU's built-in digital temperature sensor. System runtime refers to the continuous runtime of the node device from its last startup to the present, obtained by reading the jiffies counter maintained by the kernel. The number of abnormal restarts refers to the statistics of unplanned system restart events, obtained by reading restart records in the system log. The first priority weight coefficient refers to the weight calculation coefficient corresponding to the historical control success rate of the node when calculating the node priority score. The second priority weight coefficient refers to the weight calculation coefficient corresponding to the node's historical control success rate when calculating the node priority score.The weighting coefficients corresponding to the communication quality score, the third priority weighting coefficient refers to the weighting coefficient corresponding to the device health status coefficient when calculating the node priority score. This embodiment does not limit the specific values ​​of the first, second, and third priority weighting coefficients; those skilled in the art can set them according to actual conditions, such as setting α1=0.6, α2=0.2, and α3=0.2 according to the proportion of importance.

[0086] Specifically, the dynamic decision-making unit assesses the term of office of commanders and re-selects commander nodes based on the term of office, as follows:

[0087] The term of office T of the commander node in the processed node device operation data is compared with the preset term of office T0. Based on the comparison result, the term of office of the commander is judged, and the commander nodes are re-selected based on the judgment result.

[0088] When T < T0, the dynamic decision-making unit determines that the commander's term of office has not ended and does not re-screen the commander node;

[0089] When T≥T0, the dynamic decision-making unit determines that the commander's term has ended and re-screens the commander nodes.

[0090] Specifically, the term of office of the commander node refers to the time during which the node operates as the commander node, and the preset term of office refers to a preset value used to judge the term of office. This embodiment does not limit the specific value of the preset term of office, and those skilled in the art can set it according to the actual situation. For example, to ensure the dynamic load balancing and fault tolerance of the system, T0 is set to 24h. The term of office of the commander refers to whether the node has ended its term of office, as judged by the term of office of the commander node and the preset term of office. The term of office of the commander includes whether the term of office has not ended and whether the term of office has ended. In this embodiment, the specific implementation process of re-selecting the commander node is as follows: re-select the node with the highest priority score in the node priority score set Q as the commander node.

[0091] Specifically, the dynamic decision-making unit performs fault assessment on the commander node based on the processed node device operating data, and optimizes the assessment process for the commander's term status based on the assessment results. Specifically:

[0092] A commander node fault diagnosis model is constructed, and the commander node operation data from the processed node device operation data is input into the commander node fault diagnosis model. The commander node fault status output by the commander node fault diagnosis model is obtained. The commander node fault status includes whether the commander node has a fault or not. The fault diagnosis process for the commander's term status is optimized based on the commander node fault status, wherein:

[0093] When the commander node failure condition is that the commander node does not have a failure, the dynamic decision-making unit does not perform fault optimization in the judgment process of the commander's term of office.

[0094] When the commander node is faulty, the dynamic decision-making unit optimizes the judgment process of the commander's term status. The optimization includes: ignoring the remaining term of the current commander node, determining its term status as ended, triggering the control scheme generation module to immediately re-screen the commander node, not including the commander node in the node priority score set Q, and notifying staff to repair the commander node.

[0095] Specifically, this embodiment does not limit the specific implementation method for constructing the commander node fault judgment model. Those skilled in the art can set it according to the actual situation. For example, a machine learning model can be trained using a command fault dataset to obtain a trained machine learning model, and then the trained machine learning model can be lightweighted to obtain the commander node fault judgment model. This embodiment does not limit the specific implementation method for lightweighting the trained machine learning model. Those skilled in the art can set it according to the actual situation. For example, the trained machine learning model can be lightweighted by pruning it. Pruning refers to removing unimportant weights or structures in the model to create a sparser and more efficient network. The command fault dataset refers to the training dataset used to construct the commander node fault judgment model. The command fault dataset includes historically acquired commander node operation data and the commander node fault status corresponding to the historically acquired commander node operation data. The presence of a fault in a commander node means that the commander node has a fault, and the absence of a fault in a commander node means that the commander node has no fault.

[0096] Specifically, the dynamic decision-making unit also calculates the task saturation index W based on the commander node's CPU utilization B1, memory occupancy B2, communication bandwidth occupancy B3, first saturation weighting coefficient γ1, second saturation weighting coefficient γ2, and third saturation weighting coefficient γ3 in the processed node device operation data. The calculation is set as W = B1 × γ1 + B2 × γ2 + B3 × γ3. The task saturation index W is compared with a preset task saturation index W0. Based on the comparison result, the commander's task saturation status is judged, and the fault optimization process is saturated and corrected according to the judgment result.

[0097] When W≤W0, the dynamic decision-making unit determines that the commander's task saturation is unsaturated and does not perform saturation correction on the fault optimization process;

[0098] When W > W0, the dynamic decision-making unit determines that the commander's task saturation is saturated and performs saturation correction on the fault optimization process. The saturation correction includes: if the commander node fault condition is that the commander node does not have a fault, the commander nodes are also re-screened, and a fault correction condition is added when re-screening the commander nodes. The fault correction condition is: the task saturation index of the commander node is the minimum value in the node priority score set, and the commander node whose task saturation is saturated is not included in the node priority score set Q.

[0099] Specifically, the commander node CPU utilization rate refers to the proportion of time the commander node's central processing unit is in a non-idle state per unit time; the commander node memory utilization rate refers to the ratio of the commander node's used physical memory to the total physical memory; the communication bandwidth utilization rate refers to the ratio of the real-time data transmission rate of the commander node's network interface to its theoretical maximum bandwidth; the first saturation weight coefficient is the weight calculation coefficient corresponding to the commander node CPU utilization rate when calculating the task saturation index; the second saturation weight coefficient is the weight calculation coefficient corresponding to the commander node memory utilization rate when calculating the task saturation index; and the third saturation weight coefficient is the weight calculation coefficient corresponding to the communication bandwidth utilization rate when calculating the task saturation index. This embodiment does not include the first saturation weight coefficient. The specific values ​​of the number, the second saturation weight coefficient, and the third saturation weight coefficient are limited. Those skilled in the art can set them according to the actual situation. For example, γ1=0.5, γ2=0.3, and γ3=0.2 can be set according to the importance ratio. The preset task saturation index refers to the preset value for judging the saturation status of the commander's task. This embodiment does not limit the specific value of the preset task saturation index. Those skilled in the art can set it according to the actual situation. For example, through multiple experiments, it is found that when the task saturation index is greater than 0.7, the commander's task saturation status is saturated. Therefore, W0=0.7 is set. The commander's task saturation status refers to whether the commander's node task is saturated according to the task saturation index and the preset task saturation index. The commander's task saturation status includes saturation and non-saturation.

[0100] Specifically, the scheme generation unit generates a fault control scheme based on the commander node, as follows:

[0101] A fault control scheme generation model is constructed using a fault control scheme generation model construction method. The electrical data of each node is acquired through the commander node to obtain an electrical dataset, which is then input into the fault control scheme generation model to obtain the fault control scheme output by the model. The fault control scheme generation model construction method includes:

[0102] Historical control datasets are acquired and divided into a 70% training set, a 20% validation set, and a 10% test set. The training set is input into a decision tree model for training. The validation set is input into the trained decision tree model for iterative hyperparameter optimization. The test set is then input into the optimized decision tree model for control testing, yielding the test results. The total number of test samples is set as f0, the number of correctly controlled test samples is f, and the control test accuracy is F, where F = f / f0. The control test accuracy F is compared with the preset control test accuracy F0. Based on the comparison results, the training performance of the iteratively optimized decision tree model is judged, and the judgment result is output.

[0103] When F≥F0, the scheme generation unit determines that the training of the iteratively optimized decision tree model has reached the target, and outputs the iteratively optimized decision tree model as the fault control scheme generation model.

[0104] When F < F0, the scheme generation unit determines that the training of the iteratively optimized decision tree model is not up to standard, updates the historical control dataset to obtain the updated historical control dataset, and trains, iteratively optimizes hyperparameters, and analyzes and tests the decision tree model based on the updated historical control dataset until the training of the decision tree model is up to standard.

[0105] Specifically, the historical control dataset includes historically acquired electrical datasets and corresponding fault control schemes. This embodiment does not limit the specific implementation method for acquiring the historical control dataset; those skilled in the art can set it according to actual conditions, such as storing historical electrical datasets and corresponding fault control schemes. The control model refers to a decision tree model that takes preprocessed semiconductor production data as input and control data as output. The control training set refers to the dataset in the historical control dataset used to train the decision tree model. The control validation set refers to the dataset in the historical control dataset used to validate the training results of the decision tree model. The control test set refers to the dataset in the historical control dataset used to test the decision tree model. The preset control test accuracy rate refers to a preset value used to judge the training compliance of the iteratively optimized decision tree model. The training compliance of the iteratively optimized decision tree model refers to the accuracy compliance of the trained decision tree model. The training compliance of the iteratively optimized decision tree model includes both training compliance and training non-compliance.

[0106] Specifically, the communication adjustment unit judges the communication status of the devices based on the processed node device operating data, and adjusts the communication in the fault control scheme generation process according to the judgment result, specifically as follows:

[0107] A historical fault control scheme database is constructed, and the signal strength M in the processed node device operation data is compared with the preset signal strength M0. Based on the comparison results, the device communication status is judged, and the communication adjustment process of the fault control scheme generation process is adjusted according to the judgment results.

[0108] When M > M0, the communication adjustment unit determines that the device communication status is qualified and does not adjust the communication during the generation process of the fault control scheme.

[0109] When M≤M0, the communication adjustment unit determines that the device communication is unqualified, adjusts the communication process of the fault control scheme generation process, cancels the generation of the fault control scheme based on the commander node, inputs the current node electrical parameters in the processed node device operation data into the historical fault scheme control database, obtains the historical fault control scheme in the historical fault scheme control database, and outputs the historical fault control scheme as the fault control scheme.

[0110] Specifically, the signal strength refers to the received signal strength indication value of the communication link between the node device and the commander node. The preset signal strength refers to a preset value used to judge the communication status of the device. This embodiment does not limit the specific value of the preset signal strength; those skilled in the art can set it according to actual conditions. For example, through multiple experiments, it was found that when the signal strength is less than -75dBm, the device communication status is unqualified. Therefore, M0 = -75dBm is set. The device communication status refers to whether the node communication signal is qualified based on the signal strength and the preset signal strength. The device communication status includes qualified communication and unqualified communication. The historical fault scheme control database is... A relational database stored on the local node contains historically collected node electrical parameters and corresponding historical fault control schemes. This embodiment does not limit the specific implementation of inputting the current node electrical parameters from the processed node equipment operation data into the historical fault control scheme database to obtain the historical fault control schemes in the historical fault control scheme database. Those skilled in the art can set it themselves according to the actual situation, such as comparing the current node electrical parameters with the historically collected node electrical parameters, finding the historically collected node electrical parameters that are consistent with the current node electrical parameters, and outputting the historical fault control scheme corresponding to the historically collected node electrical parameters.

[0111] It is understood that in this embodiment, the system will collect electrical datasets and corresponding fault control schemes from the past 15 days as update data to supplement the historical fault control scheme database and update the historical fault control scheme database periodically.

[0112] Please see Figure 3 As shown, it is a flowchart illustrating the method of the intelligent control system for a secondary integrated pole-mounted circuit breaker in this embodiment. The method includes:

[0113] Step S1: Collect the operating data of the node devices;

[0114] Step S2: Process the node device operation data to obtain processed node device operation data;

[0115] Step S3 involves filtering commander nodes, generating a fault control scheme based on the commander nodes and the processed node equipment operation data, judging the commander's term of office, re-filtering commander nodes based on the commander's term of office, and adjusting the communication in the fault control scheme generation process.

[0116] Step S4: Perform fault control according to the fault control plan;

[0117] Step S5: Monitor the bus voltage drop based on the processed node equipment operation data, perform voltage calibration on the commander's term assessment process based on the bus voltage drop, and revise the communication adjustment process based on the bus voltage drop.

[0118] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A primary and secondary integrated pole-mounted circuit breaker intelligent control system, characterized in that, include: The data sensing module is used to collect operational data from node devices; The data processing module is used to process the node device's operating data to obtain the processed node device operating data. The control scheme generation module is used to filter commander nodes, generate fault control schemes based on the operation data of commander nodes and processed node equipment, judge the term of command and re-filter commander nodes based on the term of command, and adjust the communication during the generation process of fault control schemes. The fault control module is used to perform fault control according to the fault control scheme; The control feedback module is used to monitor the bus voltage drop based on the processed node equipment operation data, and to perform voltage calibration in the judgment process of the commander's term of office based on the bus voltage drop. It is also used to revise the voltage in the communication adjustment process based on the bus voltage drop. The control scheme generation module includes: The dynamic decision-making unit is used to filter commander nodes through a consensus mechanism to obtain commander nodes. It is also used to judge the term of command and re-filter commander nodes based on the term of command. Furthermore, it is used to judge the faults of commander nodes based on the processed node equipment operation data and optimize the judgment process of commander term status based on the judgment results. It is also used to judge the task saturation of commanders based on the processed node equipment operation data and perform saturation correction in the fault optimization process based on the judgment results. Finally, it is used to judge the communication status of equipment based on the processed node equipment operation data and adjust the communication in the generation process of fault control scheme based on the judgment results. The scheme generation unit is used to generate fault control schemes based on the operational data of the commander node and the processed node equipment; The dynamic decision-making unit selects commander nodes through a consensus mechanism to obtain commander nodes, specifically as follows: The dynamic decision-making unit calculates the node priority score q based on the node's historical control success rate A1, communication quality score A2, equipment health status coefficient A3, first priority weight coefficient α1, second priority weight coefficient α2, and third priority weight coefficient α3. The score is set as q = α1 × A1 + α2 × A2 + α3 × A3. This yields the node priority score q for all nodes, resulting in a node priority score set Q{q1, q2, q3, ..., qn-1, qn}. The node with the highest reputation score is selected as the commander node from this set Q. Here, q1 refers to the node priority score of the first node, q2 refers to the node priority score of the second node, q3 refers to the node priority score of the third node, qn-1 refers to the node priority score of the (n-1)th node, and qn refers to the node priority score of the nth node, where n is the order of the nodes. The dynamic decision-making unit assesses the term of office of commanders and re-selects commander nodes based on the term of office, specifically as follows: The term of office T of the commander node in the processed node device operation data is compared with the preset term of office T0. Based on the comparison result, the term of office of the commander is judged, and the commander nodes are re-selected based on the judgment result. When T < T0, the dynamic decision-making unit determines that the commander's term of office has not ended and does not re-screen the commander node; When T≥T0, the dynamic decision-making unit determines that the commander's term has ended and re-screens the commander nodes; The dynamic decision-making unit performs fault assessment on the commander node based on the processed node device operation data, and optimizes the assessment process for the commander's term status based on the assessment results, specifically as follows: A commander node fault diagnosis model is constructed, and the commander node operation data from the processed node device operation data is input into the commander node fault diagnosis model. The commander node fault status output by the commander node fault diagnosis model is obtained. The commander node fault status includes whether the commander node has a fault or not. The fault diagnosis process for the commander's term status is optimized based on the commander node fault status, wherein: When the commander node failure condition is that the commander node does not have a failure, the dynamic decision-making unit does not perform fault optimization in the judgment process of the commander's term of office. When the commander node failure condition is that the commander node is faulty, the dynamic decision-making unit optimizes the judgment process of the commander's term status. The failure optimization includes: ignoring the remaining term time of the current commander node, judging its term status as the end of the term, triggering the control scheme generation module to immediately re-screen the commander node, not including the commander node in the node priority score set Q, and notifying the staff to repair the commander node. The dynamic decision-making unit determines the saturation status of the commander node based on the processed node equipment operating data, and performs saturation correction on the fault optimization process based on the determination result, specifically as follows: Based on the commander node's CPU utilization (B1), memory usage (B2), communication bandwidth usage (B3), first saturation weighting coefficient (γ1), second saturation weighting coefficient (γ2), and third saturation weighting coefficient (γ3) from the processed node device operation data, the task saturation index W is calculated, set as W = B1 × γ1 + B2 × γ2 + B3 × γ3. The task saturation index W is compared with the preset task saturation index W0. Based on the comparison result, the commander's task saturation status is judged, and the fault optimization process is saturated and corrected according to the judgment result. When W≤W0, the dynamic decision-making unit determines that the commander's task saturation is unsaturated and does not perform saturation correction on the fault optimization process; When W > W0, the dynamic decision-making unit determines that the commander's task saturation is saturated and performs saturation correction on the fault optimization process. The saturation correction includes: if the commander node fault condition is that the commander node does not have a fault, the commander nodes are also re-screened, and a fault correction condition is added when re-screening the commander nodes. The fault correction condition is: the task saturation index of the commander node is the minimum value in the node priority score set, and the commander node whose task saturation is saturated is not included in the node priority score set Q.

2. The intelligent control system for primary and secondary integrated pole-mounted circuit breakers according to claim 1, characterized in that, The data processing module processes the node device's operating data to obtain processed node device operating data, specifically: The data processing module performs data cleaning on the node device operation data to obtain cleaned node device operation data, then fills the cleaned node device operation data to obtain filled node device operation data, and further deletes invalid data from the filled node device operation data to obtain processed node device operation data.

3. The intelligent control system for primary and secondary integrated pole-mounted circuit breakers according to claim 1, characterized in that, The scheme generation unit generates a fault control scheme based on the commander node, specifically as follows: The fault control scheme generation model is constructed using the fault control scheme generation model construction method. The electrical data of each node is obtained through the commander node to obtain the electrical dataset. The electrical dataset is then input into the fault control scheme generation model to obtain the fault control scheme output by the fault control scheme generation model.

4. The intelligent control system for primary and secondary integrated pole-mounted circuit breakers according to claim 3, characterized in that, The communication adjustment unit judges the communication status of the devices based on the processed node device operating data, and adjusts the communication in the fault control scheme generation process according to the judgment result, specifically as follows: A historical fault control scheme database is constructed, and the signal strength M in the processed node device operation data is compared with the preset signal strength M0. Based on the comparison results, the device communication status is judged, and the communication adjustment process of the fault control scheme generation process is adjusted according to the judgment results. When M > M0, the communication adjustment unit determines that the device communication status is qualified and does not adjust the communication during the generation process of the fault control scheme. When M≤M0, the communication adjustment unit determines that the device communication is unqualified, adjusts the communication process of the fault control scheme generation process, cancels the generation of the fault control scheme based on the commander node, inputs the current node electrical parameters in the processed node device operation data into the historical fault scheme control database, obtains the historical fault control scheme in the historical fault scheme control database, and outputs the historical fault control scheme as the fault control scheme. The control feedback module monitors the bus voltage drop based on the processed node equipment operating data, and performs voltage calibration in the process of judging the commander's term of office based on the bus voltage drop. Specifically: The instantaneous bus voltage drop rate Sj is calculated based on the current bus voltage value V(t), the bus voltage value V(t-Δt) of the previous calculation cycle, and the calculation cycle Δt in the processed node equipment operation data. Sj is set to [V(t) - V(t-Δt)] / Δt. The instantaneous bus voltage drop rate Sj is compared with the preset instantaneous bus voltage drop rate Sj0. Based on the comparison result, the bus voltage drop situation is judged. Based on the judgment result, voltage calibration is performed during the judgment process for the commander's term of office. Wherein: When Sj≤Sj0, the control feedback module determines that the bus voltage drop is a normal drop and does not perform voltage calibration in the judgment process of the commander's term of office. When Sj > Sj0, the control feedback module determines that the bus voltage drop is abnormal and performs voltage calibration on the judgment process of the commander's term status. The specific content of the voltage calibration is: ignoring the remaining term time of the current commander node, determining that its term status has ended, and triggering the control scheme generation module to immediately re-select the commander nodes. The control feedback module revises the communication adjustment process based on the bus voltage drop, specifically as follows: When the bus voltage drop is a normal drop, the control feedback module does not perform voltage correction during the communication adjustment process; When the bus voltage drops abnormally, the control feedback module performs voltage revision during the communication adjustment process. The voltage revision includes: acquiring the fault status of the adjacent nodes of the node, inputting the fault status into a preset rule judgment library, acquiring the preset control scheme output by the preset rule judgment library, and outputting the preset control scheme as the fault control scheme.

5. A method applied to the intelligent control system of a primary and secondary integrated pole-mounted circuit breaker as described in any one of claims 1-4, characterized in that, include: Step S1: Collect the operating data of the node devices; Step S2: Process the node device operation data to obtain processed node device operation data; Step S3 involves filtering commander nodes, generating a fault control scheme based on the commander nodes and the processed node equipment operation data, judging the commander's term of office, re-filtering commander nodes based on the commander's term of office, and adjusting the communication in the fault control scheme generation process. Step S4: Perform fault control according to the fault control plan; Step S5: Monitor the bus voltage drop based on the processed node equipment operation data, perform voltage calibration on the commander's term assessment process based on the bus voltage drop, and revise the communication adjustment process based on the bus voltage drop.

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