Monitoring and adaptive strategy adjusting method for self-healing line of power distribution network and related device

Abstract

The invention relates to the technical field of data processing, and discloses a power distribution network self-healing line monitoring and self-adaptive strategy adjusting method and a related device, and the method comprises the steps: collecting the operation data of a power distribution network, and transmitting the operation data of the power distribution network through a wireless sensor network employing a multipath routing algorithm; performing key data verification processing on the operation data of the power distribution network to obtain key data of the power distribution network; performing anomaly detection on the key data of the power distribution network to obtain abnormal data of the power distribution network; performing adaptive strategy adjustment processing on the abnormal data of the power distribution network to generate a reference power distribution network adaptive strategy; performing distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy; according to the reference power distribution network self-adaptive strategy and the power distribution network self-adaptive strategy, a power distribution network simulation model is used for strategy optimization processing, a target power distribution network self-adaptive strategy is obtained, and the purposes of on-line monitoring of the power distribution network and accurate self-adaptive strategy adjustment can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and related devices for monitoring and self-adaptive strategy adjustment of a self-healing line in a distribution network. Background Art

[0002] With the continuous expansion and complexity of the power system, the stability and reliability of the distribution network are the core elements to ensure the quality of power supply, which is directly related to the continuity and safety of social and economic activities. In the traditional mode, the configuration of the self-healing line of the distribution network is highly dependent on manual inspection and maintenance operations. This practice is not only inefficient and consumes a lot of human resources, but also increases the possibility of misjudgment or omission due to the uncertainty of human factors. More importantly, it cannot meet the high requirements of modern power grids for real-time and accuracy, especially in the face of sudden failures caused by natural disasters, equipment aging or operational errors. The lack of immediate and effective online monitoring means that the problem is difficult to be quickly identified and properly handled in the early stage, which may lead to the expansion of the scope of the fault and the extension of the repair time, seriously affecting the safe operation of the power grid and the reliability of power supply. In addition, the limitation of manual inspection is that it is difficult to fully cover every node and line of the distribution network, especially in areas with complex geographical environments or large network structures. This limitation is more obvious. Due to the inability to achieve all-weather and all-round monitoring of the system status, many potential safety hazards and performance degradation problems may be ignored, posing a huge challenge to the stable operation of the power system. Therefore, how to achieve online monitoring of the distribution network and accurately adjust and optimize the adaptive strategy has become a problem that needs to be solved urgently. Summary of the invention

[0003] The present invention provides a method and related device for monitoring and self-adaptive strategy adjustment of a distribution network self-healing line, so as to achieve the purpose of online monitoring of the distribution network and accurately adjusting and optimizing the self-adaptive strategy.

[0004] In a first aspect, a method for monitoring and adaptively adjusting a self-healing line of a distribution network is provided, comprising:

[0005] Collect distribution network operation data and transmit it using a wireless sensor network with a multipath routing algorithm;

[0006] Perform key data verification on the distribution network operation data to obtain key data of the distribution network;

[0007] Perform anomaly detection on key data of distribution network to obtain abnormal data of distribution network;

[0008] Perform adaptive strategy adjustment on abnormal data of distribution network and generate reference distribution network adaptive strategy;

[0009] Perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy;

[0010] According to the reference distribution network adaptive strategy and the distribution network adaptive strategy, the distribution network simulation model is used to perform strategy optimization processing to obtain the target distribution network adaptive strategy.

[0011] In a second aspect, a monitoring and adaptive strategy adjustment device for a self-healing line of a distribution network is provided, comprising:

[0012] The collection and transmission module is used to collect the distribution network operation data and transmit the distribution network operation data using the wireless sensor network with multipath routing algorithm;

[0013] The first processing module is used to perform key data verification processing on the distribution network operation data to obtain the key data of the distribution network;

[0014] The second processing module is used to perform anomaly detection on key data of the distribution network to obtain abnormal data of the distribution network;

[0015] The third processing module is used to perform adaptive strategy adjustment processing on the abnormal data of the distribution network and generate a reference distribution network adaptive strategy;

[0016] A fourth processing module is used to perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy;

[0017] The fifth processing module is used to perform strategy optimization processing using a distribution network simulation model according to a reference distribution network adaptive strategy and a distribution network adaptive strategy to obtain a target distribution network adaptive strategy.

[0018] In a third aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for monitoring and adaptively adjusting the self-healing strategy of the distribution network self-healing line are implemented.

[0019] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned method for monitoring and adaptively adjusting the self-healing line of the distribution network are implemented.

[0020] In the scheme implemented by the above-mentioned distribution network self-healing line monitoring and adaptive strategy adjustment method and related devices, by collecting distribution network operation data and transmitting the distribution network operation data using a wireless sensor network with a multipath routing algorithm, the distribution network operation data can be subjected to key data verification processing to obtain distribution network key data, and the distribution network key data can be subjected to abnormality detection to obtain distribution network abnormal data, so that the distribution network abnormal data can be subjected to adaptive strategy adjustment processing to generate a reference distribution network adaptive strategy, so as to further perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy, and then the distribution network simulation model can be used to perform strategy optimization processing according to the reference distribution network adaptive strategy and the distribution network adaptive strategy to obtain a target distribution network adaptive strategy, which is beneficial to improving the accuracy of the monitoring and adaptive strategy adjustment process for the distribution network self-healing line, so as to achieve the purpose of online monitoring of the distribution network and accurately adjusting and optimizing the adaptive strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0022] Figure 1 It is a technical architecture diagram of a method for monitoring and adaptive strategy adjustment of a self-healing line of a distribution network in one embodiment of the present invention;

[0023] Figure 2 It is a flow chart of a method for monitoring and adaptive strategy adjustment of a self-healing line of a distribution network in one embodiment of the present invention;

[0024] Figure 3 It is a schematic diagram of a process of data transmission in a wireless sensor network using a multipath routing algorithm in one embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of a key data verification process in one embodiment of the present invention;

[0026] Figure 5A is a schematic diagram of a distribution network self-healing control area in one embodiment of the present invention;

[0027] Figure 5B is a schematic diagram of a fault wiring in one embodiment of the present invention;

[0028] Figure 6 is a schematic diagram of a process for generating an adaptive adjustment strategy in one embodiment of the present invention;

[0029] Figure 7 It is a schematic diagram of a process of using a binary particle swarm algorithm to perform distribution network reconstruction in one embodiment of the present invention;

[0030] Figure 8 It is a schematic diagram of a process of automatically sensing and updating a self-healing line type change in one embodiment of the present invention;

[0031] Fig. 9 It is a structural schematic diagram of a device for monitoring and adaptively adjusting a self-healing line of a distribution network in one embodiment of the present invention;

[0032] Fig.10 is a schematic diagram of a structure of a computer device in one embodiment of the present invention;

[0033] Fig.11 It is another structural schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1 , Figure 1 The technical architecture diagram of the method for monitoring and adaptive strategy adjustment of the self-healing line of the distribution network provided by the present application is exemplarily shown. Figure 1 As shown in the figure, this solution achieves a comprehensive improvement in the intelligent management of distribution networks through key technical means such as online visual monitoring, real-time adaptive strategy adjustment, and simulation verification.

[0036] This application aims to solve the key problems faced by the distribution network during operation, such as intelligent management, fault self-healing ability improvement, and distribution network structure optimization. Through online visual monitoring of distribution network operation data, the system can grasp the operation status of the distribution network in real time, and promptly discover and handle abnormal situations. At the same time, a self-healing function feature library is established to collect and organize the key information and self-healing strategies of terminal equipment, providing strong support for subsequent adaptive adjustments. In order to improve the efficiency and accuracy of data processing, this application combines cloud computing technology and visual management tools to efficiently analyze and mine a large amount of data, convert complex data sets into easy-to-understand visual reports, and help operation and maintenance personnel better understand the operation status of the distribution network. In addition, the system can also use genetic algorithms to generate adaptive adjustment strategies, including line adjustment and switch adjustment, to optimize the operation status of the distribution network. The distribution network is reconstructed through the binary particle swarm algorithm to further optimize the distribution network structure and improve its reliability and economy. In terms of self-healing line type changes, this application can also dynamically monitor the terminal information point table of the automatic switch, providing an important basis for subsequent optimization. This application significantly enhances the self-healing ability of the distribution network, improves the level of intelligent management of the power grid, and lays a solid technical foundation for building a more reliable and efficient energy supply network.

[0037] See also Figure 2 As shown, Figure 2 A flow chart of a method for monitoring and adaptively adjusting a self-healing line in a distribution network provided by an embodiment of the present invention includes the following steps:

[0038] S10: Collecting distribution network operation data, and transmitting the distribution network operation data using a wireless sensor network with a multipath routing algorithm.

[0039] It should be noted that collecting distribution network operation data is the starting point of the entire process. Through various sensors and monitoring equipment, key parameters such as voltage, current, and power of the distribution network are collected in real time to obtain the operating status information of the distribution network. Optionally, data preprocessing technology can be used to perform preliminary processing on the data. The preprocessing may include but is not limited to data cleaning, removal of outliers, normalization, and early warning of abnormal phenomena. The above steps can monitor the distribution network operation data in real time online, so that the operation and maintenance personnel can monitor, query, and analyze the distribution network data, and receive early warning signals.

[0040] Wireless sensor networks using multipath routing algorithms can transmit the collected data. Among them, the multipath routing algorithm is a routing selection strategy for data transmission in wireless sensor networks. It can transmit data by using multiple paths at the same time, aiming to improve the reliability and performance of data transmission. The algorithm selects multiple different paths in the network so that data can be transmitted through different nodes and links, thereby increasing the alternative paths for data transmission and reducing the risk of single path transmission failure. Wireless Sensor Network (WSN) is a self-organizing network composed of many wireless sensor nodes scattered over a wide area. The sensor nodes have the ability to perceive a variety of environmental information, such as temperature, humidity, light intensity and pressure, and can transmit the collected data to a base station or monitoring center through wireless communication.

[0041] See also Figure 3 , Figure 3 The following is a schematic diagram showing a flow chart of data transmission in a wireless sensor network using a multipath routing algorithm. Figure 3 As shown, data transmission in a wireless sensor network using a multipath routing algorithm can be achieved through topology construction, path selection, data aggregation, routing maintenance, network coding application, data compression and filtering, data coding, source node determination and data aggregation, multipath routing construction and data aggregation, and routing recovery and analysis.

[0042] It is understandable that the use of multipath routing algorithms for wireless sensor networks for data transmission has significant advantages. This transmission method can select multiple paths to transmit data simultaneously to improve the reliability of data transmission and the fault tolerance of the network. In addition, the multipath routing algorithm can optimize resource utilization, improve network performance, and support load balancing. When faced with complex and changing network environments, the algorithm can flexibly adapt, reduce data transmission delays, and improve data transmission security.

[0043] S20: Perform key data verification processing on the distribution network operation data to obtain key data of the distribution network.

[0044] Key data verification is a prerequisite for operation mode calculation. Key data verification mainly involves extracting the main network and distribution network data from the database, parsing, splitting and classifying them, splicing the distribution network model into a new overall model, and analyzing and verifying the accuracy and correspondence of the graphics, topology and data of the new model to ensure that the data is accurate and reliable before being used for operation mode calculation. The purpose of key data verification is to provide a correct data basis for operation mode calculation.

[0045] The server can first extract the main network and distribution network data from the database and parse it using file parsing software; after obtaining the complete data, it can be reasonably split and classified; by analyzing the model topology attributes, the distribution network model can be spliced ​​into a new overall model; the accuracy and correspondence of the graphics, topology and data of the new model are further analyzed and verified, and only when the verification results are correct can they be output for calculation. Optionally, if the system finds an error, it can prompt the operation and maintenance personnel, and the erroneous data enters the manual process; professionals can read and re-verify the data, confirm the error, correct it, and re-enter the system for verification until it passes the verification and is correctly output.

[0046] See also Figure 4 , Figure 4 A schematic diagram of a key data verification process is shown as an example. Figure 4 As shown, the server can first obtain the distribution network data to splice the data into a model, so as to verify whether there are errors when checking the key data. If an error is found, a prompt can be given and manual modification can be performed; if no error is found, the data can be output, and this application does not limit this.

[0047] Optionally, the server can further combine the above data collection and key data verification with cloud computing technology and visual management to achieve efficient management of data. In order to manage distribution network data more efficiently, the computing resources of the cloud platform can be used to prepare for data analysis through pre-processing operations such as data cleaning, conversion, and integration; then use big data analysis tools or custom algorithms to perform data analysis; and then use visualization tools, such as Tableau used in this application, to visualize distribution network data, thereby converting complex data sets into intuitive, easy-to-understand visual reports, which are convenient for operation and maintenance personnel to monitor and analyze.

[0048] S30: Performing anomaly detection on the key data of the distribution network to obtain abnormal data of the distribution network.

[0049] After key data verification processing is performed on the data, the key data of the distribution network can be further monitored for abnormalities to detect abnormal situations of the key data of the distribution network that has been determined to be relatively accurate, thereby discovering data that does not conform to normal operating conditions, and then determining whether there are abnormal situations in the distribution network.

[0050] Optionally, the server can establish a self-healing function feature library. Online visual monitoring of distribution network operation data provides necessary data support for the establishment of the self-healing function feature library; the creation of the self-healing function feature library also improves the adjustment efficiency for subsequent real-time adaptive strategy adjustment. Finally, the combination of cloud computing technology and visual management can be used to efficiently manage and analyze the self-healing function feature library.

[0051] (1) Design of self-healing function feature library. To realize the self-healing strategy adjustment, in addition to monitoring the basic parameters such as current and voltage of the distribution network operation data, it is also necessary to collect and monitor key information such as the name, type, and connection status of the terminal equipment. And classify and organize the information such as fault detection and fault isolation in self-healing to improve the efficiency of real-time adaptive strategy adjustment as a whole. The design of the self-healing function feature library includes creating a feature signal library for classifying the switch self-healing type and creating a terminal information point table.

[0052] 1) Classification of switch self-healing types. Self-healing types are mainly classified according to fault detection and isolation methods. In actual applications, the self-healing type can be selected according to specific needs and scenarios. The switch self-healing types are divided into main station centralized type, extreme difference protection type, voltage time type, and intelligent distributed type.

[0053] Master station centralized type: Through two-way communication between the distribution master station and the distribution terminal, the distribution master station automatically calculates or manually controls the switching of switch equipment based on the real-time collected distribution network and distribution equipment operation information and fault signals, thereby optimizing the distribution network operation mode, quickly isolating faults and restoring power supply.

[0054] Extreme difference protection type: Extreme difference protection type self-healing generally includes a multi-level protection mechanism, which determines the fault interval by comparing the difference in protection signals of switches at different locations. For example, when a certain section of cable fails, the upstream switch will trip and send a protection signal, but the downstream switch will not send a protection signal, so the fault interval can be located.

[0055] Voltage-time type: Voltage-time type self-healing mainly judges faults based on voltage changes and time delays. When a fault occurs, the tripping of the feeder switch will cause a loss of voltage, and then the load switch will be opened; when the feeder switch is reclosed, the fault interval is judged based on the delayed closing of the load switch, and then the power supply to the non-fault area is restored.

[0056] Intelligent distributed type: Intelligent distributed self-healing uses intelligent equipment and distributed control technology to achieve rapid detection and isolation of faults. For example, the circuit breaker and the contact switch can simultaneously detect the forward fault current and trip, and then close the contact switch through self-healing action based on the protection action information and the voltage loss in the previous section, thereby restoring power supply.

[0057] By classifying the switch self-healing types and further analyzing the characteristics of the switch self-healing types, a feature signal library is established. The characteristics of the switch self-healing types are shown in the following table:

[0058]

[0059]

[0060] 2) Create a terminal information point table. The terminal information point table is a table that records the information of all terminal devices in the network in detail, including key information such as the Internet Protocol (IP) address, Media Access Control (MAC) address, device name, device type, and connection status of each device. The main purpose of this table is to help network administrators or system maintenance personnel quickly understand and grasp the operating status of each terminal device in the network, so as to facilitate troubleshooting, performance monitoring, and security management.

[0061] (2) Combine cloud computing technology with visual management to manage data efficiently. In order to more efficiently manage the self-healing function feature library and distribution network data, the computing resources of the cloud platform are used to prepare for data analysis through pre-processing operations such as data cleaning, conversion, and integration. Then use big data analysis tools or custom algorithms to analyze the data; then use visualization tools. This patent uses Tableau to realize the visualization of distribution network data, converting complex data sets into intuitive and easy-to-understand visual reports for easy monitoring and analysis by operation and maintenance personnel. Such data management can improve the quality of master station data and terminal data. The quality of master station data reaches: the master station diagram topology information, terminal ledger entry information, protection status and switch association, equipment listing, substation outgoing line switch rated current, etc. are correct; the quality of terminal data reaches: primary and secondary equipment abnormalities / defects are handled, the terminal three remote signals are normal, and there are no abnormalities in remote signaling and telemetry status.

[0062] S40: performing adaptive strategy adjustment processing on the abnormal data of the distribution network to generate a reference distribution network adaptive strategy.

[0063] Once an anomaly is detected, that is, when the above-mentioned distribution network abnormal data is obtained, in order to restore the normal operation of the distribution network and optimize its performance, the adaptive strategy adjustment process can be started. This application generates a reference distribution network adaptive strategy and uses a distribution network simulation model to perform strategy optimization processing to obtain a target distribution network adaptive strategy, thereby adjusting the distribution network to solve abnormal problems and thereby improve the operating efficiency and reliability of the distribution network.

[0064] In the process of implementing real-time adaptive strategy adjustment, the server can first determine the self-healing control area of ​​the distribution network to ensure that the system can respond quickly and effectively manage faults; next, the genetic algorithm can be used to generate adaptive adjustment strategies, including but not limited to switch adjustment and line adjustment, to optimize the operating status of the distribution network. The adaptive strategy adjustment process can realize real-time monitoring and adaptive adjustment of the distribution network, which is conducive to ensuring the stable operation of the power system.

[0065] The core goal of distribution network self-healing control can be to optimize the application effect of distribution network self-healing technology. Its fundamental purpose is to prevent the occurrence of power grid failures and limit power outages to the smallest possible range when unavoidable failures occur, so as to reduce the impact on daily life and production activities. In addition, distribution network self-healing control can effectively reduce the scope of power outages and shorten the duration of power outages, thereby minimizing the losses caused by power outages. Therefore, clarifying the distribution network self-healing control area is conducive to the subsequent generation of adaptive adjustment strategies.

[0066] When abnormal data of the distribution network is detected, these abnormal data can reflect areas in the distribution network where there may be faults or unstable operation. By analyzing the abnormal data, the scope of the area where self-healing control may be required can be preliminarily determined. After determining the self-healing control area, the specific conditions in the area, such as line connection, switch status, load distribution and other data, can be focused on, such as using genetic algorithms, to generate adaptive adjustment strategies.

[0067] See also Figure 5A , Figure 5A A schematic diagram of a distribution network self-healing control area is shown as an example. The self-healing control area is a specific area in the distribution network that is demarcated to quickly locate, isolate and restore power supply to faults. Within this area, the distribution network can automatically respond to and handle various fault conditions through its own control and regulation capabilities to minimize the power outage time and impact range and improve power supply reliability. After determining the self-healing control area based on abnormal data and then generating a strategy, it can better adapt to the needs of specific areas, achieve more accurate fault handling and operation optimization, and improve the self-healing ability and overall operation efficiency of the distribution network.

[0068] It should be understood that the adaptive strategy adjustment processing of the abnormal data of the distribution network to generate the reference distribution network adaptive strategy refers to the process of how to generate the reference distribution network adaptive strategy. Among them, step S40, that is, the adaptive strategy adjustment processing of the abnormal data of the distribution network to generate the reference distribution network adaptive strategy, includes the following steps:

[0069] S41: determining a self-healing control area according to the abnormal data of the distribution network to obtain a target self-healing control area;

[0070] S42: performing target initialization using a genetic algorithm according to the target self-healing control area and the abnormal data of the distribution network to obtain initialization population data;

[0071] S43: Perform fitness evaluation according to the initialized population data to obtain a fitness evaluation data set;

[0072] S44: performing a population update operation according to the fitness evaluation data set to obtain target population data;

[0073] S45: Determine a reference distribution network adaptive strategy according to the target population data and preset conditions.

[0074] In the self-healing control of smart distribution networks, the adaptive adjustment strategies generated by genetic algorithms can mainly include line adjustment and switch adjustment. Among them, line adjustment can optimize the topology of the distribution network and improve the reliability and economy of power supply by changing the line connection method; while switch adjustment focuses on real-time monitoring and control of switchgear in the distribution network to quickly respond to faults and isolate problem areas, thereby reducing the scope and duration of power outages. The above two adjustment strategies complement each other and together constitute the core content of the self-healing control of smart distribution networks.

[0075] For example, for switch adjustment, when a line is detected to be overloaded, it can automatically switch to a backup line to balance the load; or when a fault occurs, the fault area can be quickly isolated to prevent the problem from spreading. It is understandable that the above adjustments can be made based on the current operating status and historical data, aiming to optimize the performance of the distribution network. Line adjustments can include measures such as replanning the line direction and adjusting the cross-sectional area of ​​the conductor to improve the transmission efficiency and reliability of the distribution network. Through continuous iteration and optimization, the line configuration scheme that best suits the current operating conditions can be found.

[0076] For example: Figure 5B Taking the fault wiring diagram shown as an example, the circuit breaker S1 trips, and the inlet and outlet switches of the line distribution network lose pressure and open; the circuit breaker S1 is reclosed, and after A2 is closed, it is closed to the fault point, and the circuit breaker S1 trips again, causing A1 and A2 to lose pressure and open again.

[0077] Signal collection: The tripping and protection action of circuit breaker S1 are forwarded to the main distribution station through the main network OCS; the opening and protection action signals of the distribution network switch of this line, the A2 blocking signal, and the A3 blocking signal are sent to the main distribution station through the distribution terminal.

[0078] On-site action: Fault location: The first reclosing to A2 causes S1 to trip again, and it is determined that a cable fault occurs between A2 and A3. Fault isolation: A2 switch is locked, A3 is locked; the distribution terminal sends the A2 and A3 locking signals to the distribution master station. Upstream power restoration: It can be determined that the fault can be self-healed using an intelligent distributed type. According to the protection action information and the voltage loss in the previous section, the contact switch is closed through self-healing action to restore power supply, that is, the second reclosing of the outgoing line switch S1 is successful, and the upstream power restoration is completed. The dispatcher manually selects the plan according to the self-healing transfer strategy of the master station, and closes A9 or A6 by remote control; the downstream switches in the fault area are closed one by one with power on site to complete the downstream power restoration.

[0079] See also Figure 6 , Figure 6 The schematic diagram of a process flow for generating an adaptive adjustment strategy is shown as an example. It can be understood that the present application applies the genetic algorithm optimization process to the optimization problem of a complex system, namely, the self-healing control of a distribution network, so as to find a near-optimal solution in a complex solution space through continuous iteration and selection.

[0080] The server can perform target initialization. First, clarify the optimization goal to determine the direction of optimization, such as minimizing the power outage range, shortening the power outage time, etc., which will serve as the basis for subsequent evaluation of individual fitness. Secondly, determine the objective function to establish a mathematical function to quantify the degree of achievement of the goal based on the optimization goal. For example, if the goal is to minimize the power outage range, the objective function may be the size of the power outage range. Further, initialize the population and randomly generate a set of possible solutions (i.e., configuration schemes, which can be called individuals). This set of possible solutions constitutes the initial population. For example, in distribution network optimization, the above individuals can be different distribution network topologies or switch configurations.

[0081] The server can further evaluate the fitness. The fitness value of each individual can be calculated, and the fitness value is related to factors such as the power outage range and power outage time. Among them, the higher the fitness value, the closer the individual is to the optimal solution, that is, the higher the degree of excellence of the individual in solving the problem. For example, in the optimization of the distribution network, the distribution network topology corresponding to the individual with high fitness may have a smaller power outage range and a shorter power outage time, which is not limited in this application.

[0082] The server can perform a population update operation to obtain a target population. It should be understood that performing a population update operation according to the fitness evaluation data set to obtain target population data refers to the process of how to obtain target population data. Among them, step S44, that is, performing a population update operation according to the fitness evaluation data set to obtain target population data, includes the following steps:

[0083] S441: performing a selection operation on each fitness evaluation data in the fitness evaluation data set to obtain a target selection configuration data set;

[0084] S442: performing a cross operation on each target selection configuration data in the target selection configuration data set to obtain a target cross configuration data set;

[0085] S443: performing a mutation operation on each target selection configuration data in the target selection configuration data set to obtain a target mutation configuration data set;

[0086] S444: Determine target population data according to the target cross configuration data set and the target variation configuration data set.

[0087] Among them, the selection operation can further select according to the fitness of the individual, and the individual with high fitness can have a greater probability of being selected into the next generation, that is, the target population. The crossover operation can cross the selected individuals (that is, the target selection configuration data in the target selection configuration data set) according to a certain probability, that is, exchange some genes of the individuals to generate new individuals. For example, in the optimization of the distribution network, the configuration of some lines or switches in two distribution network topologies may be exchanged. The mutation operation can mutate certain genes of the individual according to a certain probability to increase the diversity of the population. For example, randomly change the state of a switch in the distribution network topology.

[0088] After performing the above operations, the server can replace a part of individuals in the initial population with newly generated individuals (such as the above target cross-configuration data set and the target variation configuration data set) to form a new generation population, namely the target population.

[0089] The preset conditions may be pre-set by the target user or set by the system as a default condition for determining termination. Common termination conditions may include, but are not limited to, reaching a preset maximum number of iterations, the fitness value reaching a preset threshold, and the fitness value having no significant change in several consecutive generations.

[0090] It is understandable that if the termination condition is met, the current optimal solution can be output. If the termination condition is not met, the iteration can continue, such as returning to the fitness evaluation step to recalculate the fitness value of the individuals in the new population, and repeating the subsequent operations until the termination condition is met.

[0091] S50: Perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy.

[0092] After the adaptive adjustment strategy is generated, further execution of the strategy may change the topology of the distribution network. Therefore, in order to ensure the operating efficiency and reliability of the distribution network, this application uses a binary particle swarm algorithm to reconstruct the distribution network to search for the optimal solution by simulating the foraging behavior of bird flocks. The core idea of ​​the algorithm is to regard each possible solution in the problem space as a "particle" and update the position and velocity of the particle according to the extreme value information of the individual and the group.

[0093] In the process of distribution network reconstruction, the position of particles can represent the topological structure of the distribution network, while the speed can reflect the changing trend of the topological structure. By continuously iteratively updating the position and speed of particles, the binary particle swarm algorithm can search for the optimal or suboptimal distribution network topological structure in the complex solution space, thereby realizing the optimal reconstruction of the distribution network.

[0094] See also Figure 7 , Figure 7A schematic diagram of a process of using a binary particle swarm algorithm to reconfigure a distribution network is shown exemplarily. It should be understood that performing distribution network reconfiguration processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy refers to the process of how to obtain a reconstruction implementation strategy. Among them, step S50, that is, performing distribution network reconfiguration processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy, includes the following steps:

[0095] S51: Determine a distribution network reconstruction set using a binary particle swarm algorithm according to the reference distribution network adaptive strategy;

[0096] S52: Obtain a first distribution network reconstruction solution from the distribution network reconstruction set;

[0097] S53: recursively solving the first distribution network reconstruction solution to obtain a first reconstruction implementation strategy;

[0098] S54: Performing a feasibility test on the first reconstruction implementation strategy to obtain a first feasibility result;

[0099] S55: When the first feasibility result indicates that the first reconstruction implementation strategy is feasible, determine the first reconstruction implementation strategy as the reconstruction implementation strategy.

[0100] The server can obtain a distribution network reconstruction set based on the binary particle swarm algorithm and the reference distribution network adaptive strategy. The distribution network reconstruction set may include one or more distribution network reconstruction solutions, which can be understood as possible solutions for distribution network reconstruction calculated using the binary particle swarm algorithm. Optionally, (H1, H2, ..., Hn) can be used to represent the distribution network reconstruction set, where n can represent the number of distribution network reconstruction solutions.

[0101] The server can select any one of the distribution network reconstruction solutions from the distribution network reconstruction set, such as selecting Hi. It can be understood that if i=1, the first solution, ie, H1, can be selected from the distribution network reconstruction solution set for the next step of analysis.

[0102] It is understandable that when the server selects i, it can determine whether i is greater than the total number of elements in the distribution network reconstruction solution set. If it is determined that the index i of the currently selected solution is less than the total number of elements in the distribution network reconstruction solution set, the currently selected Hi can be processed in the next step; if it is determined that the index i of the currently selected solution is greater than the total number of elements in the distribution network reconstruction solution set, it may indicate that all solutions have been processed, but no feasible reconstruction implementation strategy has been found, and the distribution network reconstruction processing can be terminated.

[0103] Furthermore, the server may recursively solve the selected distribution network reconstruction solution Hi that needs to be processed in the next step to find its corresponding reconstruction implementation strategy.

[0104] The server can further check whether the distribution network reconstruction implementation strategy obtained by recursive solution is safe and feasible. If the distribution network reconstruction implementation strategy obtained by solution is safe and feasible, the reconstruction implementation strategy is output and the distribution network reconstruction processing flow ends; if the distribution network reconstruction implementation strategy obtained by solution is not safe and feasible, the next distribution network reconstruction solution is judged and solved.

[0105] The process of judging and solving the next distribution network reconstruction solution can be to increase the index i by 1, that is, start checking the next distribution network reconstruction solution; then return to the step of "Is i greater than the total number of elements in the distribution network reconstruction solution array?" to continue to loop and check the next solution.

[0106] like Figure 7 As shown in the figure, the binary particle swarm algorithm is used to find the distribution network reconstruction implementation strategy, that is, by traversing all possible distribution network reconstruction solutions and checking whether each solution has a safe and feasible implementation strategy until a feasible strategy is found or all solutions are checked, so as to find the optimized distribution network reconstruction scheme, which is beneficial to improve the operation efficiency and reliability of the distribution network.

[0107] As long-term distribution network self-healing events accumulate, the terminal information point table of the self-healing characteristic signal library will also update data in real time, which will involve the need to update the self-healing line type to ensure the real-time self-healing of the distribution network. The automatic perception and update of self-healing line type changes is to dynamically monitor the terminal information point table of the automatic switch. When the self-healing characteristic signal in the information point table changes, an alarm prompt is given, and an adaptive matching modification strategy for the switch and line self-healing type is provided. After manual confirmation, the modification is automatically completed and stored. The specific design method is as follows:

[0108] (1) Self-healing characteristic signal change detection:

[0109] Characteristic signal analysis: Using data analysis technology, the data obtained from the terminal information point table is deeply analyzed to identify any characteristic signal changes that may indicate the need for self-healing. These changes may include abnormal current fluctuations, voltage drops, frequent switching operations, etc.

[0110] Alarm mechanism triggering: Once changes in the above characteristic signals are detected, the system will immediately generate an alarm prompt to notify the operation and maintenance personnel to pay attention to potential problem areas or impending failure events.

[0111] (2) Providing adaptive matching modification strategies

[0112] Strategy library query: Based on the alarm information, the system will automatically query the pre-established self-healing function feature library to find the best self-healing solution that matches the current situation. This feature library contains various types of self-healing strategies and their applicable conditions.

[0113] Strategy evaluation and selection: Based on historical experience and real-time data analysis results, evaluate the effectiveness of different self-healing strategies and select one or more combinations that best suit the current situation.

[0114] (3) After manual confirmation, the modification is automatically completed and stored

[0115] User interface interaction: Displays recommended strategy details to operation and maintenance personnel, including expected effects, potential risks and other factors, for their reference and decision-making. If necessary, it also allows manual adjustment of certain parameters to better adapt to the actual situation.

[0116] Automatic execution and feedback loop: Once confirmed, the system will automatically implement the selected self-healing strategy and track and record the entire process. At the same time, the results of this operation will also be collected as one of the important bases for future optimization strategies.

[0117] See also Figure 8 , Figure 8 The schematic diagram of a process for automatically sensing and updating the type change of a self-healing line is shown as an example. Through such a complete implementation method, not only can the self-healing ability of the distribution network in the face of emergencies be effectively improved, but also lessons can be accumulated continuously to further improve the safety and stability of the entire power grid.

[0118] This application realizes intelligent management of distribution network, improves fault self-healing capability and optimizes distribution network structure by online real-time monitoring of distribution network operation data, establishing a self-healing function feature library, combining cloud computing and visualization management technology, and using genetic algorithm and binary particle swarm algorithm to generate adaptive adjustment strategy, thereby improving the reliability and economy of distribution network.

[0119] S60: According to the reference distribution network adaptive strategy and the distribution network adaptive strategy, a distribution network simulation model is used to perform strategy optimization processing to obtain a target distribution network adaptive strategy.

[0120] After generating the adaptive strategy and obtaining the reconstruction implementation strategy, simulation verification can be performed before implementation to improve the effective implementation of the strategy. This application uses MATLAB / Simulink simulation tools to build and verify the self-healing adaptive strategy model of the distribution network. The powerful programming capabilities of the matrix laboratory (MATLAB) and the visual modeling function of the dynamic system simulation software package (Simulink) can be used to accurately simulate the operating status and fault conditions of the distribution network, and on this basis, the adaptive adjustment strategy generated by the genetic algorithm is applied for verification.

[0121] During the simulation process, we can observe the changes in the distribution network topology after the implementation of the strategy, the improvement of fault handling efficiency, and the reduction of power outage scope and time. By comparing the simulation results under different strategies, we can evaluate the advantages and disadvantages of each strategy and select the best adaptive strategy. In addition, simulation verification can also discover potential problems and provide strong support for the optimization of the actual distribution network system.

[0122] It should be understood that, according to the reference distribution network adaptive strategy and the distribution network adaptive strategy, using the distribution network simulation model to perform strategy optimization processing to obtain the target distribution network adaptive strategy refers to the process of obtaining the target distribution network adaptive strategy. Among them, step S60, that is, according to the reference distribution network adaptive strategy and the distribution network adaptive strategy, using the distribution network simulation model to perform strategy optimization processing to obtain the target distribution network adaptive strategy, includes the following steps:

[0123] S61: Determine basic information of the distribution network according to the reference distribution network adaptive strategy and the distribution network adaptive strategy;

[0124] S63: Establishing a distribution network model using a distribution network simulation model according to the basic information of the distribution network;

[0125] S64: Perform simulation processing according to the power distribution network model and preset simulation parameters to obtain a distribution network simulation result;

[0126] S65: Evaluate the operation status according to the distribution network simulation result to obtain distribution network performance evaluation data;

[0127] S66: Optimizing and improving the distribution network performance evaluation data to obtain a target distribution network adaptive strategy.

[0128] Optionally, the distribution network modeling, simulation analysis and optimization process is carried out based on MATLAB and Simulink. The specific steps are as follows:

[0129] 1. Establishing the power distribution network model

[0130] The server can first collect basic information, such as basic information of the distribution network, which may include but is not limited to the parameters and topological structure of transformers, lines, and loads, so as to establish the distribution network model in the subsequent process. Secondly, the server can select appropriate modules, such as using the SimPowerSystems toolbox in MATLAB to select appropriate power modules (such as voltage sources or current sources) and connect them to the distribution network. Furthermore, the server can add components, such as adding transformers, switches, loads and other components, and connect them through wires to form a complete distribution network model. Finally, the server can set parameters, such as setting the parameters of components, such as the transformation ratio of the transformer, the current and voltage of the circuit, etc. Among them, the setting of the parameters may affect the accuracy of the model in the subsequent simulation process.

[0131] 2. Conduct simulation analysis

[0132] The server can start the Simulink model, such as selecting the "Simulink Model" command to enter the Simulink interface and prepare for simulation. Create a system simulation model, such as using the Simulink Library Browser window, select the required modules and drag them into the model editing window, and then connect the modules to form a system simulation model. Set the simulation parameters, such as clicking the small gear on the model editing window to enter the simulation parameter setting interface and set the step type, solver and other parameters. Run the simulation. After completing the parameter setting, you can click the Run button to start the simulation and obtain the simulation results.

[0133] 3. Analyze simulation results and propose optimization solutions

[0134] The server can further evaluate the operation of the distribution network, such as evaluating the operation of the distribution network through simulation results, such as voltage stability, short-circuit faults, etc., so as to understand the operation performance of the distribution network. The server can optimize and improve the distribution network based on the simulation results. For example: increase the capacity of the static var generator (SVG): improve the compensation capacity of SVG and further improve the power quality of the power grid; balance the distribution system: reduce the imbalance of three-phase current and improve the power quality and stability.

[0135] It is understandable that the entire process, through the combination of MATLAB and Simulink, can realize the process from modeling to simulation analysis and optimization and improvement of the distribution network, which is helpful to improve the operating efficiency and reliability of the distribution network, and is conducive to improving the accuracy of the monitoring and adaptive strategy adjustment process for the distribution network self-healing lines.

[0136] After simulation according to the adaptive strategy, the selected optimal adaptive strategy is automatically transmitted to the distribution network OCS system for execution through an automated process. As the core control system of the distribution network, the OCS system converts the received strategy into specific control instructions and adjusts the operating parameters and topology of the distribution network in real time. In this way, it can ensure that the adaptive strategy is effectively applied in the actual system, further improving the intelligence level and operational stability of the distribution network.

[0137] The purpose of this application is to realize the intelligent management and efficient operation of the distribution network, and to improve its self-repair ability, safety and stability. Through online visual monitoring of distribution network operation data, the operation status of the distribution network can be mastered in real time, and the wireless sensor network with multipath routing algorithm can be used to ensure the stability and reliability of data transmission, and key data verification can be performed to improve the intelligent management level of the distribution network; a self-healing function feature library is established to collect and classify key information related to the monitoring of distribution network operation, provide support for self-healing strategy adjustment, and combine cloud computing technology with visual management to efficiently manage and analyze it to improve data quality; real-time adaptive strategy adjustment is realized, the distribution network self-healing control area is clarified, and the adaptive adjustment strategy including switch adjustment and line adjustment is generated by genetic algorithm to optimize the operation status of the distribution network, and then the binary particle swarm algorithm is used to reconstruct the distribution network, further improve the reliability and economy of the distribution network, and ensure the stable operation of the power system; the automatic perception and update of self-healing line type changes are realized, the terminal information point table is dynamically monitored, and an alarm prompt is given when the self-healing characteristic signal changes, and an adaptive matching modification strategy is provided. After manual confirmation, the modification is automatically completed and stored, thereby ensuring the real-time self-healing of the distribution network, and constantly accumulating experience to improve the safety and stability of the entire power grid. In general, this application provides a method for online monitoring and adaptive adjustment of distribution network data, which effectively improves the level of intelligent management of the power grid.

[0138] It can be seen that in the above scheme, the service end collects the distribution network operation data and transmits the distribution network operation data using a wireless sensor network with a multipath routing algorithm. It can perform key data verification processing on the distribution network operation data to obtain the distribution network key data, and perform abnormal detection on the distribution network key data to obtain the distribution network abnormal data, so that the distribution network abnormal data can be adaptively adjusted and processed to generate a reference distribution network adaptive strategy, so as to further reconstruct the distribution network according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy, and then the distribution network simulation model can be used to perform strategy optimization processing according to the reference distribution network adaptive strategy and the distribution network adaptive strategy to obtain the target distribution network adaptive strategy, which is conducive to improving the accuracy of the monitoring and adaptive strategy adjustment process for the distribution network self-healing line, so as to achieve the purpose of online monitoring of the distribution network and accurately adjusting and optimizing the adaptive strategy.

[0139] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0140] In one embodiment, a device for monitoring and adaptively adjusting a self-healing line of a distribution network is provided, and the device for monitoring and adaptively adjusting a self-healing line of a distribution network corresponds one-to-one to the monitoring and adaptively adjusting method for self-healing line of a distribution network in the above embodiment. Fig. 9 As shown, the monitoring and adaptive strategy adjustment device for the self-healing line of the distribution network includes a collection and transmission module 101, a first processing module 102, a second processing module 103, a third processing module 104, a fourth processing module 105 and a fifth processing module 106. The functional modules are described in detail as follows:

[0141] The collection and transmission module 101 is used to collect the distribution network operation data and transmit the distribution network operation data using a wireless sensor network with a multipath routing algorithm;

[0142] The first processing module 102 is used to perform key data verification processing on the distribution network operation data to obtain key data of the distribution network;

[0143] The second processing module 103 is used to perform anomaly detection on the key data of the distribution network to obtain abnormal data of the distribution network;

[0144] The third processing module 104 is used to perform adaptive strategy adjustment processing on the abnormal data of the distribution network to generate a reference distribution network adaptive strategy;

[0145] The fourth processing module 105 is used to perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy;

[0146] The fifth processing module 106 is used to perform strategy optimization processing using a distribution network simulation model according to the reference distribution network adaptive strategy and the distribution network adaptive strategy to obtain a target distribution network adaptive strategy.

[0147] In one embodiment, the third processing module 104 is used to perform adaptive strategy adjustment processing on the abnormal data of the distribution network to generate a reference distribution network adaptive strategy, specifically for:

[0148] Determine the self-healing control area according to the abnormal data of the distribution network to obtain a target self-healing control area;

[0149] According to the target self-healing control area and the abnormal data of the distribution network, a genetic algorithm is used to perform initialization processing to obtain initialization population data;

[0150] Performing fitness evaluation according to the initialized population data to obtain a fitness evaluation data set;

[0151] Performing a population update operation according to the fitness evaluation data set to obtain target population data;

[0152] According to the target population data and preset conditions, a reference distribution network adaptive strategy is determined.

[0153] In one embodiment, the third processing module 104 is used to perform a population update operation according to the fitness evaluation data set to obtain target population data, specifically for:

[0154] Performing a selection operation on each fitness evaluation data in the fitness evaluation data set to obtain a target selection configuration data set;

[0155] Performing a cross operation on each target selection configuration data in the target selection configuration data set to obtain a target cross configuration data set;

[0156] Performing a mutation operation on each target selection configuration data in the target selection configuration data set to obtain a target mutation configuration data set;

[0157] Target population data is determined according to the target crossover configuration data set and the target mutation configuration data set.

[0158] In one embodiment, the fourth processing module 105 is used to perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy, specifically for:

[0159] According to the reference distribution network adaptive strategy, a binary particle swarm algorithm is used to determine a distribution network reconstruction set;

[0160] Acquire a first distribution network reconstruction solution from the distribution network reconstruction set;

[0161] Recursively solving the first distribution network reconstruction solution to obtain a first reconstruction implementation strategy;

[0162] Performing a feasibility test on the first reconstruction implementation strategy to obtain a first feasibility result;

[0163] When the first feasibility result indicates that the first reconstruction implementation strategy is feasible, the first reconstruction implementation strategy is determined as the reconstruction implementation strategy.

[0164] In one embodiment, the fifth processing module 106 is used to perform strategy optimization processing using a distribution network simulation model according to the reference distribution network adaptive strategy and the distribution network adaptive strategy to obtain a target distribution network adaptive strategy, specifically for:

[0165] Determining basic information of the distribution network according to the reference distribution network adaptive strategy and the distribution network adaptive strategy;

[0166] According to the basic information of the distribution network, a distribution network model is established using a distribution network simulation model;

[0167] Perform simulation processing according to the distribution network model and preset simulation parameters to obtain distribution network simulation results;

[0168] Evaluate the operation status according to the distribution network simulation results to obtain distribution network performance evaluation data;

[0169] An optimization and improvement process is performed based on the distribution network performance evaluation data to obtain a target distribution network adaptive strategy.

[0170] The present invention provides a monitoring and adaptive strategy adjustment device for a self-healing line of a distribution network. By collecting distribution network operation data and transmitting the distribution network operation data using a wireless sensor network with a multipath routing algorithm, key data verification processing can be performed on the distribution network operation data to obtain distribution network key data, and abnormality detection can be performed on the distribution network key data to obtain distribution network abnormal data, so that adaptive strategy adjustment processing can be performed on the distribution network abnormal data to generate a reference distribution network adaptive strategy, so as to further perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy, and then strategy optimization processing can be performed using a distribution network simulation model according to the reference distribution network adaptive strategy and the distribution network adaptive strategy to obtain a target distribution network adaptive strategy, which is beneficial to improving the accuracy of the monitoring and adaptive strategy adjustment process for the self-healing line of the distribution network, so as to achieve the purpose of online monitoring of the distribution network and accurately adjusting and optimizing the adaptive strategy.

[0171] For the specific definition of the monitoring and adaptive strategy adjustment device for the self-healing line of the distribution network, please refer to the definition of the monitoring and adaptive strategy adjustment method for the self-healing line of the distribution network mentioned above, which will not be repeated here. Each module in the above-mentioned monitoring and adaptive strategy adjustment device for the self-healing line of the distribution network can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0172] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.10As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client through a network connection. When the computer program is executed by the processor, it realizes the functions or steps of a service-side method for monitoring and adaptive strategy adjustment of a distribution network self-healing line.

[0173] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Fig.11 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, the functions or steps on the client side of a method for monitoring and adaptive strategy adjustment of a distribution network self-healing line are implemented.

[0174] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0175] Collecting distribution network operation data and transmitting the distribution network operation data using a wireless sensor network with a multipath routing algorithm;

[0176] Performing key data verification processing on the distribution network operation data to obtain key data of the distribution network;

[0177] Performing anomaly detection on the key data of the distribution network to obtain abnormal data of the distribution network;

[0178] Performing adaptive strategy adjustment processing on the abnormal data of the distribution network to generate a reference distribution network adaptive strategy;

[0179] Perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy;

[0180] According to the reference distribution network adaptive strategy and the distribution network adaptive strategy, a distribution network simulation model is used to perform strategy optimization processing to obtain a target distribution network adaptive strategy.

[0181] The present invention provides a computer device, which can collect distribution network operation data and transmit the distribution network operation data using a wireless sensor network with a multipath routing algorithm, perform key data verification processing on the distribution network operation data to obtain distribution network key data, and perform abnormality detection on the distribution network key data to obtain distribution network abnormal data, so that the distribution network abnormal data can be adaptively adjusted and processed to generate a reference distribution network adaptive strategy, so as to further perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy, and then perform strategy optimization processing using a distribution network simulation model according to the reference distribution network adaptive strategy and the distribution network adaptive strategy to obtain a target distribution network adaptive strategy, which is beneficial to improving the accuracy of the monitoring of the distribution network self-healing line and the adaptive strategy adjustment process, so as to achieve the purpose of online monitoring of the distribution network and accurately adjusting and optimizing the adaptive strategy.

[0182] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0183] Collecting distribution network operation data and transmitting the distribution network operation data using a wireless sensor network with a multipath routing algorithm;

[0184] Performing key data verification processing on the distribution network operation data to obtain key data of the distribution network;

[0185] Performing anomaly detection on the key data of the distribution network to obtain abnormal data of the distribution network;

[0186] Performing adaptive strategy adjustment processing on the abnormal data of the distribution network to generate a reference distribution network adaptive strategy;

[0187] Perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy;

[0188] According to the reference distribution network adaptive strategy and the distribution network adaptive strategy, a distribution network simulation model is used to perform strategy optimization processing to obtain a target distribution network adaptive strategy.

[0189] The present invention provides a computer-readable storage medium. By collecting distribution network operation data and transmitting the distribution network operation data using a wireless sensor network with a multipath routing algorithm, key data verification processing can be performed on the distribution network operation data to obtain distribution network key data, and abnormality detection can be performed on the distribution network key data to obtain distribution network abnormal data, so that adaptive strategy adjustment processing can be performed on the distribution network abnormal data to generate a reference distribution network adaptive strategy, so as to further perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy, and then strategy optimization processing can be performed using a distribution network simulation model according to the reference distribution network adaptive strategy and the distribution network adaptive strategy to obtain a target distribution network adaptive strategy, which is beneficial to improving the accuracy of the monitoring of the distribution network self-healing line and the adaptive strategy adjustment process, so as to achieve the purpose of online monitoring of the distribution network and accurately adjusting and optimizing the adaptive strategy.

[0190] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0191] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0192] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0193] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for monitoring and adaptive strategy adjustment of self-healing lines in a distribution network, characterized in that: The method comprises: Collecting distribution network operation data and transmitting the distribution network operation data using a wireless sensor network with a multipath routing algorithm; Performing key data verification processing on the distribution network operation data to obtain key data of the distribution network; Performing anomaly detection on the key data of the distribution network to obtain abnormal data of the distribution network; Performing adaptive strategy adjustment processing on the abnormal data of the distribution network to generate a reference distribution network adaptive strategy; Perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy; According to the reference distribution network adaptive strategy and the distribution network adaptive strategy, a distribution network simulation model is used to perform strategy optimization processing to obtain a target distribution network adaptive strategy.

2. The method for monitoring and adaptive strategy adjustment of self-healing lines in a distribution network according to claim 1, characterized in that: The step of performing adaptive strategy adjustment processing on the abnormal data of the distribution network to generate a reference distribution network adaptive strategy includes: Determine the self-healing control area according to the abnormal data of the distribution network to obtain a target self-healing control area; According to the target self-healing control area and the abnormal data of the distribution network, a genetic algorithm is used to perform initialization processing to obtain initialization population data; Performing fitness evaluation according to the initialized population data to obtain a fitness evaluation data set; Performing a population update operation according to the fitness evaluation data set to obtain target population data; According to the target population data and preset conditions, a reference distribution network adaptive strategy is determined.

3. The method for monitoring and adaptive strategy adjustment of self-healing lines in a distribution network according to claim 2, characterized in that: The performing of a population update operation according to the fitness evaluation data set to obtain target population data includes: Performing a selection operation on each fitness evaluation data in the fitness evaluation data set to obtain a target selection configuration data set; Performing a cross operation on each target selection configuration data in the target selection configuration data set to obtain a target cross configuration data set; Performing a mutation operation on each target selection configuration data in the target selection configuration data set to obtain a target mutation configuration data set; Target population data is determined according to the target crossover configuration data set and the target mutation configuration data set.

4. The method for monitoring and adaptive strategy adjustment of self-healing lines in a distribution network according to claim 3, characterized in that: The step of performing distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy includes: According to the reference distribution network adaptive strategy, a binary particle swarm algorithm is used to determine a distribution network reconstruction set; Acquire a first distribution network reconstruction solution from the distribution network reconstruction set; Recursively solving the first distribution network reconstruction solution to obtain a first reconstruction implementation strategy; Performing a feasibility test on the first reconstruction implementation strategy to obtain a first feasibility result; When the first feasibility result indicates that the first reconstruction implementation strategy is feasible, the first reconstruction implementation strategy is determined as the reconstruction implementation strategy.

5. The method for monitoring and adaptive strategy adjustment of a self-healing line in a distribution network according to any one of claims 1 to 4, characterized in that: The method of performing strategy optimization processing using a distribution network simulation model according to the reference distribution network adaptive strategy and the distribution network adaptive strategy to obtain a target distribution network adaptive strategy includes: Determining basic information of the distribution network according to the reference distribution network adaptive strategy and the distribution network adaptive strategy; According to the basic information of the distribution network, a distribution network model is established using a distribution network simulation model; Perform simulation processing according to the distribution network model and preset simulation parameters to obtain distribution network simulation results; Evaluate the operation status according to the distribution network simulation results to obtain distribution network performance evaluation data; An optimization and improvement process is performed based on the distribution network performance evaluation data to obtain a target distribution network adaptive strategy.

6. A device for monitoring and adaptive strategy adjustment of self-healing lines in a distribution network, characterized in that: The monitoring and adaptive strategy adjustment device of the distribution network self-healing line includes: A collection and transmission module is used to collect distribution network operation data and transmit the distribution network operation data using a wireless sensor network with a multipath routing algorithm; A first processing module is used to perform key data verification processing on the distribution network operation data to obtain key data of the distribution network; A second processing module is used to perform anomaly detection on the key data of the distribution network to obtain abnormal data of the distribution network; A third processing module is used to perform adaptive strategy adjustment processing on the abnormal data of the distribution network to generate a reference distribution network adaptive strategy; A fourth processing module, configured to perform distribution network reconstruction processing according to the reference distribution network adaptive strategy to obtain a reconstruction implementation strategy; The fifth processing module is used to perform strategy optimization processing using a distribution network simulation model according to the reference distribution network adaptive strategy and the distribution network adaptive strategy to obtain a target distribution network adaptive strategy.

7. The device for monitoring and self-adaptive strategy adjustment of a self-healing line in a distribution network according to claim 6, characterized in that: The third processing module is used to perform adaptive strategy adjustment processing on the abnormal data of the distribution network to generate a reference distribution network adaptive strategy, specifically for: Determine the self-healing control area according to the abnormal data of the distribution network to obtain a target self-healing control area; According to the target self-healing control area and the abnormal data of the distribution network, a genetic algorithm is used to perform initialization processing to obtain initialization population data; Performing fitness evaluation according to the initialized population data to obtain a fitness evaluation data set; Performing a population update operation according to the fitness evaluation data set to obtain target population data; According to the target population data and preset conditions, a reference distribution network adaptive strategy is determined.

8. The device for monitoring and self-adaptive strategy adjustment of self-healing lines in a distribution network according to claim 7, characterized in that: The third processing module is used to perform a population update operation according to the fitness evaluation data set to obtain target population data, specifically for: Performing a selection operation on each fitness evaluation data in the fitness evaluation data set to obtain a target selection configuration data set; Performing a cross operation on each target selection configuration data in the target selection configuration data set to obtain a target cross configuration data set; Performing a mutation operation on each target selection configuration data in the target selection configuration data set to obtain a target mutation configuration data set; Target population data is determined according to the target crossover configuration data set and the target mutation configuration data set.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for monitoring and adaptively adjusting the self-healing line of the distribution network as described in any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for monitoring and adaptively adjusting the self-healing line of a distribution network as described in any one of claims 1 to 5 is implemented.

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