Distribution network line dynamic optimization method based on multi-fault mode analysis

Abstract

The invention provides a distribution network line dynamic optimization method based on multi-fault mode analysis, and relates to the technical field of distribution network management. Comprising the following steps: acquiring electrical parameters and real-time monitoring data of each fault section in a distribution network line, and acquiring a topological structure, a load demand and historical fault data of the distribution network line; carrying out load distribution optimization on the distribution network lines by adopting a dynamic load optimization algorithm according to the topological structure and the load demand of the distribution network lines in combination with the electrical parameters and the historical fault data, and adjusting the load distribution of each distribution network line based on optimization; and when a distribution network fault occurs, positioning a fault point and implementing a repair measure by combining the topological information of the distribution network and the electrical parameters of the fault section. According to the invention, by constructing the multi-fault mode analysis model, the fault occurrence position of the distribution network is detected in real time, and the fault point is quickly positioned; and in combination with an optimized repair strategy, the fault recovery time is effectively shortened, and the distribution network recovery efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network management, and in particular to a distribution network line dynamic optimization method based on multiple fault mode analysis. Background Art

[0002] The existing distribution network optimization dispatching technology mainly focuses on load distribution and load optimization, but fails to fully consider the impact of the distribution network system's failure mode and its dynamic changes on load dispatching;

[0003] After a fault occurs, there is often a large delay in load redistribution and line load recovery operations, and there is a lack of effective algorithm support for fault recovery strategies and repair path selection for distribution network lines;

[0004] Such problems accumulate in multiple links, making the dynamic optimization of distribution network lines face serious challenges. Especially in large-scale distribution networks, how to quickly and accurately locate the fault point and perform effective load scheduling after the fault is repaired has become a key issue that needs to be solved urgently. Summary of the invention

[0005] The present invention provides a dynamic optimization method for distribution network lines based on multiple fault mode analysis, aiming to solve the technical problem that the related technology fails to fully consider the impact of the failure modes of the distribution network system and its dynamic changes on load scheduling, avoids the use problem that load redistribution and line load recovery operations often have large delays, and avoids the subsequent problem of lack of effective algorithm support for the fault recovery strategy and repair path selection of the distribution network lines.

[0006] In order to achieve the above purpose, the embodiments of the present application are implemented in the following manner:

[0007] The present application provides a method for dynamic optimization of distribution network lines based on multiple fault mode analysis, comprising the following steps:

[0008] Obtain the electrical parameters and real-time monitoring data of each fault section in the distribution network, and obtain the topological structure, load demand and historical fault data of the distribution network.

[0009] According to the topological structure and load demand of the distribution network lines, combined with electrical parameters and historical fault data, a dynamic load optimization algorithm is used to optimize the load distribution of the distribution network lines, and the load distribution of each distribution network line is adjusted based on the optimization.

[0010] When a distribution network fault occurs, the fault point is located and repair measures are implemented based on the distribution network topology information and the electrical parameters of the faulty section.

[0011] According to the fault mode data of the fault section, the recovery strategy of the distribution network is optimized by establishing a dynamic fault recovery model.

[0012] Continuously obtain the operating data of the distribution network lines, input the operating data into the dynamic optimization model, the dynamic optimization model optimizes the load scheduling of the distribution network lines, and uses an adaptive reinforcement learning algorithm to make real-time adjustments to the dynamic optimization model.

[0013] Based on the operating status and load change trend, a dynamic optimization report of the distribution network line is generated, and fault warning and load scheduling suggestions for the distribution network line are provided based on the dynamic optimization report.

[0014] Furthermore, according to the topological structure and load demand of the distribution network line, combined with electrical parameters and historical fault data, a dynamic load optimization algorithm is used to optimize the load distribution of the distribution network line, and the load distribution of each distribution network line is adjusted based on the optimization, specifically including:

[0015] By analyzing the topological structure of the distribution network, the load transmission relationship between each distribution node is determined, and a load transmission model is constructed. The load transmission model is expressed as:

[0016] L i =f(T i , P i , D i )

[0017] Where, L i is the load of the ith distribution node, T i is the topological parameter of distribution node i, P i is the electrical parameter of distribution node i, D i is the load demand of distribution node i.

[0018] According to the historical fault data, the load transmission model of the distribution network line is optimized. According to the fault mode and recovery strategy recorded in the historical data, the fault recovery time and distribution network loss are minimized, and the load optimization model is further adjusted. The optimized load distribution formula is:

[0019]

[0020] In the formula, is the load of the i-th distribution node after optimization, α ij is the load transfer coefficient between distribution nodes i and j, L j is the load of distribution node j, β i is the fault recovery adjustment coefficient of distribution node i, R i is the fault recovery time or recovery strategy parameter of distribution node i.

[0021] During the load optimization process, load balance constraints are introduced to ensure that the total load of all distribution nodes in the distribution network does not exceed their maximum load carrying capacity. The load balance constraints are:

[0022]

[0023] Where, L max Indicates the maximum load carrying capacity of the distribution network system. is the load of the i-th distribution node after optimization.

[0024] Furthermore, when a distribution network fault occurs, the fault point is located and repair measures are implemented based on the distribution network topology information and the electrical parameters of the faulty section, including:

[0025] According to the electrical parameters of the distribution network fault section, it is combined with the distribution network topology information to determine the fault section and its characteristics. According to the electrical parameters of the fault section, the fault point is located according to the preset fault location model. The fault location model is:

[0026] F=f(E i 'T i ′P i )

[0027] Where, F represents the location of the fault point; E i represents the electrical parameters of the ith distribution node, T i represents the topology information of the ith distribution node, P i Represents the power load information of the i-th distribution node.

[0028] After the fault point is determined, the optimal path algorithm is used in combination with the topological information of the distribution network to determine the path from the fault point to the repair point. The path planning formula is:

[0029]

[0030] Where P repair represents the optimal path from the fault point to the repair point, Cost(i) represents the repair cost of the i-th node in the path, and n represents the number of nodes on the path.

[0031] According to the optimized repair path, distribution network resources are dispatched in real time to perform repair operations.

[0032] Furthermore, the fault location model is optimized using a fusion algorithm that integrates multiple electrical parameters, including:

[0033] According to the topological information of the distribution network line and the electrical parameters of the fault section, a fusion algorithm is constructed to fuse multiple electrical parameters to enhance the accuracy of fault location. The fusion algorithm is expressed as:

[0034]

[0035] Where F represents the location of the fault point, E i represents the electrical parameters of the ith distribution node, Ti is the topological information of the i-th node, P i is the power load information, w i is the weight coefficient, indicating the importance of each electrical parameter, and m is the number of electrical parameters.

[0036] Furthermore, according to the fault mode data of the fault section, a dynamic fault recovery model is established to optimize the recovery strategy of the distribution network, including:

[0037] According to the fault mode data of the fault section, machine learning is used to establish a fault recovery model, which is expressed as:

[0038] R = f(F mode , P topology , H history )

[0039] In the formula, R represents the fault recovery strategy, F mode Represents failure mode data, including electrical parameters and failure types; P topology Represents the distribution network topology information, which is used to describe the node connection relationship of the distribution network line; H history Represents historical fault data, which is used to record the recovery strategies and effects of previous faults.

[0040] According to the recovery strategy generated by the dynamic fault recovery model, combined with the real-time load data of the distribution network, the power supply priority of the distribution network is adjusted. The priority adjustment formula is:

[0041]

[0042] Where P priority is the priority of the recovery operation, L critical is the power demand of the critical load, L total is the total power demand of the entire distribution network.

[0043] Furthermore, the dynamic load optimization algorithm optimizes the load dispatching of distribution network lines by introducing a multi-objective optimization strategy, including:

[0044] Combining the topological structure, load demand and electrical parameters of the distribution network, a load distribution model based on multi-objective optimization is constructed. This model simultaneously optimizes the load balance and fault recovery capability of the distribution network. The optimization model is:

[0045]

[0046] Where, L i represents the load of the ith distribution node, α i is the load balancing factor of distribution node i, ΔL i Indicates the load fluctuation amplitude, β iis the load fluctuation suppression coefficient; according to the historical fault data and the real-time operation status of the distribution network lines, the multi-objective optimization model is adjusted in real time.

[0047] Furthermore, the operation data of the distribution network lines is continuously obtained and input into the dynamic optimization model. The dynamic optimization model optimizes the load scheduling of the distribution network lines and uses an adaptive reinforcement learning algorithm to adjust the dynamic optimization model in real time, including:

[0048] The electrical parameters of the distribution network lines are monitored in real time, and the acquired operating data is input into the dynamic optimization model. The dynamic optimization model optimizes the load scheduling through the optimization algorithm according to the current status, topology and load demand of the distribution network lines to achieve distribution network load distribution.

[0049] An adaptive reinforcement learning algorithm is applied to make real-time adjustments to the decision-making process of the dynamic optimization model. Through the reinforcement learning algorithm, after each optimization cycle, the parameters of the dynamic optimization model are updated according to the execution effect of load scheduling and the target value, and the scheduling strategy is continuously improved.

[0050] According to the optimization results, the operating status of the distribution network lines is dynamically adjusted to achieve real-time balanced scheduling of the load.

[0051] Furthermore, the fault recovery model optimizes the fault recovery strategy through deep learning, including:

[0052] According to the historical fault mode data and electrical parameters of the fault section, a fault recovery model is established using a deep learning algorithm. The fault recovery model is expressed as:

[0053]

[0054] In the formula, R represents the recovery strategy, represents historical failure mode data, Represents real-time topology information, H history Represents historical fault data and its recovery strategy, and optimizes fault recovery strategy and power supply priority based on adaptive adjustment.

[0055] Furthermore, based on the operating status and load change trend, a dynamic optimization report of the distribution network line is generated, and fault warning and load dispatching suggestions of the distribution network line are provided based on the dynamic optimization report, including:

[0056] The real-time collected operation data is combined with historical data and analyzed, and potential risks and abnormal trends in the operation of distribution network lines are identified based on the analysis.

[0057] According to the load change trend and the operating status of the distribution network line, data mining and trend analysis are used to predict future load fluctuations and potential fault points, and generate a dynamic optimization report for the distribution network line.

[0058] According to the dynamic optimization report, a deep learning model is used to predict the fault risk of the distribution network lines, and corresponding fault warnings are provided based on the prediction results.

[0059] Based on the load distribution and fault warning in the dynamic optimization report, load scheduling suggestions for distribution network lines are generated. The load scheduling suggestions will provide load optimization strategies based on the current load demand, load change trend and load capacity of the distribution network.

[0060] The beneficial effects of the present application are: by integrating the electrical parameters, topology, load demand and historical fault data of the distribution network lines, a multiple fault mode analysis model is constructed, the precise location of the distribution network fault is detected in real time, and the fault point is quickly located; combined with the optimized repair strategy, the fault recovery time is effectively reduced and the distribution network recovery efficiency is improved.

[0061] Through the dynamic load optimization algorithm, based on the topological structure and load demand of the distribution network line, combined with electrical parameters and historical fault data, load scheduling optimization is carried out; when a fault occurs, the load is redistributed to avoid load concentration caused by the fault, ensuring the smooth operation of the distribution network during the recovery process; according to the real-time operation data and load change trend, the load distribution strategy is adaptively adjusted to improve the stability of the distribution network in a dynamic environment;

[0062] Based on fault mode analysis, historical fault data and the operating status of distribution lines, potential faults are predicted and early warnings are given in a timely manner. Through dynamic optimization models, recovery strategies for different fault modes are provided, and combined with the real-time status of the power grid, the load recovery path of the distribution lines is optimized to further shorten the fault recovery time and ensure the stability of power supply.

[0063] Providing dynamic optimization reports can reflect key data such as load distribution, operating status and fault warning of the distribution network in real time, helping power dispatchers to make quick decisions in emergency situations; through in-depth analysis of load trends and fault predictions, potential risk points can be identified in advance, and dispatch recommendations can be generated based on load changes and fault warnings to further optimize the load dispatch and maintenance of distribution network lines.

[0064] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 A flowchart of a method for dynamic optimization of distribution network lines based on multiple fault mode analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0068] See also Figure 1 , Figure 1 A flowchart of a method for dynamic optimization of distribution network lines based on multiple fault mode analysis is provided for an embodiment of the present application.

[0069] In this embodiment, the method for dynamic optimization of distribution network lines based on multiple fault mode analysis may include step S100, step S200, step S300, step S400, step S500 and step S600.

[0070] Step S100: Obtain electrical parameters and real-time monitoring data of each fault section in the distribution network line, and obtain the topological structure, load demand and historical fault data of the distribution network line.

[0071] Here, it should be explained that sensors are arranged at substations, access points and distribution boxes in the distribution network line; the sensors transmit the collected electrical data in real time through wireless communication modules.

[0072] It should be further explained that electrical parameters are obtained, including current, voltage, power factor, load current fluctuation and frequency fluctuation; through the changes in these electrical parameters, the load condition of the line and the precursors of faults are monitored in real time.

[0073] It should be further explained that, in order to ensure the accuracy of the data, the data obtained by the sensor is processed in real time, including noise filtering, data verification and anomaly detection, to ensure that the electrical data obtained is correct.

[0074] It should be further explained that the obtained electrical data is transmitted to the real-time monitoring platform of the distribution network. The platform analyzes the electrical parameters through data analysis and monitoring software to identify possible fault sections in a timely manner.

[0075] It is further necessary to explain that topological data is generated through smart meters and sensors; for the real-time monitored topological structure, the sensor node data in the real-time monitoring platform is synchronized with the distribution network design diagram through the Internet of Things technology, so as to update the topological structure; the topological structure model is constructed through the equipment location, connection relationship and electrical parameter data of the distribution network; the topological structure model is used to determine the specific location and connection method of each node and fault section, and support the analysis of fault location and load scheduling.

[0076] It is further necessary to explain that load monitoring sensors are installed at important nodes of the distribution network to collect load data of each node in real time; the load data is transmitted to the central server through wireless communication; through the combination of historical load data and real-time data, the LSTM model is used to predict future load demand and identify peak load periods and special load change trends.

[0077] It should be further explained that historical fault data is obtained through the fault records and operation and maintenance records of the distribution network; the historical fault data includes the time, location, electrical parameters, fault type, repair time and repair path of each fault; all historical fault data are centrally stored in the cloud platform, and the data is classified and managed by time, region, and equipment to support rapid retrieval and analysis of subsequent data; historical fault data is processed based on big data analysis technology, and a fault pattern recognition model is established. Based on the model, the fault type and fault-inducing cause can be quickly determined when a fault occurs.

[0078] Step S200: According to the topological structure and load demand of the distribution network line, combined with electrical parameters and historical fault data, a dynamic load optimization algorithm is used to optimize the load distribution of the distribution network line, and the load distribution of each distribution network line is adjusted based on the optimization.

[0079] Here, specifically include:

[0080] By analyzing the topological structure of the distribution network, the load transmission relationship between each distribution node is determined, and a load transmission model is constructed. The load transmission model is expressed as:

[0081] L i =f(T i , P i , D i )

[0082] Where, L i is the load of the ith distribution node, T i is the topological parameter of distribution node i, P i is the electrical parameter of distribution node i, D i is the load demand of distribution node i.

[0083] It should be noted that before constructing the load transmission model, the topological data of the distribution network line is first obtained; the topological data includes the connection mode of the line, node distribution and load points; and the topological structure model of the distribution network line is established based on the obtained topological data.

[0084] According to the historical fault data, the load transmission model of the distribution network line is optimized. According to the fault mode and recovery strategy recorded in the historical data, the fault recovery time and distribution network loss are minimized, and the load optimization model is further adjusted. The optimized load distribution formula is:

[0085]

[0086] In the formula, is the load of the i-th distribution node after optimization, α ij is the load transfer coefficient between distribution nodes i and j, L j is the load of distribution node j, β i is the fault recovery adjustment coefficient of distribution node i, R i is the fault recovery time or recovery strategy parameter of distribution node i.

[0087] It should be noted that the historical fault data of the distribution network lines are analyzed to analyze the fault mode, recovery strategy, recovery time and its impact on load distribution; through the study and analysis of historical fault data, the potential risk points of the distribution network are identified, and a basis for improvement is provided for load optimization; according to the fault types and fault recovery strategies in the historical data, the load transmission model is tuned to optimize the recovery time and reduce the loss of the distribution network system; when a distribution node fails, the load distribution of other nodes will be adjusted according to the recovery strategies of similar faults in the historical data to speed up the recovery time.

[0088] During the load optimization process, load balance constraints are introduced to ensure that the total load of all distribution nodes in the distribution network does not exceed their maximum load carrying capacity. The load balance constraints are:

[0089]

[0090] Where, L max Indicates the maximum load carrying capacity of the distribution network system. is the load of the i-th distribution node after optimization; the introduction of load balancing constraints ensures that the load distribution process is consistent with the actual carrying capacity and prevents overload; the realization of this constraint condition relies on real-time monitoring and data analysis functions, and the load data is compared with the actual carrying capacity to ensure that the maximum load value is not exceeded during the optimization process:

[0091] Step S300: When a distribution network fault occurs, the fault point is located and repair measures are implemented in combination with the distribution network topology information and the electrical parameters of the fault section.

[0092] Here, specifically include:

[0093] According to the electrical parameters of the distribution network fault section, it is combined with the distribution network topology information to determine the fault section and its characteristics. According to the electrical parameters of the fault section, the fault point is located according to the preset fault location model. The fault location model is:

[0094] F=f(E i , T i , P i )

[0095] Where, F represents the location of the fault point; E i represents the electrical parameters of the ith distribution node, T i represents the topology information of the ith distribution node, P i Represents the power load information of the i-th distribution node.

[0096] It should be noted that when a distribution network fault occurs, the electrical parameters of the faulty section are obtained in real time through the distribution network sensors; after the electrical parameters of the faulty section are obtained, the electrical parameters of the faulty section are combined with the distribution network topology information as input for fault location.

[0097] It should be further explained that the fault location model is optimized using a fusion algorithm that integrates multiple electrical parameters, including:

[0098] According to the topological information of the distribution network line and the electrical parameters of the fault section, a fusion algorithm is constructed to fuse multiple electrical parameters to enhance the accuracy of fault location. The fusion algorithm is expressed as:

[0099]

[0100] Where F represents the location of the fault point, E i represents the electrical parameters of the ith distribution node, T i is the topological information of the i-th node, P i is the power load information, w i is the weight coefficient, indicating the importance of each electrical parameter, and m is the number of electrical parameters. The fault location model optimizes the accuracy of fault point location by integrating multiple electrical parameters.

[0101] Among them, it should be noted that w i Through weighted regression analysis combined with historical fault data for training, the weight of each electrical parameter is automatically adjusted to ensure that in actual applications, the contribution of different fault types to the model is reasonably reflected.

[0102] After the fault point is determined, the optimal path algorithm is used in combination with the topological information of the distribution network to determine the path from the fault point to the repair point. The path planning formula is:

[0103]

[0104] Where P repair represents the optimal path from the fault point to the repair point, Cost(i) represents the repair cost of the i-th node in the path, and n represents the number of nodes on the path.

[0105] It is further necessary to explain that the topology information and electrical parameter information are used in combination with the path planning algorithm to calculate the shortest path from the fault point to the repair point; the repair cost of the node needs to be taken into account in the path planning, and the repair cost includes the physical distance, the power restoration priority during repair, and the resource scheduling factor; the repair cost in the path is weighted to select the most optimized repair plan.

[0106] It should be noted that the repair cost of each path node is based on the difficulty of repairing the node, the required repair time and the availability of the repair equipment; it is obtained through a comprehensive evaluation of multiple factors; specifically, the repair cost evaluation model is based on historical fault data and is modeled through fuzzy logic.

[0107] According to the optimized repair path, the distribution network resources are dispatched in real time to perform repair operations. Once the optimal repair path is determined, the distribution network resources will be dispatched in real time according to the path planning results to perform fault repair operations. According to the real-time load status, equipment availability and repair strategy of the distribution network, maintenance personnel, equipment and materials are dispatched to the fault location for repair. The specific repair process includes switching the fault section, enabling the backup power supply and adjusting the load distribution. During the repair process, the repair progress is monitored in real time and dynamically adjusted according to the actual situation to ensure the smooth progress of the repair operation. After the repair is completed, the repair effect will be verified, and the electrical parameters of the repair node will be monitored to ensure that the power grid resumes normal operation. Fault recovery verification is carried out through automated testing to ensure the accuracy of the repair.

[0108] Step S400: Optimize the recovery strategy of the distribution network by establishing a dynamic fault recovery model based on the fault mode data of the fault section.

[0109] Here, specifically include:

[0110] According to the fault mode data of the fault section, machine learning is used to establish a fault recovery model, which is expressed as:

[0111] R = f(F mode , P topology , H history )

[0112] In the formula, R represents the fault recovery strategy, F mode Represents failure mode data, including electrical parameters and failure types; P topology Represents the distribution network topology information, which is used to describe the node connection relationship of the distribution network line; H history Represents historical fault data, which is used to record the recovery strategies and effects of previous faults.

[0113] It should be noted that when a distribution network fault occurs, the electrical parameters and fault type data of the faulty section are collected in real time; the electrical parameters and fault type data of the faulty section are collected in real time by distribution network sensors and recorded as fault mode data.

[0114] It should be further explained that the above-mentioned machine learning algorithm is used to train historical fault data, specifically the random forest algorithm, to establish a dynamic fault recovery model; the model analyzes the recovery strategies and effects of historical faults, learns the relationship between different fault modes and recovery strategies, and thus can predict the best recovery strategy based on the current fault mode data; during the model training process, historical fault data provides samples for training the machine learning model; the data includes the time, type, recovery time and repair method of the fault. By learning from these historical cases, the model can quickly determine the most appropriate recovery strategy when facing new faults.

[0115] According to the recovery strategy generated by the dynamic fault recovery model, combined with the real-time load data of the distribution network, the power supply priority of the distribution network is adjusted. The priority adjustment formula is:

[0116]

[0117] Where P priority is the priority of the recovery operation, L critical is the power demand of the critical load, L total is the total power demand of the entire distribution network.

[0118] It should be noted that critical load refers to facilities and areas that are vital to social and industrial production. By calculating the ratio of critical load to total load in real time, the priority of recovery operations is dynamically adjusted to ensure the rapid recovery of critical facilities.

[0119] It should be further explained that the dynamic fault recovery model generates a recovery strategy based on the current fault mode data. The recovery strategy includes the order of recovery, recovery means and resource scheduling content. Specifically, the recovery strategy needs to combine the distribution network topology information and historical fault data to determine the recovery operation of the faulty section and how to effectively restore operation. After the recovery strategy is generated, it is implemented and the execution effect of the strategy is evaluated in real time. The strategy is adjusted according to the actual operating conditions to optimize the fault recovery process.

[0120] It should be noted that the dynamic load optimization algorithm optimizes the load dispatching of distribution network lines by introducing a multi-objective optimization strategy, which includes:

[0121] Combining the topological structure, load demand and electrical parameters of the distribution network, a load distribution model based on multi-objective optimization is constructed. This model simultaneously optimizes the load balance and fault recovery capability of the distribution network. The optimization model is:

[0122]

[0123] Where, L i represents the load of the ith distribution node, α i is the load balancing factor of distribution node i, ΔL i represents the load fluctuation amplitude, βi is the load fluctuation suppression coefficient; the multi-objective optimization model is adjusted in real time according to the historical fault data and the real-time operation status of the distribution network lines.

[0124] It should be noted that when performing load optimization, it is necessary to consider the topological structure of the distribution network line, the real-time load demand and the electrical parameters, and use the particle swarm optimization in the multi-objective optimization algorithm for calculation to ensure that the load distribution is balanced and that there is no overload problem during the fault recovery process.

[0125] Step S500: continuously obtain the operation data of the distribution network line, input the operation data into the dynamic optimization model, the dynamic optimization model optimizes the load scheduling of the distribution network line, and uses an adaptive reinforcement learning algorithm to adjust the dynamic optimization model in real time.

[0126] Here, specifically include:

[0127] The electrical parameters of the distribution network lines are monitored in real time, and the acquired operating data is input into the dynamic optimization model. The dynamic optimization model optimizes the load scheduling through the optimization algorithm according to the current status, topology and load demand of the distribution network lines to achieve distribution network load distribution.

[0128] It should be noted that the electrical parameters acquired in real time are input into the dynamic optimization model through the data interface; the dynamic optimization model will perform optimization calculations for load scheduling based on the current state and topology of the distribution network, as well as load demand.

[0129] An adaptive reinforcement learning algorithm is applied to make real-time adjustments to the decision-making process of the dynamic optimization model. Through the reinforcement learning algorithm, after each optimization cycle, the parameters of the dynamic optimization model are updated according to the execution effect of load scheduling and the target value, and the scheduling strategy is continuously improved.

[0130] It should be noted that the dynamic optimization model optimizes load scheduling according to the current electrical state, topology and load demand of the distribution network line: the dynamic optimization model calculates the load distribution through the genetic algorithm in the optimization algorithm to ensure the load balance of the distribution network and meet the safety requirements of operation;

[0131] Load scheduling optimization: including optimization objectives and topology constraints.

[0132] Optimization objectives: maximize load balancing, minimize transmission losses, ensure priority restoration of key load areas, and avoid distribution network overload;

[0133] Topology constraint: During the optimization process, the topology structure of the distribution network line is used as a constraint to ensure that the optimization result conforms to the actual structure of the distribution network.

[0134] It should be noted that after each optimization cycle, the parameters of the optimization model are adjusted in real time according to the deviation between the load scheduling execution result and the target value based on the adaptive reinforcement learning algorithm; through reinforcement learning, the scheduling strategy is continuously learned and optimized during operation.

[0135] It should be further explained that the status and reward definitions are as follows:

[0136] Status: electrical parameters, topology information, load status of the current distribution network lines, and current decisions of the optimization model.

[0137] Reward: The reward function is defined based on the effect of load scheduling and the achievement of optimization objectives. If the load is balanced after scheduling and the power supply requirements are met, the reward is high; if there is an uneven load or power outage, the reward is low.

[0138] Action: The reinforcement learning algorithm selects the optimal scheduling action based on the current state and updates the decision strategy based on reward feedback.

[0139] It should be further explained that the adaptive reinforcement learning algorithm continuously adjusts the parameters of the dynamic optimization model according to the feedback results to achieve continuous optimization scheduling of the distribution network load; as the training progresses, the reinforcement learning algorithm will gradually improve the quality of the scheduling strategy, making the distribution network load scheduling intelligent and responding to different load demand changes and fault conditions.

[0140] According to the optimization results, the operating status of the distribution network lines is dynamically adjusted to achieve real-time balanced scheduling of the load.

[0141] It should be noted that according to the optimization results, the operating status of the distribution network lines is dynamically adjusted. The dynamic adjustment includes: changing the load distribution of the lines, enabling the backup power supply and adjusting the power flow of the nodes; ensuring real-time balanced scheduling of the load based on dynamic adjustment; implementing adjustment operations by issuing instructions; the adjustment can be performed through manual intervention, which depends on the degree of automation of the distribution network lines; the actual operating status of the distribution network is compared with the expected target and fed back to the optimization model; the feedback mechanism is used to understand whether the current scheduling has achieved the expected results, and adjustments are made in the next round of optimization, thereby improving the accuracy of the scheduling.

[0142] It should be noted that the fault recovery model optimizes the fault recovery strategy through deep learning, including:

[0143] According to the historical fault mode data and electrical parameters of the fault section, a fault recovery model is established using a deep learning algorithm. The fault recovery model is expressed as:

[0144]

[0145] In the formula, R represents the recovery strategy, represents historical failure mode data, Represents real-time topology information, H history Represents historical fault data and its recovery strategy, and optimizes fault recovery strategy and power supply priority based on adaptive adjustment.

[0146] It should be noted that the fault recovery model not only uses deep learning optimization strategies, but also combines real-time feedback to adaptively adjust the recovery strategy and power supply priority to improve the efficiency of fault recovery.

[0147] Step S600: Generate a dynamic optimization report of the distribution network line based on the operating status and load change trend, and provide fault warning and load scheduling suggestions for the distribution network line based on the dynamic optimization report.

[0148] Here, specifically include:

[0149] The real-time collected operation data is combined with historical data and analyzed, and potential risks and abnormal trends in the operation of distribution network lines are identified based on the analysis.

[0150] According to the load change trend and the operating status of the distribution network line, data mining and trend analysis are used to predict future load fluctuations and potential fault points, and generate a dynamic optimization report for the distribution network line.

[0151] It should be noted that the operating data and historical data of the distribution network lines are collected in real time; the data is collected through distribution network sensors and uploaded to the cloud platform through wireless transmission technology; data analysis is performed based on the combination of real-time data and historical data to identify potential risks and abnormal trends in the operation of distribution network lines; cluster analysis in data mining algorithms is used to identify operating modes related to faults and to mine potential fault points in distribution network operation; and the sliding average method in time series analysis methods is used to analyze the distribution network load change trend and equipment operating status to predict the potential risk of equipment failure.

[0152] It should be further explained that, based on the real-time load data and the historical load distribution, the trend of excessive or unbalanced load fluctuations can be identified; the deviation between the real-time electrical parameters and the normal operating state can be compared, the abnormal fluctuations of the electrical parameters can be identified, and potential line failures can be warned.

[0153] According to the dynamic optimization report, a deep learning model is used to predict the fault risk of the distribution network lines, and corresponding fault warnings are provided based on the prediction results.

[0154] It should be noted that according to the dynamic optimization report, a deep neural network is used to extract deep features from the input data and output the failure risk probability of the distribution network line to determine whether there is a potential failure risk in the operating status of the distribution network line.

[0155] Based on the load distribution and fault warning in the dynamic optimization report, load scheduling suggestions for distribution network lines are generated. The load scheduling suggestions will provide load optimization strategies based on the current load demand, load change trend and load capacity of the distribution network.

[0156] It should be noted that an optimized load scheduling plan is provided based on the load change trend and current load demand, combined with the load capacity of the distribution network; the priority of load scheduling is adjusted according to the urgency of the load demand, the load demand of hospitals and core power equipment, and the remaining load capacity of the current distribution network; the balance of load distribution is improved to avoid overload of the distribution network, while ensuring priority power supply to key load areas; dispatch instructions are issued to each node of the distribution network through the dispatch center, the distribution network load is adjusted in real time, the power transmission path is optimized, and the stability of power supply is ensured.

[0157] At this point, the dynamic optimization method for distribution network lines based on multiple fault mode analysis is completed.

[0158] The above are only specific embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the embodiments of the present invention, which should be included in the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention should be based on the protection scope of the claims.

Claims

1. A distribution network line dynamic optimization method based on multiple fault mode analysis, characterized in that: The steps include: Obtain electrical parameters and real-time monitoring data of each fault section in the distribution network, and obtain the topology, load demand and historical fault data of the distribution network; According to the topological structure and load demand of the distribution network line, combined with electrical parameters and historical fault data, a dynamic load optimization algorithm is used to optimize the load distribution of the distribution network line, and the load distribution of each distribution network line is adjusted based on the optimization; When a distribution network fault occurs, the fault point is located and repair measures are implemented based on the distribution network topology information and the electrical parameters of the faulty section; Based on the fault mode data of the fault section, the restoration strategy of the distribution network is optimized by establishing a dynamic fault recovery model; Continuously obtain the operating data of the distribution network lines and input the operating data into the dynamic optimization model. The dynamic optimization model optimizes the load scheduling of the distribution network lines and uses an adaptive reinforcement learning algorithm to adjust the dynamic optimization model in real time. Based on the operating status and load change trend, a dynamic optimization report of the distribution network line is generated, and fault warning and load scheduling suggestions for the distribution network line are provided based on the dynamic optimization report.

2. The method for dynamic optimization of distribution network lines based on multiple fault mode analysis according to claim 1, characterized in that: According to the topological structure and load demand of the distribution network line, combined with electrical parameters and historical fault data, a dynamic load optimization algorithm is used to optimize the load distribution of the distribution network line, and the load distribution of each distribution network line is adjusted based on the optimization, including: By analyzing the topological structure of the distribution network line, the load transmission relationship between each distribution node is determined, and a load transmission model is constructed. The load transmission model is expressed as: L i =f(T i ,P i ,D i ) Where, L i is the load of the ith distribution node, T i is the topological parameter of distribution node i, P i is the electrical parameter of distribution node i, D i is the load demand of distribution node i; According to the historical fault data, the load transmission model of the distribution network line is optimized. According to the fault mode and recovery strategy recorded in the historical data, the fault recovery time and distribution network loss are minimized, and the load optimization model is further adjusted. The optimized load distribution formula is: In the formula, is the load of the i-th distribution node after optimization, α ij is the load transfer coefficient between distribution nodes i and j, L j is the load of distribution node j, β i is the fault recovery adjustment coefficient of distribution node i, R i is the fault recovery time or recovery strategy parameter of distribution node i; During the load optimization process, a load balance constraint is introduced to ensure that the total load of all distribution nodes in the distribution network does not exceed its maximum load carrying capacity. The load balance constraint is: Where, L max Indicates the maximum load carrying capacity of the distribution network system. is the load of the i-th distribution node after optimization.

3. The method for dynamic optimization of distribution network lines based on multiple fault mode analysis according to claim 1, characterized in that: When a distribution network fault occurs, the fault point is located and repair measures are implemented based on the distribution network topology information and the electrical parameters of the faulty section, including: According to the electrical parameters of the distribution network fault section, it is combined with the distribution network topology information to determine the fault section and its characteristics. According to the electrical parameters of the fault section, the fault point is located according to the preset fault location model. The fault location model is: F=f(E i ,T i ,P i ) Where, F represents the location of the fault point; E i represents the electrical parameters of the ith distribution node, T i represents the topology information of the i-th distribution node, P i represents the power load information of the i-th distribution node; After the fault point is determined, the optimal path algorithm is used in combination with the topological information of the distribution network to determine the path from the fault point to the repair point. The path planning formula is: Where P repair represents the optimal path from the fault point to the repair point, Cost(i) represents the repair cost of the i-th node in the path, and n represents the number of nodes on the path; According to the optimized repair path, distribution network resources are dispatched in real time to perform repair operations.

4. The method for dynamic optimization of distribution network lines based on multiple fault mode analysis according to claim 1, characterized in that: The fault location model is optimized by using a fusion algorithm that integrates multiple electrical parameters, specifically including: According to the topological information of the distribution network line and the electrical parameters of the fault section, a fusion algorithm is constructed to fuse multiple electrical parameters to enhance the accuracy of fault location. The fusion algorithm is expressed as: Where F represents the location of the fault point, E i represents the electrical parameters of the ith distribution node, T i is the topological information of the i-th node, P i is the power load information, w i is the weight coefficient, indicating the importance of each electrical parameter, and m is the number of electrical parameters.

5. The method for dynamic optimization of distribution network lines based on multiple fault mode analysis according to claim 1, characterized in that: According to the fault mode data of the fault section, a dynamic fault recovery model is established to optimize the recovery strategy of the distribution network, including: According to the fault mode data of the fault section, machine learning is used to establish a fault recovery model, which is expressed as: R=f(F mode ,P topology ,H history ) In the formula, R represents the fault recovery strategy, F mode Represents failure mode data, including electrical parameters and failure types; P topology Represents the distribution network topology information, which is used to describe the node connection relationship of the distribution network line; H history Represents historical fault data, used to record the recovery strategies and effects of previous faults; According to the recovery strategy generated by the dynamic fault recovery model, combined with the real-time load data of the distribution network, the power supply priority of the distribution network is adjusted. The priority adjustment formula is: Where P priority is the priority of the recovery operation, L critical is the power demand of the critical load, L total is the total power demand of the entire distribution network.

6. The method for dynamic optimization of distribution network lines based on multiple fault mode analysis according to claim 1, characterized in that: The dynamic load optimization algorithm optimizes the load dispatching of distribution network lines by introducing a multi-objective optimization strategy, which specifically includes: Combining the topological structure, load demand and electrical parameters of the distribution network line, a load distribution model based on multi-objective optimization is constructed. The model simultaneously optimizes the load balance and fault recovery capability of the distribution network line. The optimization model is: Where, L i represents the load of the ith distribution node, α i is the load balancing factor of distribution node i, ΔL i Indicates the load fluctuation amplitude, β i is the load fluctuation suppression coefficient; according to the historical fault data and the real-time operation status of the distribution network lines, the multi-objective optimization model is adjusted in real time.

7. The method for dynamic optimization of distribution network lines based on multiple fault mode analysis according to claim 1, characterized in that: Continuously obtain the operating data of the distribution network lines and input the operating data into the dynamic optimization model. The dynamic optimization model optimizes the load scheduling of the distribution network lines and uses an adaptive reinforcement learning algorithm to adjust the dynamic optimization model in real time, including: Real-time monitoring of the electrical parameters of the distribution network lines, inputting the acquired operating data into the dynamic optimization model, which optimizes the load scheduling through the optimization algorithm according to the current status, topology and load demand of the distribution network lines to achieve distribution network load distribution; Adaptive reinforcement learning algorithms are used to adjust the decision-making process of the dynamic optimization model in real time. Through the reinforcement learning algorithm, after each optimization cycle, the parameters of the dynamic optimization model are updated according to the execution effect of load scheduling and the target value, and the scheduling strategy is continuously improved. According to the optimization results, the operating status of the distribution network lines is dynamically adjusted to achieve real-time balanced scheduling of the load.

8. The method for dynamic optimization of distribution network lines based on multiple fault mode analysis according to claim 1, characterized in that: The fault recovery model optimizes the fault recovery strategy through deep learning, specifically including: According to the historical fault mode data and electrical parameters of the fault section, a fault recovery model is established using a deep learning algorithm. The fault recovery model is expressed as: In the formula, R represents the recovery strategy, represents historical failure mode data, Represents real-time topology information, H history Represents historical fault data and its recovery strategy, and optimizes fault recovery strategy and power supply priority based on adaptive adjustment.

9. The method for dynamic optimization of distribution network lines based on multiple fault mode analysis according to claim 1, characterized in that: Generate a dynamic optimization report for distribution network lines based on the operating status and load change trend, and provide fault warnings and load dispatching suggestions for distribution network lines based on the dynamic optimization report, including: Combine and analyze the real-time collected operation data with historical data, and identify potential risks and abnormal trends in the operation of distribution network lines based on the analysis; According to the load change trend and the operating status of the distribution network line, data mining and trend analysis are used to predict future load fluctuations and potential fault points, and generate dynamic optimization reports for distribution network lines; According to the dynamic optimization report, a deep learning model is used to predict the fault risk of the distribution network line, and corresponding fault warnings are provided based on the prediction results; Based on the load distribution and fault warning in the dynamic optimization report, load scheduling suggestions for distribution network lines are generated. The load scheduling suggestions will provide load optimization strategies based on the current load demand, load change trend and load capacity of the distribution network.

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