Intelligent master station self-healing control method and device and computer program product

Abstract

The invention discloses an intelligent master station self-healing control method and device and a computer program product, and the method comprises the steps: building a self-healing control evaluation index system which comprises a response time index, an accuracy index and a validity index; constructing a self-healing control model based on the index system, wherein the self-healing control model comprises an action judgment logic, a switch refusing judgment logic and a fault recovery constraint condition; a multi-objective ant lion optimization algorithm is improved, an initial population is generated through chaotic mapping, and algorithm parameters are dynamically adjusted; and solving the self-healing control model by using an improved algorithm, generating a Pareto frontier solution set through multi-objective optimization, and outputting an optimized fault isolation and power restoration control strategy. Through deep combination of algorithm optimization and model constraint, high precision, quick response and global optimization of self-healing control are realized, the reliability and anti-interference capability of a power system are remarkably improved, and continuous and safe operation of a power grid under a fault is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular, to an intelligent master station self-healing control method, device, and computer program product. Background Art

[0002] Different from traditional centralized power systems, the operating environment of smart grids is more complex and faces more diverse challenges. In recent years, the large-scale access of distributed energy has not only improved the cleanliness and utilization efficiency of electric energy but also brought great challenges to the dispatching and control of the power grid. In addition, with the rapid growth of social electricity demand, the power load fluctuates frequently and randomly, further increasing the operating pressure of the power system. The frequent occurrence of natural disasters and the inevitability of equipment failures have added uncertainty and complexity to the safe and stable operation of the power grid. In the above context, in order to ensure that the power grid can cope with various complex working conditions, the power grid self-healing technology has gradually become a key research and application direction.

[0003] Currently, the centralized self-healing control of intelligent master stations is mainly divided into three categories: simulation modeling analysis, mathematical optimization algorithms, and artificial intelligence algorithms. Simulation modeling analysis mainly conducts dynamic analysis and processing of faults by establishing a physical model of the power grid and combining electrical characteristics, and can relatively truly reflect the system operating state. However, simulation models are usually based on static or simplified operating conditions and are insufficiently adaptable to sudden and changing actual working conditions. Mathematical optimization algorithms (such as linear programming, non-linear programming, etc.) achieve self-healing control by constructing objective functions and constraint conditions and solving for the optimal solution. However, traditional mathematical optimization methods are prone to falling into local optima and are difficult to comprehensively explore the solution space of complex power grids, especially under multi-objective and multi-constraint conditions. In recent years, artificial intelligence algorithms have shown great potential in the field of smart grids, are suitable for non-linear, multi-dimensional, and multi-objective problems, and are particularly prominent in scenarios with distributed energy access and complex power grid topologies. Summary of the Invention

[0004] The technical problem to be solved by the embodiments of the present invention is to provide an intelligent master station self-healing control method, device, and computer program product to improve the fault response speed, control strategy accuracy, and fault isolation and power restoration efficiency of intelligent master station self-healing control, shorten the system recovery time, and improve power supply reliability and economy.

[0005] To solve the above technical problem, the present invention provides an intelligent master station self-healing control method, including:

[0006] Step S1, establishing a self-healing control evaluation index system, where the index system includes a response time index, an accuracy index, and an effectiveness index;

[0007] Step S2: Construct a self-healing control model based on the index system. The self-healing control model includes action judgment logic, switch refusal discrimination logic, and fault recovery constraint conditions;

[0008] Step S3: Improve the multi-objective ant lion optimization algorithm, generate an initial population through chaotic mapping, and dynamically adjust the algorithm parameters;

[0009] Step S4: Use the improved algorithm to solve the self-healing control model, generate a Pareto front solution set through multi-objective optimization, and output an optimized fault isolation and power restoration control strategy.

[0010] Preferably, the response time index is specifically:

[0011] T S = T 收集 + T 定位 + T 策略生成

[0012] where, T S is the response time index, T 收集 is the signal collection time, T 定位 is the fault location time, T 策略生成 is the control strategy generation time T 策略生成 ;

[0013] The accuracy index is specifically:

[0014] A Z = A 执行 + A 拓扑 + A 成功率

[0015] where, A Z is the accuracy index, A 执行 is the execution accuracy of the control strategy, A 拓扑 is the topology matching accuracy, A 成功率 is the success rate of self-healing remote control;

[0016] The effectiveness index is specifically:

[0017] R Z = R 恢复 + R 隔离

[0018] where, R Z is the effectiveness index, R 恢复 is the load recovery rate, R 隔离 is the fault area isolation rate.

[0019] Preferably, the action judgment logic in step S2 is set according to the fault type and location, and satisfies the following logic:

[0020]

[0021] The logic for judging the failure of the switch to operate is that if the switch fails to operate, it is re-planned according to the alternative plan, and the calculation method is:

[0022]

[0023] Among them, Switch is the number or identifier of a certain switch device in the power grid, Switch i = 1 indicates that switch i can perform an action; Status is the current state or action situation of the corresponding switch device, Status i = 1 indicates that switch i has successfully operated.

[0024] Preferably, the restoration logic constraints upstream and downstream of the fault point in step S2 include the voltage value, frequency, and the relationship between load and generation power. The specific expression is:

[0025]

[0026]

[0027]

[0028] Among them, V i is the voltage value of the master station i in the restoration area; V min , V max are the allowed minimum and maximum voltage limits respectively; f min , f max are the allowed minimum and maximum frequency limits respectively; f i is the master station system frequency value; P i,load is the internal load of the master station i in the restoration area; P i,gen is the total generation power from within the master station i or other distributed power sources.

[0029] Preferably, step S3 uses the Tent mapping to generate the initial positions of ants and antlions, and its formula is:

[0030] x ij = x min,j + z n ·(x max,j - x min,j )

[0031] Among them, x min,j , x max,j are the lower and upper limit values of the jth dimension respectively; x ij is the initial position of the ith individual in the population in the jth dimension.

[0032] Preferably, when adjusting the algorithm parameters dynamically in step S3, the inertia weight is adjusted according to a non-linear function:

[0033]

[0034] where t is the current iteration number; T max is the maximum iteration number; ω max and ω min are the minimum and maximum values of the inertia weight respectively; b is a dynamic coefficient and is greater than 1.

[0035] Preferably, the dynamic adjustment formula for the predation radius is:

[0036]

[0037] where r max is the initial predation radius; α is a control coefficient;

[0038] and the hybrid weight parameter β(t) is adjusted according to a sine function:

[0039]

[0040] where β max and β min are the maximum and minimum step sizes respectively.

[0041] Preferably, when generating the Pareto front solution set through multi-objective optimization:

[0042] Calculate the crowding distance of the solution set:

[0043]

[0044] where m is the number of objective functions; is the maximum value of the k-th objective function; is the minimum value of the k-th objective function; is the maximum value of the (k + 1)-th objective function; is the minimum value of the (k + 1)-th objective function;

[0045] In the hybrid search strategy, some individuals update their positions according to the particle swarm dynamics formula

[0046]

[0047] where L elite and L best are the local and global optimal solutions respectively; c1, c2 are learning factors; r1, r2 are random numbers; x ij is the initial position of the i-th individual in the population in the j-th dimension.

[0048] The present invention also provides an intelligent master station self-healing control device, including:

[0049] One or more processors;

[0050] A memory;

[0051] One or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the one or more processors, and the one or more applications are configured to execute the intelligent master station self-healing control method described above.

[0052] The present invention also provides a computer program product, including computer instructions, and the computer instructions direct a computer device to perform the operations corresponding to the method.

[0053] Implementing the present invention has the following beneficial effects: By systematically analyzing the action process and time characteristics of the intelligent master station self-healing, the present invention constructs a self-healing control evaluation index system covering response time, accuracy, and effectiveness, providing a comprehensive quantitative evaluation basis for the formulation of self-healing strategies, and significantly improving the system recovery efficiency and reliability. Based on the action judgment logic, switch refusal discrimination logic, and fault recovery constraint conditions, a self-healing control model with complete constraint conditions is established, effectively avoiding unreasonable actions and enhancing the system stability and security. Aiming at the limitations of traditional optimization algorithms, the present invention innovatively uses Tent chaotic mapping to generate the initial population, enhancing the diversity of the solution space; and through the non-linear control parameter strategy, significantly enhancing the global search ability and convergence speed of the multi-objective ant lion optimization algorithm, solving the problem that traditional algorithms are prone to falling into local optima, and improving the solution accuracy and efficiency. The improved algorithm generates an optimization strategy through the Pareto front solution set, can quickly locate faults, accurately isolate the fault area, and maximize the load recovery rate, effectively shortening the system recovery time, reducing economic losses and maintenance costs. Through the deep combination of algorithm optimization and model constraints, the present invention realizes high-precision, fast response, and global optimality of self-healing control, significantly improving the reliability and anti-interference ability of the power system, and ensuring the continuous safe operation of the power grid under faults. Description of the Drawings

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0055] Figure 1 It is a schematic flowchart of an intelligent master station self-healing control method according to Embodiment 1 of the present invention.

[0056] Figure 2 It is a schematic flow chart of using the improved algorithm to solve the self-healing control model in the embodiments of the present invention.

[0057] Figure 3 It is a schematic wiring diagram of the field test line.

[0058] Figure 4 It is a convergence comparison curve graph between the present invention and the traditional algorithm. Specific Embodiments

[0059] The following descriptions of each embodiment refer to the accompanying drawings to illustrate specific embodiments in which the present invention can be implemented.

[0060] Please refer to Figure 1 As shown, Embodiment 1 of the present invention provides an intelligent master station self-healing control method, including:

[0061] Step S1, establish a self-healing control evaluation index system, and the index system includes response time index, accuracy index, and effectiveness index;

[0062] Step S2, construct a self-healing control model based on the index system, and the self-healing control model includes action judgment logic, switch refusal discrimination logic, and fault recovery constraint conditions;

[0063] Step S3, improve the multi-objective ant lion optimization algorithm, generate an initial population through chaotic mapping, and dynamically adjust the algorithm parameters;

[0064] Step S4, use the improved algorithm to solve the self-healing control model, generate a Pareto front solution set through multi-objective optimization, and output an optimized fault isolation and power restoration control strategy.

[0065] Specifically, in Step S1 of the embodiment of the present invention, the action process and time characteristics of the intelligent master station self-healing are first analyzed, and an index system for the centralized self-healing of the intelligent master station is constructed;

[0066] The action process specifically includes:

[0067] After a fault occurs, the distribution automation terminal uploads relevant information to the master station. The master station accurately locates the fault point through analysis and calculation, generates corresponding control strategies, and issues instructions to the line sectionalizing switch to quickly isolate the fault area and restore power supply to the non-fault area at the same time. The specific actions are as follows:

[0068] Fault tripping: When a fault occurs, after the protection device such as a relay detects an abnormal current or voltage signal, it quickly issues a tripping instruction, and the circuit breaker cuts off the fault line or equipment to isolate the fault source, preventing the fault from further expanding and causing a chain effect on other equipment and systems.

[0069] System signal collection, topology analysis and fault location: After a fault trip, the intelligent master station collects signals through the real-time monitoring system, including data such as current, voltage, and breaker status. Using this data and combining with the system topology information, the master station analyzes the current operating state of the power grid, identifies the affected areas, and accurately locates the fault point to ensure the accuracy of the isolation plan.

[0070] Self-healing function fault isolation and power restoration control: According to the fault location results, the intelligent master station activates the self-healing control function and formulates isolation and power restoration plans. While isolating the fault point, it adjusts the power grid topology or dispatching standby power sources to restore power supply to non-fault areas.

[0071] The time characteristics specifically include:

[0072] Centralized self-healing of the intelligent master station. From the start of self-healing analysis to the completion of automatic control execution, it can be divided into three time characteristic stages, specifically:

[0073] Signal collection stage: The master station system collects relevant information from substation switch protection devices, action signals, and distribution line automation terminal protection devices through a specific time delay. The main influencing factors for signal acquisition include the communication time between the master station and the terminal and the quality of the communication channel, which are directly related to the timeliness and accuracy of data transmission.

[0074] Fault location stage: The system uses the real-time topology model of the distribution line, combines information such as the opening and closing states of sectional switches, and comprehensively analyzes and judges the location of the fault point. After the location is completed, the system generates corresponding fault isolation and power restoration strategies. The key factors affecting this stage include the calculation speed of the master station and the scale and complexity of the real-time topology model.

[0075] Automatic control stage: The master station system sends control commands to on-site terminals according to the generated control strategies to complete the remote control isolation and power restoration operations of the fault area. The main influencing factors in this stage include the response speed of the three-remote terminals (remote measurement, remote signaling, remote control), the remote control execution speed of primary equipment, and the terminal communication time, which determine the efficiency and accuracy of the operation.

[0076] The constructed index system specifically includes: response time index, accuracy index, effectiveness index.

[0077] (1) Response time index T S Specifically includes:

[0078] Signal collection time T 收集 : The time required for the master station to collect necessary signals from terminal devices. The formula is:

[0079] T 收集 = T 通信 + T 传输

[0080] Where: T 通信 is the communication delay between the master station and the terminal; T 传输 is the time for data to be transmitted in the channel;

[0081] The fault location time T 定位 : The time for the master station to judge the fault by combining the real-time topology model and the switch state. The formula is:

[0082] T 定位 = T 模型加载 + T 计算

[0083] Where: T 模型加载 is the time required to load the real-time topology model; T 计算 is the time required for the system to comprehensively analyze and locate the fault point;

[0084] The control strategy generation time T 策略生成 : The time for the master station to generate isolation and power restoration strategies based on the fault location result. The formula is:

[0085] T 策略生成 = T 逻辑计算 + T 指令生成

[0086] Where: T 逻辑计算 is the time to analyze the fault situation and determine the control logic; T 指令生成 is the time to convert the control logic into executable instructions.

[0087] In summary, the response time index is specifically: T S = T 收集 + T 定位 + T 策略生成 .

[0088] (2) Accuracy index A Z Specifically includes:

[0089] The control strategy execution accuracy A 执行 : Evaluate the accuracy of the control strategy generated by the master station during actual execution. The formula is:

[0090]

[0091] Where: A 成功执行 is the successfully executed strategy; A 总策略 is the total number of implemented strategies;

[0092] The topology matching accuracy A 拓扑 : The degree of coincidence between the real-time topology model and the actual power grid operation state. The formula is:

[0093]

[0094] Where: A 拓扑误差 The incorrect part in the topological state; A 总拓扑 Is the total part in the topological state;

[0095] The success rate A of self-healing remote control 成功率 : The ratio of the remote control instructions sent by the master station that are successfully received and executed by the terminal device. The formula is:

[0096]

[0097] Where: A 成功遥控 Is the number of successfully executed remote control instructions; A 总遥控 Is the total number of remote control instructions issued by the master station;

[0098] In summary, the accuracy index is specifically A z = A 执行 + A 拓扑 + A 成功率 .

[0099] (3) Effectiveness index R Z Specifically includes:

[0100] Load recovery rate R 恢复 : The degree of power supply restoration during the fault handling process. The formula is:

[0101]

[0102] Where: R 恢复量 Is the total power consumption of the power supply load during the fault; R 总负荷 Is the power demand of the total load in the system;

[0103] Fault area isolation rate R 隔离 : The ratio of the system successfully isolating the fault area. The formula is:

[0104]

[0105] Where: R 成功隔离 Is the length of the isolated fault area; R 故障区域 Is the total length (or capacity) of the fault area.

[0106] In summary, the effectiveness index is specifically R Z = R 恢复 + R 隔离 .

[0107] Step S2 is adjusted based on logics such as action judgment and switch refusal judgment, and a self-healing control model based on self-healing characteristics and evaluation indicators is established, specifically including:

[0108] Constraints:

[0109] Action judgment logic: According to the fault type and location, the following logic is satisfied:

[0110]

[0111] Switch refusal judgment logic: If the switch refuses to act, re-plan according to the alternative plan:

[0112]

[0113] Where: Switch is the number or identifier of a switch device in the power grid, Switch i = 1 indicates that switch i can perform an action; Status is the current state or action of the corresponding switch device, Status i = 1 indicates that switch i has successfully acted;

[0114] Restoration logic constraints for upstream and downstream of the fault point:

[0115]

[0116] Where: V i is the voltage value of the master station i in the restoration area; V min , V max are the allowed minimum and maximum voltage limits respectively; f min , f max are the allowed minimum and maximum frequency limits respectively; f i is the frequency value of the master station system; P i,load is the internal load of the master station i in the restoration area; P u,gen is the total power generation from within the master station i or other distributed power sources;

[0117] Objective function: The objective function should be constructed in combination with the indicators of intelligent master station centralized self-healing:

[0118] maxf1(x) = -ω1T S

[0119] maxf2(x) = ω2A Z

[0120] maxf3(x) = ω3R Z

[0121] Where: ω1, ω2, ω3 are the weight values of each indicator respectively.

[0122] Step S3 uses the Tent chaotic mapping and non-linear control parameter strategy to improve the key parameters of the multi-objective ant lion optimization (MOALO) algorithm, specifically including:

[0123] Step S31, Tent chaos mapping formula

[0124] The Tent mapping is used to generate an initial population with high distribution uniformity and adjust dynamic parameters. Its formula is as follows:

[0125]

[0126] In the formula: z n is the chaos variable at the nth iteration, z n ∈[0,1]; μ∈(0,1] is the mapping parameter, and usually the best effect is obtained when μ = 0.5;

[0127] In the algorithm, it is used for population initialization: using the Tent mapping to generate the initial positions of ants and antlions, enhancing the diversity of solutions. Its formula is:

[0128] x ij = x min,j + z n ·(x max,j - x min,j )

[0129] In the formula: x min,j , x max,j are respectively the lower limit value and the upper limit value of the jth dimension; x ij is the initial position of the ith individual in the population at the jth dimension.

[0130] Step S32, the non - linear control parameter strategy adjusts the key parameters through non - linear functions. The specific description is as follows:

[0131] Step S321, non - linear inertia weight

[0132] The inertia weight controls the balance of the solution search process. Its non - linear change formula is:

[0133]

[0134] In the formula: t is the current iteration number; T max is the maximum iteration number; ω max , ω min are respectively the minimum value and the maximum value of the inertia weight; b is a dynamic coefficient and greater than 1. The weight decreases slowly in the initial stage of the search and quickly in the later stage.

[0135] Step S322, predation radius r t Non - linear control

[0136] The predation radius affects the search range of ants. Its dynamic adjustment formula is:

[0137]

[0138] where: r max is the initial predation radius; α is the control coefficient used to control the convergence speed;

[0139] Step S323, non-linearly adjust the hybrid weight parameter β(t)

[0140] The step size factor β(t) in the antlion update formula is dynamically adjusted to:

[0141]

[0142] where: β max , β min are the maximum and minimum step sizes respectively; the sine function is used to achieve non-linear reduction, making the search flexible.

[0143] Step S4 then uses the improved MOALO algorithm to solve the aforementioned centralized self-healing control model and generate an optimized processing strategy, specifically including:

[0144] Step S41, population initialization: Use Tent chaos mapping to generate the initial population;

[0145] Step S42, multi-objective function evaluation: Calculate the objective function values, update the non-dominated solution set P using the Pareto dominance rule, and calculate the crowding distance D i ;

[0146]

[0147] where: m is the number of objective functions; is the maximum value of the k-th objective function; is the minimum value of the k-th objective function; is the maximum value of the (k + 1)-th objective function; is the minimum value of the (k + 1)-th objective function;

[0148] Step S43, ant random walk: The ant position update follows the random walk model, combined with the Tent chaos sequence and the dynamic predation radius;

[0149] The ant random walk is specifically:

[0150] x ij (t + 1) = x ij (t) + r t ·norm(R(t))

[0151] where: norm(R(t)) is the normalized value of the random walk direction, following the Tent chaos distribution;

[0152] Step S44, Antlion predation update: The update of the antlion position is based on the step factor β(t) and the predation behavior of the target individual;

[0153] The antlion predation update is specifically as follows:

[0154] L ij (t + 1) = L ij (t) + β(t)·(x ij -L ij )

[0155] In the formula: L ij is the position vector of the i-th antlion individual in the j-th dimension.

[0156] Step S45, Hybrid search strategy: In each generation, randomly select some ants and antlions to update their positions using the PSO dynamics formula

[0157] The hybrid search strategy is specifically as follows:

[0158]

[0159] In the formula: L elite 、L best are the local and global optimal solutions respectively; c1, c2 are learning factors; r1, r2 are random numbers; x ij is the initial position of the i-th individual in the population in the j-th dimension;

[0160] Step S46, Pareto front update: Determine whether the new solutions generated by ants and antlions dominate the current Pareto front. If they do, update the Pareto solution set and calculate the crowding distance of the new solutions;

[0161] Step S47, Termination condition: When the change in the Pareto solution set is less than the set threshold, stop the iteration;

[0162] Step S48, Output the Pareto front solution set and its corresponding non-dominated solutions.

[0163] To verify the effectiveness of the intelligent master station self-healing control method of the embodiments of the present invention, taking a 10kV overhead line in a certain area as an example, as Figure 3 shown, the on-site test line is configured with 1 circuit breaker and 4 hybrid switches.

[0164] Scenario 1: Instantaneous fault of the f4 hybrid switch

[0165] When FB1 trips, if the first reclosing function is configured, the reclosing operation will be successfully completed. If FB1 is not configured with the first reclosing, the master station will issue a closing lock command for the tie switch and KS2, and effectively restore the power supply in areas B and C within the specified response time.

[0166] Scenario 2: Permanent fault of the f4 combined switch

[0167] When FB1 is not equipped with the first reclosing function, the operation is the same as described above; if FB1 is configured with the reclosing function, the reclosing may fail. At this time, the master station will issue a closing lock command for the tie switch and KS2, and remotely control FB1 to trip on the premise of ensuring sufficient power supply for FB1, so as to restore the power supply in areas B and C.

[0168] Compare the convergence situation of the present invention with that of traditional artificial intelligence methods, as Figure 4 shown, the present invention can quickly achieve iterative convergence, and compared with traditional methods, its convergence value is significantly lower. This fully proves the superiority of the present invention in improving efficiency and optimizing performance. Through rapid convergence and a lower convergence value, the present invention can obtain more accurate results in a shorter time, thus significantly improving the overall performance of the system. Compared with traditional methods, the present invention not only greatly improves the accuracy and efficiency of self-healing control, but also significantly improves the stability and reliability of the system, reduces the outage time and maintenance cost, and provides strong support for the efficient operation of the intelligent grid master station.

[0169] Corresponding to the intelligent master station self-healing control method described in the foregoing Embodiment 1 of the present invention, Embodiment 2 of the present invention further provides an intelligent master station self-healing control device, including:

[0170] One or more processors;

[0171] A memory;

[0172] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the intelligent master station self-healing control method described in the foregoing Embodiment 1 of the present invention.

[0173] Corresponding to the intelligent master station self-healing control method described in the foregoing Embodiment 1 of the present invention, Embodiment 3 of the present invention further provides a computer program product, including computer instructions, and the computer instructions instruct a computer device to execute the operations corresponding to the intelligent master station self-healing control method described in the foregoing Embodiment 1 of the present invention.

[0174] Preferably, the processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor. The processor is the control center of the device, and connects various parts of the device through various interfaces and circuits.

[0175] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc., and the data storage area can store relevant data, etc. In addition, the memory may be a high-speed random access memory, or may also be a non-volatile memory, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., or the memory may also be other volatile solid-state storage devices.

[0176] It should be noted that the above device may include but is not limited to a processor and a memory, which can be understood by those skilled in the art.

[0177] For the working principles and processes of the above embodiments, refer to the description of Embodiment 1 of the present invention above, and details are not described herein again.

[0178] As can be seen from the above description, compared with the prior art, the beneficial effects of the present invention are as follows: By systematically analyzing the action process and time characteristics of the self-healing of the intelligent master station, the present invention constructs a self-healing control evaluation index system covering response time, accuracy and effectiveness, provides a comprehensive quantitative evaluation basis for the formulation of self-healing strategies, and significantly improves the system recovery efficiency and reliability. Based on the action judgment logic, switch refusal judgment logic and fault recovery constraint conditions, a self-healing control model with complete constraint conditions is established, effectively avoiding unreasonable actions and enhancing the system stability and security. Aiming at the limitations of traditional optimization algorithms, the present invention innovatively uses Tent chaotic mapping to generate the initial population, improving the diversity of the solution space; and through the non-linear control parameter strategy, significantly enhancing the global search ability and convergence speed of the multi-objective ant lion optimization algorithm, solving the problem that traditional algorithms are prone to fall into local optima, and improving the solution accuracy and efficiency. The improved algorithm generates an optimization strategy through the Pareto front solution set, can quickly locate faults, accurately isolate the fault area and maximize the load recovery rate, effectively shorten the system recovery time, and reduce the economic loss and maintenance cost. Through the deep combination of algorithm optimization and model constraint, the present invention realizes high-precision, fast response and global optimum of self-healing control, significantly improves the reliability and anti-interference ability of the power system, and ensures the continuous safe operation of the power grid under faults.

[0179] The above-disclosed are only the preferred embodiments of the present invention, and of course, the scope of the rights of the present invention cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. An intelligent master station self-healing control method, characterized in that Including: Step S1, establish a self-healing control evaluation index system, where the index system includes a response time index, an accuracy index, and an effectiveness index; Step S2, construct a self-healing control model based on the index system, where the self-healing control model includes an action judgment logic, a switch refusal movement discrimination logic, and a fault recovery constraint condition; Step S3, improve the multi-objective ant lion optimization algorithm, generate an initial population through chaotic mapping, and dynamically adjust the algorithm parameters; Step S4, use the improved algorithm to solve the self-healing control model, generate a Pareto front solution set through multi-objective optimization, and output an optimized fault isolation and power restoration control strategy.

2. The method according to claim 1, wherein The specific response time index is: T S = T 收集 + T 定位 + T 策略生成 Among them, T S is the response time index, T 收集 is the signal collection time, T 定位 is the fault location time, T 策略生成 is the control strategy generation time T 策略生成 ; The specific accuracy index is: A Z = A 执行 + A 拓扑 + A 成功率 Among them, A Z is the accuracy index, A 执行 is the execution accuracy of the control strategy, A 拓扑 is the topological matching accuracy, A 成功率 is the success rate of self-healing remote control; The specific effectiveness index is: R Z = R 恢复 + R 隔离 Among them, R Z is the effectiveness index, R 恢复 is the load recovery rate, and R 隔离 is the fault area isolation rate.

3. The method according to claim 1, wherein The action judgment logic in the step S2 is set according to the fault type and location, and satisfies the following logic: The switch refusal movement judgment logic is that if the switch refuses to move, re-plan according to the alternative plan, and the calculation method is: Among them, Switch is the number or identifier of a certain switching device in the power grid, Switch i = 1 indicates that switch i can perform an action; Status is the current state or action situation of the corresponding switching device, Status i = 1 indicates that switch i has successfully performed an action.

4. The method according to claim 3, characterized in that, The upstream and downstream recovery logic constraints of the fault point in the step S2 include the relationship between voltage value, frequency, and load and power generation power, and the specific expression is: Among them, V i is the voltage value of the main station i in the restoration area; V min , V max are the allowed minimum and maximum voltage limits respectively; f min , f max are the allowed minimum and maximum frequency limits respectively; f i is the system frequency value of the main station; P i,load is the internal load of the main station i in the restoration area; P i,gen is the total power generation from within the main station i or other distributed power sources.

5. The method according to claim 1, wherein The step S3 uses the Tent mapping to generate the initial positions of ants and ant lions, and its formula is: x ij = x min,j + z n · (x max,j - x min,j ) where x min,j and x max,j are the lower limit value and the upper limit value of the j-th dimension respectively; x ij is the initial position of the i-th individual in the population in the j-th dimension.

6. The method according to claim 5, characterized in that When dynamically adjusting the algorithm parameters in the step S3, the inertia weight is adjusted according to a non-linear function: where \(t\) is the current iteration number; \(T\) max is the maximum number of iterations; \(\omega\) max , \(\omega\) min are the minimum and maximum values of the inertia weight respectively; \(b\) is a dynamic coefficient and greater than 1.

7. The method according to claim 6, wherein The dynamic adjustment formula of the predation radius is: where r max is the initial predation radius; α is the control coefficient; And the mixed weight parameter β(t) is adjusted according to a sine function: where β max and β min are the maximum and minimum step sizes respectively.

8. The method according to claim 1, characterized in that, When generating the Pareto front solution set through multi-objective optimization: Calculate the crowding distance of the solution set: where m is the number of objective functions; is the maximum value of the k-th objective function; is the minimum value of the k-th objective function; is the maximum value of the (k + 1)-th objective function; is the minimum value of the (k + 1)-th objective function; In the hybrid search strategy, some individuals update their positions according to the particle swarm dynamics formula Among them, L elite and L best are the local and global optimal solutions respectively; c1 and c2 are learning factors; r1 and r2 are random numbers; x ij is the initial position of the i-th individual in the j-th dimension of the population.

9. An intelligent master station self-healing control device, characterized in that, Including: One or more processors; A memory; One or more applications, where the one or more applications are stored in the memory and are configured to be executed by the one or more processors, and the one or more applications are configured to execute the intelligent master station self-healing control method according to any one of claims 1 to 8.

10. A computer program product, characterized in that, Including computer instructions, where the computer instructions instruct the computer device to execute the operations corresponding to the method according to any one of claims 1 to 8.

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