Relay protection starting point intelligent selection and efficient setting method based on quantum heuristic recombination optimization and dynamic adaptive genetic algorithm

Abstract

The invention discloses a relay protection starting point intelligent selection and efficient setting method based on quantum heuristic recombination optimization and a dynamic adaptive genetic algorithm, and the method comprises the following steps: 1) constructing a protection cooperation importance comprehensive index, so as to quantify the influence of misoperation caused by protection mismatch on a power grid; 2) based on a protection cooperation importance comprehensive index, constructing a starting point optimization and setting calculation model based on the minimum mismatch importance and the protection adjustment number; and 3) solving the starting point optimization and setting calculation model based on the minimum mismatch importance degree and the protection adjustment number to obtain a relay protection starting point setting result. According to the method, the calculation efficiency of relay protection setting is remarkably improved, the consumed time of starting point optimization and setting calculation is shortened from several hours to tens of seconds through the QIRO-DAGA fusion algorithm, and the problems that manual setting is slow in response and long in period are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation, specifically a method for intelligent selection and efficient setting of relay protection starting points based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithms. Background Technology

[0002] In recent years, with the large-scale investment in power generation and substation facilities and the dynamic adjustment of power grid operation lines, the scale of the power grid has continued to expand and its structure has become increasingly complex, posing greater challenges to the design and setting of relay protection.

[0003] In engineering practice, improper protection configuration settings can lead to incoordination between devices, i.e., protection mismatch. When a fault occurs on the line where the mismatch point is located, it can easily trigger cascading protection actions, expanding the power outage area and exacerbating losses. This problem is particularly prominent in complex ring network structures, where protection coordination is in a closed-loop form, and the setting of any protection is closely related to the selection of its starting point. Traditional starting point selection often relies on manual experience; if the selection is inappropriate, it will not only exacerbate the risk of mismatch in subsequent settings but may also trigger large-scale cascading adjustments to protection settings, increasing the workload of settings and introducing new operational uncertainties. Therefore, researching setting strategies that can intelligently optimize the selection of starting points is of great significance for improving the reliability of power grid operation.

[0004] Currently, the industry still relies on repeated calculations, verifications, and corrections based on human experience for protection settings. While this method can handle routine scenarios based on experience, it is difficult to make rapid and scientific decisions on multiple starting points from a global perspective in complex situations such as frequent line switching or temporary maintenance. This results in long setting cycles and slow responses, making it difficult to meet the modern power grid's requirements for a balance between setting accuracy and speed.

[0005] Regarding the selection of optimal starting points, existing research mainly focuses on the assessment of protection importance and the construction of optimization models. At the assessment level, researchers primarily evaluate protection importance by analyzing its topological location, such as the density of nodes associated with the protection, or by considering the consequences of protection failures, such as power flow shifts. While these methods provide some basis for selecting mismatch points, they often emphasize a single indicator and fail to systematically integrate the adverse effects caused by initial steady-state power flow and mismatch consequences, making it difficult to support a comprehensive assessment of the importance of protection coordination relationships. At the model level, research is mostly based on 0-1 integer programming models for starting points, introducing minimum coordination action time limits, maximum number of independent starting points, and minimum starting point importance as optimization objectives. While these studies help avoid excessively long ring network protection setting action times and consider starting point protection coordination issues, they fail to effectively combine relay protection setting principles to coordinate the overall mismatch importance with the number of existing protection adjustments. This results in limited applicability of the obtained schemes in actual setting, making it difficult to form a complete closed loop of "starting point selection - setting calculation," and the results are difficult to directly apply to engineering practice. Therefore, it is necessary to combine the relay protection setting principles to establish a mixed integer programming model with the goal of minimizing the importance of mismatch and the number of adjustments.

[0006] At the algorithm level, researchers have applied various intelligent optimization algorithms, such as genetic algorithms, particle swarm optimization, and ant colony optimization, to solve the aforementioned 0-1 integer programming model. These methods have made some progress in improving computational efficiency. However, with the expansion of the power grid leading to a surge in the number of ring networks and protection systems, these algorithms generally face limitations such as difficulty in setting hyperparameters and a tendency to get trapped in local optima, making it difficult to dynamically adapt to complex engineering constraints. Summary of the Invention

[0007] The purpose of this invention is to provide a method for intelligent selection and efficient setting of relay protection starting points based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithms, comprising the following steps:

[0008] Step 1) Construct a comprehensive index of the importance of protection coordination to quantify the impact of maloperation caused by protection mismatch on the power grid;

[0009] Step 2) Based on the comprehensive index of protection coordination importance, construct a starting point optimization and tuning calculation model based on the minimum mismatch importance and the number of protection adjustments;

[0010] Step 3) Solve the starting point optimization and setting calculation model based on the minimum mismatch importance and the number of protection adjustments to obtain the relay protection starting point setting results.

[0011] Furthermore, in step 1), the comprehensive index of the importance of protection coordination is as follows:

[0012] (1)

[0013] (2)

[0014] In the formula, These are the weighting coefficients for each indicator; ; Branch power index; As a load loss indicator; For branch line overload indicators; As an indicator of tidal current transfer rate; It is an exponential utility function; Input metrics; To protect the comprehensive index of importance.

[0015] Furthermore, branch power indicators Used to reflect the power load of the branch where the protection coordination relationship is located in the initial operating mode, that is:

[0016] (3)

[0017] In the formula: For the first The sum of active power of the branches associated with each protection coordination relationship; This represents the total active power of the original system.

[0018] Load loss index Used to characterize the degree of load loss caused by escalating actions due to mismatch in protection coordination, i.e.:

[0019] (4)

[0020] In the formula: To disconnect the first The total active power of the system after the protection coordination relationship is associated with the branch;

[0021] Branch overload index Used to assess the average overload severity of branches in the system after mismatch, i.e.:

[0022] (5)

[0023] In the formula: For overloaded branch sets; and for Overloaded branches in the network The magnitude of the active power transmitted and its power limit;

[0024] Current transfer index Used to measure the health and stability risk of system operation after protection mismatch, namely:

[0025] (6)

[0026] (7)

[0027] In the formula: For branch road collection; For generator sets; and Branch roads Reactance and active power of the branch; branch road Power limit; For generator Maximum effective output; , , Network power average transmission distance, branch average load rate, generator average load rate; subscript and Corresponding to the initial Network and disconnection A protective cooperation relationship formed network.

[0028] Furthermore, the optimization and tuning calculation model based on the starting point of minimum mismatch importance and the number of protection adjustments is shown below:

[0029] (8)

[0030] In the formula: These are the initial decision variables; The overall objective function; These are the weighting coefficients. This is used to balance the risk of mismatch with the cost of adjustment; It is the set of all protection coordination relationships in the system; For the first A comprehensive index indicating the importance of each protective and cooperative relationship; As an indicator function, when the starting point scheme Leading to the The value is 1 when the protection pairing relationship is mismatched, and 0 otherwise. This represents the total number of protections in the system. As an indicator function, when protection Section II fixed value Compared to its initial value The value is 1 when a change occurs, and 0 otherwise. Indicates As input, the complete set of protection setting schemes is obtained after executing the constraint processing strategy; This represents the set of all feasible tuning schemes that satisfy the actual tuning principle.

[0031] Furthermore, decision variables As shown below:

[0032] (9)

[0033] In the formula: the first column vector Representing the The starting point pair of a ring network. Components and Both are integers, representing the starting protection number selected for the forward and reverse setting calculations in the ring network, respectively.

[0034] Furthermore, the steps for implementing the constraint handling strategy include:

[0035] Step 1) Calculate the fixed setting values ​​for all protections, including the first-stage setting values, based on the power grid parameters and setting principles. Sensitivity requirements for segment II And the initial settings for coordination with the lower-level protection stage I. ;

[0036] Step 2) Initialize the coordination matrix RDR; the dimensions of the coordination matrix RDR are... ,element Characterization protection and protection The number of coordination segments between them; if protection and protection The cooperation satisfies Then place Set to 1 if the value is 1, otherwise set to 0.

[0037] Step 3) Decision variables at the starting point To guide the determination of the tuning sequence, tuning calculations are performed step-by-step along the positive and negative directions of the ring network; during the calculation process, if The value is 1, and the coordination value with the lower-level protection stage I is adopted. ;like The value is set to 0, adjusted to coordinate with the lower-level protection stage II, and the setting is adopted. and will Change to 2;

[0038] Step 4) Take the minimum value among all possible combination values ​​as the final value for segment II;

[0039] Whenever the stage II setting of a lower-level protection is determined, the relevant coordination settings of the upper-level protection are updated. And verify whether it meets the sensitivity requirements of the upper-level protection; if it still does not meet the sensitivity requirements when combined with stage II, then... Setting it to 0 indicates a mismatch in the pairing relationship;

[0040] Step 5) Based on the final RDR matrix and the complete set of settings, calculate the total importance of system mismatch and the number of protection adjustments, and then calculate candidate solutions. If a complete constant value scheme cannot be generated during the process of determining the objective function value, then the candidate solution is determined to be infeasible.

[0041] Further, in step 3), the QIRO-DAGA fusion algorithm is used to solve the starting point optimization and tuning calculation model based on the minimum mismatch importance and the number of protection adjustments.

[0042] Furthermore, in step 3), the steps for solving the starting point optimization and tuning calculation model based on the minimum mismatch importance and the number of protection adjustments include:

[0043] Step 3.1) Input the power grid parameters and setting principles, and calculate the importance of all protection coordination relationships based on the protection coordination relationship importance assessment strategy;

[0044] Set algorithm parameters, including population size and maximum number of iterations;

[0045] Step 3.2) Perform QIRO global exploration to generate a high-quality population;

[0046] Step 3.3) Using the high-quality population output by QIRO as the initial population, a local search is performed using an adaptive evolutionary mechanism to generate the optimal starting point scheme. ;

[0047] Step 3.4) Based on the optimal starting point scheme It performs setting calculations and outputs a complete scheme including the settings, operating times, and coordination relationships of each protection.

[0048] Furthermore, step 3.2) involves performing a global QIRO exploration to generate a high-quality population, including:

[0049] Step 3.2.1) Initialize the quantum population, encode each protected cooperative state as a quantum state, and collapse each quantum individual into the starting point scheme through quantum measurement. ;

[0050] Among them, the A quantum state with a protective coordination relationship As shown below:

[0051] (10)

[0052] In the formula: The probability amplitudes correspond to the three states; To protect the cooperative relationship In the first The probability of a certain state;

[0053] Starting point scheme In the middle, the first The state of a protective cooperation relationship As shown below:

[0054] (11)

[0055] In the formula: For the first A protection coordination status;

[0056] Step 3.2.2) Apply constraint handling strategies to each starting point scheme, determine feasibility, and calculate the fitness value. ;

[0057] Step 3.2.3) Based on fitness value The quantum state probability amplitude is updated using quantum rotation gate probability and interference operation;

[0058] Among them, for the first For a quantum state with a protected coordination relationship, the update operation is as follows:

[0059] (12)

[0060] For the The interference operation for a quantum state with a protected coordination relationship is as follows:

[0061] (13)

[0062] In the formula: These represent the period before and after the interference, respectively. Among the candidate solutions, the th... The probability amplitude of a protection cooperation relationship being in a certain state; Population size; For the first The and the first The random phase angle between the candidate solutions follows a certain order. Uniform distribution on; It is a constant interference factor used to control the intensity of the interference effect; The rotation angle;

[0063] Step 3.2.4) Repeat steps 3.2.2)-3.2.3) when the algorithm reaches the maximum number of iterations, terminate and output the high-quality population.

[0064] Furthermore, in step 3.3), the step of using the adaptive evolutionary mechanism for local search includes:

[0065] Step 3.3.1) Use the high-quality population output by QIRO as the initial population;

[0066] Step 3.3.2) Dynamically calculate the adaptive crossover and mutation probabilities according to the adaptive mechanism, and perform selection, crossover, and mutation operations; among which the mutation probability introduces the importance of protection matching relationship, and prioritizes the adjustment of high-risk mismatch points;

[0067] Crossover probability With the probability of mutation The dynamic adjustment method is as follows:

[0068] (14)

[0069] (15)

[0070] In the formula: These are the maximum and average fitness of the current population, respectively. The highest fitness among the individuals to be crossed; The fitness of the individual to be mutated; These are the boundaries of the crossover probability and the mutation probability, respectively;

[0071] No. The actual mutation probabilities of each protection coordination relationship are shown below:

[0072] (16)

[0073] In the formula: For the first The actual probability of variation of a protection coordination relationship; For the first The importance of the protection and cooperation relationship; This is the adjustment coefficient;

[0074] Step 3.3.3) Employ an elitist retention strategy to ensure that the optimal solution is not lost, and initiate a local search if the algorithm fails to improve the optimal solution over multiple generations, ultimately outputting the optimal starting point scheme. .

[0075] The operation of performing a local search on elite individuals is as follows:

[0076] (17)

[0077] In the formula: For new individuals; For elite individuals; It is a Gaussian perturbation with a mean of 0; The variance is time-varying. This represents the current iteration number; This represents the total number of iterations.

[0078] It is worth noting that this invention assesses the importance of protection coordination by analyzing the consequences of mismatch, constructs an optimal starting point selection model based on minimizing the importance of mismatch and the original number of protection adjustments, and finally proposes a fusion algorithm based on QIRO and DAGA to solve the problem, thereby optimizing the selection of the starting point and improving the setting efficiency.

[0079] The technical effects of this invention are undeniable, and its beneficial effects are as follows:

[0080] 1) This invention significantly improves the calculation efficiency of relay protection setting. By using the QIRO-DAGA fusion algorithm, the time spent on starting point optimization and setting calculation is reduced from several hours to tens of seconds, effectively solving the problems of slow response and long cycle of manual setting.

[0081] 2) This invention effectively reduces the risk of protection mismatch. By using a protection coordination relationship importance assessment strategy based on steady-state power flow and mismatch consequence analysis, the risk level of protection coordination relationship is systematically quantified, and high-risk mismatch points are accurately identified and prioritized for elimination.

[0082] 3) This invention fully considers the feasibility of engineering implementation. By establishing an optimization mechanism aimed at minimizing the number of protection adjustments, it improves system security while minimizing the scope of protection setting changes, reducing the cost of setting implementation and operation and maintenance risks, and ensuring the practical application value of the optimization results.

[0083] This invention can be applied to the optimization and setting calculation process of the starting point of power system relay protection. Attached Figure Description

[0084] Figure 1 This is a flowchart for assessing the importance of protection coordination based on the consequences of tuning mismatch analysis;

[0085] Figure 2 This is a schematic diagram of the bidirectional setting calculation for ring network protection.

[0086] Figure 3 The process of optimizing the selection and tuning of the calculation model for the starting point;

[0087] Figure 4 This is a model solution method based on QIRO-DAGA fusion;

[0088] Figure 5 This is the topology of a certain 220kV power grid;

[0089] Figure 6 This is the topology of a 220kV power grid. Detailed Implementation

[0090] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0091] Example 1:

[0092] See Figures 1 to 6 A method for intelligent selection and efficient setting of relay protection starting points based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm includes the following steps:

[0093] Step 1) Construct a comprehensive index of the importance of protection coordination to quantify the impact of maloperation caused by protection mismatch on the power grid;

[0094] Step 2) Based on the comprehensive index of protection coordination importance, construct a starting point optimization and tuning calculation model based on the minimum mismatch importance and the number of protection adjustments;

[0095] Step 3) Solve the starting point optimization and setting calculation model based on the minimum mismatch importance and the number of protection adjustments to obtain the relay protection starting point setting results.

[0096] Example 2:

[0097] A method for intelligent selection and efficient setting of relay protection starting points based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm, with the same technical content as in Example 1, further, in step 1), the comprehensive index of protection coordination importance is as follows:

[0098] (1)

[0099] (2)

[0100] In the formula, These are the weighting coefficients for each indicator; ; Branch power index; As a load loss indicator; For branch line overload indicators; As an indicator of tidal current transfer rate; It is an exponential utility function; Input metrics; To protect the comprehensive index of importance.

[0101] Example 3:

[0102] A method for intelligent selection and efficient setting of relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm, with the same technical content as any one of embodiments 1-2, further including branch power index. Used to reflect the power load of the branch where the protection coordination relationship is located in the initial operating mode, that is:

[0103] (3)

[0104] In the formula: For the first The sum of active power of the branches associated with each protection coordination relationship; This represents the total active power of the original system.

[0105] Load loss index Used to characterize the degree of load loss caused by escalating actions due to mismatch in protection coordination, i.e.:

[0106] (4)

[0107] In the formula: To disconnect the first The total active power of the system after the protection coordination relationship is associated with the branch;

[0108] Branch overload index Used to assess the average overload severity of branches in the system after mismatch, i.e.:

[0109] (5)

[0110] In the formula: For overloaded branch sets; and for Overloaded branches in the network The magnitude of the active power transmitted and its power limit;

[0111] Current transfer index Used to measure the health and stability risk of system operation after protection mismatch, namely:

[0112] (6)

[0113] (7)

[0114] In the formula: For branch road collection; For generator sets; and Branch roads Reactance and active power of the branch; branch road Power limit; For generator Maximum effective output; , , Network power average transmission distance, branch average load rate, generator average load rate; subscript and Corresponding to the initial Network and disconnection A protective cooperation relationship formed network.

[0115] Example 4:

[0116] A method for intelligent selection and efficient setting of relay protection starting points based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm, with the same technical content as any one of embodiments 1-3, further wherein the starting point optimization and setting calculation model based on minimum mismatch importance and protection adjustment quantity is as follows:

[0117] (8)

[0118] In the formula: These are the initial decision variables; The overall objective function; These are the weighting coefficients. This is used to balance the risk of mismatch with the cost of adjustment; It is the set of all protection coordination relationships in the system; For the first A comprehensive index indicating the importance of each protective and cooperative relationship; As an indicator function, when the starting point scheme Leading to the The value is 1 when the protection pairing relationship is mismatched, and 0 otherwise. This represents the total number of protections in the system. As an indicator function, when protection Section II fixed value Compared to its initial value The value is 1 when a change occurs, and 0 otherwise. Indicates As input, the complete set of protection setting schemes is obtained after executing the constraint processing strategy; This represents the set of all feasible tuning schemes that satisfy the actual tuning principle.

[0119] Example 5:

[0120] A method for intelligent selection and efficient setting of relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm, with the same technical content as any one of embodiments 1-4, further comprising decision variables. As shown below:

[0121] (9)

[0122] In the formula: the first column vector Representing the The starting point pair of a ring network. Components and Both are integers, representing the starting protection number selected for the forward and reverse setting calculations in the ring network, respectively.

[0123] Example 6:

[0124] A method for intelligent selection and efficient setting of relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm, with the same technical content as any one of embodiments 1-5, further comprising the following steps for executing the constraint processing strategy:

[0125] Step 1) Calculate the fixed setting values ​​for all protections, including the first-stage setting values, based on the power grid parameters and setting principles. Sensitivity requirements for segment II And the initial settings for coordination with the lower-level protection stage I. ;

[0126] Step 2) Initialize the coordination matrix RDR; the dimensions of the coordination matrix RDR are... ,element Characterization protection and protection The number of coordination segments between them; if protection and protection The cooperation satisfies Then place Set to 1 if the value is 1, otherwise set to 0.

[0127] Step 3) Decision variables at the starting point To guide the determination of the tuning sequence, tuning calculations are performed step-by-step along the positive and negative directions of the ring network; during the calculation process, if The value is 1, and the coordination value with the lower-level protection stage I is adopted. ;like The value is set to 0, adjusted to coordinate with the lower-level protection stage II, and the setting is adopted. and will Change to 2;

[0128] Step 4) Take the minimum value among all possible combination values ​​as the final value for segment II;

[0129] Whenever the stage II setting of a lower-level protection is determined, the relevant coordination settings of the upper-level protection are updated. And verify whether it meets the sensitivity requirements of the upper-level protection; if it still does not meet the sensitivity requirements when combined with stage II, then... Setting it to 0 indicates a mismatch in the pairing relationship;

[0130] Step 5) Based on the final RDR matrix and the complete set of settings, calculate the total importance of system mismatch and the number of protection adjustments, and then calculate candidate solutions. If a complete constant value scheme cannot be generated during the process of determining the objective function value, then the candidate solution is determined to be infeasible.

[0131] Example 7:

[0132] A method for intelligent selection and efficient setting of relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm, with the same technical content as any one of embodiments 1-6, further wherein, in step 3), the starting point optimization and setting calculation model based on minimum mismatch importance and protection adjustment quantity is solved by QIRO-DAGA fusion algorithm.

[0133] Example 8:

[0134] A method for intelligent selection and efficient setting of relay protection starting points based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm, with the same technical content as any one of embodiments 1-7, further comprising, in step 3), solving the starting point optimization and setting calculation model based on minimum mismatch importance and protection adjustment quantity, including:

[0135] Step 3.1) Input the power grid parameters and setting principles, and calculate the importance of all protection coordination relationships based on the protection coordination relationship importance assessment strategy;

[0136] Set algorithm parameters, including population size and maximum number of iterations;

[0137] Step 3.2) Perform QIRO global exploration to generate a high-quality population;

[0138] Step 3.3) Using the high-quality population output by QIRO as the initial population, a local search is performed using an adaptive evolutionary mechanism to generate the optimal starting point scheme. ;

[0139] Step 3.4) Based on the optimal starting point scheme It performs setting calculations and outputs a complete scheme including the settings, operating times, and coordination relationships of each protection.

[0140] Example 9:

[0141] A method for intelligent selection and efficient setting of relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm, with the same technical content as any one of embodiments 1-8, further comprising the following steps: Step 3.2) performing QIRO global exploration to generate a high-quality population includes:

[0142] Step 3.2.1) Initialize the quantum population, encode each protected cooperative state as a quantum state, and collapse each quantum individual into the starting point scheme through quantum measurement. ;

[0143] Among them, the A quantum state with a protective coordination relationship As shown below:

[0144] (10)

[0145] In the formula: The probability amplitudes correspond to the three states; To protect the cooperative relationship In the first The probability of a certain state;

[0146] Starting point scheme In the middle, the first The state of a protective cooperation relationship As shown below:

[0147] (11)

[0148] In the formula: For the first A protection coordination status;

[0149] Step 3.2.2) Apply constraint handling strategies to each starting point scheme, determine feasibility, and calculate the fitness value. (Formula 8);

[0150] Step 3.2.3) Based on fitness value The quantum state probability amplitude is updated using quantum rotation gate probability and interference operations; specifically: first, a quantum rotation gate operation is performed, followed by a quantum interference operation, and the final quantum state probability amplitude is... ;

[0151] Among them, for the first For a quantum state with a protected coordination relationship, the update operation is as follows:

[0152] (12)

[0153] For the The interference operation for a quantum state with a protected coordination relationship is as follows:

[0154] (13)

[0155] In the formula: These represent the period before and after the interference, respectively. Among the candidate solutions, the th... The probability amplitude of a protection cooperation relationship being in a certain state; Population size; For the first The and the first The random phase angle between the candidate solutions follows a certain order. Uniform distribution on; It is a constant interference factor used to control the intensity of the interference effect; The rotation angle;

[0156] Step 3.2.4) Repeat steps 3.2.2)-3.2.3) when the algorithm reaches the maximum number of iterations, terminate and output the high-quality population.

[0157] Example 10:

[0158] A method for intelligent selection and efficient setting of relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm, with the same technical content as any one of embodiments 1-9, further comprising the following steps in step 3.3): The step of using an adaptive evolutionary mechanism for local search includes:

[0159] Step 3.3.1) Use the high-quality population output by QIRO as the initial population;

[0160] Step 3.3.2) Dynamically calculate the adaptive crossover and mutation probabilities according to the adaptive mechanism, and perform selection, crossover, and mutation operations; among which the mutation probability introduces the importance of protection matching relationship, and prioritizes the adjustment of high-risk mismatch points;

[0161] Crossover probability With the probability of mutation The dynamic adjustment method is as follows:

[0162] (14)

[0163] (15)

[0164] In the formula: These are the maximum and average fitness of the current population, respectively. The highest fitness among the individuals to be crossed; The fitness of the individual to be mutated; These are the boundaries of the crossover probability and the mutation probability, respectively;

[0165] No. The actual mutation probabilities of each protection coordination relationship are shown below:

[0166] (16)

[0167] In the formula: For the first The actual probability of variation of a protection coordination relationship; For the first The importance of the protection and cooperation relationship; This is the adjustment coefficient;

[0168] Step 3.3.3) Employ an elitist retention strategy to ensure that the optimal solution is not lost, and initiate a local search if the algorithm fails to improve the optimal solution over multiple generations, ultimately outputting the optimal starting point scheme. .

[0169] The operation of performing a local search on elite individuals is as follows:

[0170] (17)

[0171] In the formula: For new individuals; For elite individuals; It is a Gaussian perturbation with a mean of 0; The variance is time-varying. This represents the current iteration number; This represents the total number of iterations.

[0172] Example 11:

[0173] A method for intelligent selection and efficient setting of relay protection starting points based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm is presented below:

[0174] First, a protection importance assessment strategy based on steady-state power flow and mismatch consequence analysis is proposed. Taking each pair of protection coordination relationships as the research object, the accident chain of "branch fault → correct operation of branch protection → maloperation of branch backup protection" is adopted. Multi-dimensional quantitative indicators are established to comprehensively consider the adverse effects of initial steady-state power flow and protection mismatch, such as load loss, branch overload, and power flow transfer, providing a basis for the objective function of starting point optimization. Then, a starting point optimization and tuning calculation model based on minimizing mismatch importance and the number of protection adjustments is constructed. This model incorporates practical tuning principles to handle complex engineering constraints, using starting point selection as the core decision variable. Tuning calculations determine whether protection coordination is necessary and the number of coordination segments, thereby reducing the number of protection mismatches and the original number of protection adjustments while ensuring selectivity. Furthermore, a solution method for the starting point optimization and tuning calculation model based on QIRO and DAGA is proposed. This algorithm integrates quantum-inspired global search and dynamic adaptive algorithm local optimization, efficiently solving for the optimal starting point while ensuring the results meet complex engineering constraints, significantly improving the efficiency and reliability of the tuning calculation. Finally, a case study was conducted based on a real 220kV power grid configuration. The results verified that the proposed method can effectively select the starting point and improve the setting efficiency.

[0175] The details are as follows.

[0176] 1. Strategy for assessing the importance of protection coordination relationships based on steady-state power flow and mismatch consequences analysis

[0177] The protection coordination relationship importance assessment process based on steady-state power flow and mismatch consequence analysis proposed in this invention is as follows: Figure 1 As shown, this strategy starts from the accident chain caused by protection mismatch: "branch fault → correct operation of branch protection → malfunction of branch backup protection". It constructs multi-dimensional quantitative indicators to provide the objective function basis for the starting point optimization and setting calculation model.

[0178] To accurately quantify the impact of maloperation caused by protection mismatch on the power grid, it is necessary to first construct a protection importance assessment index:

[0179] (1) Branch power index This reflects the power load of the branch where the protection coordination relationship is located in the initial operating mode.

[0180] (1)

[0181] In the formula: For the first The sum of active power of the branches associated with each protection coordination relationship; This represents the total active power of the original system.

[0182] (2) Load loss index This characterizes the degree of load loss caused by overstepping of protection levels due to mismatch in protection coordination.

[0183] (2)

[0184] In the formula: To disconnect the first The total active power of the system after the protection coordination relationship is associated with the branch.

[0185] (3) Branch overload index : Assess the average overload severity of branches in the system after mismatch.

[0186] (3)

[0187] In the formula: For overloaded branch sets; and for Overloaded branches in the network The amount of active power transmitted and its power limit.

[0188] (4) Current transfer index Define the average transmission distance of network power respectively. Average load rate of branch lines Generator average load rate As shown in equation (4).

[0189] (4)

[0190] In the formula: For branch road collection; For generator sets; and Branch roads Reactance and active power of the branch; branch road Power limit; For generator Maximum contribution.

[0191] Measuring the power transmission efficiency of a network, and To measure the static safety margin of equipment, the power flow transfer index can be defined as shown in Equation (5). This index comprehensively measures the health and stability risk of the system operation after protection mismatch.

[0192] (5)

[0193] In the formula: subscript and Corresponding to the initial Network and disconnection A protective cooperation relationship formed network.

[0194] Furthermore, for indicators that significantly impact the system, such as load loss and branch overload, an exponential utility function is introduced for mapping to highlight the importance of "low-probability, high-risk" events. The function used is shown in equation (6):

[0195] (6)

[0196] Define the comprehensive index of the importance of protection coordination As shown in equation (7):

[0197] (7)

[0198] In the formula, These are the weighting coefficients for each indicator, satisfying... Comprehensive indicators By using a weighted summation method, the importance of protection coordination relationships was comprehensively measured from multiple perspectives, including the initial network state and the system state after mismatch.

[0199] 2. Construct a starting point optimization and tuning calculation model based on minimum mismatch importance and the number of protection adjustments.

[0200] A starting point optimization and tuning calculation model was established. This model uses the selection of the starting point as the core decision variable and minimizes the importance of mismatch and the number of adjustments to the original protection as the objective function. Simultaneously, the model strictly adheres to practical tuning principles, embedding network topology loop unblocking, protection coordination requirements, and sensitivity verification into the constraint processing to ensure that the optimization results meet engineering feasibility.

[0201] Relay protection setting schemes need to be evaluated from multiple dimensions. On the one hand, the overall mismatch risk of protection coordination should be minimized to ensure system safety. On the other hand, adjustments to existing protection settings should be reduced as much as possible to lower implementation costs and avoid introducing new uncertainties. Therefore, this section establishes the following bi-objective optimization function:

[0202] 1) Minimize the overall system mismatch risk. This objective pursues optimal global security, the core of which is to avoid high-risk mismatch points. This is based on a comprehensive index of the importance of protection coordination relationships. The objective function is defined as follows.

[0203] (8)

[0204] In the formula, The set of all mismatched points; For the first A comprehensive index indicating the importance of each protective and cooperative relationship; A 0-1 variable, representing the first... Whether a protection pairing is mismatched (1 indicates mismatch, 0 indicates pairing). This objective aims to prioritize eliminating high-risk mismatches by assigning differentiated importance weights to different protection pairings.

[0205] 2) Minimize the number of protection adjustments. In power grid operation, frequent and large-scale modifications to protection settings introduce new uncertainties and increase operation and maintenance costs. Therefore, this objective aims to improve safety while minimizing changes to existing settings. The objective function is defined as follows.

[0206] (9)

[0207] In the formula, To protect the total number; A variable consisting of 0 or 1 indicates protection. Whether the set value is adjusted (1 indicates adjustment, 0 indicates keeping the original value).

[0208] Target and Since they are usually in competition, this section uses the linear weighted sum method to transform it into a single-objective problem for efficient solution. The final comprehensive optimization objective function is as follows.

[0209] (10)

[0210] In the formula, The objective is to minimize the overall system mismatch risk. To protect the goal of minimizing the number of adjustments, The weighting coefficients are used to balance the two optimization objectives of mismatch risk and adjustment cost. This model employs a dual-objective optimization design, considering both system security and engineering implementation costs. To ensure the overall system mismatch risk is minimized, This will maximize the stability of the existing protection configuration and avoid introducing new uncertainties by frequently modifying the settings.

[0211] For a containing For a system with a ring network, the selection of the starting point is determined by decision variables. Characterization. Since the tuning calculations for each ring network need to be performed independently in two directions, the calculation process is as follows: Figure 2 As shown, therefore, each ring network needs to specify a pair of starting points. Define decision variables. For one The matrix:

[0212] (11)

[0213] In the formula: the first column vector Representing the The starting point pair of a ring network. Components and Both are integers, representing the starting protection number selected for the forward and reverse setting calculations in the ring network, respectively.

[0214] To ensure that the optimization results conform to engineering practice, this invention deeply integrates relay protection setting principles into the model construction process, forming a complete constraint processing mechanism. The core process of this mechanism is as follows: Figure 3 As shown.

[0215] The specific steps are as follows:

[0216] Step 1: Initialization Calculation. Based on the power grid parameters and setting principles, calculate the fixed setting values ​​for all protections, including the first-stage setting values. Sensitivity requirements for segment II And the initial settings for coordination with the lower-level protection stage I. .

[0217] Step 2: Construct the coordination matrix. Initialize the coordination matrix RDR, with dimensions [missing information]. ,element Characterization protection and protection The number of coordination segments between them. If protection... and protection The cooperation satisfies Then place Set to 1 if the value is 1, otherwise set to 0.

[0218] Step 3: Perform two-way tuning calculations. Starting with the decision variables... To guide the determination of the tuning sequence, tuning calculations are performed step-by-step along the forward and reverse directions of the ring network. During the calculation process, the coordination logic is determined based on the current state in the RDR matrix: if The value is 1, and the coordination value with the lower-level protection stage I is adopted. ;like A value of 0 indicates that the coordination value with the lower-level protection stage I does not meet the sensitivity requirements. It should be adjusted to coordinate with the lower-level protection stage II, and the setting value should be adopted. and will Change it to 2.

[0219] Step 4: Dynamic Verification and Update. Following the "strictest principle," the minimum value among all possible coordination values ​​is taken as the final Stage II setting. Whenever the Stage II setting of a lower-level protection is determined, the relevant coordination settings of its upper-level protection are updated. And verify whether it meets the sensitivity requirements of the superior protection. If it still does not meet the sensitivity requirements when combined with stage II, then... Setting it to 0 indicates a mismatch in the pairing relationship.

[0220] Step 5: Scheme Evaluation. Based on the final RDR matrix and the complete set of settings, calculate the total importance of system mismatch and the number of protection adjustments, and then calculate candidate solutions. The objective function value. If a complete constant value scheme cannot be generated during this process, the candidate solution is determined to be infeasible.

[0221] Based on the above optimization objectives and constraint handling strategies, the calculation model for starting point optimization and tuning is established as follows:

[0222] (12)

[0223] In the formula: These are the initial decision variables; The overall objective function; These are the weighting coefficients. This is used to balance the risk of mismatch with the cost of adjustment; It is the set of all protection coordination relationships in the system; For the first A comprehensive index indicating the importance of each protective and cooperative relationship; As an indicator function, when the starting point scheme Leading to the The value is 1 when the protection pairing relationship is mismatched, and 0 otherwise. This represents the total number of protections in the system. As an indicator function, when protection Section II fixed value Compared to its initial value The value is 1 when a change occurs, and 0 otherwise. Indicates As input, execute Figure 3 The complete set of protection setting schemes obtained after the constraint processing strategy; This represents the set of all feasible tuning schemes that satisfy the actual tuning principle.

[0224] 3. Starting point optimization and tuning calculation model solution method based on QIRO-DAGA fusion algorithm

[0225] In high-dimensional, discrete, and multi-constraint combinatorial optimization problems involving starting point optimization and tuning computational models, traditional optimization algorithms often get stuck in local optima due to insufficient initial global exploration. To address this, this invention designs a two-stage solution strategy, achieving efficient solution through a collaborative mechanism of Quantum Inspired Recombination Optimization (QIRO) and Dynamic Adaptive Genetic Algorithm (DAGA). This method first utilizes the quantum parallelism of QIRO for global exploration, quickly identifying a region of high-quality solutions; then, DAGA is used to perform a refined local search within this region, ultimately obtaining the optimal starting point scheme. The method flow is as follows: Figure 4 As shown, the specific steps are as follows.

[0226] Step 1: Initialization and Preprocessing. Input power grid parameters and setting principles, and calculate the importance of all protection coordination relationships based on the importance assessment strategy. Set algorithm parameters, including population size and maximum number of iterations, to complete the solution preparation.

[0227] Step 2: QIRO Global Exploration. First, initialize the quantum population, and then use quantum measurement to collapse each quantum individual into a specific starting point scheme. Secondly, a constraint handling strategy is applied to each starting point scheme to determine its feasibility and calculate its fitness value. Finally, based on fitness values The quantum state probability amplitude is updated using quantum rotation gate probability and interference operations. This process is iterated to transfer the current high-quality population to the DAGA stage.

[0228] QIRO achieves efficient parallel exploration of the solution space by simulating superposition states and interference principles in quantum computing. Its core lies in encoding each protected coordination state as a quantum state, allowing a quantum population to simultaneously characterize multiple possible combinations of coordination states, as detailed below.

[0229] 1) Quantum State Encoding: The algorithm uses qubits as the basic unit of information. A qubit can be in one of the following states: , Or its superposition state, its state can be represented as:

[0230] (13)

[0231] In the formula: They represent state or The complex form of the probability of a state occurring, i.e., the probability amplitude; This represents the probability that a qubit is in a corresponding state.

[0232] Since a single qubit corresponds to only two ground states, this invention uses two qubits to encode a single protection pair relationship to characterize the three states of the protection pair relationship. This encoding system has four ground states. The first three ground states are chosen to characterize the three coordination states mentioned above. A quantum state with a protective coordination relationship It can be represented as:

[0233] (14)

[0234] In the formula: The probability amplitudes correspond to the three states; To protect the cooperative relationship In the first The probability of each state. Initially, all probability magnitudes are set to... This ensures that the three states are selected with equal probability, maintaining the uniformity of the initial search.

[0235] 2) Quantum Measurement and Candidate Solution Generation: Through quantum measurement operations, quantum states are... It collapses into a definite classical solution. For the th A protection relationship is established, and a random number is generated. Its state It is determined by equation (15).

[0236] (15)

[0237] In the formula: For the first Each protection coordination state. All This constitutes a complete protection coordination strategy and corresponds to a starting point scheme. .

[0238] 3) Quantum Gate Update and Interference: To guide the population towards a better solution, a quantum rotation gate is used to adjust the probability amplitude of each qubit. For the ... The update operation for a quantum state with a protected coordination relationship is as follows.

[0239] (16)

[0240] In the formula: The rotation angle is adaptively adjusted by the difference between the current solution and the historical best solution, with its magnitude and direction determined by the difference between the current solution and the historical best solution. However, a single rotation gate update can cause the population to converge rapidly, potentially leading to a loss of diversity. To address this, a quantum interference operation is introduced after the rotation gate update. This operation simulates the wave properties of quantum states, maintaining population diversity through the mutual influence of probability amplitudes among individuals.

[0241] (17)

[0242] In the formula: These represent the period before and after the interference, respectively. Among the candidate solutions, the th... The probability amplitude of a protection cooperation relationship being in a certain state; Population size; For the first The and the first The random phase angle between the candidate solutions follows a certain order. Uniform distribution on; It is a constant interference factor used to control the intensity of the interference effect.

[0243] Step 3: DAGA Local Optimization. First, the high-quality population output by QIRO is used as the initial population. Second, adaptive crossover and mutation probabilities are dynamically calculated based on the adaptive mechanism, and selection, crossover, and mutation operations are performed. The mutation probability incorporates the importance of protecting matching relationships, prioritizing adjustments to high-risk mismatch points. Finally, an elite retention strategy is adopted to ensure that the optimal solution is not lost, and local search is initiated if the algorithm fails to improve the optimal solution over multiple generations. Ultimately, the optimal starting point scheme is output. .

[0244] Starting with the high-quality initial population output by QIRO, DAGA conducts a refined local search by introducing an adaptive evolutionary mechanism, as detailed below.

[0245] 1) Adaptive probability mechanism: crossover probability With the probability of mutation The strategy is to dynamically adjust based on population fitness, as follows.

[0246] (18)

[0247] (19)

[0248] In the formula: These are the maximum and average fitness of the current population, respectively. The highest fitness among the individuals to be crossed; The fitness of the individual to be mutated; These represent the boundaries of the crossover probability and the mutation probability, respectively. This mechanism allows the algorithm to maintain global exploration in the early stages of evolution and enhance its local exploration capabilities in the later stages.

[0249] 2) Targeted mutation strategy: To prioritize the elimination of high-risk mismatches, a targeted mutation strategy is designed.

[0250] (20)

[0251] In the formula: For the first The actual probability of variation of a protection coordination relationship; For the first The importance of the protection and cooperation relationship; This is the adjustment coefficient. This strategy primarily optimizes based on the protection coordination relationships with high importance in the system.

[0252] 3) Elite Preservation and Local Search: To ensure that the optimal solution is not lost, an elite preservation strategy is adopted. When the algorithm fails to improve the optimal solution for several consecutive generations, a local search is performed on the elite individuals. The specific operation is as follows:

[0253] (twenty one)

[0254] In the formula: For new individuals; For elite individuals; It is a Gaussian perturbation with a mean of 0; The variance is time-varying and decreases with increasing generation number to ensure stable convergence at the end of the algorithm; This represents the current iteration number; This represents the total number of iterations. If a new individual is superior to the current elite individual, a replacement operation is performed. This mechanism aims to improve the algorithm's local search capability in the later stages to avoid premature convergence.

[0255] Step 4: Based on the optimal starting point scheme It performs setting calculations and outputs a complete scheme including the settings, operating times, and coordination relationships of each protection.

[0256] Example 12:

[0257] The verification of a method for intelligent selection and efficient setting of relay protection starting points based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm is as follows:

[0258] This embodiment uses a simulation verification based on the actual configuration of a 220kV power grid. The network contains 7 nodes, 9 branches, and corresponding to 18 protection devices. The system topology is as follows: Figure 5 As shown, node A and node C are the hub nodes connecting the power supply. Based on this topology, a total of 32 protection coordination relationships are formed.

[0259] To verify the rationality of the protection coordination relationship importance assessment strategy constructed in this invention, the importance of all protection coordination relationships was analyzed, and the weight coefficient of each indicator was taken as follows: The results of each indicator are shown in Table 1.

[0260] Table 1. Evaluation Indicators for Protection and Coordination Relationships

[0261]

[0262] The results show that, as hub nodes connecting power supplies, mismatches in the protection coordination relationships B1-A2, B1-A8, G4-C2, G4-C3, and G4-C5 between nodes A and C will trigger large-scale power flow shifts, adversely affecting system stability. This result demonstrates that the importance assessment strategy proposed in this invention can effectively identify high-risk protection coordination relationships in the system due to mismatches, and the assessment results are consistent with practical engineering experience. Accurate quantification of the importance of protection coordination relationships provides a reliable objective function basis for subsequent starting point optimization, verifying the rationality and practicality of the assessment strategy.

[0263] Example 13:

[0264] The verification of a method for intelligent selection and efficient setting of relay protection starting points based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm is as follows:

[0265] To further verify the actual setting calculation performance of the proposed method, this embodiment uses a real 220kV power grid configuration for verification analysis, the topology of which is as follows: Figure 6 As shown.

[0266] The network comprises 12 nodes, 21 branches, and 42 protection devices. Based on the optimal starting point scheme, the protection settings were calculated, and the resulting protection settings are shown in Table 2.

[0267] Table 2 Tuning Calculation Results

[0268]

[0269] Analysis results show that the present invention can effectively guide the system mismatch point to the A2-C2 branch, consistent with the actual engineering setting strategy. Setting results indicate that the deviations between the setting values ​​of most protections, such as G6, E7, and F14, and the actual results are less than 5%, demonstrating the good computational accuracy of the proposed method. Furthermore, the operating times of all protections meet the graded coordination requirements, proving the good engineering feasibility of the proposed method. This method can meet practical engineering requirements while ensuring setting calculation accuracy, achieving intelligent optimization selection and efficient setting of the starting point in complex power grid structures, thus verifying the effectiveness of the optimization method proposed in this invention.

Claims

1. A method for intelligent selection and efficient setting of relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm, characterized in that, Includes the following steps: Step 1) Construct a comprehensive index of the importance of protection coordination to quantify the impact of maloperation caused by protection mismatch on the power grid; Step 2) Based on the comprehensive index of protection coordination importance, construct a starting point optimization and tuning calculation model based on the minimum mismatch importance and the number of protection adjustments; Step 3) Solve the starting point optimization and setting calculation model based on the minimum mismatch importance and the number of protection adjustments to obtain the relay protection starting point setting results.

2. The intelligent selection and efficient setting method for relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm as described in claim 1, characterized in that, In step 1), the comprehensive index of the importance of protection coordination is as follows: (1) (2) In the formula, These are the weighting coefficients for each indicator; ; Branch power index; As a load loss indicator; For branch line overload indicators; As an indicator of tidal current transfer rate; It is an exponential utility function; Input metrics; To protect the comprehensive index of importance.

3. The intelligent selection and efficient setting method for relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm according to claim 2, characterized in that, Branch power index Used to reflect the power load of the branch where the protection coordination relationship is located in the initial operating mode, that is: (3) In the formula: For the first The sum of active power of the branches associated with each protection coordination relationship; This represents the total active power of the original system. Load loss index Used to characterize the degree of load loss caused by escalating actions due to mismatch in protection coordination, i.e.: (4) In the formula: To disconnect the first The total active power of the system after the protection coordination relationship is associated with the branch; Branch overload index Used to assess the average overload severity of branches in the system after mismatch, i.e.: (5) In the formula: For overloaded branch sets; and for Overloaded branches in the network The amount of active power transmitted and the power limit; Current transfer index Used to measure the health and stability risk of system operation after protection mismatch, namely: (6) (7) In the formula: For branch road collection; For generator sets; and Branch roads Reactance and active power of the branch; branch road Power limit; For generator Maximum effective output; , , Network power average transmission distance, branch average load rate, generator average load rate; subscript and Corresponding to the initial Network and disconnection A protective cooperation relationship formed network.

4. The intelligent selection and efficient setting method for relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm as described in claim 1, characterized in that, The optimization and tuning calculation model based on the starting point of minimum mismatch importance and protection adjustment quantity is shown below: (8) In the formula: These are the initial decision variables; The overall objective function; These are the weighting coefficients. This is used to balance the risk of mismatch with the cost of adjustment; It is the set of all protection coordination relationships in the system; For the first A comprehensive index indicating the importance of each protective and cooperative relationship; As an indicator function, when the starting point scheme Leading to the The value is 1 when the protection pairing relationship is mismatched, and 0 otherwise. This represents the total number of protections in the system. As an indicator function, when protection Section II fixed value Compared to its initial value The value is 1 when a change occurs, and 0 otherwise. Indicates As input, the complete set of protection setting schemes is obtained after executing the constraint processing strategy; This represents the set of all feasible tuning schemes that satisfy the actual tuning principle.

5. The intelligent selection and efficient setting method for relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm according to claim 4, characterized in that, Decision variables As shown below: (9) In the formula: the first column vector Representing the The starting point pair of the ring network; components and Both are integers, representing the starting protection number selected for the forward and reverse setting calculations in the ring network, respectively.

6. The intelligent selection and efficient setting method for relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm according to claim 4, characterized in that, The steps for implementing constraint handling strategies include: Step 1) Calculate the fixed setting values ​​for all protections, including the first-stage setting values, based on the power grid parameters and setting principles. Sensitivity requirements for segment II And the initial settings for coordination with the lower-level protection stage I. ; Step 2) Initialize the coordination matrix RDR; the dimensions of the coordination matrix RDR are... ,element Characterization protection and protection The number of coordination segments between them; if protection and protection The cooperation satisfies Then place Set to 1 if the value is 1, otherwise set to 0. Step 3) Decision variables at the starting point To guide the determination of the tuning sequence, tuning calculations are performed step-by-step along the positive and negative directions of the ring network; during the calculation process, if The value is 1, and the coordination value with the lower-level protection stage I is adopted. ;like The value is set to 0, adjusted to coordinate with the lower-level protection stage II, and the setting is adopted. and will Change to 2; Step 4) Take the minimum value among all possible combination values ​​as the final value for segment II; Whenever the stage II setting of a lower-level protection is determined, the relevant coordination settings of the upper-level protection are updated. And verify whether it meets the sensitivity requirements of the upper-level protection; if it still does not meet the sensitivity requirements when combined with stage II, then... Setting it to 0 indicates a mismatch in the pairing relationship; Step 5) Based on the final RDR matrix and the complete set of settings, calculate the total importance of system mismatch and the number of protection adjustments, and then calculate candidate solutions. If a complete constant value scheme cannot be generated during the process of determining the objective function value, then the candidate solution is determined to be infeasible.

7. The intelligent selection and efficient setting method for relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm as described in claim 1, characterized in that, In step 3), the QIRO-DAGA fusion algorithm is used to solve the starting point optimization and tuning calculation model based on the minimum mismatch importance and the number of protection adjustments.

8. The intelligent selection and efficient setting method for relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm according to claim 1, characterized in that, Step 3) involves solving the starting point optimization and tuning calculation model based on the minimum mismatch importance and the number of protection adjustments, including: Step 3.1) Input the power grid parameters and setting principles, and calculate the importance of all protection coordination relationships based on the protection coordination relationship importance assessment strategy; Set algorithm parameters, including population size and maximum number of iterations; Step 3.2) Perform QIRO global exploration to generate a high-quality population; Step 3.3) Using the high-quality population output by QIRO as the initial population, a local search is performed using an adaptive evolutionary mechanism to generate the optimal starting point scheme. ; Step 3.4) Based on the optimal starting point scheme It performs setting calculations and outputs a complete scheme including the settings, operating times, and coordination relationships of each protection.

9. The intelligent selection and efficient setting method for relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm according to claim 8, characterized in that, Step 3.2) The steps for performing QIRO global exploration to generate a high-quality population include: Step 3.2.1) Initialize the quantum population, encode each protected cooperative state as a quantum state, and collapse each quantum individual into the starting point scheme through quantum measurement. ; Among them, the A quantum state with a protective coordination relationship As shown below: (10) In the formula: The probability amplitudes correspond to the three states; To protect the cooperative relationship In the first The probability of a certain state; Starting point scheme In the middle, the first The state of a protective cooperation relationship As shown below: (11) In the formula: For the first A protection coordination status; Step 3.2.2) Apply constraint handling strategies to each starting point scheme, determine feasibility, and calculate the fitness value. ; Step 3.2.3) Based on fitness value The quantum state probability amplitude is updated using quantum rotation gate probability and interference operation; Among them, for the first For a quantum state with a protected coordination relationship, the update operation is as follows: (12) For the The interference operation for a quantum state with a protected coordination relationship is as follows: (13) In the formula: These represent the period before and after the interference, respectively. Among the candidate solutions, the th... The probability amplitude of a protection cooperation relationship being in a certain state; Population size; For the first The and the first The random phase angle between the candidate solutions follows a certain order. Uniform distribution on; It is a constant interference factor used to control the intensity of the interference effect; The rotation angle; Step 3.2.4) Repeat steps 3.2.2)-3.2.3) when the algorithm reaches the maximum number of iterations, terminate and output the high-quality population.

10. The intelligent selection and efficient setting method for relay protection starting point based on quantum-inspired recombination optimization and dynamic adaptive genetic algorithm according to claim 8, characterized in that, In step 3.3), the steps for local search using the adaptive evolutionary mechanism include: Step 3.3.1) Use the high-quality population output by QIRO as the initial population; Step 3.3.2) Dynamically calculate the adaptive crossover and mutation probabilities according to the adaptive mechanism, and perform selection, crossover, and mutation operations; among which the mutation probability introduces the importance of protection matching relationship, and prioritizes the adjustment of high-risk mismatch points; Crossover probability With the probability of mutation The dynamic adjustment method is as follows: (14) (15) In the formula: These are the maximum and average fitness of the current population, respectively. The highest fitness among the individuals to be crossed; The fitness of the individual to be mutated; These are the boundaries of the crossover probability and the mutation probability, respectively; No. The actual mutation probabilities of each protection coordination relationship are shown below: (16) In the formula: For the first The actual probability of variation of a protection coordination relationship; For the first The importance of the protection and cooperation relationship; This is the adjustment coefficient; Step 3.3.3) Employ an elitist retention strategy to ensure that the optimal solution is not lost, and initiate a local search if the algorithm fails to improve the optimal solution over multiple generations, ultimately outputting the optimal starting point scheme. ; The operation of performing a local search on elite individuals is as follows: (17) In the formula: For new individuals; For elite individuals; It is a Gaussian perturbation with a mean of 0; The variance is time-varying. This represents the current iteration number; This represents the total number of iterations.

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