A power distribution network relay protection setting optimization method considering distributed power supply access

Abstract

The application discloses a distribution network relay protection setting optimization method considering distributed power supply access, and comprises the following optimization steps: S1, respectively establishing a distribution network relay protection setting optimization objective function and a constraint condition; S2, combining the objective function and the constraint condition through a penalty function to obtain a distribution network relay protection setting optimization model; S3, adopting an improved particle swarm algorithm to solve the distribution network relay protection setting optimization model; and S4, obtaining optimal setting parameters according to the optimal solution obtained by the improved particle swarm algorithm. The application can improve the relay protection performance and ensure safe, reliable and stable operation of the distribution network.

Description

Technical Field

[0001] This invention belongs to the field of distribution network relay protection technology, specifically relating to a method for optimizing distribution network relay protection settings that takes into account distributed power source access. Background Technology

[0002] Against the backdrop of large-scale renewable energy power generation grid connection and continuous growth of various types of electricity load, the grid load has been at a high level for a long time, and the system operation pressure has continued to increase. Simple faults are increasingly likely to evolve into serious and complex faults. As the first line of defense to ensure grid security, the accurate and rapid action of relay protection is crucial to ensuring the safety of the large power grid and improving the power supply reliability of the distribution network.

[0003] With the increasing complexity of the power grid structure and the integration of a large number of new energy sources into power grids of various voltage levels, traditional line protection systems that rely on power source characteristics are showing significant inadequacy. In particular, due to the uncertainty of the boosting effect of each power source under the fluctuation of new energy output, and the limiting of inverter control strategies, the weak feedback characteristics of new energy sources are obvious. This makes it impossible for traditional line protection systems to effectively achieve differential coordination and setting at each level. When a fault occurs in the distribution network, relays can hardly play their protective role effectively.

[0004] Therefore, in order to solve the above problems, it is necessary to develop a method for optimizing the setting of relay protection in distribution networks that takes into account the access of distributed power sources. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for optimizing the setting of relay protection in distribution networks that takes into account the access of distributed power sources, thereby improving the performance of relay protection and ensuring the safe, reliable and stable operation of the distribution network.

[0006] The objective of this invention is achieved as follows: a method for optimizing the setting of relay protection in distribution networks considering distributed generation access, comprising the following optimization steps:

[0007] S1. Establish the objective function and constraints for optimizing the setting of relay protection in the distribution network;

[0008] S11. The minimum total coordinated operation time of relay protection is taken as the objective function for optimizing the setting of relay protection in distribution network.

[0009] S12. Establish optimization constraints for distribution network relay protection settings; including relay setting coefficient constraints, relay operating time constraints, relay starting current constraints, and relay coordination time interval constraints.

[0010] S2. The objective function and constraints are combined using a penalty function to obtain the optimal model for setting the relay protection of the distribution network.

[0011] S3. An improved particle swarm optimization algorithm is used to solve the optimization model of the relay protection setting of the distribution network.

[0012] S31. The population of the improved particle swarm optimization algorithm is initialized using the Logistic chaotic equation.

[0013] S32. The inertial weight of the particle swarm algorithm is calculated using a dynamic weighting strategy based on particle spacing.

[0014] S33. The optimal position in the particle swarm optimization algorithm is optimized using the Lozi equation of a two-dimensional discontinuous discrete chaotic dynamic system.

[0015] S4. Obtain the optimal tuning parameters based on the optimal solution obtained by the improved particle swarm optimization algorithm.

[0016] Furthermore, the objective function for minimizing the total sum of relay coordinated action times in step S11 is expressed as: In the formula, Indicates distribution network lines The probability of failure. Indicates distribution network lines The probability of different fault types occurring Indicates relay right The response time of the fault that occurred at that location , Indicates relay The setting coefficient, For the fault current flowing through the relay, This is the starting current of the relay.

[0017] Furthermore, the relay setting coefficient constraint in step S12 is expressed as follows: , , Relays The minimum and maximum values ​​of the time setting coefficient; the relay action time constraint is expressed as: , For relays The inherent minimum operating time; the relay starting current constraint is expressed as: , This is the maximum load current of the relay. The minimum fault current flowing through the relay; the relay coordination time interval constraint is expressed as: , For relays right The response time of the fault that occurred at that location For time intervals.

[0018] Furthermore, the penalty function in step S2 is expressed as: In the formula, Let be the objective function. As a penalty factor, For constraint functions, A composite function constructed based on constraint functions. As a penalty item, For the number of iterations, Let be the number of particles; and let the penalty function satisfy the condition. .

[0019] Furthermore, the Logistic chaotic equation in step S31 is expressed as: In the formula, Represents the parameters of the chaotic mapping. The system is in a chaotic state. The system is in a state of complete chaos. This represents the corresponding chaotic variable within the system. exist Randomly traverse within the range and ensure that no two elements are repeated.

[0020] Furthermore, the calculation of the inertia weight in step S32 is expressed as follows: In the formula, , These are the maximum and minimum inertia weight values, respectively. , These represent the current number of iterations and the maximum number of iterations, respectively. Let the weight adjustment factor be determined by the particle spacing, then the first... During the nth iteration The weight adjustment factor for each particle is expressed as: , , They are respectively The maximum and minimum values, , These are the maximum and minimum particle spacing, respectively. For the first During the nth iteration Particle spacing of individual particles, For the first During the nth iteration The position vector of the i-th particle One portion, For the first The th iteration of the population's historical best position vector One portion, Let be the dimension of the particle.

[0021] Furthermore, the Lozi equation in step S33 is expressed as: In the formula, For the number of iterations, For chaotic variables, parameters Then it enters chaotic motion, parameters Take 0.5.

[0022] Furthermore, in step S33, the Lozi equation is used to determine the optimal position in the particle swarm optimization algorithm. Chaos optimization specifically includes the following steps: ① Through Will Mapped to Above; ② Using the Lozi equation to... conduct The next iteration yields the chaotic sequence. ③ Through By performing an inverse mapping on the chaotic sequence to return it to the original solution space, a sequence of feasible solutions for chaotic variables is obtained. ④ Calculate the fitness value of the feasible solution vector, and retain the feasible solution corresponding to the optimal fitness value, denoted as . ⑤ Randomly select the position of a particle from the current particle swarm, and use... replace.

[0023] Due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0024] (1) By establishing the objective function and constraints for the setting optimization of the distribution network relay protection separately, and combining them with the penalty function to construct the setting optimization model of the distribution network relay protection, the improved particle swarm optimization algorithm is used to optimize and solve the parameters. It can adapt to more complex distribution network configurations and has stronger anti-interference and regulation capabilities, effectively improving the relay protection performance and ensuring the reliability of distribution network operation.

[0025] (2) By using the Logistic chaotic equation to initialize the population of the improved particle swarm algorithm, the distribution of particles in the initial population is more uniform, and the population will not be too sparse or too dense, thus avoiding the defects of local optima and premature convergence that may occur in the global optimization search process.

[0026] (3) By using a dynamic weighting strategy based on particle distance to calculate the inertial weight of the particle swarm algorithm, and using the Lozi equation of the two-dimensional discontinuous discrete chaotic dynamic system to perform chaotic optimization on the optimal position in the particle swarm algorithm, not only can the search ability of the particles be enhanced, but also the algorithm can avoid getting trapped in local optimal solutions, increase particle disorder, maintain the diversity of the population, and effectively improve the ability of the particle swarm algorithm to get rid of local extreme points. Attached Figure Description

[0027] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0028] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0029] like Figure 1 As shown, a method for optimizing the relay protection settings of a distribution network considering distributed generation access includes the following optimization steps:

[0030] S1. Establish the objective function and constraints for optimizing the setting of relay protection in the distribution network.

[0031] S11. The objective function for optimizing the setting of relay protection in distribution networks is to minimize the total time of coordinated operation of relay protection.

[0032] Preferably, the objective function for minimizing the total sum of relay coordinated action times in step S11 is expressed as: In the formula, Indicates distribution network lines The probability of failure. Indicates distribution network lines The probability of different fault types occurring Indicates relay right The response time of the fault that occurred at that location , Indicates relay The setting coefficient, For the fault current flowing through the relay, The starting current of the relay is determined by... and Parameter optimization is performed to achieve tuning optimization.

[0033] Specifically, the power distribution network lines Probability of failure Represented as: , For the line Number of faults per unit time; the distribution network lines Probability of different fault types For: When the fault type is a three-phase short circuit, When the fault type is a two-phase short circuit, the value is 0.05. When the fault type is a two-phase-to-ground short circuit, the value is 0.20. When the fault type is a single-phase ground fault, the value is 0.10. Take 0.65.

[0034] S12. Establish optimization constraints for distribution network relay protection settings, including relay setting coefficient constraints, relay operating time constraints, relay starting current constraints, and relay coordination time interval constraints.

[0035] Preferably, the relay setting coefficient constraint in step S12 is expressed as follows: , , Relays The minimum and maximum values ​​of the time setting coefficient; the relay action time constraint is expressed as: , For relays The inherent minimum operating time; the relay starting current constraint is expressed as: , This is the maximum load current of the relay. The minimum fault current flowing through the relay; the relay coordination time interval constraint is expressed as: , For relays right The response time of the fault that occurred at that location For time intervals.

[0036] S2. The objective function and constraints are combined using a penalty function to obtain the distribution network relay protection setting optimization model.

[0037] Preferably, the penalty function in step S2 is expressed as: In the formula, Let be the objective function. As a penalty factor, For constraint functions, A composite function constructed based on constraint functions. As a penalty item, For the number of iterations, The number of particles; and the condition is satisfied. .

[0038] S3. An improved particle swarm optimization algorithm is used to solve the distribution network relay protection setting optimization model.

[0039] S31. The population of the improved particle swarm algorithm is initialized using the Logistic chaotic equation.

[0040] Preferably, the Logistic chaotic equation in step S31 is expressed as: In the formula, Represents the parameters of the chaotic mapping. The system is in a chaotic state. The system is in a state of complete chaos. This represents the corresponding chaotic variable within the system. exist Randomly traverse within the range and ensure that no two elements are repeated.

[0041] S32. The inertial weight of the particle swarm algorithm is calculated using a dynamic weighting strategy based on particle spacing.

[0042] Preferably, the calculation of the inertia weight in step S32 is expressed as follows: In the formula, , These are the maximum and minimum inertia weight values, respectively. , These represent the current number of iterations and the maximum number of iterations, respectively. Let the weight adjustment factor be determined by the particle spacing, then the first... During the nth iteration The weight adjustment factor for each particle is expressed as: , , They are respectively The maximum and minimum values, , These are the maximum and minimum particle spacing, respectively. For the first During the nth iteration Particle spacing of individual particles, For the first During the nth iteration The position vector of the i-th particle One portion, For the first The th iteration of the population's historical best position vector One portion, Let be the dimension of the particle; specifically, The bigger The smaller the inertial weight, the greater the particle's inertial weight, and the greater its velocity, which is beneficial for global searching. Conversely, the smaller the inertial weight, the greater the particle's inertial weight. smaller The larger the inertial weight, the smaller the particle velocity, which is beneficial for particles to perform local searches.

[0043] S33. The optimal position in the particle swarm optimization algorithm is optimized using the Lozi equation of a two-dimensional discontinuous discrete chaotic dynamic system.

[0044] Preferably, the Lozi equation in step S33 is expressed as: In the formula, For the number of iterations, For chaotic variables, parameters Then it enters chaotic motion, parameters Take 0.5.

[0045] Preferably, in step S33, the Lozi equation is used to determine the optimal position in the particle swarm optimization algorithm. Chaos optimization specifically includes the following steps: ① Through Will Mapped to Above; ② Using the Lozi equation to... conduct The next iteration yields the chaotic sequence. ③ Through By performing an inverse mapping on the chaotic sequence to return it to the original solution space, a sequence of feasible solutions for chaotic variables is obtained. ④ Calculate the fitness value of the feasible solution vector, and retain the feasible solution corresponding to the optimal fitness value, denoted as . ⑤ Randomly select the position of a particle from the current particle swarm, and use... replace.

[0046] Specifically, step S3, which uses an improved particle swarm optimization algorithm to solve the model, includes the following steps: ① Initializing algorithm parameters, including particle swarm size N, particle dimension D, and learning factor. and ① The population is initialized using the Logistic chaotic equation; ② The fitness function value of each particle is calculated. If the fitness value of the current particle is better than the fitness value corresponding to the historical best position, the historical best position is updated to the current particle position; specifically, the fitness function value is calculated by a penalty function composed of the objective function and a penalty term; ③ For each particle, if the fitness value of its current position is better than the fitness value corresponding to the global best position, the global best position is updated to the current position of the particle; ⑤ The inertia weight value is calculated using a dynamic weighting strategy based on particle spacing; ⑥ The velocity and position of each particle are updated; where the velocity update formula is expressed as: , , The first The velocity of each particle before and after iteration For the number of iterations, For inertial weights, , As a learning factor, , for Random numbers between , The first The optimal particle position and the global optimal position at the next iteration. Number the particles. The number of particles, For the first During the nth iteration The position of each particle; where the position update formula is expressed as: ⑦ Use the Lozi equation for a two-dimensional discontinuous discrete chaotic dynamic system to perform chaotic optimization on the optimal position in the particle swarm optimization algorithm; ⑧ Determine whether the iteration stopping condition is met. If it is met, stop and output the result; otherwise, return to step ③ to continue.

[0047] S4. Obtain the optimal setting parameters for the relay based on the optimal solution obtained from the improved particle swarm optimization algorithm. The setting coefficient and relay starting current .

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the setting of relay protection in a distribution network considering distributed generation access, characterized in that: The optimization steps include the following: S1. Establish the objective function and constraints for optimizing the setting of relay protection in the distribution network; S11. The minimum total coordinated operation time of relay protection is taken as the objective function for optimizing the setting of relay protection in distribution network. S12. Establish optimization constraints for distribution network relay protection settings; including relay setting coefficient constraints, relay operating time constraints, relay starting current constraints, and relay coordination time interval constraints. S2. The objective function and constraints are combined using a penalty function to obtain the optimal model for setting the relay protection of the distribution network. S3. An improved particle swarm optimization algorithm is used to solve the optimization model of the relay protection setting of the distribution network. S31. The population of the improved particle swarm optimization algorithm is initialized using the Logistic chaotic equation. S32. The inertial weight of the particle swarm algorithm is calculated using a dynamic weighting strategy based on particle spacing. S33. The optimal position in the particle swarm optimization algorithm is optimized using the Lozi equation of a two-dimensional discontinuous discrete chaotic dynamic system. S4. Obtain the optimal tuning parameters based on the optimal solution obtained by the improved particle swarm optimization algorithm.

2. The method for optimizing the setting of relay protection in a distribution network considering distributed generation access, as described in claim 1, is characterized in that: The objective function for minimizing the total sum of relay coordinated action times in step S11 is expressed as: In the formula, Indicates distribution network lines The probability of failure. Indicates distribution network lines The probability of different fault types occurring Indicates relay right The response time of the fault that occurred at that location , Indicates relay The setting coefficient, For the fault current flowing through the relay, This is the starting current of the relay.

3. The method for optimizing the setting of relay protection in a distribution network considering distributed generation access, as described in claim 1, is characterized in that: The relay setting coefficient constraint in step S12 is expressed as follows: , , Relays The minimum and maximum values ​​of the time setting coefficient; the relay action time constraint is expressed as: , For relays The inherent minimum operating time; the relay starting current constraint is expressed as: , This is the maximum load current of the relay. The minimum fault current flowing through the relay; the relay coordination time interval constraint is expressed as: , For relays right The response time of the fault that occurred at that location For time intervals.

4. The method for optimizing the setting of relay protection in a distribution network considering distributed generation access, as described in claim 1, is characterized in that: The penalty function in step S2 is expressed as follows: In the formula, Let be the objective function. As a penalty factor, For constraint functions, A composite function constructed based on constraint functions. As a penalty item, For the number of iterations, Let be the number of particles; and let the penalty function satisfy the condition. .

5. The method for optimizing the setting of relay protection in a distribution network considering distributed generation access, as described in claim 1, is characterized in that: The Logistic chaotic equation in step S31 is expressed as follows: In the formula, Represents the parameters of the chaotic mapping. The system is in a chaotic state. The system is in a state of complete chaos. This represents the corresponding chaotic variable within the system. exist Randomly traverse within the range and ensure that no two elements are repeated.

6. The method for optimizing the setting of relay protection in a distribution network considering distributed generation access, as described in claim 1, is characterized in that: The calculation of the inertia weight in step S32 is expressed as follows: In the formula, , These are the maximum and minimum inertia weight values, respectively. , These represent the current number of iterations and the maximum number of iterations, respectively. Let the weight adjustment factor be determined by the particle spacing, then the first... During the nth iteration The weight adjustment factor for each particle is expressed as: , , They are respectively The maximum and minimum values, , These are the maximum and minimum particle spacing, respectively. For the first During the nth iteration Particle spacing of individual particles, For the first During the nth iteration The position vector of the i-th particle One portion, For the first The th iteration of the population's historical best position vector One portion, Let be the dimension of the particle.

7. The method for optimizing the setting of relay protection in a distribution network considering distributed generation access, as described in claim 1, is characterized in that: The Lozi equation in step S33 is expressed as follows: In the formula, For the number of iterations, For chaotic variables, parameters Then it enters chaotic motion, parameters Take 0.

5.

8. The method for optimizing the setting of relay protection in a distribution network considering distributed generation access, as described in claim 7, is characterized in that: In step S33, the Lozi equation is used to determine the optimal position in the particle swarm optimization algorithm. Chaos optimization specifically includes the following steps: ① Through Will Mapped to Above; ② Using the Lozi equation to... conduct The next iteration yields the chaotic sequence. ③ Through By performing an inverse mapping on the chaotic sequence to return it to the original solution space, a sequence of feasible solutions for chaotic variables is obtained. ④ Calculate the fitness value of the feasible solution vector, and retain the feasible solution corresponding to the optimal fitness value, denoted as . ⑤ Randomly select the position of a particle from the current particle swarm, and use... replace.

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