Inverse time limit current protection method for power distribution network

Abstract

The invention discloses an inverse time limit current protection method for a power distribution network, and the method comprises the following steps: S1, constructing an objective function, obtaining a set value of a time setting coefficient of each relay in the power distribution network, and constructing a time setting coefficient set; s2, acquiring bus voltage at the installation position of the protection resistor, constructing a fault current coefficient and a fault impedance coefficient, constructing a composite fault compensation factor based on the fault current coefficient and the fault impedance coefficient, and optimizing the time setting coefficient set based on the composite fault compensation factor to obtain an optimized time setting coefficient set; s3, constructing an SOS algorithm and optimizing the SOS algorithm; and S4, optimizing the optimized time setting coefficient set based on the optimized SOS algorithm to obtain an optimal time setting coefficient, actuating the relay, and accelerating the protection action response.

Description

Technical Field

[0001] The invention relates to the technical field of distribution network protection, and more particularly to a distribution network inverse time current protection method. Background Art

[0002] Inverse time current protection is widely used in the protection of power equipment such as generators, transformers, motors and distribution networks. For example, in the protection of power plant motors, inverse time current protection can quickly cut off the fault current to prevent motor damage; in the distribution network, inverse time current protection can ensure that the fault line is disconnected in time in the case of short circuit or overload, protecting the stable operation of the entire power grid. Its basic principle is: within any period of time, when the current reaches a certain value, the protection device will send a disconnection signal, thereby achieving the purpose of protecting equipment and systems. This protection method is named because its action time is inversely proportional to the fault current (that is, the larger the fault current, the shorter the action time).

[0003] The publication number is CN106816855A, and the name is a method for inverse current protection, which includes (1) respectively calculating the action delay time of the high-resistance distance relay at the sending end and the receiving end when the three-phase A, B, and C faults occur at the protection installation; (2) respectively calculating the zero-sequence current inverse time protection action time of the sending end and the receiving end of the three-phase high-resistance distance relay; (3) determining the time relationship between the high-resistance distance protection at the sending end and the receiving end of the three-phase high-resistance distance relay and the zero-sequence current inverse time action protection. The zero-sequence current inverse time protection and distance protection coordination method proposed in the present invention determines the approximate range of the fault through distance protection, accelerates the action speed of the zero-sequence current inverse time protection, can effectively shorten the action time of the inverse time protection, and prevents its overstepping action, thereby improving the action performance of the zero-sequence current inverse time protection under high-resistance fault conditions.

[0004] The publication number is CN115588960A, and the name is a method and device for calculating the constant value of an inverse time current protection. The method includes: establishing an optimization model for the constant value of an inverse time zero-sequence current protection according to the network topology, impedance data and parameter data of an inverse time zero-sequence current protection device of an ultra-high voltage power grid; performing linear fitting on the relationship between the protection action time and the starting current through a fitting module to generate a linear fitting result; performing simplified processing on the optimization model for the constant value of an inverse time zero-sequence current protection according to the linear fitting result through a generation module to generate a quadratic target quadratic constraint model; performing calculation and solving according to the quadratic target quadratic constraint model through a calculation module to generate a constant value of an inverse time current protection. The embodiment of the present invention can improve the calculation efficiency of the constant value of the inverse time current protection.

[0005] However, the setting of the time setting coefficient in the above-mentioned prior art relies on experience or fixed rules, which is difficult to adapt to complex power grid structures and changing fault conditions. It does not take into account the impact of fault current and fault impedance on the protection action time, and cannot enable the protection system to automatically adjust the protection parameters according to the real-time fault conditions.

[0006] Therefore, how to provide a method for solving the optimization problem of the time setting coefficient, introducing a fault compensation factor, improving the SOS algorithm and increasing the protection action speed is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0007] In view of this, the present invention provides a distribution network inverse time current protection method, aiming to solve the above technical problems.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] A method for inverse time current protection of a distribution network comprises the following steps:

[0010] S1. Construct an objective function, obtain the set value of the time setting coefficient of each relay in the distribution network, and construct a set of time setting coefficients;

[0011] S2. Obtain the bus voltage at the location where the protection resistor is installed, construct a fault current coefficient and a fault impedance coefficient, construct a composite fault compensation factor based on the fault current coefficient and the fault impedance coefficient, optimize the time setting coefficient set based on the composite fault compensation factor, and obtain an optimized time setting coefficient set;

[0012] S3, build SOS algorithm and optimize it;

[0013] S4. Based on the optimized SOS algorithm, the optimized time setting coefficient set is optimized to obtain the optimal time setting coefficient, operate the relay, and speed up the protection action response.

[0014] Furthermore, the objective function constructed in S1 is:

[0015]

[0016] In the formula, is the time setting coefficient, is the starting current;

[0017] Build constraints:

[0018] T j,k -T i,k ≥CTI

[0019] Where CTI is the time difference between the main protection and backup protection of the relay, T j,kis the action time of the relay at the fault point k, R i Relay is the main protection, T i,k is the action time of the relay at the fault point k, and the relay R j Relay R i backup protection;

[0020] I pi,min ≤I p ≤I pi,max

[0021] In the formula, I pi,min is the minimum starting current of the relay, I pi,max is the maximum starting current of the relay;

[0022] T i,min ≤T≤T i,max

[0023] Where, T i,min is the minimum operating time of the relay, T i,max It is the maximum operating time of the relay.

[0024] Furthermore, the S2 is specifically:

[0025] Assume protection relay R i The bus voltages measured before and after the fault at the installation site are Constructing the protection relay R i Fault current coefficient at

[0026]

[0027] Based on the relationship between the measured impedance and the fault distance, a protection relay R i Fault impedance factor

[0028]

[0029] In the formula, Z L is the total impedance from bus 1 at the common connection point to bus 3 at the end of the line, Z Ri To measure the impedance at the protection relay installation location;

[0030] Construct a composite fault compensation factor based on the fault voltage coefficient and the fault impedance coefficient:

[0031]

[0032] Based on the composite fault compensation factor, the time setting coefficient set is optimized to obtain the optimized time setting coefficient set, that is, according to the composite fault compensation factor Ci Get the changes as the fault condition changes

[0033]

[0034] Furthermore, the optimized SOS algorithm in S3 is specifically as follows:

[0035] Improve the beneficial factors BF1 and BF2 in the SOS algorithm to ABF1 and ABF2:

[0036]

[0037] Furthermore, the S4 is specifically:

[0038] S41, generating N initial individuals X based on the time setting coefficient set i , set the total number of iterations K and initialize the current number of iterations;

[0039] S42, the number of iterations increases by 1;

[0040] S43. For each individual X in N i , randomly select another individual X j , and the two individuals are different, the two individuals are mutually beneficially symbiotic to generate two new individuals;

[0041] S44, comparing the new individual with the original individual, if the new individual is better than the initial individual, updating the initial individual to the new individual, if the initial individual is better than the new individual, retaining the initial individual;

[0042] S45, the initial individuals in N are subjected to partial symbiosis to obtain new individuals, and the new individuals are compared with the original individuals. If the new individuals are better than the initial individuals, the initial individuals are updated to the new individuals; if the initial individuals are better than the new individuals, the initial individuals are retained;

[0043] S46, obtaining an initial individual from N, constructing a random vector, modifying the initial individual based on the random vector to generate a mutant individual, comparing the mutant individual with the original individual, and if the mutant individual is better than the initial individual, updating the initial individual to the mutant individual; if the initial individual is better than the mutant individual, retaining the initial individual;

[0044] S47, determine whether the total number of iterations has been reached, if so, output a new set of time setting coefficients, if not, return to S42.

[0045] Furthermore, the generation of the initial individual in S41 is specifically as follows:

[0046] Assume that the initial number of individuals is N, the upper limit of the individual search range is U, the lower limit is L, and N initial individuals are randomly generated:

[0047] X i =L+rand(1,D)×(UL)

[0048] In the formula, X i is the i-th individual in the time setting coefficient set, rand is a random number between [0,1], and D is the dimension of the optimization problem.

[0049] Furthermore, the mutualism in S43 specifically includes:

[0050]

[0051] In the formula, R MV For X i and X j The interaction relationship between rand(0,1) is a random number between (0,1), X best is the optimal individual, bf1 and bf2 are benefit factors, indicating the level of benefit that individuals obtain from their mutual relationship, and the values ​​of ABF1 and ABF2 are randomly selected to be 1 or 2, indicating partial benefit or full benefit.

[0052] Furthermore, the determination method in S44, S45 and S46 is:

[0053]

[0054] In the formula, N is the number of individuals, i is the individual serial number, and N max is the maximum selected pressure, N min To minimize the selection pressure, arrange the obtained P in order from large to small, and select the individual corresponding to the largest P as the optimal individual.

[0055] Furthermore, the partial beneficial symbiosis in S45 is specifically:

[0056]

[0057] In the formula, For the new individual, X i is the initial individual, rand(-1,1) is a random number between (-1,1), X best -X j For X j Help X i Improved adaptability, X best The optimal individual.

[0058] Furthermore, the generation of the variant individuals in S46 is specifically as follows:

[0059] Select individual X from the whole N iAs a parasitic vector, a random vector is constructed, and individuals are replicated and modified based on the random vector to generate a mutation vector R PV :

[0060] R PV =X i

[0061] R PV (pick)=rand(1,length(pick))*(ub(pick)-lb(pick))+lb(pick)

[0062] In the formula, pick is the mutant individual, ub is the search upper bound, and lb is the search lower bound.

[0063] The above technical solution includes at least the following technical effects:

[0064] It can be known from the above technical solutions that the present invention discloses a method for inverse time current protection of a distribution network. Compared with the prior art, the present invention ensures that the set value of the time setting coefficient is optimized under the premise of satisfying the protection selectivity and sensitivity by constructing an objective function and constraints, thereby improving the accuracy of the protection action. A composite fault compensation factor is introduced to dynamically adjust the time setting coefficient according to the fault current coefficient and the fault impedance coefficient, so that the protection system can better adapt to the protection requirements under different fault conditions. The SOS algorithm is improved, and the search efficiency and convergence speed of the algorithm are improved by adjusting the beneficial factors and improving the strategy, making it more efficient when solving the optimal time setting coefficient. The optimized time setting coefficient set is optimized through the SOS algorithm to obtain the optimal time setting coefficient, thereby accelerating the reaction speed of the protection action and reducing the impact of the fault on the equipment and system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0066] Figure 1 The present invention is a flow chart of a method for inverse time current protection of a distribution network. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] See attached Figure 1 The embodiment of the present invention discloses a method for inverse time current protection of a distribution network, comprising the following steps:

[0069] S1. Construct an objective function, obtain the set value of the time setting coefficient of each relay in the distribution network, and construct a set of time setting coefficients;

[0070] S2. Obtain the bus voltage at the location where the protection resistor is installed, construct a fault current coefficient and a fault impedance coefficient, construct a composite fault compensation factor based on the fault current coefficient and the fault impedance coefficient, optimize the time setting coefficient set based on the composite fault compensation factor, and obtain an optimized time setting coefficient set;

[0071] S3, build SOS algorithm and optimize it;

[0072] S4. Based on the optimized SOS algorithm, the optimized time setting coefficient set is optimized to obtain the optimal time setting coefficient, operate the relay, and speed up the protection action response.

[0073] To further optimize the above technical solution, the objective function constructed in S1 is:

[0074]

[0075] In the formula, is the time setting coefficient, is the starting current;

[0076] Build constraints:

[0077] T j,k -T i,k ≥CTI

[0078] Where CTI is the time difference between the main protection and backup protection of the relay, T j,k is the action time of the relay at the fault point k, R i Relay is the main protection, T i,k is the action time of the relay at the fault point k, and the relay R j Relay R i backup protection;

[0079] I pi,min ≤I p ≤Ipi,max

[0080] In the formula, I pi,min is the minimum starting current of the relay, I pi,max is the maximum starting current of the relay;

[0081] T i,min ≤T≤T i,max

[0082] Where, T i,min is the minimum operating time of the relay, T i,max It is the maximum operating time of the relay.

[0083] To further optimize the above technical solution, S2 is specifically:

[0084] Assume protection relay R i The bus voltages measured before and after the fault at the installation site are Constructing the protection relay R i Fault current coefficient at

[0085]

[0086] Based on the relationship between the measured impedance and the fault distance, a protection relay R i Fault impedance factor

[0087]

[0088] In the formula, Z L is the total impedance from bus 1 at the common connection point to bus 3 at the end of the line, Z Ri To measure the impedance at the protection relay installation location;

[0089] Construct a composite fault compensation factor based on the fault voltage coefficient and the fault impedance coefficient:

[0090]

[0091] Based on the composite fault compensation factor, the time setting coefficient set is optimized to obtain the optimized time setting coefficient set, that is, according to the composite fault compensation factor C i Get the changes as the fault condition changes

[0092]

[0093] To further optimize the above technical solution, the optimized SOS algorithm in S3 is as follows:

[0094] Improve the beneficial factors BF1 and BF2 in the SOS algorithm to ABF1 and ABF2:

[0095]

[0096] To further optimize the above technical solution, S4 is specifically:

[0097] S41, generating N initial individuals X based on the time setting coefficient set i , set the total number of iterations K and initialize the current number of iterations;

[0098] S42, the number of iterations increases by 1;

[0099] S43. For each individual X in N i , randomly select another individual X j , and the two individuals are different, the two individuals are mutually beneficially symbiotic to generate two new individuals;

[0100] S44, comparing the new individual with the original individual, if the new individual is better than the initial individual, updating the initial individual to the new individual, if the initial individual is better than the new individual, retaining the initial individual;

[0101] S45, the initial individuals in N are subjected to partial symbiosis to obtain new individuals, and the new individuals are compared with the original individuals. If the new individuals are better than the initial individuals, the initial individuals are updated to the new individuals; if the initial individuals are better than the new individuals, the initial individuals are retained;

[0102] S46, obtaining an initial individual from N, constructing a random vector, modifying the initial individual based on the random vector to generate a mutant individual, comparing the mutant individual with the original individual, and if the mutant individual is better than the initial individual, updating the initial individual to the mutant individual; if the initial individual is better than the mutant individual, retaining the initial individual;

[0103] S47, determine whether the total number of iterations has been reached, if so, output a new set of time setting coefficients, if not, return to S42.

[0104] To further optimize the above technical solution, the initial individuals generated in S41 are specifically:

[0105] Assume that the initial number of individuals is N, the upper limit of the individual search range is U, the lower limit is L, and N initial individuals are randomly generated:

[0106] X i =L+rand(1,D)×(UL)

[0107] In the formula, X iis the i-th individual in the time setting coefficient set, rand is a random number between [0,1], and D is the dimension of the optimization problem.

[0108] To further optimize the above technical solution, the mutualism in S43 is specifically as follows:

[0109]

[0110] In the formula, R MV For X i and X j The interaction relationship between rand(0,1) is a random number between (0,1), X best is the optimal individual, bf1 and bf2 are benefit factors, indicating the level of benefit that individuals obtain from their mutual relationship, and the values ​​of ABF1 and ABF2 are randomly selected to be 1 or 2, indicating partial benefit or full benefit.

[0111] To further optimize the above technical solution, the judgment method in S44, S45 and S46 is:

[0112]

[0113] In the formula, N is the number of individuals, i is the individual serial number, and N max is the maximum selected pressure, N min To minimize the selection pressure, arrange the obtained P in order from large to small, and select the individual corresponding to the largest P as the optimal individual.

[0114] To further optimize the above technical solution, the partial beneficial symbiosis in S45 is specifically as follows:

[0115]

[0116] In the formula, For the new individual, X i is the initial individual, rand(-1,1) is a random number between (-1,1), X best -X j For X j Help X i Improved adaptability, X best The optimal individual.

[0117] To further optimize the above technical solution, the generation of mutant individuals in S46 is specifically as follows:

[0118] Select individual X from the whole N i As a parasitic vector, a random vector is constructed, and individuals are replicated and modified based on the random vector to generate a mutation vector R PV :

[0119] R PV =Xi

[0120] R PV (pick)=rand(1,length(pick))*(ub(pick)-lb(pick))+lb(pick)

[0121] In the formula, pick is the mutant individual, ub is the search upper bound, and lb is the search lower bound.

[0122] The method includes four main steps: constructing an objective function and a set of time setting coefficients (S1), optimizing the time setting coefficients based on fault information (S2), optimizing the SOS algorithm (S3), and optimizing the time setting coefficients based on the optimized SOS algorithm (S4).

[0123] S1: Construct objective function and time setting coefficient set

[0124] Objective function construction

[0125] We assume a general form that aims to minimize the combined cost of protection action time and system stability. Assume the objective function is:

[0126]

[0127] In the formula, is the time setting coefficient, is the starting current;

[0128] According to the design parameters or historical data of each relay in the distribution network, the set value of the time setting coefficient of each relay is collected to construct a time setting coefficient set.

[0129] Build constraints:

[0130] T j,k -T i,k ≥CTI

[0131] Where CTI is the time difference between the main protection and backup protection of the relay, T j,k is the action time of the relay at the fault point k, R i Relay is the main protection, T i,k is the action time of the relay at the fault point k, and the relay R j Relay R i backup protection;

[0132] I pi,min ≤I p ≤I pi,max

[0133] In the formula, I pi,min is the minimum starting current of the relay, Ipi,max is the maximum starting current of the relay;

[0134] T i,min ≤T≤T i,max

[0135] Where, T i,min is the minimum operating time of the relay, T i,max It is the maximum operating time of the relay.

[0136] S2: Time setting coefficient optimization based on fault information

[0137] At the protection relay installation, use voltage measurement equipment to obtain the bus voltage before and after the fault

[0138] Constructing the protection relay R i Fault current coefficient at

[0139]

[0140] Based on the relationship between the measured impedance and the fault distance, a protection relay R i Fault impedance factor

[0141]

[0142] In the formula, Z L is the total impedance from bus 1 at the common connection point to bus 3 at the end of the line, Z Ri To measure the impedance at the protection relay installation location;

[0143] Construct a composite fault compensation factor based on the fault voltage coefficient and the fault impedance coefficient:

[0144]

[0145] Based on the composite fault compensation factor, the time setting coefficient set is optimized to obtain the optimized time setting coefficient set, that is, according to the composite fault compensation factor C i Get the changes as the fault condition changes

[0146]

[0147] The SOS algorithm simulates the symbiotic relationship between organisms in nature, including mutualism and partial mutualism. The algorithm searches for the optimal solution by continuously updating individuals.

[0148] The beneficial factors in the SOS algorithm are improved to improve the search efficiency and convergence speed of the algorithm.

[0149] Improve the beneficial factors BF1 and BF2 in the SOS algorithm to ABF1 and ABF2:

[0150]

[0151] Generate N initial individuals X based on the time setting coefficient set i , set the total number of iterations K and initialize the current number of iterations; set the initial number of individuals to N, the upper limit of the individual search range to U, the lower limit to L, and randomly generate N initial individuals:

[0152] X i =L+rand(1,D)×(UL)

[0153] In the formula, X i is the i-th individual in the time setting coefficient set, rand is a random number between [0,1], and D is the dimension of the optimization problem.

[0154] The number of iterations increases by 1;

[0155] For each individual X in N i , randomly select another individual X j , and the two individuals are different, the two individuals are mutually beneficially symbiotic to generate two new individuals;

[0156] Mutualism is specifically:

[0157]

[0158] In the formula, R MV For X i and X j The interaction relationship between rand(0,1) is a random number between (0,1), X best is the optimal individual, bf1 and bf2 are benefit factors, indicating the level of benefit that individuals obtain from their mutual relationship, and the values ​​of ABF1 and ABF2 are randomly selected to be 1 or 2, indicating partial benefit or full benefit.

[0159] Compare the new individual with the original individual. If the new individual is better than the initial individual, update the initial individual to the new individual. If the initial individual is better than the new individual, keep the initial individual.

[0160]

[0161] In the formula, N is the number of individuals, i is the individual serial number, and N max is the maximum selected pressure, N min To minimize the selection pressure, arrange the obtained P in order from large to small, and select the individual corresponding to the largest P as the optimal individual.

[0162] The initial individuals in N are subjected to partial mutualism to obtain new individuals, and the new individuals are compared with the original individuals. If the new individuals are better than the initial individuals, the initial individuals are updated to the new individuals. If the initial individuals are better than the new individuals, the initial individuals are retained.

[0163] Partial symbiosis is specifically:

[0164]

[0165] In the formula, For the new individual, X i is the initial individual, rand(-1,1) is a random number between (-1,1), X best -X j For X j Help X i Improved adaptability, X best The optimal individual.

[0166] Compare the new individual with the original individual. If the new individual is better than the initial individual, update the initial individual to the new individual. If the initial individual is better than the new individual, keep the initial individual.

[0167]

[0168] In the formula, N is the number of individuals, i is the individual serial number, and N max is the maximum selected pressure, N min To minimize the selection pressure, arrange the obtained P in order from large to small, and select the individual corresponding to the largest P as the optimal individual.

[0169] Get an initial individual from N, construct a random vector, modify the initial individual based on the random vector, generate a mutant individual, compare the mutant individual with the original individual, if the mutant individual is better than the initial individual, update the initial individual to the mutant individual, if the initial individual is better than the mutant individual, retain the initial individual;

[0170] Select individual X from the whole N i As a parasitic vector, a random vector is constructed, and individuals are replicated and modified based on the random vector to generate a mutation vector R PV :

[0171] R PV =X i

[0172] R PV (pick)=rand(1,length(pick))*(ub(pick)-lb(pick))+lb(pick)

[0173] In the formula, pick is the mutant individual, ub is the search upper bound, and lb is the search lower bound.

[0174] Compare the new individual with the original individual. If the new individual is better than the initial individual, update the initial individual to the new individual. If the initial individual is better than the new individual, keep the initial individual.

[0175]

[0176] In the formula, N is the number of individuals, i is the individual serial number, and N max is the maximum selected pressure, N min To minimize the selection pressure, arrange the obtained P in order from large to small, and select the individual corresponding to the largest P as the optimal individual.

[0177] The individuals that have undergone mutualism, selection, partial mutualism and mutation operations are reassembled into a new population. Check whether the total number of iterations K is reached. If so, the optimal individual in the current population is output as the final time setting coefficient set; if not, the number of iterations is updated and the iteration is returned to S42 to continue.

[0178] According to a distribution network inverse time current protection method designed by the present invention, compared with the prior art, the present invention ensures that the set value of the time setting coefficient is optimized under the premise of satisfying the protection selectivity and sensitivity by constructing the objective function and constraint conditions, thereby improving the accuracy of the protection action. A composite fault compensation factor is introduced, and the time setting coefficient is dynamically adjusted according to the fault current coefficient and the fault impedance coefficient, so that the protection system can better adapt to the protection requirements under different fault conditions. The SOS algorithm is improved, and the search efficiency and convergence speed of the algorithm are improved by adjusting the beneficial factors and improving the strategy, making it more efficient when solving the optimal time setting coefficient. The optimized time setting coefficient set is optimized by the SOS algorithm, and the optimal time setting coefficient is obtained, thereby accelerating the reaction speed of the protection action and reducing the impact of the fault on the equipment and system.

[0179] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0180] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A distribution network inverse time current protection method, characterized in that: The following steps are involved: S1. Construct an objective function, obtain the set value of the time setting coefficient of each relay in the distribution network, and construct a set of time setting coefficients; S2. Obtain the bus voltage at the location where the protection resistor is installed, construct a fault current coefficient and a fault impedance coefficient, construct a composite fault compensation factor based on the fault current coefficient and the fault impedance coefficient, optimize the time setting coefficient set based on the composite fault compensation factor, and obtain an optimized time setting coefficient set; S3, build SOS algorithm and optimize it; S4. Based on the optimized SOS algorithm, the optimized time setting coefficient set is optimized to obtain the optimal time setting coefficient, operate the relay, and speed up the protection action response.

2. A distribution network inverse time current protection method according to claim 1, characterized in that: The objective function constructed in S1 is: In the formula, is the time setting coefficient, is the starting current; Build constraints: T j,k -T i,k ≥CTI Where CTI is the time difference between the main protection and backup protection of the relay, T j，k is the action time of the relay at the fault point k, R i Relay is the main protection, T i,k is the action time of the relay at the fault point k, and the relay R j Relay R i backup protection; I pi,min ≤I p ≤I pi,max In the formula, I pi,min is the minimum starting current of the relay, I pi,max is the maximum starting current of the relay; T i,min ≤T≤T i,max Where, T i,min is the minimum operating time of the relay, T i,max It is the maximum operating time of the relay.

3. A distribution network inverse time current protection method according to claim 1, characterized in that: The S2 is specifically: Assume protection relay R i The bus voltages measured before and after the fault at the installation site are Constructing the protection relay R i Fault current coefficient at Based on the relationship between the measured impedance and the fault distance, a protection relay R i Fault impedance factor In the formula, Z L is the total impedance from bus 1 at the common connection point to bus 3 at the end of the line, Z Ri To measure the impedance at the protection relay installation location; Construct a composite fault compensation factor based on the fault voltage coefficient and the fault impedance coefficient: Based on the composite fault compensation factor, the time setting coefficient set is optimized to obtain the optimized time setting coefficient set, that is, according to the composite fault compensation factor C i Get the changes as the fault condition changes 4. A distribution network inverse time current protection method according to claim 1, characterized in that: The optimized SOS algorithm in S3 is specifically as follows: Improve the beneficial factors BF1 and BF2 in the SOS algorithm to ABF1 and ABF2:

5. A distribution network inverse time current protection method according to claim 1, characterized in that: The S4 is specifically: S41, generating N initial individuals X based on the time setting coefficient set i , set the total number of iterations K and initialize the current number of iterations; S42, the number of iterations increases by 1; S43. For each individual X in N i , randomly select another individual X j , and the two individuals are different, the two individuals are mutually beneficially symbiotic to generate two new individuals; S44, comparing the new individual with the original individual, if the new individual is better than the initial individual, updating the initial individual to the new individual, if the initial individual is better than the new individual, retaining the initial individual; S45, the initial individuals in N are subjected to partial symbiosis to obtain new individuals, and the new individuals are compared with the original individuals. If the new individuals are better than the initial individuals, the initial individuals are updated to the new individuals; if the initial individuals are better than the new individuals, the initial individuals are retained; S46, obtaining an initial individual from N, constructing a random vector, modifying the initial individual based on the random vector to generate a mutant individual, comparing the mutant individual with the original individual, and if the mutant individual is better than the initial individual, updating the initial individual to the mutant individual; if the initial individual is better than the mutant individual, retaining the initial individual; S47, determine whether the total number of iterations has been reached, if so, output a new set of time setting coefficients, if not, return to S42.

6. A distribution network inverse time current protection method according to claim 5, characterized in that: The specific steps of generating the initial individual in S41 are: Assume that the initial number of individuals is N, the upper limit of the individual search range is U, the lower limit is L, and N initial individuals are randomly generated: X i =L+rand(1,D)×(U-L) Where, X i is the i-th individual in the time setting coefficient set, rand is a random number between [0, 1], and D is the dimension of the optimization problem.

7. A distribution network inverse time current protection method according to claim 5, characterized in that: The mutualism in S43 is specifically: In the formula, R MV For X i and X j The interaction relationship between rand(0,1) is a random number between (0,1), X best is the optimal individual, bf1 and bf2 are benefit factors, indicating the level of benefit that individuals obtain from their mutual relationship, and the values ​​of ABF1 and ABF2 are randomly selected to be 1 or 2, indicating partial benefit or full benefit.

8. A distribution network inverse time current protection method according to claim 5, characterized in that: The judgment method in S44, S45 and S46 is: In the formula, N is the number of individuals, i is the individual serial number, and N max is the maximum selected pressure, N min To minimize the selection pressure, arrange the obtained P in order from large to small, and select the individual corresponding to the largest P as the optimal individual.

9. A distribution network inverse time current protection method according to claim 5, characterized in that: The specific partial beneficial symbiosis in S45 is: In the formula, For the new individual, X i is the initial individual, rand(-1,1) is a random number between (-1,1), X best -X j For X j Help X i Improved adaptability, X best The optimal individual.

10. A distribution network inverse time current protection method according to claim 5, characterized in that: The specific steps of generating the mutant individuals in S46 are: Select individual X from the whole N i As a parasitic vector, a random vector is constructed, and individuals are replicated and modified based on the random vector to generate a mutation vector R PV : R PV =X i R PV (pick)=rand(1,length(pick))*(ub(pick)-lb(pick))+lb(pick) In the formula, pick is the mutant individual, ub is the search upper bound, and lb is the search lower bound.

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