CN112541299A - Relay protection fixed value optimization method based on genetic algorithm - Google Patents

Relay protection fixed value optimization method based on genetic algorithm Download PDF

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CN112541299A
CN112541299A CN202011362316.7A CN202011362316A CN112541299A CN 112541299 A CN112541299 A CN 112541299A CN 202011362316 A CN202011362316 A CN 202011362316A CN 112541299 A CN112541299 A CN 112541299A
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谌江波
汤文杰
王玥
刘庆丰
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Abstract

The invention discloses a relay protection fixed value optimization method based on a genetic algorithm, which relates to the field of relay protection and comprises five steps of (1) establishing a relay protection fixed value global optimization model according to a relay protection global optimization multi-objective, multi-variable and multi-constraint complex system; (2) determining a setting variable in a relay protection constant value global optimization model based on a target function; (3) establishing a constraint interval coding diagram of the setting variable; (4) carrying out fitness function detection based on a relay protection setting value global optimization model function; (5) and optimizing the relay protection fixed value by adopting a master-slave PGA model in a parallel genetic algorithm. The invention can realize the online optimization of the relay protection constant value for the complexity and nonlinearity of the relay protection system, has high calculation speed and good convergence, and reduces the maintenance workload of the relay protection constant value.

Description

Relay protection fixed value optimization method based on genetic algorithm
Technical Field
The invention relates to the field of relay protection, in particular to a relay protection fixed value optimization method based on a genetic algorithm.
Background
The relay protection device is one of important devices in the power system, and whether the relay protection device can meet the 'quartic' proposed by the power grid depends on whether the relay protection setting value is reasonable or not. An accurate and proper relay protection constant value scheme plays a key role in safe and stable operation of a power system and selectivity and sensitivity of protection actions.
In China, setting principles of 'step-by-step matching and step-by-step setting' are generally adopted for setting protection, and if selectivity or sensitivity cannot meet requirements, related protection matching fixed values must be manually adjusted to minimize the number of mismatch protection within a system range. The setting method takes single protection as a setting object, lacks the capability of global coordination, does not comprehensively consider the mutual influence among the protection fixed values, and is difficult to obtain the protection fixed value with the optimal overall protection performance.
Disclosure of Invention
The invention aims to solve the technical problems, and adopts the technical scheme that a relay protection fixed value optimization method based on a genetic algorithm is provided, a global optimization model is established for power grid relay protection, and a parallel genetic algorithm is adopted to optimize the relay protection fixed value.
A relay protection fixed value optimization method based on a genetic algorithm comprises the following steps:
(1) establishing a relay protection fixed value global optimization model according to a relay protection global optimization multi-objective, multi-variable and multi-constraint complex system:
f＝min∑WiTik
wherein, TikFor protecting R when k branches have faultsiTime of operation of (W)iIs a weight systemCounting;
(a) protection action characteristic constraints: t ═ PTDS (PI)p)
PTDS＝(K10+K11TDS+K12TDS2+K13TDS3)
Figure BDA0002804330560000021
Figure BDA0002804330560000022
Wherein T is the protection action time, IpTDS is the time coefficient, K, for starting the current10、K11、K12、K13、A0～A4Is constant, I is fault current;
(b) and (3) constraint of a main backup protection coordination relationship: t is1k-Tik≥ΔT
Wherein, T1kWhen the k line fails, protection RiΔ T is the minimum time interval of the primary backup protection action time;
(c) and (3) restricting the parameter interval of the protection device: t isikmin≤Tik≤Tikmax
TDSimin≤TDSi≤TDimax
Ipmin≤Ip≤Ipmax
(2) Determining a setting variable in a relay protection constant value global optimization model based on a target function: setting the action time and setting the starting current value;
(3) establishing a constraint interval coding graph based on the integer variable;
coding by adopting a constraint interval, dividing a constant value solution space into a certain number of constraint spaces according to protection action characteristic constraint, main backup protection matching relation constraint and protection device parameter interval constraint conditions, and performing gene coding by taking each constraint interval as an independent variable; the upper limit or the lower limit of the constraint interval corresponding to each code is the protection fixed value represented by the code.
(4) Carrying out fitness function detection based on a relay protection setting value global optimization model function;
(5) and optimizing the relay protection fixed value by adopting a master-slave PGA model in a parallel genetic algorithm.
Preferably, the specific process of solving the optimal relay protection setting value by using the master-slave PGA model in the parallel genetic algorithm in the step (5) includes:
(1) randomly generating L initial individuals to form an initial population Q according to the coding method;
(2) calculating the number P of individuals shared by each slave node, wherein when the number N of the CPUs can divide the number of the individuals in the population, the number P of the individuals to be calculated by each node is L/N; when N cannot be divided completely, the remaining individuals are sent to CPUs with larger numbers one more, and during cloud computing, each slave node only needs to process one individual respectively due to numerous computing nodes;
(3) each slave processor determines the fixed value and time matching relation of each protection action according to the chromosome code;
(4) each slave processor performs whole network setting according to the matching relation to determine each protection action time and each fixed value;
(5) each slave processor judges each protection constraint condition and determines the number of the protection unsatisfied constraint conditions and punishment time;
(6) and each slave processor determines the fitness of the chromosome according to a fitness evaluation formula and returns the fitness value of the chromosome to the master processor corresponding to the chromosome. The main processor judges whether the maximum iteration times or the target fitness value is reached, if so, the optimizing process is ended, otherwise, the chromosomes are sorted according to the fitness value, and the global optimal chromosome fitness is memorized;
(7) the main processor selectively carries out cross and variation operation on the chromosome according to the fitness;
(8) sending the chromosomes which are subjected to selection, crossing and mutation operation to the slave processor again, calculating the fitness, returning to the master processor, namely returning to the step (2), and continuously circulating until the end condition is met;
(9) and after the end condition is met, determining the chromosome with the highest fitness, and finding out the corresponding fixed value, namely the globally optimal protection fixed value.
The invention has the beneficial effects that: the method comprises the steps of establishing a global optimization model of the relay protection constant value, carrying out fitness function detection on a relay protection matching relation and a constraint relation, establishing a constraint interval coding diagram on a relay protection setting variable, optimizing the relay protection constant value by adopting a parallel genetic algorithm, realizing online optimization of the relay protection constant value for complexity and nonlinearity of a relay protection system, having high calculation speed and good convergence, and reducing the maintenance workload of the relay protection constant value.
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FIG. 1 is a flow chart of the steps of the present invention.
FIG. 2 is a flow chart of the algorithm of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
As shown in fig. 1 to 2, a relay protection fixed value optimization method based on a genetic algorithm includes the following steps:
(1) establishing a relay protection fixed value global optimization model according to a relay protection global optimization multi-objective, multi-variable and multi-constraint complex system:
f＝min∑WiTik
wherein, TikFor protecting R when k branches have faultsiTime of operation of (W)iIs a weight coefficient;
(a) protection action characteristic constraints: t ═ PTDS (PI)p)
PTDS＝(K10+K11TDS+K12TDS2+K13TDS3)
Figure BDA0002804330560000041
Figure BDA0002804330560000042
Wherein T is the protection action time, IpTDS is the time coefficient, K, for starting the current10、K11、K12、K13、A0～A4Is constant, I is fault current;
(b) and (3) constraint of a main backup protection coordination relationship: t is1k-Tik≥ΔT
Wherein, T1kWhen the k line fails, protection RiΔ T is the minimum time interval of the primary backup protection action time;
(c) and (3) restricting the parameter interval of the protection device: t isikmin≤Tik≤Tikmax
TDSimin≤TDSi≤TDimax
Ipmin≤Ip≤Ipmax
(2) Determining a setting variable in a relay protection constant value global optimization model based on a target function: setting the action time and setting the starting current value;
(3) establishing a constraint interval coding graph based on the integer variable;
coding by adopting a constraint interval, dividing a constant value solution space into a certain number of constraint spaces according to protection action characteristic constraint, main backup protection matching relation constraint and protection device parameter interval constraint conditions, and performing gene coding by taking each constraint interval as an independent variable; the upper limit or the lower limit of the constraint interval corresponding to each code is the protection fixed value represented by the code.
(4) Carrying out fitness function detection based on a relay protection setting value global optimization model function;
(5) and optimizing the relay protection fixed value by adopting a master-slave PGA model in a parallel genetic algorithm.
In this embodiment, the specific process of solving the optimal relay protection setting value by using the master-slave PGA model in the parallel genetic algorithm in step (5) includes:
(1) randomly generating L initial individuals to form an initial population Q according to the coding method;
(2) calculating the number P of individuals shared by each slave node, wherein when the number N of the CPUs can divide the number of the individuals in the population, the number P of the individuals to be calculated by each node is L/N; when N cannot be divided completely, the remaining individuals are sent to CPUs with larger numbers one more, and during cloud computing, each slave node only needs to process one individual respectively due to numerous computing nodes;
(3) each slave processor determines the fixed value and time matching relation of each protection action according to the chromosome code;
(4) each slave processor performs whole network setting according to the matching relation to determine each protection action time and each fixed value;
(5) each slave processor judges each protection constraint condition and determines the number of the protection unsatisfied constraint conditions and punishment time;
(6) and each slave processor determines the fitness of the chromosome according to a fitness evaluation formula and returns the fitness value of the chromosome to the master processor corresponding to the chromosome. The main processor judges whether the maximum iteration times or the target fitness value is reached, if so, the optimizing process is ended, otherwise, the chromosomes are sorted according to the fitness value, and the global optimal chromosome fitness is memorized;
(7) the main processor selectively carries out cross and variation operation on the chromosome according to the fitness;
(8) sending the chromosomes which are subjected to selection, crossing and mutation operation to the slave processor again, calculating the fitness, returning to the master processor, namely returning to the step (2), and continuously circulating until the end condition is met;
(9) and after the end condition is met, determining the chromosome with the highest fitness, and finding out the corresponding fixed value, namely the globally optimal protection fixed value.
It will be appreciated by those skilled in the art that the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the meaning and range of equivalency of the invention are to be embraced within their scope.

Claims (2)

1.一种基于遗传算法的继电保护定值优化方法，其特征在于，包括以下步骤：1. a kind of relay protection fixed value optimization method based on genetic algorithm, is characterized in that, comprises the following steps: (1)根据继电保护全局优化多目标、多变量和多约束的复杂系统建立继电保护定值全局优化模型:(1) According to the complex system of multi-objective, multi-variable and multi-constraint global optimization of relay protection, a global optimization model of relay protection fixed value is established: f＝min∑WiTik f=min∑W i T ik 其中，Tik为k条支路出现故障时，保护Ri的动作时间，Wi为权重系数；Wherein, T ik is the action time of the protection Ri when the k branches are faulty , and Wi is the weight coefficient; (a)保护动作特性约束：T＝(PTDS)(PIp)(a) Constraint of protection action characteristic: T=(PTDS)(PI p ) PTDS＝(K10+K11TDS+K12TDS2+K13TDS3)PTDS=(K 10 +K 11 TDS + K 12 TDS 2 +K 13 TDS 3 )
Figure FDA0002804330550000011
Figure FDA0002804330550000011
Figure FDA0002804330550000012
Figure FDA0002804330550000012
 其中，T为保护动作时间，Ip为启动电流，TDS为时间系数，K10、K11、K12、K13、A0～A4为常数，I为故障电流；Among them, T is the protection action time, I p is the starting current, TDS is the time coefficient, K 10 , K 11 , K 12 , K 13 , A 0 ~ A 4 are constants, and I is the fault current; (b)主后备保护配合关系约束：T1k-Tik≥ΔT(b) The main and backup protection coordination relationship constraints: T 1k -T ik ≥ΔT 其中，T1k表示第k条线路发生故障时，保护Ri的第一后备保护动作时间，ΔT为主后备保护动作时间的最小时间间隔；Among them, T 1k represents the first backup protection action time of the protection R i when the kth line fails, and ΔT is the minimum time interval of the main backup protection action time; (c)保护装置参数区间约束：Tikmin≤Tik≤Tikmax (c) Constraints on the parameter interval of the protection device: T ikmin ≤T ik ≤T ikmax TDSimin≤TDSi≤TDimax TDS imin ≤TDS i ≤TD imax Ipmin≤Ip≤Ipmax I pmin ≤I p ≤I pmax (2)基于目标函数中确定继电保护定值全局优化模型中的整定变量：动作时间整定和启动电流值整定；(2) Based on the objective function, determine the setting variables in the global optimization model of the relay protection setting value: the setting of the action time and the setting of the starting current value; (3)基于对整定变量建立其约束区间编码图；(3) Based on the setting variable, establish its constraint interval coding map; 采用约束区间进行编码，根据保护动作特性约束、主后备保护配合关系约束、保护装置参数区间约束条件，将定值解空间划分成一定数目的约束空间，以及各个约束区间作为自变量进行基因编码；每一编码对应的约束区间的上限或下限即为该编码代表的保护定值；The constraint interval is used for coding, and the fixed value solution space is divided into a certain number of constraint spaces according to the protection action characteristic constraints, the main and backup protection coordination relationship constraints, and the protection device parameter interval constraints, and each constraint interval is used as an independent variable for genetic coding; The upper limit or lower limit of the constraint interval corresponding to each code is the protection value represented by the code; (4)基于继电保护定值全局优化模型函数进行适应度函数检测；(4) The fitness function detection is carried out based on the relay protection fixed value global optimization model function; (5)采用并行遗传算法中的主从PGA模型对继电保护定值进行优化。(5) Using the master-slave PGA model in the parallel genetic algorithm to optimize the relay protection settings. 2.根据权利要求1所述的一种基于遗传算法的继电保护定值优化方法，其特征在于：所述步骤(5)中采用并行遗传算法中的主从PGA模型求解继电保护最优定值具体流程包括：2. a kind of relay protection fixed value optimization method based on genetic algorithm according to claim 1, is characterized in that: in described step (5), adopt the master-slave PGA model in parallel genetic algorithm to solve relay protection optimal The specific process of setting value includes: (1)按照本文所述编码方法，随机生成L个初始个体组成初始群体Q；(1) According to the coding method described in this paper, randomly generate L initial individuals to form an initial group Q; (2)计算每个从节点分担的个体数目P，当CPU数目N能整除种群中的个体数时，每个节点要计算的个体数目为P＝L/N；当N不能整除时，将剩余的个体给编号较大的那些CPU各多发送一个，在云计算时，由于计算节点众多，仅需要每个从节点分别处理一个个体即可；(2) Calculate the number of individuals P shared by each slave node. When the number of CPUs N can divide the number of individuals in the population, the number of individuals to be calculated by each node is P=L/N; In cloud computing, due to the large number of computing nodes, each slave node only needs to process one individual separately; (3)各从处理器根据染色体编码，确定各保护动作定值、时间配合关系；(3) Each slave processor determines the setting value and time coordination relationship of each protection action according to the chromosome code; (4)各从处理器根据配合关系，进行全网整定确定各保护动作时间和定值；(4) According to the cooperation relationship, each slave processor performs network-wide tuning to determine the action time and fixed value of each protection; (5)各从处理器对每个保护约束条件进行判断，确定保护不满足约束条件数目及惩罚时间；(5) Each slave processor judges each protection constraint condition, and determines the number of protection that does not meet the constraint condition and the penalty time; (6)各从处理器按照适应度评价公式确定该染色体的适应度，将染色体适应度值返回主处理器对应该染色体。主处理器判断是否达到最大迭代次数或达到目标适应度值，如果达到就结束寻优过程，否则就对各染色体按适应度值大小进行排序，记忆全局最优染色体适应度；(6) Each slave processor determines the fitness of the chromosome according to the fitness evaluation formula, and returns the chromosome fitness value to the master processor corresponding to the chromosome. The main processor judges whether the maximum number of iterations or the target fitness value is reached, and if so, the optimization process ends; otherwise, each chromosome is sorted according to the fitness value, and the global optimal chromosome fitness is memorized; (7)主处理器按照适应度大小选择性的对染色体进行交叉、变异运算；(7) The main processor selectively performs crossover and mutation operations on chromosomes according to the size of fitness; (8)将经过选择、交叉、变异运算完成的染色体再次发送到从处理器，计算适应度，返回给主处理器，也就是又回到步骤(2)，不断循环得到满足结束条件为止。(8) The chromosomes that have been selected, crossed, and mutated are sent to the slave processor again, the fitness is calculated, and returned to the master processor, that is, back to step (2), and the cycle is repeated until the end condition is met. (9)满足结束条件后，确定出适应度最高的染色体，找到对应定值即为全局最优的保护定值。(9) After the end condition is satisfied, the chromosome with the highest fitness is determined, and the corresponding fixed value is found to be the globally optimal protection fixed value.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114285012A (en) * 2021-12-23 2022-04-05 中国电力科学研究院有限公司 Relay protection device action setting optimization method, system, equipment and storage medium
CN114285012B (en) * 2021-12-23 2023-11-17 中国电力科学研究院有限公司 Relay protection device action fixed value optimization method, system, equipment and storage medium

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