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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relay protection
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fitness
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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. A relay protection fixed value optimization method based on a genetic algorithm is characterized by comprising 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 FDA0002804330550000011
Figure FDA0002804330550000012
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 of the main backup protection operation timeSpacing;
(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.
2. The relay protection fixed value optimization method based on the genetic algorithm as claimed in claim 1, wherein: the specific process of solving the optimal relay protection setting value by adopting the master-slave PGA model in the parallel genetic algorithm in the step (5) comprises the following steps:
(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) and (4) 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.
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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 fixed value 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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Application publication date: 20210323