CN114444762A - Power failure maintenance optimization method based on multi-target particle swarm - Google Patents

Power failure maintenance optimization method based on multi-target particle swarm Download PDF

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CN114444762A
CN114444762A CN202111538461.0A CN202111538461A CN114444762A CN 114444762 A CN114444762 A CN 114444762A CN 202111538461 A CN202111538461 A CN 202111538461A CN 114444762 A CN114444762 A CN 114444762A
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李存
袁丁
梁改革
刘璐
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State Grid Xuzhou Power Supply Co
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Abstract

The invention discloses a power failure overhaul optimization method based on multi-objective particle swarm, which introduces a game theory mechanism, calculates an Archive optimal solution set which enables a plurality of power failure optimization objective functions to be minimum, and generates a power failure overhaul optimization model with safety and economy. Aiming at the power failure equipment, designing a power failure optimization multi-objective function; initializing a population before model training, setting a game order to obtain a leader, and establishing an Archive set for storing historical optimal solutions; selecting a non-dominated solution to enter an Archive set, updating iterative replacement, and exiting and outputting a training result if an iterative termination condition is met; and if the iteration condition is not met, selecting a target solution leader in the Archive set as an optimal solution, and updating the population according to the current solution, including updating the game sequence, updating the particle position and velocity, and updating the Archive set. The invention utilizes a game mechanism to carry out game selection on the historical excellent solution, ensures the diversity of the population, realizes the high efficiency and economy of power failure maintenance, and meets the requirement of a power failure plan of a large power grid.

Description

Power failure maintenance optimization method based on multi-target particle swarm
Technical Field
The invention relates to the field of power failure maintenance, in particular to a power failure maintenance optimization method based on multi-target particle swarm.
Background
With the increasingly complex structure of a power transmission system, the influence caused by power failure accidents caused by faults in a power grid is larger and larger, and the power grid overhauls line equipment to ensure the power supply safety of the system. In the period of power failure maintenance, how to realize safe maintenance and low-cost influence is the main content of power failure management and control.
At present, a large number of target optimization algorithms exist, such as an immune algorithm, an ant colony optimization algorithm, a particle swarm optimization algorithm and the like, wherein the particle swarm optimization is widely researched and applied by the advantages of simple concept, high convergence speed, easiness in implementation and the like, but diversity loss exists and the particle swarm optimization falls into a local optimal condition.
Disclosure of Invention
Aiming at solving the defects in the prior art, the invention aims to provide a power failure maintenance optimization method based on multi-target particle swarm, provides a particle swarm optimization algorithm aiming at minimizing influence, optimizing economy and shortening maintenance time, and selects an optimal solution from solutions of solving a multi-target function by using a game theory through the particle swarm optimization algorithm, so that the population diversity is promoted and ensured, the high efficiency and economy of power failure maintenance are realized, and the requirement of a power failure plan of a large power grid is met.
In order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows:
a power failure maintenance optimization method based on multi-target particle swarm comprises the following steps:
(1) aiming at power failure equipment, designing a power failure optimization multi-objective function, which comprises a power failure influence minimum function, a power failure cost minimum function and a maintenance time minimum function;
(2) initializing a population before model training, setting a game order to obtain a leader, and establishing an Archive set for storing historical optimal solutions;
(3) selecting a non-dominated solution to enter an Archive set, updating iterative replacement, exiting if an iteration termination condition is met, and outputting a training result;
(4) and if the iteration condition is not met, selecting a target solution leader in the Archive set as an optimal solution, updating the population according to the current solution, including updating the game sequence, updating the positions and the speeds of the particles, updating the Archive set, and returning to the step 3.
Further, in step 1, the power outage influence minimum function:
Figure BDA0003413240400000021
wherein, FiFrequency of power failure for equipment i, NiThe number of users is influenced by the power failure of equipment i, M is the number of users,
Figure BDA0003413240400000023
the total number of the power failure equipment.
Further, in step 1, the power outage cost minimum function:
Figure BDA0003413240400000022
wherein, CiFor the loss of electricity charge per unit time of blackout, T, of the equipment iiIs the time of the power failure of the apparatus, RiWhich is a maintenance expense.
Further, in step 1, the shortest function of repair time:
min f3＝tli+R(c1,c2,xi)τ(xi)+tfi
wherein, the journey time tliTime t for inspectionfiCooperative latency τ (x)i)。
Further, in step 1, the power outage optimization multi-objective function is as follows:
min f(x)＝(f1(x),f2(x),...,fk(x)),x∈Ω
the optimized inner vector x consists of n-dimensional variables, and the target vector f (x) consists of k-dimensional targets, and is a plurality of optimized target functions in the power failure plan; wherein the multi-objective function f maps n as a space Ω into a k-dimensional target space.
Further, step 4 specifically includes:
(4.1) initializing the particle swarm to generate an initial swarm P1And in the optimization process, the historical optimal solution is stored in Archive files to obtain A1；
(4.2) updating the game sequence to obtain a leader, updating the leader particle, updating the population information, updating the position and the speed of the particle in the population, and seeking the optimal solution under the guidance of two extreme values of the position and the speed;
(4.3) obtaining a new population P through continuous updating iterationt+1Storing the optimal solution in the new group into an Aichive archive set;
and (4.4) outputting the particle information concentrated by the Archive, including the position and the speed.
Further, when the Archive set is not empty, as long as P ist+1A medium particle is superior to or independent of a particle in the Archive set, and the particle is inserted into the Archive set.
Further, when the number of particles in an Archive set exceeds a prescribed size, it is necessary to delete extra individuals to maintain a stable Archive set size;
number of particles to delete PN:
Figure BDA0003413240400000031
wherein Int () is a rounding function At+1Representing the updated memory set and Grid representing the number of particles in the Grid.
Further, in the step 2, in the multi-target particle swarm optimization algorithm based on the game theory, the leader game party controls the searching process of the population, and other choices accept the former strategy.
Further, in step 3, the iteration is terminated when the iteration number is greater than the set iteration number.
Compared with the prior art, the method has the advantages that compared with the traditional particle swarm algorithm, the method is more economical for the optimization strategy result after the power failure maintenance of the equipment, on the premise that the minimum objective function value of a plurality of power failure maintenance is met, the game mechanism theory can enable the decision result to keep the population diversity, the overall maintenance consumption is the lowest, and the method provided by the invention is more practical.
The method introduces a game theory mechanism, and builds a multi-target particle swarm power failure maintenance optimization model based on the game mechanism; the model is adopted to calculate the Archive optimal solution set which enables a plurality of power failure optimization objective functions to be minimum, and a power failure maintenance optimization model with safety and economy is generated.
The invention utilizes a game mechanism to carry out game selection on the historical excellent solution, ensures the diversity of the population, realizes the high efficiency and economy of power failure maintenance, and meets the requirement of a power failure plan of a large power grid.
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FIG. 1 is a flow chart of a power failure maintenance optimization method based on multi-objective particle swarm.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings and the embodiment. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in FIG. 1, the method for optimizing power outage maintenance based on multi-target particle swarm comprises the following steps:
(1) aiming at the power failure equipment, designing and calculating a power failure optimization multi-objective function, wherein the power failure optimization multi-objective function comprises a power failure influence minimum function, a power failure cost minimum function and a maintenance time minimum function;
the power outage optimization multi-objective function comprises the following steps:
power outage impact minimization function:
comprehensively considering the influence of power failure on users, establishing a power failure influence function by taking the current power failure frequency as a basis:
Figure BDA0003413240400000032
wherein, FiFrequency of power failure for equipment i, NiThe number of users is influenced by the power failure of equipment i, M is the number of users,
Figure BDA0003413240400000033
the total number of the power failure equipment.
Power outage cost minimum function:
one of the final goals of blackout overhaul optimization is to minimize overhaul costs, including the sum of electricity sales losses due to blackouts, overhaul equipment operating expenses:
Figure BDA0003413240400000041
wherein, CiFor the loss of electricity charge per unit time of blackout, T, of the equipment iiIs the time of the power failure of the apparatus, RiWhich is a maintenance expense.
Shortest function of repair time:
the maintenance time is the journey time t in the processliTime t for inspectionfiCooperative latency τ (x)i). When two teams c1,c2During cooperation, the corresponding overhaul time is as follows:
min f3＝tli+R(c1,c2,xi)τ(xi)+tfi
the objective of multi-objective optimization is to achieve the optimization of multiple requirements in a constrained region without loss of generality. The concept function is as follows:
min f(x)＝(f1(x),f2(x),...,fk(x)),x∈Ω
the optimized inner vector x is composed of n-dimensional variables; the target vector f (x) is composed of k-dimensional targets and is a plurality of optimization target functions in the power failure plan; wherein the multi-objective function f maps n as a space Ω into a k-dimensional target space.
(2) Before model training, a population needs to be initialized, and a game order is set to obtain a leader; establishing an Archive set and storing historical excellent solutions;
in a multi-target particle swarm optimization algorithm based on a game theory, a game party controls a searching process of a population. For example, in a two-party game, the yield of the selected game party is as large as possible, and then the other party decides whether to accept the former strategy, if the other party also benefits, the selection of the next round is accepted and carried out, otherwise, the selection can be rejected.
Strategy 1: the revenue expectation when adopting the acceptance strategy is: e1＝f1，f1The former benefits from the optimal value of the objective function.
Strategy 2: the expected revenue when adopting the non-acceptance strategy is as follows: e2＝p×f2+(1-p)×f3P is the latter benefit probability, f2For the optimal value of the objective function at this time, 1-p is the probability of damage, f3The optimal value of the objective function at this time.
When the game process is carried out in the Archive file set, the definition of non-inferior solutions shows that no solution which enables each target to obtain better simultaneously exists. Consider also that when the gaming process is conducted prior to entering a non-inferior zone, the benefit generally offered by the gaming party itself is better than the benefit offered by the other gaming parties, i.e., for f1,f2In general, f1＜f2＜f3At this time E1-E2Is less than 0. Occasionally f appears1＞f2But due to f1-f3< 0, whereby:
E1-E2＝f1-f3+p×(f3-f2)+ε(ε→0)
E1-E2＜0
in either case, the betting party treats the offers of the other betting parties with policy 1 to expect the maximum revenue obtained.
(3) Selecting a non-dominated solution to enter an Archive set, updating iterative replacement, exiting if an iteration termination condition is met, and outputting a training result;
when the Archive file set has a plurality of target solutions, such as: s1And s2When s is1Is superior to s2Then call s1Domination s2If s is1Is not dominated by other solutions, s1Referred to as the non-dominated solution.
The iteration number is set to be 100, and when the iteration number is larger than 100, the iteration is terminated.
(4) And if the iteration condition is not met, selecting a target solution leader in the Archive set, updating the game order, updating the particle position and the particle speed, and returning to the step 3 after updating the Archive set.
When a game mechanism is used for game calculation, a target solution leader is selected from an Archive set as an optimal solution, the population is updated according to the current solution, and the game sequence and the particle position and speed are updated. The calculation of the model is based on the game under the premise of a historical optimal solution set, updated particles are generated in the solution set to play the game, a game sequence is set, a leader is obtained, the leader leads the particles to update, and then a new group is obtained until a termination condition is met.
The multi-target particle swarm is realized by the following steps:
(4.1) initializing particle swarm and particle speed, giving initial value to parameter, and generating initial population P1And in the optimization process, the historical optimal solution is stored in Archive files to obtain A1；
(4.2) updating the population information; updating the position and the speed of the particles in the group, and seeking an optimal solution under the guidance of two extreme values of the position and the speed;
(4.3) updating the storage set: obtaining a new group P through continuous updating iterationt+1If the optimal solution in the new group is consistent with the step 1, storing the optimal solution in the new group into an Aichive archive set;
when the Archive set is not empty, as long as Pt+1A medium particle is superior to or independent of a particle in the Archive set, and the particle is inserted into the Archive set.
(4.4) truncation operation of Archive set: when the number of particles in an Archive set exceeds a predetermined size, it is necessary to delete extra individuals to maintain a stable Archive set size.
Number of particles to delete PN:
Figure BDA0003413240400000051
wherein Int () is a rounding function At+1Representing the updated memory set and Grid representing the number of particles in the Grid.
And (4.5) outputting the particle information concentrated by the Archive, including the position and the speed.
Compared with the prior art, the method has the advantages that compared with the traditional particle swarm algorithm, the method is more economical for the optimization strategy result after the power failure maintenance of the equipment, on the premise that the minimum objective function value of a plurality of power failure maintenance is met, the game mechanism theory can enable the decision result to keep the population diversity, the overall maintenance consumption is the lowest, and the method provided by the invention is more practical.
The method introduces a game theory mechanism, and builds a multi-target particle swarm power failure maintenance optimization model based on the game mechanism; the model is adopted to calculate the Archive optimal solution set which enables a plurality of power failure optimization objective functions to be minimum, and a power failure maintenance optimization model with safety and economy is generated.
The invention utilizes a game mechanism to carry out game selection on the historical optimal solution, ensures the diversity of the population, realizes the high efficiency and economy of power failure maintenance, and meets the requirement of a power failure plan of a large power grid.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.

Claims (10)

1. A power failure maintenance optimization method based on multi-target particle swarm is characterized by comprising the following steps:
(1) aiming at power failure equipment, designing a power failure optimization multi-objective function, which comprises a power failure influence minimum function, a power failure cost minimum function and a maintenance time minimum function;
(2) initializing a population before model training, setting a game order to obtain a leader, and establishing an Archive set for storing historical optimal solutions;
(3) selecting a non-dominated solution to enter an Archive set, updating iterative replacement, exiting if an iteration termination condition is met, and outputting a training result;
(4) and if the iteration condition is not met, selecting a target solution leader in the Archive set as an optimal solution, updating the population according to the current solution, including updating the game sequence, updating the particle position and velocity, updating the Archive set, and returning to the step 3.
2. The multi-objective particle swarm-based outage overhaul optimization method according to claim 1,
in step 1, the power failure influence minimum function:
Figure FDA0003413240390000011
wherein, FiFrequency of power failure, N, for device iiThe number of users is influenced by the power failure of equipment i, M is the number of users,
Figure FDA0003413240390000012
the total number of the power failure equipment.
3. The multi-objective particle swarm-based outage overhaul optimization method according to claim 1,
in step 1, the power outage cost minimum function:
Figure FDA0003413240390000013
wherein, CiFor the loss of electricity charge per unit time of blackout, T, of the equipment iiIs the time of the power failure of the apparatus, RiWhich is a maintenance expense.
4. The multi-objective particle swarm-based outage overhaul optimization method according to claim 1,
in step 1, the shortest function of overhaul time:
min f3＝tli+R(c1,c2,xi)τ(xi)+tfi
wherein, the journey time tliTime t for inspectionfiCooperative latency τ (x)i)。
5. The multi-objective particle swarm-based outage overhaul optimization method according to claim 1,
in step 1, the power outage optimization multi-objective function is as follows:
min f(x)＝(f1(x),f2(x),...,fk(x)),x∈Ω
the optimized inner vector x consists of n-dimensional variables, and the target vector f (x) consists of k-dimensional targets, and is a plurality of optimized target functions in the power failure plan; wherein the multi-objective function f maps n as a space Ω into a k-dimensional target space.
6. The multi-objective particle swarm-based outage overhaul optimization method according to claim 1,
in the step 4, the method specifically comprises the following steps:
(4.1) initializing the particle swarm to generate an initial swarm P1And in the optimization process, the historical optimal solution is stored in Archive files to obtain A1；
(4.2) updating the game sequence to obtain a leader, updating the leader particle, updating the population information, updating the position and the speed of the particle in the population, and seeking the optimal solution under the guidance of two extreme values of the position and the speed;
(4.3) obtaining a new population P through continuous updating iterationt+1Storing the optimal solution in the new group into an Aichive archive set;
and (4.4) outputting particle information in the Archive set, including position and speed.
7. The multi-objective particle swarm-based outage overhaul optimization method according to claim 6,
when an Archive set is not empty, as long as Pt+1A medium particle is superior to or independent of a particle in the Archive set, and the particle is inserted into the Archive set.
8. The multi-objective particle swarm-based outage overhaul optimization method according to claim 6,
when the number of particles in an Archive set exceeds a specified size, redundant individuals need to be deleted to maintain a stable Archive set size;
number of particles to delete PN:
Figure FDA0003413240390000021
wherein Int () is a rounding function At+1Representing the updated memory set and Grid representing the number of particles in the Grid.
9. The multi-objective particle swarm-based outage overhaul optimization method according to claim 1,
in the step 2, in the multi-target particle swarm optimization algorithm based on the game theory, the leader game party controls the searching process of the population, and other choices accept the former strategy.
10. The multi-objective particle swarm-based outage overhaul optimization method according to claim 1,
in step 3, the iteration is terminated when the iteration number is greater than the set iteration number.
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