Power-system reactive power optimization method and device

Abstract

The embodiment of the invention discloses a power-system reactive power optimization method and device. The method includes the steps that a target function is established, and target parameters are initialized; according to a combined chaotic sequence, parent speeds and parent positions corresponding to all particles in a parent particle swarm are obtained; according to the garget function, optimal positions of the particles and the optimal position of a population are determined; according to the updating rule, child speeds and child positions are obtained in an updated mode; according to the child speeds and the inertia weight rule, the inertia weight of an child particle swarm is adjusted; through the target function, adaptive degrees of all particles in the child particle swarm after chaotization treatment are calculated, a particle with the optimal adaptive degree is reserved and serves as a parent particle swarm of next iteration, and updating of the optimal positions of the particles and updating of the optimal position of the population are completed; when the number of iteration reaches the number of preset largest iteration, operation is end; if the number of iteration does not reach the number of preset largest iteration, particle-group updating operation is repeated. According to the technical scheme, the whole optimization searching performance of the particle swarm algorithm is improved, and reactive power optimization of a power system is achieved.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a reactive power optimization method and device for a power system. Background technique [0002] Power system reactive power optimization is a discrete, nonlinear, multi-objective mixed integer programming problem. When making multi-objective decision-making, the objective functions may conflict with each other, and it is impossible to make them optimal together at the same time. Therefore, in order to find a set of solutions that are more suitable for each objective, we must base on each objective The relationship between them, clarify the weight of each objective in the total objective function, and combine multiple sub-objectives linearly weighted into a single objective. The advantage of this method is that it is easy to calculate, but the disadvantage is that it is difficult to determine the weight of each objective function. [0003] For a long time, many scholars ...

AI Technical Summary

Problems solved by technology

However, the standard particle swarm optimization algorithm also has shortcomings such as initial value sensitivity, easy to fall into local extreme points, and low search accuracy during the evolution process.

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