A Reactive Power Optimization Method of Power System Based on Improved Crossover Algorithm

Abstract

The invention discloses a reactive power optimization method of an electric power system based on an improved CSO algorithm. The algorithm is a swarm intelligent search algorithm based on an improved CSO (ICSO) algorithm. The reactive power optimization method mainly comprises a horizontal cross operator, a longitudinal cross operator and a differential mutation operator. In horizontal cross, every two of all particles in a population are non-repeatedly paired in the horizontal cross, and the paired particles and the edges thereof are searched and updated in real time; in longitudinal cross, all dimensions are paired and then subjected to arithmetic cross; in differential mutation, all particles are subjected to mutation disturbance and cross and finally subjected to preferential selection; the three operators update the population through the selection operation, so that the convergence rate is accelerated and the population diversity is kept. The reactive power optimization method has the beneficial effects that the convergence speed of the ICSO algorithm is high, information exchange among individuals in the population is complete, the global convergence capability is strong, the particle diversity is good, and the reaction power optimization method has good applicability aiming at the high-dimensionality, multi-constraint and nonlinear complicated practical problem of reactive optimization of the electric power system.

Description

technical field [0001] The invention relates to a reactive power optimization method of a power system, in particular to a reactive power optimization method of a power system based on an improved CSO (ICSO) algorithm. Background technique [0002] Power system reactive power optimization is of great significance to the efficient, stable and economical operation of the power system. As a typical optimization problem in the power system, reactive power optimization refers to the rational configuration of the power grid by controlling the extreme voltage output of the generator, the reactive power compensation capacity, and the tap of the on-load tap changer on the basis of satisfying various constraints. Reactive power flow distribution minimizes network transmission loss, improves system voltage quality, and improves system security and economy. This is an optimization problem with nonlinear, multi-dimensional, multi-constrained, combination of continuous and non-continuous...

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Problems solved by technology

[0004] Although these algorithms have their own characteristics and have made some progress to varying degrees, there are still many disadvantages for reactive power optimization of power systems, a complex optimization problem with multiple constraints, nonlinearity, and high latitude.

Such as: Genetic Algorithm (GA) runs for a long time; Particle Swarm Optimization (PSO) is easy to fall into local optimum; Evolutionary Algorithm (EA), Artificial Bee Colony Algorithm (ACO), Group Search Algorithm (GSO) and other particle diversity are poor, and the control parameters Wait a minute

In addition, many algorithms have poor global convergence ability. For the complex model of reactive power optimization, the global convergence ability is not strong enough, and it is easy to fall into local optimum.

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