Power system reactive power optimization method of wind power field

Abstract

The invention relates to a reactive power optimization of a power system and specifically relates to a power system reactive power optimization method of a wind power field. The method includes random initialization of population, linear annealing weight introduction, gene fusion of genes of individuals in a new population and individual in a original population under a CR weight, target population generation, cross operation implementation, target individual fitness value calculation, one-to-one comparison of target individual fitness values and original individual fitness values, preferential saving, new population generation, and iteration search in the maximal evolution algebra range until the large evolution algebra is reached. According to the invention, dynamic adjustment is performed on parameters of a differential algorithm and a variation strategy of linear annealing is adopted for overlapped individuals in the population, so that a condition that the algorithm falls into local optimum is avoided, optimization and overall search capability are improved, the calculation time is shortened, influence on power grid reactive power distribution and voltage problems by the wind power field are eliminated, system grid loss is reduced and voltage level is improved.

Description

technical field [0001] The invention relates to a reactive power optimization method for a power system, in particular to a reactive power optimization method for a wind farm power system. Background technique [0002] Power system reactive power optimization refers to when the structural parameters and load conditions of the system are given, through the optimization of control variables, under all specified constraints, find the reactive power when one or more performance indicators of the system are optimal. means of adjustment. The purpose of reactive power optimization is to reduce the active network loss by adjusting the distribution of reactive power flow, and to ensure that the voltage is at the best level. Network loss, to achieve the smallest active network loss. Reactive power optimization is a multi-objective, multi-variable, multi-constraint hybrid nonlinear programming problem. The optimization variables include both continuous variables such as node voltages...

AI Technical Summary

Problems solved by technology

[0005] The disadvantage is that the simulated annealing algorithm and the genetic algorithm have many parameters, the parameter selection is more troublesome, and the dependence on the problem is too strong; the artificial neural network needs to invest in a large number of samples for training in the early stage, the self-learning cycle is long, and it is easy to fall into local extremum; Fuzzy optimization algorithm is too complicated to solve precise problems

Since it still boils down to a linear or nonlinear programming problem in the end, the calculation speed is not significantly improved, which will cause the model to become nonlinear and affect the calculation efficiency

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