Improved Genetic Algorithm In Reactive Power Optimization

Reactive power optimization in power system can not only reduce power loss, but also improve voltage quality. So it is of great importance to security and economic operation of power system.Reactive optimization of power systems is a mixed optimizing question, which its operating variables include the continual and the separate, its solved process is quite complex. Conventional algorithms such as nonlinear programming, linear programming, mix integer rely on the precise mathematical model, in general, and request the objective function of solved problem to be continual, differential. The final result obtained by the algorithm closely correlates with the initialization, only this initialization approaches optimal solution, the algorithm could acquire the optimal result. In addition, these traditional optimizing algorithms can not precisely dispose of the separate variable, when applied to the reactive power optimization, its result would have a biggish error. For the sake of mending the deficiency, researchers gradually make use of artificial intelligence method to solve reactive optimization. On the base of comprehensively grasping the current reactive optimization of power system ,This paper systematically discusses the classical and the modern artificial intelligence algorithms that applied to reactive optimization problem , Analyze and summarize their respective characteristics and their application. In order to improve result and increase computational speed, this thesis deep studies the mathematical model and algorithms of reactive optimization, so, proposes a kind of modified genetic algorithm.For the economic need of power system operation ,therefore, this paper take power loss minimum as objective function, simultaneously, considering the security operation of electrical network, deal the restriction of voltage and reactive power produced by generator with the penalty function , establishes mathematical model that makes three aspects integrative optimization., moreover, for the increasing the computing speed of reactive power optimization by genetic algorithm ,this paper synthetically compared three kinds to the penalty coefficient and take the exponential variation rule .The simple genetic algorithm is constrained by its poor converging performance , readily leads to local optimization, its computing speed is slow ,and can not precisely deal with separate variable, in view of the deficiency of simple genetic algorithm, this paper make some improvement, employing hybrid-coding to handle separate variable and different fitness functions at different stage, using the dissimilar reproducing in the evolutional process, according as coding ,still make use of arithmetic crossover and small mutation. Except the improved algorithm retained performances of multi-spots search and the strong robust, its convergent rate obtains the enhancement, the algorithm's applicability and global searching ability increase.When applied to reactive power optimization, the genetic algorithm's computing speed is quite slow, besides algorithm self reason, another important factor is of repeatedly solving the power flow equation. Calculating speed of power flow directly influence entire algorithm's speed. Based on comparing power flow algorithms, the paper adopts the fast decouple method, and has made the improvement to it in genetic algorithm reactive power optimization procedure application from the two sides, one side , because matrix X' is foreign to the individual of genetic population ,so making matrix X' is once ,or not repetitive, in course of computing, the calculating program directly use this matrix. On the other hand, convergent precision is also influence computing speed and population's updating, thus power flow which its convergent precision is low is employed in prophase of evolution, however which its convergent precision is high in anaphase. By means of the improvement, speed of the reactive power optimization may availably enhanced , the computing time can be saved.The paper finally used the MATLAB language to compile the simple genetic algorithm and the improvement genetic algorithm reactive power optimization program, the IEEE14-bus and the IEEE30-bus system simulation result indicated, when the improvement algorithm applied to the reactive power optimization, compared with the simple genetic algorithm, it had a better ability of the global research and the higher convergence rate.