Study On Reactive Power Optimization Based On The Improved Genetic Algorithm

With the expansion of the scale of power systems and grid structure becomingincreasingly complex, reactive power optimization in power system gradually attractspeople’s attention. It can not only effectively reduce the power loss of the system, butalso improve the voltage quality, security, stability and economic operation of thesystem has very important significance.Traditional reactive power optimization algorithm relies on a precisemathematical model，there are high demands to the the objective function. It easilyfall into local optimum, and can not accurately deal with discrete variables, and otherdefects. To solve this problem, researchers introduce intelligent optimizationalgorithms to the optimization of reactive power. Complete control of a variety ofoptimization algorithms in the application on the basis of the current power system,the thesis systematically summarizes the application of the traditional algorithms andsmart algorithms in reactive power optimization, and analyzes respective advantagesand disadvantages. In order to improve computational efficiency and seek the optimalsolution, the thesis does a more in-depth study with the objective function and thesolution algorithm, and proposes the improved genetic algorithm application inreactive power optimization.The mathematical model is based on universal mathematical model of reactivepower optimization,the objective function is the power loss minimum, taking intoaccount the safe operation of the grid, the introduction of state variables to change theway the penalty function to handle distribution of0,1, the establishment of threeintegrated optimal mathematical model of the target. Simple genetic algorithm inpower system is the drawback of slow convergence and easy to fall into localoptimum, the processing of the control variables also kept a large error. In view of this,the simple genetic algorithm has been improved, hybrid coding of integers and realnumbers deals with discrete variables,and proposed adaptive crossover and mutationoperations, so that the diversity of population increases, resulting in more outstandingindividual.Finally, using the MATLAB language write a simple genetic algorithm and anImproved Genetic Algorithm program.Combined with the IEEE14-bus and IEEE30-bus system reactive power optimization simulation,two different algorithms werecompared. The results showed that, compared with the simple genetic algorithm, global search ability and convergence speed are improved in the improved geneticalgorithm.Simulation results show that the improved genetic algorithm is valid andcorrect in application of reactive power optimization.