Comparing For Algorithms On Researching Of Active Power Optimization In The Power Systems

The active power optimization of the power system has important research significance for the economic operation of the power system.The classic method of active power optimization has the characteristics of fast calculation speed and high reliability,and it is an important method for power system optimization.With the development of power systems,intelligent algorithms have begun to be used in the field of power systems.Genetic algorithms have been adopted more and more because of their robustness and wide range of applications.This paper introduces the classic method and genetic algorithm and proposes improvement measures based on it.The content of this article can be divided into three parts.The first part introduces the content of power flow calculation firstly,which mainly includes the formation of node admittance matrix,the method of solving network equations,and the method of power flow calculation,and proposes a new type of admittance matrix storage.And on this basis,the network equation solution and power flow calculation methods are improved,followed by the introduction of four kinds of network loss micro-increasing solutions,which are polar impedance matrix method,rectangular coordinate impedance matrix method,polar coordinate admittance matrix method and jacobian matrix method.An improved algorithm is proposed and applied to active optimization algorithm.The second part is the content of the genetic algorithm.It introduces the principle of the genetic algorithm in detail.It also introduces measures to improve the specific operation of the algorithm to make up for the shortcomings such as the lack of precision of the genetic algorithm.The algorithm is applied to active optimization.The third part is the comparison of the active power optimization results of the classical method and the improved genetic algorithm.The feasibility of improving the intelligent algorithm is verified by an example.