Research Of Reactive Power Optimization In Bameng Power System

Reactive power optimization is one of the most important control methods to ensure power system operation securely and economically, and an effective measure to improve the voltage quality and reduce the transmission loss. Study on reactive power optimization has the great significance in theory and practical application.Reactive power optimization ,a classical problem in power system is a large-scale nonlinear optimization problem in maths with a large number of variables and uncertain parameters, the operating variables include continuous and discrete variables, so the optimization becomes very complex. A novel algorithm, immune genetic algorithm (IGA) which is based on the theory of biological immune system is used in this papeT. This algorithm inherits and develops the merits of genetic algorithm (GA) such as multi points searching, dealing with the discrete variables, application in large-scale range and so on. At the same time, with self-regulation of antibodies, IGA greatly improves the diversity of antibodies, achieves a good dynamic balance between individual diversity and population global convergence, avoids the shortage of traditional GA converging to a local optimal solution because of reducing the diversity of individuals. Because of the good nature of IGA, it is chosen to be applied to reactive power optimization in power system.According to the characteristics of reactive power optimization, this paper analyses the fundamental reason of the voltage and reactive power problem in BAYANNAOER power system, and proposed detailed and practical regulating measure aiming at reducing power loss and improving voltage quality. The corresponding reactive power optimization software is programmed.The computing results against the actual BAYANNAOER network prove that the method of reactive power optimization based on IGA is effective and possesses the excellent the value in theory and practice. The optimization scheme achieved can improve the voltage level and reactive power distributing of BAYANNAOER network.