Research On Reactive Power Optimization Of Changyi City Power Grid Based On Improved Genetic Algorithm

Reasonable and efficient utilization of the reactive power in the power supply network is very necessary.Optimization and control of reactive power not only can improve the voltage quality,but also can help us to reduce the loss of power supply network,so as to achieve the function of electrical energy saving,the measure is also in line with the sustainable development of energy targets.Reactive power optimization and control of power system also can ensure the safety and economy of power system operation,therefore,plays an important role in this initiative and the long-term healthy development of safe and stable operation of the power grid.In order to solve the problem of reactive power optimization and control in power system,the mathematical model and the optimal power flow algorithm to be used are the first to be solved.The mathematical model,we achieve the purpose according to the different selected by choosing different mathematical models to achieve the purpose of selecting different objective functions and for the optimization algorithm.We pay more attention to is the convergence speed of the algorithm and the convergence performance of how to.Here we select the minimum active power loss as the objective function of the mathematical model to be done.This mathematical model contains both equality constraints and inequality constraints.With equality constraints of active power,such as the node injected reactive power,inequality constraints include the generator reactive power output,parallel compensation capacity and the transformer ratio etc.Method selection genetic algorithm for reactive power optimization and control in power system.In order to achieve the purpose of reactive power optimization of power supply network,we use MATLAB tools to compile,Newton Ralph Xun Cartesian coordinate MATLAB flow calculation program and the genetic algorithm for reactive power optimization of MATLAB power flow calculation program.Finally,the practicality and accuracy of genetic algorithm are illustrated by a concrete example.After calculation,we select a specific node,is analyzed in detail in the modified Changyi city 14 bus system and IEEE30 bus system,the comparison of various algorithms,finally obtained the improved genetic algorithms to solve multi variable,nonlinear,discontinuous and multi constraint problems concerned with accuracy and practical features.