Study On Reactive Power Optimization Based On The Improved Genetic Algorithm

With the development of national economy, the demands of power supply quality from all kinds of industries are increased. Rational distribution of reactive power in system is the prior condition which can ensure voltage quality and reduce the network loss. Reactive power optimization is a kind of complicated nonlinear optimization problem. Efficent and reasonable reactive power optimization not only can ensure voltage quality and reduce the net loss, but also be very important for security and economics of power system.In this paper, the study contents and present status of reactive power optimization are represented. After researched kinds of methods for reactive power optimization, the advantages and disadvantages and application of these methods are analyzed. A mathematical model for reactive power optimization is established to obtain minimization of network loss with satisfying constraints of power flow and security, and the gradients of constraints and object which are the basement of using classic algorithms for promble solving are derived.After compared the merits and faults of optimization methods in classic algorithms for nonlinear optimization problem, the Sequential Quadratic Programming (SQP) and Primal-Dual Interior Point (PDIP) methods which are representative are adopted for reactive power optimization. The principles of two methods are introduced, then the steps for reactive power optimization are designed and the optimization programs for reactive power optimization are compiled using MATLAB languages. The simulation results prove that though the convergence speed for the two kinds of methods is fast, the methods are indepent on the maths model and initial point and usually can reach the local optimum result.Using Genetic Algorithm (GA) to solve the reactive power optimization problem not only can avoid the faults of classic algorithms such as depending on the model, initial start points and so on, but also have the ability to search the global optimum result. In this paper, two kinds of GA which are standard GA and Genetic algorithm for numerical optimization of constrained problems (a kind of multi-pop synergism evlution GA) are employed to solve the reactive power optimization. Some faults are improved and counterpart programs are compiled. The simulation results prove that though there are hardly limits for GA sovling the problem and the GA can effectively get the global optimum result, there are some defaults that GA is lack of ability to search local domain quickly and easy to be premature.Combined the merits of classic algorithm and GA, three kinds of hybrid GA are composed.(1) The hybrid GA with GA and SQP. In this kind of hybrid GA, GA is firstly used for searching domain and obtains a group of middle results, and then SQP is employed for searching finally optimum result using this group of middle results as its initial point. The hybrid GA can not only solve the problem of selecting initial point for SQP, but also increase the convergence speed.(2) The hybrid GA with GA and PDIP. PDIP is firstly used for searching a group of suboptimum results, and then GA is employed for searching the finally optimum result. The hybrid GA can not only take advantage of computiton speed of PDIP to remedy the computition speed of GA computing the power flow in the beginning, but also ensure the global result can be obtained.(3) The hybrid multi-pop GA. This kind of hybrid GA uses SQP to replace the genetic operation in birthing reference population is represented. The hybrid multi-population GA can partly decrease the number of computing the adaptive values and develop the search efficiency.At last, all the proposed methods are applied to IEEE 14-bus system. The numerical simulation results compared with exsited literature demonstrate the validity and efficiency of these algorithms.