Multi-objective Optimal Power Flow Of Power System Based On The Improved Cuckoo Search Algorithm

Optimal power flow(OPF) plays an important role in the process of planning,scheduling and operation of modern power system,which can meet the requirements of the power system for economy,security and stability.The main purpose of OPF is to make the selected objective function optimal by adjusting the control variables on the basis of satisfying various system constraints.In recent years,the OPF problem has attracted extensive attention of scholars,and its model and algorithm have been perfected day by day.With the increasing demand for electricity and the increasing complexity of power system,researchers have focused more attention on the study of multi-objective optimal power flow(MOOPF).For MOOPF problem,because of the competitive relationship and different dimension between different objective functions,the MOOPF problem is much more difficult to solve than OPF.In this paper,we first introduce the power flow calculation,which is the core of OPF problem,and then build the mathematical model of OPF problem.Cuckoo search(CS) method is selected for solving OPF problem.On the basis of CS method,feedback control strategy,quasi-opposition based learning mechanism,best-solution-guided search mechanism and constraint handling mechanism have been introduced to propose three improved algorithm,that is,FCS?QFCS and IQFCS.In order to test the improvement effect of the improved mechanism to the CS algorithm,this paper uses 15 widely used benchmark functions to carry out simulation experiments,and the optimal results are compared to other evolutionary algorithms in literatures.The experimental results indicate that three improved mechanisms can improve the performance of CS algorithm to some extent,and the IQFCS method is superior to other methods in the optimal values of most functions.For the MOOPF problem,this paper firstly introduces some key concepts in the multi-objective optimization method,including dominated principle,Pareto optimal solution set,non-dominated sorting,crowding distance sorting,and best compromise solution.The MO-IQFCS algorithm is proposed by combining the multi-objective optimization method with the cuckoo algorithm and the improved cuckoo algorithm.The optimal objectives selected in this paper include fuel cost,active power losses,voltage deviation and emission of atmospheric pollutions,and then three MOOPF problems are proposed according to the different requirements of the system.Simulation experiments are carried out in two standard test systems of IEEE 30 and IEEE 57 for three multi-objective functions.The experimental results indicate that the MO-IQFCS method obtains better compromise solution and optimal Pareto fronts than other algorithms.Finally,in order to better illustrate the superiority of MO-IQFCS algorithm,this article uses three different performance indexes to analyze the optimal sets obtained by different algorithms,and further proves that the proposed MO-IQFCS algorithm can efficiently solve the MOOPF problem.