Multi-objective optimal power filter planning in distribution network based on fast nondominated sorting genetic algorithms

In this paper, firstly the defects of current algorithms for optimal power filter planning are analyzed, and then a new model, taking both filter costs and network loss as objective functions is proposed. In this model, reactive power compensation is also taken into account. Fast nondominated sorting genetic algorithm (NSGA-II), a new multi-objective genetic algorithm, is used to solve the model. The simulation results show that the obtained Pareto-optimal solutions have much better spread of solutions, better convergence and robustness, which provide decision-makers with a wide choice of filter optimization plan. The comparison with the classical single objective optimal filter planning demonstrated the proposed model solved by improved NSGA-II can obtain better optimum solutions. In addition, three widely used methods of transformer harmonic loss are analyzed in this paper. And through a case a more accurate method under varying range of harmonic distortion for voltage is used in the optimization model.