Multi-objective Optimization Design Of Passive Power Filter

Recently, with a large number of nonlinear device’s input, the power grid harmonicproblem is increasingly serious, which led to the decrease of the power quality seriously.At this time, more and more people begin to pay close attention to passive power filterwhich has many virtues such as simple construction, lower investment cost, higherreliability and lower operation cost. Thus, it becomes the most widely harmonicsuppression equipment in electric system. In this paper, we intensive study the passivepower filter parameter design which is widely used in the power system.Firstly, we analyze the structural style and working principle of single-tuned filterand high-pass filter. Furthermore, we study some parameters’(such as equivalentdeviation, reactive compensation capacity, sharpness of tuning and so on) affect topassive power filter’s performance, we also analyze the determination of systemharmonic impedance and the relation between system harmonic impedance and filterperformance.Secondly, we introduce the basic principle, mathematics model, basic steps ofstandard particle swarm. We also analyze the algorithm parameters. Aimed at thelimitations of standard particle swarm optimization algorithm and the insufficiency, weadd the chaos optimization and the elite strategy into the standard particle swarmoptimization algorithm: Through the verification test function, we find that the improvedparticle swarm algorithm has more advantages.Once more, we analyze and give the mathematical description, which is about theoptimization design of the passive power filter, and then it is obtained the equalityconstraints and inequality constraints from the mathematical description. On this basis,we can obtain the flow chart of solving the filter parameters based on improved particleswarm optimization.Finally, through establish simulation model of passive power filter, we comparisonpassive power filter’s traditional design method, the standard particle swarm optimizationand the improved particle swarm optimization algorithm. In the end, we find that theimproved particle swarm optimization algorithm has more effectiveness.