Improved Particle Swarm Optimization Of Passive Filter

With the use of large nonlinear load causes serious harmonic pollution of power grids produces a lot of power quality problems. Passive power filter which is of simple structure, less investment, high reliability and low cost is one of harmonic control equipments commonly used in the power system. In this paper, parameters optimization method of the passive power filter is studied.Analyze the structure and working principle of monotone filters and high-pass filters in passive power filters and their related parameters. Mainly study several aspects which affect passive power filters, such as equivalent frequency deviation, quality factor, reactive compensation capacity and system harmonic impedance. Analyze sorts of design ways of filter devices deduce filter parameter processes of the smallest capacitor install capacity method, reactive compensation capacity distribution method and over-voltage limit method so on.The particle swarm optimization algorithm based on random weight is proposed to solve the passive filter parameters. The paper makes integrated optimization of filter parameters from aberrtion rate value, practical economy and reactive power compensation so on. At the same time, this paper proposes "filter economy" to judge the filter effect of the filter. Compared with the filter effect in engineering design, the "filter economy" proposed in the paper verify the superiority and feasibility of the multi-objective optimization design.From aberrtion rate value , practical economy and reactive power compensation, the paper simulates and analyzes parameter filter effects of the multi-objective optimization design and engineering design respectively. The results show filter parameters, compared to the engineering design achieving a single goal , obtained in multi-objective optimization design are better in harmonic control, cost and reactive power compensation. The paper verifies the feasibility of multi-object parameter optimization.