Multi-objective Reactive Power Optimization Of Power System With Wind Farm

With the continuous development of social economy and the deepening adjustment of energy structure, the impact of wind turbine generation on electric power system has been paid more and more attention. Due to its intermittent and randomness, the insertion of wind turbine generation, especially the large scale wind turbine generation, has great influenced on active power flow and reactive power flow. Due to reasonable distribution of reactive power directly affecting the safe operation and stability of the electric power system, reactive power optimization has become an important research of electric power system. The problem of reactive power optimization of power system has not yet been satisfactorily resolved because of the discrete, nonlinear, large-scale and convergence dependent on initial value. Therefore, according to the analysis of the electric power system flow with wind farm, using combined chaotic sequence dynamically particle swarm optimization algorithm (CCPSO) and fuzzy entropy weight algorithm, this thesis launched the study of reactive power optimization which contained wind farm.First, according to the prediction of wind speed, this thesis explores the relationship among the output mechanical power of wind turbine, wind speed, pitch angle, tip speed ratio wind turbine structure, obtains that the output mechanical power is determined by wind speed while wind turbine case is sure. It analysis steady-state model and bus type which are important in power flow calculation asynchronous generator, doubly-fed induction generator. Through overhead line model and cable line model, this article analysis the model of collecting line model in wind farm. Based on the Park model, we introduce a far wake and near wake effect model. Combined above four point analysis, a way of power flow calculation which contains asynchronous generator and doubly-fed induction generator is discussed.Second, this paper presents a combined chaotic sequence dynamic particle swarm optimization algorithm. Based on Logistic chaotic optimization algorithm, a combined chaotic sequence which combined Chebyshev chaotic map with logistic chaotic map is applied to obtain a uniform initial value so as to improved the global optimization ability of the algorithm; As the problem of inertia weight, the algorithm give a dynamic inertia weight which based on population velocity.Third, the mathematical model of reactive power optimization of power system is established. Based on the analysis of entropy weight and fuzzy weight in multi-objective decision making, combining their advantages in objective and subjective decision, this paper introduces a fuzzy entropy weight method to solve reactive power optimization by MATLAB.Last, According to the results of the power flow calculation which contained wind farm, combined with CCPSO algorithm and fuzzy entropy weight method, Reactive power optimization of IEEE-14bus system which contained wind farm is solved by MATLAB programming. Compared with PSO algorithm and CPSO algorithm, this result shows that the algorithm proposed in this paper has a better global search ability and convergence speed.