Reactive Power Optimization Of Power System With Wind Farm

Our China is rich in wind resources. The development of wind power which is green, non-polluting and renewable brings an infinite prospect to the power industry. Due to the characteristics of the wind, the output of wind power is kind of randomness and volatility; large-scale wind power grid will bring the stability of the system a vital problem.For power system, reactive power balance plays an important role in voltage quality and the power system’s stable operation. Because of its variables and constraints, reactive power optimization is an extremely complex question. Because of the large-scale wind power grid’ connection, reactive power optimization is faced with a new challenge. The wind speed is hard to predict and output data is hard to get. Traditional algorithm of power flow is not convenient for the wind power nodes. Various algorithms for optimization problems still have some shortcomings and need to be improved.In this paper, to deal with the problem of wind velocity’s unpredictability, wind farm power output model which is with a certain accuracy and easy to calculate is established. The model describes the wind speed with the Weibull Distribution. With wind power scenarios probability model, wind power output is acquired. Taking the particularity of wind power grid nodes into consideration, power flow calculation only needs to modify the Jacobi matrix with the Newton-Raphson method.In this paper, Particle swarm optimization (PSO) is selected to optimize the model. Its objective function is designed to be active power loss of the system, in which the node voltage beyond limited and the generator reactive power output beyond limited are considered in the way of penalty function. In order to overcome the shortcoming of particle swarm optimization (PSO) algorithm, which is easy to fall into local optimal, a new method called Frog-Chaotic particle swarm optimization (F-CPSO)will be proposed. Compared with Standard Particle Swarm Optimization Algorithm, the new method divides the population into several groups by value of the fitness. A learning factor of the optimal particle is added in the process of evolution. In order to increase the diversity of the population, the particle with the worst fitness will be replaced by a new one generated randomly.In order to verify the effectiveness and applicability, this paper finally uses the modified particle swarm optimization algorithm in IEEE-30 nodes standard test systems, which take the wind turbines nodes into consideration. This paper uses MATLAB language for programming and simulation, and compares with the Standard Particle Swarm Optimization (SPSO).Through the analysis and comparison of the simulation results, it verifies its effectiveness, using modified particle swarm optimization algorithm can effectively reduce the system power loss and improve voltage value, compared with the standard Particle Swarm Optimization, the improved particle swarm algorithm has a better global convergence performance.