Research On Reactive Power Optimization Of Wind Power Integration System With The Improved QPSO Algorithm

Under the pressure of energy shortage and environmental deterioration, the development and utilization of renewable energy have received widespread attentions. The wind power becomes the most promising new energy with its advantage of clean, no pollution and extensive distribution. However, in practical applications, the connection to the system of wind farm will change the flow distribution of the network and influence the power loss and node voltage of the power system due to the volatility and uncertainty of wind power. Hence, it is significant to perform research on the reactive power optimization considering wind power’s uncertainty for safe and stable operation of the system and wind power reliable integration.Firstly, this paper describes the current development of wind power and the affect that wind power integration has on system reactive power optimization. Then the status of reactive power optimization of wind power integration system is introduced in detail. The steady-state model of asynchronous wind turbine and its node approach in flow calculations are made analysis based on the introduction of three kinds of wind turbines’structures. Considering the distribution reactive power optimization’s uncertainty which results from that asynchronous wind turbines integration, a scenario analysis method based on wind speed prediction is presented to convert the uncertainty model to several typical certainty scenario problem. The system scene partitioning methods of asynchronous wind turbines integration is discussed in applications, Thus multi-objective reactive power optimization models which take into account the economic and security stability of the system under the whole scene is established. A multi-objective model is transformed into a single objective model according to maximum-satisfaction criterion on fuzzy set theory and is solved by an improved quantum-behaved particle swarm optimization(IQPSO) algorithm. The algorithm improves quantum-behaved particle swarm optimization(QPSO) by introducing an adaptive weighting factor, Cauchy mutation operator and the adaptive strategies of contraction-expansion coefficient, which improves the defects of slow searching speed and premature convergences that easily happen when solving complex multimodal functions. And a standard feature function is adopted to verity the validity of the improved algorithm.The improved distribution system of IEEE69 nodes is applied for simulation analysis and the results show that the constructed model of reactive power optimization can better adapt to the random variations of wind speed, and can well coordinate the relationship of system’s economy and secure stability. The convergence and superiority of the proposed IQPSO in solving models is verified by comparing and analyzing the three algorithms results in details, which also provides a theoretical basis for other forms of new energy to the grid.