利用强度Pareto进化算法的多目标无功优化

Multi-objective Reactive Power Optimization Using SPEA2

为更好地解决电力系统多目标无功优化问题,分析了当前多目标无功优化算法存在的缺陷,首次将强度Pareto进化算法(SPEA2)应用于多目标无功优化,为真正意义上的多目标无功优化提供了依据。SPEA2是一种新型的多目标进化算法,参数设置少,收敛速度快,寻优能力强,求得的Pareto最优解分布均匀。IEEE30节点测试系统的算例结果表明所提出的算法在多目标无功优化中具有良好的效果,为各目标之间的权衡分析提供了有效工具,是一种求解多目标无功优化问题的有效方法。

Strength Pareto Evolutionary Algorithm （SPEA2） is a new multi-objective evolutionary algorithm. This algorithm needs only a few parameters but has fast convergence and powerful optimization capability. It can obtain well-distributed solutions in objective function spaces. Reactive power optimization has great influences on power system security and economic operation. It is a kind of complicated nonlinear constrained optimization problem. A variety of methods have been devoted to solve the problem. In this paper, firstly the defects of current methods are summarized, then a model of multi-objective reactive power optimization is established, which takes into account of loss minimization, voltage stability margin maximization and high service equality. In addition, fitness assignment strategy and environmental selection technique of SPEA2 is described simply. To overcome the shortcomings of the existing methods, SPEA2 is applied to reach the final solution of multi-objective reactive power optimization. This approach can avoid the blindness of selecting weight factors. It offers an effective tool for measuring the performance of different sub-objective functions, which provides a basis to the real multi-objective reactive power optimization. Based on the proposed mathematical model and algorithm, an optimization program is developed. In this program, hybrid-coding of integer and real numbers is used. Finally this method is applied to IEEE 30-bus testing system. In comparison with standard genetic algorithm, the results show that SPEA2 has better convergence and flexibility. The quality of optimal solution is improved. Thus the validity and efficiency of proposed algorithm are confirmed. SPEA2 provides a new thought for multi-objective reactive power optimization.