动态多目标无功/电压规划的Pareto最优集的求取

动态多目标无功/电压规划问题是一个复杂的多目标非线性优化问题。广泛使用的先评价方法通常只能得到这一问题的单一解，并且在确定各目标间的权重关系时难以给出统一的标准，因而不利于作为实际控制的参考。该文通过创建该问题的多目标数学模型，将动作次数限制归纳为优化目标之一，并应用改进的非支配遗传算法(NSGA-II)，实现了动态多目标无功优化问题的Pareto近似最优集的求解。通过IEEE14、30节点电网模型及实际电网模型的计算，验证了该方法的有效性。

Pareto-Based Approach to Multi-Objective Dynamic Volt/NAR Planning

Multi-objective dynamic voltage/reactive power control is a complicated non-linear problem. Conventional pre-evaluated methods can get one solution only. A novel multi-objective dynamic reactive power optimization model is created in this paper and, for solving the model, a relevant improvement has been made in the non-dominated sorting genetic algorithm II (NSGA-II). This approach has been applied to the IEEE 14 nodes and 30 nodes systems and an actual system. The result shows that the approach has encouraging performance in dynamic reactive power optimization with objectives of minimizing reactive power loss, voltage deviation, and the number of operations as well.