基于改进的遗传–模拟退火算法和误差度分析原理的PMU多目标优化配置

Research on Multi-objective Optimal PMU Placement Based on Error Analysis Theory and Improved GASA

为了进一步优化同步相量测量单元(phasor measurement unit,PMU)配置的合理性和效率,提出了一种新的误差度分析原理,并使用改进的遗传–模拟退火算法对多个IEEE标准测试系统进行了优化配置。该原理同时考虑了测量冗余度和状态估计的精度,并且避免引入雅可比矩阵,还具备可观测性分析的功能。研究结果表明:该算法不仅可以找到满足全网可观测性的所有PMU数目的配置解,而且进一步提升了全网的测量精度,从而证明了其有效性和优越性。

Aiming at enhancing rationality and efficiency of optimal PMU placement, the paper proposed a new error analysis method and use improved Genetic-simulated Annealing Algorithm to optimize the PMU placement in several IEEE standard test systems. The new error analysis method takes both measurement redundancy and state estimation accuracy into account without calculating Jacobian matrix, and can also obtain the network observability. The research results prove that the method and algorithm used here can not only find all solutions of PMU placements satisfying full network observability, but also make a further progress in whole system measurement accuracy, and thus, its effectiveness and advantages can be varified.