【摘要】 以提高动态状态估计精度为目标,采用离散粒子群优化(discreteparticleswarmoptimization,DPSO)算法对同步相量测量单元(phsormeasurementunit,PMU)的配置点进行优化。该方法克服了传统解析优化方法难以适应不连续目标函数和不连通约束域等情况的缺点,同时,在配置有限PMU的情况下使PMU量测量发挥最大效益。最后对基于扩展Kalman滤波算法的动态状态估计模型进行仿真,证明了经DPSO优化后的配置与随机配置相比最大可能地利用了PMU的高精度量测信息,充分发挥了PMU量测的优点,大大提高了动态状态估计的精度。更多还原

【Abstract】 To improve the accuracy of dynamic state estimation, the discrete particle swarm optimization (DPSO) algorithm is adopted to optimize the placement of phasor measurement unit (PMU). This method can overcome the defects of traditional analytical optimization methods: they are hard to suit the conditions of discontinuous objective function and disconnected constrained domain. At the same time, the quantities measured by PMU can fully give play to maximum advantage while limited PMUs are placed. The dynamic state estimation model based on extended Kalman filter theory is simulated. It is proved by simulation results that comparing with random placement of PMU, the proposed PMU placement optimized by DPSO can utilize the measurement information with high accuracy as much as possible and the advantage of measurement by PMU can be fully developed, thus the accuracy of dynamic state estimation can be evidently improved.更多还原