基于混合量测的电力系统线性动态状态估计算法

A Mixed Measurement-based Linear Dynamic State Estimation Algorithm for Power Systems

针对当前电力系统中广域测量系统(WAMS)和数据采集与监控(SCADA)系统并存的现状,利用量测变换技术,将SCADA系统下支路功率量测和节点注入功率量测转换为等效的电流相量量测,并与WAMS组成混合量测系统,在此基础上提出了直角坐标系下的线性动态状态估计算法。该算法采用Holt两参数线性指数平滑技术,结合线性定常系统Kalman滤波原理,实现了系统状态的预测和估计。该算法具有常数雅可比矩阵,从而大大减少了动态状态估计的计算时间,保证了动态状态估计的计算精度。通过IEEE14节点系统的仿真结果,验证了该算法的有效性和优越性。

At present, the measurements of the wide area measurement system （WAMS） and supervisory control and data acquisition （SCADA） are both available in power systems, but the measuring accuracy of the WAMS is much better than that of SCADA. Hence, a linear dynamic state estimation algorithm is proposed to deal with this problem. The measurements of the active and reactive power flow and power injections at the nodes of the SCADA system are transformed into the equivalent current phasors by measurement transformation, so that these measurements can be combined with those of WAMS and form a mixed measuring system. The algorithm proposed is denoted by rectangular coordinates and the linear exponential smoothing technique and linear time-invariant Kalman filtering method are used to implement the forecasting and estimation. Since the proposed algorithm has a constant Jacobian matrix, the calculating time can be significantly reduced and the calculating accuracy ensured. Simulations are carried out in an IEEE 14-bus test system to demonstrate the validity and advantages of the algorithm proposed.