Study On WAMS/SCADA Hybrid Measurement Based Power System State Estimation

State estimation provides the real-time operational state of power system, and it’s a prerequisite to ensure the normal operation of the system. The conventional state estimation which based on SCADA has the disadvantages of low accuracy and poor synchronization, and the emergence and development of WAMS brings new opportunities for improving the performance of state estimation. From the economy and technique views to consider, WAMS and SCADA exists at the same time is the inexorable trend. However, WAMS and SCADA based on different technical platforms, so how to combine the WAMS data with the SCADA data is a difficult problem. Aiming at some basic questions of WAMS/SCADA hybrid state estimation, this paper makes in-depth and careful studies on static state estimation algorithm, dynamic state estimation algorithm, optimal PMU placement, compatibility problem, identification and correction of the parameters errors. The main achievements of this paper are as follows:This paper proposes two kinds of static state estimation algorithms which based on WAMS/SCADA hybrid data. The first algorithm constructs a state estimation objective function which combined with the constraints of WAMS and zero-injection nodes. The second algorithm expands the observation range of PMU by introducing pseudo measurements. Then, analyzing and comparing the adaptability of two algorithms under different working conditions.This paper establishs mathematical model about the compatible problem of WAMS/SCADA hybrid data, and analyzes the main factors which effect data compatibility. This solution implements the synchronization of WAMS and SCADA data through the correlation analysis, and then re-quantified the measure weights by considering the delay distribution of measurement data, to make hybrid data have compatibility.On the basis of WAMS/SCADA hybrid measurements, a new method to identify and correct the parameters errors is proposed in this paper. Combining the residuals power and zero-injection node power flows acquired by WAMS measurements with the residuals of SCADA measurements, using the optimization theory to construct the sensibility matrix with respect to the residuals and parameters errors. And then, the identification and correction of parameters errors can be achieved. This paper presents a new method for an optimal placement of PMU in hybrid measurement system. This method establishs the coupling relationship of network nodes based on electrical distance, and uses the Fuzzy c-means clustering technique to group the network nodes, a class of nodes which have similar dynamic characteristics can be replaced by grouping node. Then, PMU which placed in the grouping node can capture the typical dynamic information.The final study of this paper is about dynamic state estimation algorithm which based on WAMS/SCADA hybrid measurements. This algorithm introduces the WAMS data to improve the accuracy of state estimation, and uses Unscented Kalman Filter (UKF) to avoid the error caused by the linearization process and the complicated calculation of Jacobian matrix, so it can estimate the state variables of dynamic system more accurately.