PMU-based Robust Power System Forecasting Aided State Estimation

Power system state estimation plays a critical role in modern energy management systems(EMS)and is also the base of decision making for operators.However,due to the intrinsic errors,transmission noise and other reasons in measuring devices,the power system measurements are always faced with contamination,causing the disturbance and inaccuracy in the state estimation,which would mislead decision-makers.Hence,it is significant for maintaining the stability of power system to improve the robustness of state estimation as well as the ability of restricting the bad data.As the measuring device develops gradually,synchronous phasor measurement unit(PMU)is applied in the power system step by step,and provides state estimation with more accurate and synchronous measurements.Meanwhile,the source of PMU measurements is different from SC AD A measurements',and is mutual independent to restrain the bad data in SCADA measurements effectively.Finally,the robustness of algorithm can be improved.Therefore,in the thesis,a more robust forecasting-aided state estimation algorithm is proposed.And the influence of PMU measurement on the accuracy and robustness of the algorithm is also investigated.The main contents of the research are as follows:1.Simply presenting the main algorithm in state estimation,including weighted least squares method,Kalman filter and extended Kalman filter.2.Based on SCADA measurements,a more robust forecasting-aided state estimation algorithm—generalized maximum-likelihood-type-extended Kalman filter(GM-EKF)is proposed.The main content is to simulate the proposed GM-EKF in different IEEE standard test systems,and to demonstrate the effectiveness and robustness of algorithm.3.According to different ways between SCADA and PMU measurements infusion.Two forecasting-aided state estimation algorithms based on mixed measurements are proposed.The main contents are to simulate the two algorithms with mixed measurements,so as to verify the effect of PMU in improving the accuracy and robustness of GM-EKF.