Thévenin Equivalent Parameter Adaptive Robust Estimation Considering the Erroneous Measurements of PMU

Parameter estimation based on the measurement data of the phasor measurement unit (PMU) is an important approach for identifying the Thévenin equivalent parameters (TEPs) of power systems. However, in the process of acquiring or transmitting data in PMU, measurement errors due to external interference or internal system faults will affect the accuracy of parameter estimation. In this paper, a TEP estimation algorithm based on local PMU measurement is proposed. The algorithm considers the errors of the PMU and introduces Huber function and projection statistics (PS) to eliminate the effects of outliers and leverage measurements, respectively. Additionally, a variable forgetting factor (VFF) is used to quickly eliminate the historical data with measurement deviation and track the changes of the system. The regularization technique is used to solve the divergence problem in the inverse process of the ill-conditioned matrix, thereby improving the stability and generalization performance of the algorithm. Finally, by minimizing the cost function of this algorithm, a recursive formula for the equivalent parameter estimation is derived. The effectiveness of the algorithm is verified on the IEEE 118-bus and IEEE 30-bus systems, and compared with recursive least squares (RLS) and Huber’s M-Estimation; the mean relative errors decreased by 94.75% and 84.77%, respectively.