Estimation Matrix Calibration of PMU Data-driven State Estimation Using Neural Network

Linear state estimation (LSE) is a phasor measurement unit (PMU) data-based power system state estimation that incorporates a linear measurement model in rectangular coordinates. Due to the high computational efficiency and high observational time-resolution, LSE can act as a supplementary state estimation in a wide-area monitoring system (WAMS). The performance of LSE is relatively sensitive to noises in measurements. Therefore, the estimation accuracy relies heavily on the accuracy of the estimation matrix, which is directly influenced by the measurement weight matrix. This paper proposes two novel calibration method of the estimation matrix using neural networks. One is based on the minimum absolute network loss (ANL), and the other is based on the minimum average squared network loss (ASNL). Both methods are tested and compared with LSE algorithms on the IEEE 14-bus system.