Improved power system state estimator with preprocessing based on the modified intersection of confidence intervals

Abstract

Abstract The paper proposes a power system state estimator upgraded by the improved relative intersection of confidence intervals (RICI) algorithm combined with the local polynomial approximation (LPA). In the proposed approach, a novel LPA-RICI denoising technique is utilized to preprocess the input measurements. The accuracy of the adaptive, data-driven LPA-RICI based state estimator was tested on the IEEE test systems with 14 and 30 buses, as well as on a model of the real-life Croatian transmission power system. Due to its adaptivity to high transitions in measurement series achieved by varying filter support size, the LPA-RICI based state estimator enhanced the estimation accuracy for all tested power systems, and at the same time reduced the number of iterations required for the estimator to converge when compared to the classical weighted least squares (WLS) based estimator. Namely, improvement when using the proposed preprocessor was obtained in estimation of the input voltage magnitudes, active and reactive power flows and injections for the Croatian transmission power system by up to 29.3% and 19.4% in terms of the reduction of the mean squared error (MSE) and mean absolute error (MAE), respectively. Furthermore, the LPA-RICI based estimator resulted in an overall estimation quality enhancement for the Croatian transmission power system by up to 13.3%, 12.3%, and 3.7%, in terms of the objective function, variance of the estimated states, and average time for the state estimator to converge, respectively.