Tracking Power System State Evolution with Maximum-correntropy-based Extended Kalman Filter

Abstract

This paper develops a novel approach to track power system state evolution based on the maximum correntropy criterion, due to its robustness against non-Gaussian errors. It includes the temporal aspects on the estimation process within a maximum-correntropy-based extended Kalman filter (MCEKF), which is able to deal with both nonlinear supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurement models. By representing the behavior of the state variables with a nonparametric model within the kernel density estimation, it is possible to include abrupt state transitions as part of the process noise with non-Gaussian characteristics. Also, a novel strategy to update the size of Parzen windows in the kernel estimation is proposed to suppress the effects of suspect samples. By properly adjusting the kernel bandwidth, the proposed MCEKF keeps its accuracy during sudden load changes and contingencies, or in the presence of bad data. Simulations with IEEE test systems and the Brazilian interconnected system are carried out. The results show that the method deals with non-Gaussian noises in both the process and measurement, and provides accurate estimates of the system state under normal and abnormal conditions.

Hybrid Physics-Based Adaptive Kalman Filter State Estimation Framework

“…One should note that in the bad data condition, there exist equations "inconsistencies" or "a structural discontinuity" of the equations describing the DSE behavior [10,25]. Otherwise, considering the large load changes, there is no structural discontinuity in system equations [10,17,25]. In this work, the distinction between these anomalies is done through the analysis of the statistical distribution of the innovation process.…”

“…Only in the late 2000s, dynamic state estimators regained significant interest, with the advent of faster computer architectures, by the allocation of new instrumentation and communication technologies with faster updating and sampling rates, and as well as the acute power system operational requirements, carried by a more volatile environment with renewables [5]. In the effort of surpassing many of the practical and theoretical challenges, different versions of the Kalman Filter were proposed, such as the Linear Kalman Filter [13], the Extended Kalman Filter [14], the Unscented Kalman Filter [15], the Cubature Kalman Filter [16], the Correntropy Kalman Filter [17] and the Ensemble Kalman Filter [18]. Nonetheless, all such approaches consider a single stationary scenario or that only known load changes occur on the system.…”

Improved unscented Kalman filter based interval dynamic state estimation of active distribution network considering uncertainty of photovoltaic and load

“…The DSE could not only estimate the current state but also predict the state of the near future (Zhao et al, 2021), which is benefit for the timely problem detection and control of ADN, such as voltage exceeding limits. The traditional DSE method mainly includes Kalman Filter (KF) (Julier andUhlmann, 2004, 2004;Valverde and Terzija, 2011;Karimipour and Dinavahi, 2015;Massignan et al, 2020). The Kalman-based DSE method, such as Extended Kalman Filter (EKF), is implemented on the assumption that the noise follows Gaussian distribution (Massignan et al, 2020).…”

“…The traditional DSE method mainly includes Kalman Filter (KF) (Julier andUhlmann, 2004, 2004;Valverde and Terzija, 2011;Karimipour and Dinavahi, 2015;Massignan et al, 2020). The Kalman-based DSE method, such as Extended Kalman Filter (EKF), is implemented on the assumption that the noise follows Gaussian distribution (Massignan et al, 2020). Unscented Kalman Filter (UKF) achieves higher accuracy than EKF due to unscented transformation, it propagates the mean and covariance through unscented transformation while capturing their nature to third order [6].…”

Outlier Reconstruction Based Distribution System State Estimation Using Equivalent Model of Long Short-term Memory and Metropolis-Hastings Sampling

“…In transmission grids, there are many methods for resisting the effects of outliers. Examples are the methods based on residuals [11], [12], maximum-likelihood (ML) [13]- [15], projection statistics (PS) [16], statistical characterization [17], S-estimator [10], least trimmed squares (LTS) [18], Mestimator [19], least absolute value (LAV) [20], [21], improved Kalman filter [22], [23], and simple hard threshold- This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). M. Xia, J.…”