Abstract
Monitoring the current operation status of the power system plays an essential role in the enhancement of the power grid for future requirements. Therefore, the real-time state estimation (SE) of the power system has been of widely-held concern. The Kalman filter is an outstanding method for the SE, and the noise in the system is generally assumed to be Gaussian noise. In the actual power system however, these measurements are usually disturbed by non-Gaussian noises in practice. Furthermore, it is hard to get the statistics of the state noise and measurement noise. As a result, a novel adaptive extended Kalman filter with correntropy loss is proposed and applied for power system SE in this paper. Firstly, correntropy is used to improve the robustness of the EKF algorithm in the presence of non-Gaussian noises and outliers. In addition, an adaptive update mechanism of the covariance matrixes of the measurement and process noises is introduced into the EKF with correntropy loss to enhance the accuracy of the algorithm. Extensive simulations are carried out on IEEE 14-bus and IEEE 30-bus test systems to verify the feasibility and robustness of the proposed algorithm.
Highlights
The power system state estimation (SE) is the foundation and core of the energy management system, and it is indispensable for power system safety, reliability, quality and economic operation [1].SE is usually divided into static state estimation (SSE) and forecasting-aided state estimation (FASE), FASE is called dynamic state estimation in some studies [2]
We perform experiments on the standard IEEE 14-bus and IEEE 30-bus test system to verify the effectiveness and superiority of the proposed algorithm compared with the EKF, UKF, A-EKF and maximum correntropy criteria (MCC)-EKF algorithms
We use the 50 time-sample intervals, which were obtained by running successful load flows under different loading conditions to simulate the slow dynamics of the power system
Summary
The power system state estimation (SE) is the foundation and core of the energy management system, and it is indispensable for power system safety, reliability, quality and economic operation [1]. In [14], a UKF based power system dynamic state estimation was proposed All these approaches mentioned above have suffered from several important defects, limiting them from being adopted for PSFASE. To be specific, they cannot handle: 1) the non-Gaussian process and observation noises of the system nonlinear dynamic models, and 2) the unknown noise covariance matrices. The SE methods based on the original EKF and UKF will show un-robustness when the system suffers from the non-Gaussian noises, that is, the state cannot be estimated correctly. A novel robust EKF based on the MCC (called MCC-EKF) was developed in which the MSE was substituted by the MCC to solve estimation issues in non-Gaussian noise environments [20,21,22].
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