Dual Extended Kalman Filter Reconstruction of Actuator and Sensor Faults in DC Microgrids with Constant Power Loads

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Vafamand, N (Author) Other; Arefi, MM (Author) Other; Asemani, MH (Author) Other; Javadi, M (Author) Other; Wang, F (Author) Other; Catalao, JPS (Author) FEUP

Conference proceedings International

Title: IEEE Industry Applications Society Annual Meeting, IAS 2021, Vancouver, BC, Canada, October 10-14, 2021

Pages: 1-6

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 5 Citations

Other information

Authenticus ID: P-00W-309

DOI: 10.1109/ias48185.2021.9677191

Abstract (EN): This paper explores the problem of model-based detecting and reconstructing occurring actuator and sensor faults in direct current (DC) microgrids (MGs) connected to resistive and constant power loads (CPLs) and energy storage units. Both the actuator and sensor faults are modeled as an additive time-varying term in the state-space representation, which highly degrade the system response performance if they are not compensated. In this paper, a novel advanced extended Kalman filter (EKF), called dualEKF (D-EKF) is proposed to estimate the system states as well as the accruing actuator and sensor faults. The main property of the developed approach is that it offers a systematic estimation procedure by dividing the estimating parameters into three parts and these parts are estimated in parallel. A first-order filter is utilized to turn the sensor faulty system into an auxiliary sensor faults-free representation. Thereby, the artificial output contains the filter states. The proposed D-EKF estimator does not require restrictive assumptions on the power system matrices and is highly robust against stochastic Gaussian noises. At the end, the proposed approach is applied on a practical faulty DC MG benchmark connected to a CPL, a resistive load, and an energy storage system and the obtained simulation results are analyzed form the accuracy and convergence speed viewpoints.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6