State Estimation MRAS and Identification of Stator Winding Phase Fault Detection of the PMSG in Wind Energy Based on the Sliding Mode Control

Author:

Bouslimani Samir1,Meradi Samir2ORCID,Drid Said1,Chrifi-Alaoui Larbi3,Bezziane Ali1

Affiliation:

1. 1 Higher National School of Renewable Energy, Environment and Sustainable Development , LSPIE Laboratory , Batna , Algeria

2. 2 Laboratory of Innovative Technologies , COSI Team, ENST , Algiers , Algeria

3. 3 Laboratory LTI , University of Picardie Jules Verne, Cuffies , Soissons , France

Abstract

Abstract This paper proposes a method for the diagnosis of stator inter-turn short-circuit fault for permanent magnet synchronous generators (PMSG). Inter-turn short-circuit currents are among the most critical in PMSG. For safety considerations, a fast detection is required when a fault occurs. This approach uses the parameter estimation of the per-phase stator resistance in closed-loop control of variable speed of wind energy conversion system (WECS). In the presence of an incipient short-circuit fault, the estimation of the resistance of the stator in the d-q reference frame does not make it possible to give the exact information. To solve this problem, a novel fault diagnosis scheme is proposed using parameter estimation of the per-phase stator resistance. The per-phase stator resistance of PMSG is estimated using the MRAS algorithm technique in real time. Based on a faulty PMSG model expressed in Park’s reference frame, the number of short-circuited turns is estimated using MRAS. Fault diagnosis is on line detected by analysing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the information on the estimated parameters can be used to improve the control performance. The simulation results demonstrate that the proposed method can estimate the faulty phase.

Publisher

Walter de Gruyter GmbH

Subject

General Medicine

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Wavelet and Hilbert Huang Transforms Applied to Park’s Vector for Fault Detection in a PMSG Wind Turbine;2023 IEEE 6th Colombian Conference on Automatic Control (CCAC);2023-10-17

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