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Open AccessArticle

LPV Model Based Sensor Fault Diagnosis and Isolation for Permanent Magnet Synchronous Generator in Wind Energy Conversion Systems

by Zhimin Yang 1,2, Yi Chai 1,2,*, Hongpeng Yin 1,2 and Songbing Tao 1,2
1
Key Laboratory of Complex System Safety and Control, Ministry of Education, Chongqing University, Chongqing 400044, China
2
School of Automation, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(10), 1816; https://doi.org/10.3390/app8101816
Received: 10 September 2018 / Revised: 29 September 2018 / Accepted: 1 October 2018 / Published: 3 October 2018
(This article belongs to the Special Issue Large Grid-Connected Wind Turbines)
This paper deals with the current sensor fault diagnosis and isolation (FDI) problem for a permanent magnet synchronous generator (PMSG) based wind system. An observer based scheme is presented to detect and isolate both additive and multiplicative faults in current sensors, under varying torque and speed. This scheme includes a robust residual generator and a fault estimation based isolator. First, the PMSG system model is reformulated as a linear parameter varying (LPV) model by incorporating the electromechanical dynamics into the current dynamics. Then, polytopic decomposition is introduced for H design of an LPV residual generator and fault estimator in the form of linear matrix inequalities (LMIs). The proposed gain-scheduled FDI is capable of online monitoring three-phase currents and isolating multiple sensor faults by comparing the diagnosis variables with the predefined thresholds. Finally, a MATLAB/SIMULINK model of wind conversion system is established to illustrate FDI performance of the proposed method. The results show that multiple sensor faults are isolated simultaneously with varying input torque and mechanical power. View Full-Text
Keywords: fault diagnosis and isolation; multiple sensor faults; LPV observer; permanent magnet synchronous generator fault diagnosis and isolation; multiple sensor faults; LPV observer; permanent magnet synchronous generator
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Yang, Z.; Chai, Y.; Yin, H.; Tao, S. LPV Model Based Sensor Fault Diagnosis and Isolation for Permanent Magnet Synchronous Generator in Wind Energy Conversion Systems. Appl. Sci. 2018, 8, 1816.

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