TY - JOUR
AU1 - Bouazza, Hadjira
AU2 - Bendaas, Mohamed Lokmane
AU3 - Allaoui, Tayeb
AU4 - Denai, Mouloud
AB - Power converters play a key-role in the grid-integration of wind power generation and as any physical device, they are prone to mal function and failure. There is, therefore, a need for converter health monitoring and fault detection to ensure a reliable and sustainable operation of the wind turbine. This paper presents different artificial intelligence-based fault detection using fuzzy and neuro-fuzzy techniques. The proposed methods are designed for the detection of one or two open-circuit fault in the power switches of the rotor side converter (RSC) of a doubly-fed induction generator (DFIG) wind energy conversion system (WECS). In the proposed detection method only the average values of the three-phase rotor current are used to identify the faulty switch. Alongside these condition monitoring strategies, the paper also present two fuzzy logic-based controllers for the regulation of the real and reactive power flow between the grid and the converter. The performances of the controllers are evaluated under different operating conditions of the power system and the reliability, feasibility and the effectiveness of the proposed fault detection have been verified under various open-switch fault conditions.
TI - Application of artificial intelligence to wind power generation: modelling, control and fault detection
JF - International Journal of Intelligent Systems Technologies and Applications
DO - 10.1504/IJISTA.2020.108083
DA - 2020-01-01
UR - https://www.deepdyve.com/lp/inderscience-publishers/application-of-artificial-intelligence-to-wind-power-generation-m6xN6nyMuO
SP - 280
EP - 305
VL - 19
IS - 3
DP - DeepDyve
ER -