Fault-tolerant optimal pitch control of wind turbines using dynamic weighted parallel firefly algorithm. (September 2022)

• Record Type: Journal Article
• Authors: Mousavi, Yashar; Bevan, Geraint; Kucukdemiral, Ibrahim Beklan
• Is Part Of: ISA transactions. Volume 128(2022)Part A
• Page Start: 301
• Page End: 317
• Publication Date: 2022-09
• Subjects: Fault-tolerant control -- Wind turbine -- Pitch control -- Optimization -- Firefly algorithm -- Fractional calculus; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.10.019
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: With steadily increasing interest in utilizing wind turbine (WT) systems as primary electrical energy generators, fault-tolerance has been considered decisive to enhance their efficiency and reliability. In this work, an optimal fault-tolerant pitch control (FTPC) strategy is addressed to adjust the pitch angle of WT blades in the presence of sensor, actuator, and system faults. The proposed scheme incorporates a fractional-calculus based extended memory (EM) of pitch angles along with a fractional-order proportional–integral–derivative (FOPID) controller to enhance the performance of the WT. A dynamic weighted parallel firefly algorithm (DWPFA) is also proposed to tune the controller parameters. The efficiency of the proposed algorithm is evaluated on the test functions adopted from 2017 IEEE congress on evolutionary computation (CEC2017). The merits of the proposed fault-tolerant approach are tested on a 4.8-MW WT benchmark model and compared to conventional PI and optimal FOPID approaches. Corresponding comparative simulation results validate the effectiveness and fault-tolerant capability of the proposed control paradigm, where it is observed that the proposed control scheme tends to be more consistent in the power generated at a given wind speed. Highlights: A fault-tolerant pitch controller is proposed to improve the power generation of WT. Fractional-calculus-based memory of the pitch system is merged in the controller. A modified FA with increased exploitation accuracy and convergence speed is proposed. The controller parameters are tuned using the proposed algorithm.