A novel fractional-order type-2 fuzzy control method for online frequency regulation in ac microgrid. (April 2020)

• Record Type: Journal Article
• Authors: Mohammadzadeh, Ardashir; Kayacan, Erkan
• Is Part Of: Engineering applications of artificial intelligence. Volume 90(2020)
• Publication Date: 2020-04
• Subjects: General type-2 fuzzy neural network -- AC microgrid -- Frequency regulation -- Learning algorithm; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103483
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In this paper, a novel adaptive fractional-order fuzzy control method is developed for frequency control in an ac microgrid (MG). A sequential general type-2 fuzzy system based on the radial basis neural network is presented for online modeling of the frequency response of the MG. Then, the parameters of the type-2 fuzzy controller based on the online estimated model are online tuned, such that the frequency deviation is minimized. The consequent parameters, i.e., centers of membership functions (MFs), the values of α -cuts, and the type-reduction parameters are optimized based on the proposed algorithm, which is inspired from the particle swarm optimization and artificial bee colony algorithm (PSO-ABC). The simulation results and comparison with other methods show that the proposed control scheme is effective, and results in a good and robust performance in the presence of variation of solar radiation, wind speed, load disturbance, and time-varying dynamics of the other units of MG. Moreover, the effectiveness of the proposed fuzzy system and the learning algorithm are examined by using white noise as the control input, and it is shown that the proposed identification scheme results in good performance even in the noisy environment.