Authors: M. Senthil Kumar; P.S. Manoharan; R. Ramachandran
Addresses: Department of EEE, Thiagarajar College of Engineering, Madurai, India ' Department of EEE, Thiagarajar College of Engineering, Madurai, India ' Department of EEE, Thiagarajar College of Engineering, Madurai, India
Abstract: This paper presents modelling and simulation of artificial neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm for PV system with modified SEPIC converter. The conventional existing MPPT methods are having major drawbacks of high oscillations at maximum power point and low efficiency due to uncertain nature of solar radiation and temperature. These mentioned problems can be solved by the proposed adaptive (ANFIS)-based MPPT. The proposed work involves ANFIS and modified single ended primary inductor converter (SEPIC) to extract maximum power from PV panel. The results obtained from proposed methodology are compared with other MPPT algorithms such as perturb and observe (P&O), incremental conductance (INC) and radial basis function network (RBFN). The improvement in voltage rating of modified SEPIC is compared with conventional SEPIC converter. The result confirms the superiority of the proposed system.
Keywords: maximum power point tracking; MPPT; modified SEPIC; artificial neuro-fuzzy inference system; ANFIS; radial basis function network; RBFN.
International Journal of Business Intelligence and Data Mining, 2019 Vol.15 No.3, pp.255 - 272
Received: 27 Apr 2017
Accepted: 06 Jul 2017
Published online: 04 Jul 2019 *