A Novel Artificial Intelligence Maximum Power Point Tracking Technique for Integrated PV-WT-FC Frameworks

Mohammad Junaid Khan, Divesh Kumar, Yogendra Narayan, Hasmat Malik, Fausto Pedro García Márquez and Carlos Quiterio Gómez Muñoz
Additional contact information
Mohammad Junaid Khan: Department of Electrical and Electronics Engineering, Mewat Engineering College (Wakf), Nuh 122107, Haryana, India
Divesh Kumar: Department of Electronics & Communication Engineering, GLA University, Mathura 281406, Uttar Pradesh, India
Yogendra Narayan: Department of Electronics & Communication Engineering, Chandigarh University, Mohali 140413, Punjab, India
Hasmat Malik: BEARS, University Town, NUS Campus, Singapore 138602, Singapore
Fausto Pedro García Márquez: Ingenium Research Group, Business and Administration Department, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain
Carlos Quiterio Gómez Muñoz: HCTLab Research Group, Electronics and Communications Technology Department, Universidad Autónoma de Madrid, 28049 Madrid, Spain

Energies, 2022, vol. 15, issue 9, 1-35

Abstract: The development of each country depends on electricity. In this regard, conventional energy sources, e.g., diesel, petrol, etc., are decaying. Consequently, the investigations of renewable energy sources (RES) are increasing as alternate energy sources for the fulfillment of energy requirements. The output characteristics of RES are becoming non-linear. Therefore, the maximum power point tracking (MPPT) techniques are critical for extracting the maximum power point (MPP) from RES, e.g., photovoltaic (PV) and wind turbines (WT). RES such as the Fuel Cell (FC) has been hailed as one of the major capable RES for automobile applications since they continually create electricity for the dc-link (even if one or both RES are not supplied by solar and wind, the FC will continue to supply to the load). Adaptive Neuro-Fuzzy Inference System (AN-FIS) MPPT for PV, WT, FC, and Hybrid RES is employed in this research article to solve this problem. The high step-ups (boost converters) are connected with PV and FC modules, and the buck converter is connected with the WT framework, to extract the maximum amount of power using MPPT algorithms. The performance of proposed frameworks based on MPPT algorithms is assessed in variable operating conditions such as Solar-Radiation (SR), Wind-Speed (WS), and Hydrogen-Fuel-Rate (HFR). A novel AN-FIS MPPT framework has enhanced the power of Hybrid RES at DC-link, and also reduced the simulation time to reach the MPP when compared to the perturb and observe (P-&-O), Fuzzy-Logic Controller (F-LC), and artificial neural network (AN-N) MPPTs.

Keywords: artificial intelligence; maximum power point tracking; technique for integration; renewable energy sources; intelligent controller (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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