Citation: Magaji, N.; Mustafa, M.W.B.; Lawan, A.U.; Tukur, A.; Abdullahi, I.; Marwan, M. Application of Type 2 Fuzzy for Maximum Power Point Tracker for Photovoltaic System. Processes 2022, 10, 1530. https://doi.org/10.3390/ pr10081530 Academic Editors: Wen-Jer Chang, Hak Keung Lam and Yongming Li Received: 17 July 2022 Accepted: 25 July 2022 Published: 4 August 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). processes Article Application of Type 2 Fuzzy for Maximum Power Point Tracker for Photovoltaic System Nuraddeen Magaji 1, *, Mohd Wazir Bin Mustafa 2 , Abdulrahman Umar Lawan 1 , Alliyu Tukur 1 , Ibrahim Abdullahi 3 and Mohd Marwan 1 1 Department of Electrical Engineering, Bayero University, Kano P.M.B. 3011, Nigeria 2 School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia 3 Department of Mechanical Engineering, Bayero University, Kano P.M.B. 3011, Nigeria * Correspondence: nmagaji2000@gmail.com; Tel.: +234-8031850106 Abstract: Photovoltaic systems (PV) are becoming more popular as a way to make electricity because they offer so many benefits, such as free solar irradiation to harvest and low maintenance costs. Moreover, the system is environmentally friendly because it neither emits noxious gases nor generates environmental noise. Consequently, during the operation of a PV system, the working environment is free of all types of pollution. Despite the aforementioned advantages, a photovoltaic (PV) system’s performance is significantly impacted by the fluctuation in electrical charges from the panel, such as shading conditions (PSC), weather conditions, and others, which significantly lowers the system’s efficiency. To operate the PV modules at their peak power, maximum-powerpoint tracking (MPPT) is employed. As a result of the various peaks present during fluctuating irradiance, the P-V curves become complex. Traditional methods, such as Perturb and Observe (P and O) have also failed to monitor the Global Maximum Power Point (GMPP), therefore they usually live in the Local Maximum Power Point (LMPP), which drastically lowers the efficiency of the PV systems. This study compares type 2 fuzzy logic (T2-FLC) with the traditional Perturb and Observe Method (P and O) in three different scenarios of irradiance, temperature, and environmental factors, in order to track the maximum power point of photovoltaics. Type 1 fuzzy logic (T1-FLC) is not appropriate for systems with a high level of uncertainty (complex and non-linear systems). By modelling the vagueness and unreliability of information, type 2 fuzzy logic is better equipped to deal with linguistic uncertainties, thereby reducing the ambiguity in a system. The result for three conditions in terms of four variables; efficiency, settling time, tracking time, and overshoot, proves that this strategy offers high efficiency, dependability, and resilience. The performance of the proposed algorithm is further validated and compared to the other three tracking techniques, which include the Perturb and Observe methods (P and O). The particle swarm algorithm (PSO) and incremental conductance method results show that type 2 fuzzy (IT2FLC) is better than the three methods mentioned above. Keywords: photovoltaic system (PV); fuzzy logic; partial shading condition (PSC); maximum power point tracking (MPPT); Perturb and Observe (P and O); interval type 2 fuzzy; incremental conductance 1. Introduction Due to rising rates of energy consumption, renewable energy sources are gaining importance in the development of modern energy-generation technologies. Photovoltaic (PV) systems are one of the most popular types of renewable energy resources and have garnered a great deal of interest over the past few decades. The power-voltage (P-V) characteristics of a photovoltaic system are influenced by environmental factors, including solar irradiance and temperature. Determining the maximum extractable power from a photovoltaic system’s nonlinear output characteristic is one of the most influential factors on the control unit’s efficiency and total cost [1]. Currently, partial shading conditions pose the greatest challenge for the PV systems (PSC). Clouds, trees, branches, buildings, towers, Processes 2022, 10, 1530. https://doi.org/10.3390/pr10081530 https://www.mdpi.com/journal/processes