International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 6, December 2022, pp. 5717~5729 ISSN: 2088-8708, DOI: 10.11591/ijece.v12i6.pp5717-5729  5717 Journal homepage: http://ijece.iaescore.com Comparative analysis of evolutionary-based maximum power point tracking for partial shaded photovoltaic Prisma Megantoro 1 , Hafidz Faqih Aldi Kusuma 1 , Lilik Jamilatul Awalin 1 , Yusrizal Afif 1 , Dimas Febriyan Priambodo 2 , Pandi Vigneshwaran 3 1 Faculty of Advance Technology and Multidiscipline, Universitas Airlangga, Surabaya, Indonesia 2 Cyber Security Engineering, Politeknik Siber dan Sandi Negara, Bogor, Indonesia 3 Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, India Article Info ABSTRACT Article history: Received Sep 28, 2021 Revised Jun 11, 2022 Accepted Jul 7, 2022 The characteristics of the photovoltaic module are affected by the level of solar irradiation and the ambient temperature. These characteristics are depicted in a V-P curve. In the V-P curve, a line is drawn that shows the response of changes in output power to the level of solar irradiation and the response to changes in voltage to ambient temperature. Under partial shading conditions, photovoltaic (PV) modules experience non-uniform irradiation. This causes the V-P curve to have more than one maximum power point (MPP). The MPP with the highest value is called the global MPP, while the other MPP is the local MPP. The conventional MPP tracking technique cannot overcome this partial shading condition because it will be trapped in the local MPP. This article discusses the MPP tracking technique using an evolutionary algorithm (EA). The EAs analyzed in this article are genetic algorithm (GA), firefly algorithm (FA), and fruit fly optimization (FFO). The performance of MPP tracking is shown by comparing the value of the output power, accuracy, time, and tracking effectiveness. The performance analysis for the partial shading case was carried out on various populations and generations. Keywords: Evolutionary algorithm Maximum power point tracking Optimization Photovoltaic Renewable energy This is an open access article under the CC BY-SA license. Corresponding Author: Prisma Megantoro Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga Surabaya, Indonesia Email: prisma.megantoro@ftmm.unair.ac.id 1. INTRODUCTION In a solar power generation system, there is a solar charge controller (SCC) device. This device is used to optimize the power harvest of photovoltaic (PV) modules. In another sense, it is increasing the efficiency and performance of the solar power generation unit. In modern solar power systems, generally, the SCC device is already a set with the inverter. A set of these devices is usually used for stand-alone or grid-tied systems. The advanced SCC device has a feature to optimize electric power harvest, namely the maximum power point tracking (MPPT) technique. This MPPT feature is used to track the maximum power point (MPP), where at this point, the maximum power harvesting process occurs [1]–[3]. The MPP point can be reached if the system operating voltage is set to the MPP voltage [4], [5]. Many studies have been carried out to apply this MPPT technique, both with conventional methods and with advanced algorithms. Lagdani et al. [6] compared the performance between three conventional algorithms, namely incremental conduction, perturb and observe, also fuzzy logic. Tracking carried out specifically for partial shading cases requires more advanced methods or algorithms. For the algorithm, Zouga et al. [7] has designed a particle swarm optimization (PSO) implementation for grid-tied solar PV