INTERNATIONAL JOURNAL of RENEWABLE ENERGY RESEARCH M. Chellal et al., Vol.11, No.1, March, 2021 Experimental Evaluation of MPPT Algorithms: A Comparative Study Majd Chellal* ‡ , Thiago Fialho Guimarães**, Vicente Leite*** *Superior School of Applied Sciences, BP 165 RP Bel horizon 13000 Tlemcen, Algeria **Technological Federal University of Paraná, Curitiba-PR, 80230-901, Brazil ***Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, Campus de Santa Apolónia - 5300-253 Bragança, Portugal (majdchellal7@gmail.com, thiagofialho.vg@gmail.com, avtl@ipb.pt) ‡ Corresponding Author; First Author, Road Aquilino Ribeiro Nº 2, 5300-087 Bragança, Portugal, Tel: +351 934 477 681, majdchellal7@gmail.com Received: 16.02.2021 Accepted:11.03.2021 Abstract- Photovoltaic (PV) energy is among the most used renewable sources. Grid-connected PV systems should yield as much energy as possible. However, external influencers such as irradiance and temperature impose a non-linear characteristic of the PV system, which hinder its operation at the maximum power point. Additionally, other factors, such as shading or internal degradation, can change this characteristic by making local maximums appear, which makes it difficult to extract the maximum available power. There are several techniques for maximum power point tracking (MPPT) and very diverse algorithms for this purpose. There are also some published works with comparative studies. However, in most of these works, the comparison is based on a literature review or on simulation. An experimental evaluation of MPPT techniques, from the simplest to the most complex, remains relevant. Thus, this paper presents an experimental analysis of five MPPT algorithms: two of the simplest and widely used (Perturb & Observe and Incremental Conductance) and three of the most complex (Fuzzy Logic Controller, Kalman Filter and Particle Swarm Optimization). The experimental tests were carried out under real test conditions, using Simulink and the dSPACE 1103 real-time controller board. The results show that the five MPPT algorithms are able to track the MPP with a difference of less than 2% in their efficiency under normal operating conditions. This difference increases under shadow effect. The PSO algorithm was the only one able to find the global MPP under the effect of partial shading. Keywords MPPT algorithms; Perturb and Observe; Incremental Conductance; Fuzzy Logic Control; Kalman filter; Particle Swarm Optimization. 1. Introduction Since the past decade, photovoltaic (PV) energy is among the most preferred source over all the other renewable sources, due to its wide range of qualities such as abundance in nature, low maintenance and high power density [1, 2]. However, the efficiency of PV systems is greatly affected by the efficiency of the inverter, the PV modules and the maximum power point tracking (MPPT) algorithms. PV inverters available on the market have achieved a maximum efficiency of 98% [3]. The increase of PV modules efficiency is under way and has been intensely investigated but it depends on complex manufacturing processes. Instead, improving the efficiency of the MPPT with various control techniques may be an alternative [4]. The main goal of these algorithms is to achieve the maximum power point (MPP) located along the nonlinear P-V characteristic, which depends on the temperature, solar irradiance and shadow situations [5]. Fig. 1 presents a generic P-V curve under normal test conditions containing a unique MPP, and under partial shading conditions, which contains a local MPP (LMPP) and a global MPP (GMPP)[5, 6]. There are about 10 main MPPT techniques [7, 8], and a few dozen variants [9] published in literature. Some of the most recent works [10-12] deal with the integration of conventional