A Comparative Study on Maximum Power Point Tracking Techniques of Photovoltaic Systems

A Comparative Study on Maximum Power Point Tracking Techniques of Photovoltaic Systems

Afef Badis (National School of Engineers of Monastr, Monastr, Tunisia), Mohamed Habib Boujmil (Higher Institute of the Technological Studies of Nabeul, Kelibia, Tunisia) and Mohamed Nejib Mansouri (National School of Engineers of Monastr, Monastr, Tunisia)
Copyright: © 2018 |Pages: 20
DOI: 10.4018/IJEOE.2018010104
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Abstract

This article concerns maximizing the energy reproduced from the photovoltaic (PV) system, ensured by using an efficient Maximum Power Point Tracking (MPPT) process. The process should be fast, rigorous and simple for implementation because the PV characteristics are extremely affected by fast changing conditions and Partial Shading (PS). PV systems are popularly known to have many peaks (one Global Peak (GP) and several local peaks). Therefore, the MPPT algorithm should be able to accurately detect the unique GP as the maximum power point (MPP), and avoid any other peak to mitigate the effect of (PS). Usually, with no shading, nearly all the conventional methods can easily reach the MPP with high efficiency. Nonetheless, they fail to extract the GP when PS occurs. To overcome this problem, Evolutionary Algorithms (AEs), namely the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are simulated and compared to the conventional methods (Perturb & Observe) under the same software.
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Introduction

Maximum power point (MPPT) is an integral part of grid connected photovoltaic (PV) systems. Nowadays, many studies have been devoted in order to enhance the performance of PV systems through developing new or improving already existed MPPT methods.

Normally, the conventional MPPT methods that includes Perturb and Observe (P&O), Incremental Conductance (IncCond), and Hill Climbing (HC) techniques, are able to track the maximum power point (MPP) and usually can achieve 99% or higher tracking efficiency (Ahmed, 2008; Esram, 2007; Hohm, 2000; Sera, 2006). The P&O is the most used for its simplicity. The system is continuously perturbed in order to reach the MPP (Zegaoui et al., 2011). Tough, the mission becomes more problematic when the PV array experiences partial shading (PS) conditions and extracting the maximum becomes a challenging task because of the non-linearity of the Power-Voltage (P-V) curve, which exhibits multiple local peaks. In fact, the half or more of the PV array receive different amount of insulation because of partially cloudy conditions caused by towers, clouds, trees, neighboring, etc. (Wasynezuk, 1983; Chowdhury, 2010; White, 2016). Thus, it is imperative to use a suitable MPPT technique which tracks the unique global peak (GP) of the shaded PV array effectively, fast and smoothly (Koutroulis & Blaabjerg, 2015). In the latter case, conventional MPPT techniques become ineffective and inaccurate because they cannot discriminate between global and local peak and they can be trapped on the first local peak which reduces the effectiveness of the whole system (Liu, 2012; Shaiek, 2013; Quaschning, 2008). Thus, the main target of this work is the study of those difficulties and limitations of the conventional methods such as P&O and introducing a more suitable method based on Evolutionary techniques (ET) mainly the Genetic Algorithm (GA) and the dynamic Particle Swarm Optimization (PSO).

The partial shade raises the power consumption and the maximum power of the partially shaded PV modules is minimized. To overcome these issue, a bypass diode is often included in parallel with each serial connection of PV cells which makes the P-V characteristic develop multiple maxima. Figure 1 shows the difference between the MPPs in a totally sunny module and a shaded one where the global and the local MPPs are indicated.

Figure 1.

Example of the power-voltage characteristics of a PV array

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