Particle swarm optimization based maximum power point tracking algorithm for photovoltaic energy
conversion system
Youcef Soufi
1, Mohcene Bechouat
2, Sami Kahla
21Labget laboratory, Department of Electrical Engineering, University Larbi Tebessi, Tebessa, Algeria
2 Department of Electronic and Telecommunication, University of 8 May 1945 of Guelma, Algeria.
y_soufi@yahoo.fr, mohcene.oui@gmail.com, samikahla40@yahoo.com
Abstract— In order to extract the maximum power from PV system, the maximum power point tracking (MPPT) method is one of the most popular and widely used and it has always been applied in photovoltaic energy conversion system. However, this method exhibits fluctuations among the maximum power point (MPP) due to the nature of unpredicted and changes of the environmental parameters. Therefore, it is significant to include an intelligent controller that can track the maximum peak
regardless of parameters variations such as: irradiation and temperature. This paper describes the design and
development of particle swarm optimization (PSO) based maximum power point tracking (MPPT) algorithm for photovoltaic energy conversion system. The proposed MPPT is simple, flexible, accurate and efficient in maximum photovoltaic power tracking. In this work, MATLAB/Simulink simulation package is used to simulate the performance of the proposed MPPT algorithm. The performance of the proposed PSO algorithm is evaluated by comparing it with the conventional P&O method in terms of tracking speed and accuracy. The simulation results demonstrate that the tracking capability of the PSO algorithm is more efficient, comparing to the traditional one, particularly under parameters variation conditions.
Keywords— Photovoltaic systems, maximum power point tracking (MPPT), particle swarm optimization (PSO), perturb and observe (P&O).
I. INTRODUCTION
In recent years, renewable energy resources are receiving considerable attention in the continued growth and development of electric power generation systems and it plays an important role in providing electrical energy than conventional sources due to fossil fuel depletion, high cost, and increasing environmental
concerns. Therefore, there is a big trend to use renewable energy resources to address the power generation especially for the isolated or remote areas [1]. The photovoltaic generation system is becoming increasingly important as renewable energy sources and recently, researchers have strongly promoted the use of solar energy as a viable source of energy due to its advantages such as absence of fuel cost, low maintenance requirement, and environmental friendliness which it can be integrated into local and regional power supplies. But, there are still some challenging problems associated with photovoltaic (PV) system such as high cost of installation, less conversion efficiency and low available power [2][3][4]. It is crucial to improve its efficiency and develop reliability of PV generation control systems. In practical applications, a PV module consists of many solar cells which are connected in series and PV modules are wired together into array both in series and in parallel to provide the necessary voltage or currents. The output characteristics of the PV system depends on the ambient temperature, solar radiation, , cloud, cell damaging, partially shading and load impedance, it is important to operate the PV panel at its maximum power point (MPP) which varies with the weather conditions [5]. The PV curve of photovoltaic system
exhibits multiple peaks under various conditions of functioning and changes in meteorological
conditions such as: radiation intensity and temperature which reduce the effectiveness of the conventional maximum power point tracking methods. Therefore, maximum-power-point tracking (MPPT) methods are required for a PV system to maintain efficient operation of the PV panels present, at their MPP and to overcome the major challenges in its dependence on the environmental parameters of the PV curve.
2018 15th International Multi-Conference on Systems, Signals & Devices (SSD)
978-1-5386-5305-0/18/$31.00 ©2018 IEEE 773
