COMPARISON AND IMPLEMENTATION OF MAXIMUM POWER POINT TECHNIQUES OF A PV SOLAR SYSTEM (USING PERTURB AND OBSERVE, INCREMENTAL CONDUCTANCE, MODIFIED PERTURB, FUZZY LOGIC AND ARTIFICIAL NEURAL NETWORK)
The working behavior of a module / PV array is non-linear and highly dependent on working conditions. As a given condition, there is only one point at which the level of available power at its output is maximum. This point varies with time, enlightenment and temperature. To ensure optimum operation, the use of MPPT control allows us to extract the maximum power. This paper presents a comparative study of four widely-adopted MPPT algorithms, such as Perturb and Observe, Incremental Conductance, Measurements of the variation of the open circuit voltage or of the short-circuit current. Their performance is evaluated using, for all these techniques. In particular, this study compares the behaviors of each technique in presence of solar irradiation variations and temperature fluctuations. These MPPT techniques will be compared using the Matlab / Simulink tool.
CHAPTER ONE
1.0 INTRODUCTION
Solar energy is one of the most important renewable energy sources. As opposed to the conventional non renewable sources such as gasoline, coal, etc. solar energy is clean, inexhaustible and free. Unfortunately, PV generation systems have two major problems: the conversion efficiency in electric power generation is low and the amount of electric power generated by solar module changes continuously with weather conditions. Moreover, the solar cell V-I characteristic is nonlinear and changes with irradiation and temperature. In general, there is a point on the V-I or V-P curve only, called the Maximum Power Point (MPP), at which the entire PV system operates with maximum efficiency and produces its maximum output power. The location of the MPP is not known, but can be located, either through calculation models or by search algorithms. Maximum Power Point Tracking (MPPT) techniques are used to maintain the PV module’s operating point at its MPP.
Many MPPT techniques have been proposed in the literature; like Incremental conductance method, Perturb and Observe method, Artificial Neural Network method, the Fuzzy Logic method, etc..But, The tracking of MPP has not yet been done ,on the P-V or I-V Curve and conventional P&O has drawbacks such as low tracking speed do not track the exact maximum power point during sudden changes of irradiation and temperature. So in this paper Modify the perturb and observe, incremental conductance, modified perturb, fuzzy logic and artificial neural network methods using MATLAB Programming/Simulink Results.
1.2 AIM OF THE STUDY
In this study, different techniques of achieving maximium power of pv solar system are perturb and observe, incremental conductance, modified perturb, fuzzy logic and artificial neural network. The main aim of this work is to compare and implement these methods.
1.3 OBJECTIVE OF THE STUDY
At the end of this work, student involved will be able:
- To define and discuss the different methods used in this work
- To understand different MPPT algorithms commonly used.
- To know the different among the techniques used
1.4 PURPOSEOF THE STUDY
The purpose of this paper is to study and compare five maximum power point tracking, (MPPT) algorithms in a photovoltaic simulation system. The Matlab/Simulink is used in this paper to establish a model of photovoltaic system with MPPT function. This system is developed by combining the models of established solar system.
1.5 SCOPE OF THE PROJECT
Maximum power point tracking (MPPT) is an essential part of the photovoltaic (PV) system to ensure that the power converters operate at the maximum power point (MPP) of the solar array. Various MPPT algorithms have been developed in [1]-[3]. In the P&O method, the voltage is being increased or decreased with a fixed step size in the direction of reaching the MPP. The process is repeated periodically until the MPP is reached. At steady state, the operating point oscillates around the MPP.
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