Maximum Power Point Tracking (MPPT) Algorithms for Photovoltaic Systems

Chapter
First Online:
Part of the Lecture Notes in Energy book series (LNEN, volume 37)

Abstract

The solar energy have become a challenging area among other renewable energy sources (RESs) since the photovoltaic (PV) systems have the advantages of not causing pollution, having low maintenance, and long-lasting operation life. Besides these advantages, a PV system has several drawbacks such as considerably higher installation cost comparing some other RESs, and limited efficiency ranges between 9–18%. The feasibility analyses have a great role in order to determine the most appropriate plant site before installation. On the other hand, the operating analyses and improvements based on maximum power point tracking (MPPT) are quite important to increase the harvested total energy. The intermittent characteristic and perturbing power curve of a PV module is one of the most important defects that should be tackled to increase the generation efficiency. The power-voltage (P-V) and current-voltage (I-V) curves are main efficiency indicators of a PV system that exhibit nonlinear characteristics in its natural structure. Furthermore, the generated maximum power with a PV panel depends on two main quantities of temperature and irradiation. However, it is possible to increase the generated power up to maximum rates by MPPT algorithms. This chapter introduces most widely used algorithms respecting to their implementation and utilization properties. The indirect, direct, and computational methods are presented considering their advantages and disadvantages. The conventional and novel algorithms are explained with flowcharts and analytical details in order to provide clear comparison. The artificial methods are expressed in the last section where fuzzy logic, artificial intelligence, and optimization-based approaches are discussed.

Keywords

Maximum power point tracking (MPPT) Perturb and observe Incremental conductance Fuzzy logic controller Artificial neural network Particle swarm optimization 

Abbreviation and Acronyms

ABC

Artificial Bee Colony

AF

Activation Function

ANN

Artificial Neural Network

COA

Centroid of Area

CV

Constant Voltage

EMI

Electromagnetic Interference

ESS

Energy Storage System

FLC

Fuzzy Logic Controller

GA

Genetic Algorithm

HC

Hill Climbing

IncCond

Incremental Conductance

I-V

Current-Voltage

MLI

Multilevel Inverter

MLP

Multilayer Perceptron

MPPT

Maximum Power Point Tracking

OV

Open Voltage

P&O

Perturb and Observe

PSO

Particle Swarm Optimization

PV

Photovoltaic

P-V

Power-Voltage

RES

Renewable Energy Source

SCPB

Short-Current Pulse-Based

THD

Total Harmonic Distortion

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Engineering and Architecture, Department of Electrical and Electronics EngineeringNevsehir HBV UniversityNevsehirTurkey

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