Maximum power point tracking for Photovoltaic systems using fuzzy logic and artificial neural networks

A. M.Zein Alabedin; E. F. El-Saadany; M. M.A. Salama

doi:10.1109/PES.2011.6039690

Maximum power point tracking for Photovoltaic systems using fuzzy logic and artificial neural networks

A. M.Zein Alabedin
, E. F. El-Saadany
, M. M.A. Salama

Electrical Engineering

University of Waterloo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

74 Scopus citations

Abstract

The maximum power point tracking (MPPT) aims to increase the efficiency of Photovoltaic (PV) systems by operating their PV panels at the optimum power point. Many strategies have been introduced to achieve this objective. However, these strategies vary in their tracking performance, computational complexity and cost. The rapid changes in environmental conditions and the nonlinearity in the current-voltage (I-V) characteristics of PV panels make the tracking problem complex. This paper presents the design of two controllers; one based on fuzzy logic, and the other based on artificial neural networks. Fuzzy logic controllers are simple, easy to implement, and does not need knowledge of the mathematical model of the system. Neural networks are known to be universal approximators for non-linear dynamic system. Thus, they can be used to estimate the reference parameters of the maximum power point. The two controllers are simulated under variable environmental factors to study their tracking performance.

Original language	British English
Title of host publication	2011 IEEE PES General Meeting
Subtitle of host publication	The Electrification of Transportation and the Grid of the Future
DOIs	https://doi.org/10.1109/PES.2011.6039690
State	Published - 2011
Event	2011 IEEE PES General Meeting: The Electrification of Transportation and the Grid of the Future - Detroit, MI, United States Duration: 24 Jul 2011 → 28 Jul 2011

Publication series

Name	IEEE Power and Energy Society General Meeting
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2011 IEEE PES General Meeting: The Electrification of Transportation and the Grid of the Future
Country/Territory	United States
City	Detroit, MI
Period	24/07/11 → 28/07/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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10.1109/PES.2011.6039690

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Alabedin, A. M. Z., El-Saadany, E. F., & Salama, M. M. A. (2011). Maximum power point tracking for Photovoltaic systems using fuzzy logic and artificial neural networks. In 2011 IEEE PES General Meeting: The Electrification of Transportation and the Grid of the Future Article 6039690 (IEEE Power and Energy Society General Meeting). https://doi.org/10.1109/PES.2011.6039690

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title = "Maximum power point tracking for Photovoltaic systems using fuzzy logic and artificial neural networks",

abstract = "The maximum power point tracking (MPPT) aims to increase the efficiency of Photovoltaic (PV) systems by operating their PV panels at the optimum power point. Many strategies have been introduced to achieve this objective. However, these strategies vary in their tracking performance, computational complexity and cost. The rapid changes in environmental conditions and the nonlinearity in the current-voltage (I-V) characteristics of PV panels make the tracking problem complex. This paper presents the design of two controllers; one based on fuzzy logic, and the other based on artificial neural networks. Fuzzy logic controllers are simple, easy to implement, and does not need knowledge of the mathematical model of the system. Neural networks are known to be universal approximators for non-linear dynamic system. Thus, they can be used to estimate the reference parameters of the maximum power point. The two controllers are simulated under variable environmental factors to study their tracking performance.",

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Alabedin, AMZ, El-Saadany, EF & Salama, MMA 2011, Maximum power point tracking for Photovoltaic systems using fuzzy logic and artificial neural networks. in 2011 IEEE PES General Meeting: The Electrification of Transportation and the Grid of the Future., 6039690, IEEE Power and Energy Society General Meeting, 2011 IEEE PES General Meeting: The Electrification of Transportation and the Grid of the Future, Detroit, MI, United States, 24/07/11. https://doi.org/10.1109/PES.2011.6039690

Maximum power point tracking for Photovoltaic systems using fuzzy logic and artificial neural networks. / Alabedin, A. M.Zein; El-Saadany, E. F.; Salama, M. M.A.
2011 IEEE PES General Meeting: The Electrification of Transportation and the Grid of the Future. 2011. 6039690 (IEEE Power and Energy Society General Meeting).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Maximum power point tracking for Photovoltaic systems using fuzzy logic and artificial neural networks

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