Intelligent controller to extract maximum power from solar park

Muhammad Sheraz; G. M. Asim Akhtar; M. A. Abido

doi:10.1109/SASG.2017.8356515

Intelligent controller to extract maximum power from solar park

Muhammad Sheraz, G. M. Asim Akhtar, M. A. Abido

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

2 Scopus citations

Abstract

A global shortage of conventional fuels and high costs have led major energy market players toward the greater use of renewable energy sources. The Kingdom of Saudi Arabia (KSA), which has a major share in the oil market, has started working to integrate solar-based electric power into the national grid, enabling KSA to move toward eco-friendly and cheaper electricity while also maintaining a large share of the oil market. This paper discusses a practical technique for harvesting the maximum power from a prospective, large photovoltaic (PV) system, also known as Solar Park, in KSA. The idea is based on tracking the maximum power point from the nonlinear output characteristics of the PV system. An intelligent technique, adaptive neuro-fuzzy inference system (ANFIS), is used to build the maximum power point tracking (MPPT) controller that is tuned to extract the maximum power from the PV system under different ambient conditions. A small test system has been developed and simulated using a real-Time digital simulator (RTDS), dSPACE, and MATLAB/Simulink to demonstrate the effectiveness of the proposed technique in comparison with the conventional algorithm of incremental conductance.

Original language	English
Title of host publication	2017 Saudi Arabia Smart Grid Conference, SASG 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-7
Number of pages	7
ISBN (Electronic)	9781538618769
DOIs	https://doi.org/10.1109/SASG.2017.8356515
State	Published - 8 May 2018

Publication series

Name	2017 Saudi Arabia Smart Grid Conference, SASG 2017

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

Artificial Intelligence
Electrical and Electronic Engineering
Energy Engineering and Power Technology

UN SDGs

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

Access to Document

10.1109/SASG.2017.8356515

Cite this

@inproceedings{dee338f689994e60bb345674ce6ddf07,

title = "Intelligent controller to extract maximum power from solar park",

abstract = "A global shortage of conventional fuels and high costs have led major energy market players toward the greater use of renewable energy sources. The Kingdom of Saudi Arabia (KSA), which has a major share in the oil market, has started working to integrate solar-based electric power into the national grid, enabling KSA to move toward eco-friendly and cheaper electricity while also maintaining a large share of the oil market. This paper discusses a practical technique for harvesting the maximum power from a prospective, large photovoltaic (PV) system, also known as Solar Park, in KSA. The idea is based on tracking the maximum power point from the nonlinear output characteristics of the PV system. An intelligent technique, adaptive neuro-fuzzy inference system (ANFIS), is used to build the maximum power point tracking (MPPT) controller that is tuned to extract the maximum power from the PV system under different ambient conditions. A small test system has been developed and simulated using a real-Time digital simulator (RTDS), dSPACE, and MATLAB/Simulink to demonstrate the effectiveness of the proposed technique in comparison with the conventional algorithm of incremental conductance.",

author = "Muhammad Sheraz and \{Asim Akhtar\}, \{G. M.\} and Abido, \{M. A.\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.",

year = "2018",

month = may,

day = "8",

doi = "10.1109/SASG.2017.8356515",

language = "English",

series = "2017 Saudi Arabia Smart Grid Conference, SASG 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Intelligent controller to extract maximum power from solar park. / Sheraz, Muhammad; Asim Akhtar, G. M.; Abido, M. A.
2017 Saudi Arabia Smart Grid Conference, SASG 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-7 (2017 Saudi Arabia Smart Grid Conference, SASG 2017).

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

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AU - Asim Akhtar, G. M.

AU - Abido, M. A.

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N2 - A global shortage of conventional fuels and high costs have led major energy market players toward the greater use of renewable energy sources. The Kingdom of Saudi Arabia (KSA), which has a major share in the oil market, has started working to integrate solar-based electric power into the national grid, enabling KSA to move toward eco-friendly and cheaper electricity while also maintaining a large share of the oil market. This paper discusses a practical technique for harvesting the maximum power from a prospective, large photovoltaic (PV) system, also known as Solar Park, in KSA. The idea is based on tracking the maximum power point from the nonlinear output characteristics of the PV system. An intelligent technique, adaptive neuro-fuzzy inference system (ANFIS), is used to build the maximum power point tracking (MPPT) controller that is tuned to extract the maximum power from the PV system under different ambient conditions. A small test system has been developed and simulated using a real-Time digital simulator (RTDS), dSPACE, and MATLAB/Simulink to demonstrate the effectiveness of the proposed technique in comparison with the conventional algorithm of incremental conductance.

AB - A global shortage of conventional fuels and high costs have led major energy market players toward the greater use of renewable energy sources. The Kingdom of Saudi Arabia (KSA), which has a major share in the oil market, has started working to integrate solar-based electric power into the national grid, enabling KSA to move toward eco-friendly and cheaper electricity while also maintaining a large share of the oil market. This paper discusses a practical technique for harvesting the maximum power from a prospective, large photovoltaic (PV) system, also known as Solar Park, in KSA. The idea is based on tracking the maximum power point from the nonlinear output characteristics of the PV system. An intelligent technique, adaptive neuro-fuzzy inference system (ANFIS), is used to build the maximum power point tracking (MPPT) controller that is tuned to extract the maximum power from the PV system under different ambient conditions. A small test system has been developed and simulated using a real-Time digital simulator (RTDS), dSPACE, and MATLAB/Simulink to demonstrate the effectiveness of the proposed technique in comparison with the conventional algorithm of incremental conductance.

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ER -

Intelligent controller to extract maximum power from solar park

Abstract

Publication series

Bibliographical note

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this