Efficient maximum power point tracking using model predictive control for photovoltaic systems under dynamic weather condition

Mostafa Mosa, Mohammad B. Shadmand, Robert Balog, Haitham Abu-Rub

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

This study presents a high-efficient maximum power point tracking (MPPT) of photovoltaic (PV) systems by means of model-predictive control (MPC) techniques that is applied to a high-gain DC-DC converter. The high variability and stochastic nature of solar energy requires that the MPPT control continuously adjust the power converter operating point in order to track the changing maximum power point; a concept well known in the literature. The main contribution of this study is a modelpredictive-based controller with a fixed step that is combined with the traditional incremental conductance (INC) method. This technique improves the speed at which the controller can track rapid changes in solar insolation and results in an increase in the overall efficiency of the PV system. The controller speeds up convergence since MPC predicts error before the switching signal is applied to the high-gain multilevel DC-DC converter and thus is able to choose the next switch event to minimise error between the commanded and actual converter operation. Comparing the proposed technique to the conventional INC method shows substantial improvement in MPPT effectiveness and PV system performance. The performance of the proposed MPCMPPT is analysed and validated experimentally.

Original languageEnglish
Pages (from-to)1401-1409
Number of pages9
JournalIET Renewable Power Generation
Volume11
Issue number11
DOIs
Publication statusPublished - 13 Sep 2017

Fingerprint

Model predictive control
DC-DC converters
Controllers
Incident solar radiation
Power converters
Solar energy
Switches

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

Cite this

Efficient maximum power point tracking using model predictive control for photovoltaic systems under dynamic weather condition. / Mosa, Mostafa; Shadmand, Mohammad B.; Balog, Robert; Abu-Rub, Haitham.

In: IET Renewable Power Generation, Vol. 11, No. 11, 13.09.2017, p. 1401-1409.

Research output: Contribution to journalArticle

@article{cd3d51bc0e32455a9f8e400e97ed62b6,
title = "Efficient maximum power point tracking using model predictive control for photovoltaic systems under dynamic weather condition",
abstract = "This study presents a high-efficient maximum power point tracking (MPPT) of photovoltaic (PV) systems by means of model-predictive control (MPC) techniques that is applied to a high-gain DC-DC converter. The high variability and stochastic nature of solar energy requires that the MPPT control continuously adjust the power converter operating point in order to track the changing maximum power point; a concept well known in the literature. The main contribution of this study is a modelpredictive-based controller with a fixed step that is combined with the traditional incremental conductance (INC) method. This technique improves the speed at which the controller can track rapid changes in solar insolation and results in an increase in the overall efficiency of the PV system. The controller speeds up convergence since MPC predicts error before the switching signal is applied to the high-gain multilevel DC-DC converter and thus is able to choose the next switch event to minimise error between the commanded and actual converter operation. Comparing the proposed technique to the conventional INC method shows substantial improvement in MPPT effectiveness and PV system performance. The performance of the proposed MPCMPPT is analysed and validated experimentally.",
author = "Mostafa Mosa and Shadmand, {Mohammad B.} and Robert Balog and Haitham Abu-Rub",
year = "2017",
month = "9",
day = "13",
doi = "10.1049/iet-rpg.2017.0018",
language = "English",
volume = "11",
pages = "1401--1409",
journal = "IET Renewable Power Generation",
issn = "1752-1416",
publisher = "Institution of Engineering and Technology",
number = "11",

}

TY - JOUR

T1 - Efficient maximum power point tracking using model predictive control for photovoltaic systems under dynamic weather condition

AU - Mosa, Mostafa

AU - Shadmand, Mohammad B.

AU - Balog, Robert

AU - Abu-Rub, Haitham

PY - 2017/9/13

Y1 - 2017/9/13

N2 - This study presents a high-efficient maximum power point tracking (MPPT) of photovoltaic (PV) systems by means of model-predictive control (MPC) techniques that is applied to a high-gain DC-DC converter. The high variability and stochastic nature of solar energy requires that the MPPT control continuously adjust the power converter operating point in order to track the changing maximum power point; a concept well known in the literature. The main contribution of this study is a modelpredictive-based controller with a fixed step that is combined with the traditional incremental conductance (INC) method. This technique improves the speed at which the controller can track rapid changes in solar insolation and results in an increase in the overall efficiency of the PV system. The controller speeds up convergence since MPC predicts error before the switching signal is applied to the high-gain multilevel DC-DC converter and thus is able to choose the next switch event to minimise error between the commanded and actual converter operation. Comparing the proposed technique to the conventional INC method shows substantial improvement in MPPT effectiveness and PV system performance. The performance of the proposed MPCMPPT is analysed and validated experimentally.

AB - This study presents a high-efficient maximum power point tracking (MPPT) of photovoltaic (PV) systems by means of model-predictive control (MPC) techniques that is applied to a high-gain DC-DC converter. The high variability and stochastic nature of solar energy requires that the MPPT control continuously adjust the power converter operating point in order to track the changing maximum power point; a concept well known in the literature. The main contribution of this study is a modelpredictive-based controller with a fixed step that is combined with the traditional incremental conductance (INC) method. This technique improves the speed at which the controller can track rapid changes in solar insolation and results in an increase in the overall efficiency of the PV system. The controller speeds up convergence since MPC predicts error before the switching signal is applied to the high-gain multilevel DC-DC converter and thus is able to choose the next switch event to minimise error between the commanded and actual converter operation. Comparing the proposed technique to the conventional INC method shows substantial improvement in MPPT effectiveness and PV system performance. The performance of the proposed MPCMPPT is analysed and validated experimentally.

UR - http://www.scopus.com/inward/record.url?scp=85030092446&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85030092446&partnerID=8YFLogxK

U2 - 10.1049/iet-rpg.2017.0018

DO - 10.1049/iet-rpg.2017.0018

M3 - Article

VL - 11

SP - 1401

EP - 1409

JO - IET Renewable Power Generation

JF - IET Renewable Power Generation

SN - 1752-1416

IS - 11

ER -