Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID

Cascade control is one of the most efficient systems for improving the performance of the conventional single-loop control, especially in the case of disturbances. Usually, controller parameters in the inner and the outer loops are identified in a strict sequence. This paper presents a novel cascade...

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Main Authors: Afef Badis, Mohamed Nejib Mansouri, Mohamed Habib Boujmil
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:International Journal of Photoenergy
Online Access:http://dx.doi.org/10.1155/2019/4325648
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spelling doaj-126e355f3d694ba38ff0ab8a1ce3009a2020-11-25T01:49:06ZengHindawi LimitedInternational Journal of Photoenergy1110-662X1687-529X2019-01-01201910.1155/2019/43256484325648Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PIDAfef Badis0Mohamed Nejib Mansouri1Mohamed Habib Boujmil2Electronics and Microelectronics Laboratory (EμE), The National Engineering School of Monastir (ENIM), University of Monastir, TunisiaElectronics and Microelectronics Laboratory (EμE), The National Engineering School of Monastir (ENIM), University of Monastir, TunisiaHigher Institute of Technological Studies of Nabeul, Nabeul, TunisiaCascade control is one of the most efficient systems for improving the performance of the conventional single-loop control, especially in the case of disturbances. Usually, controller parameters in the inner and the outer loops are identified in a strict sequence. This paper presents a novel cascade control strategy for grid-connected photovoltaic (PV) systems based on fractional-order PID (FOPID). Here, simultaneous tuning of the inner and the outer loop controllers is proposed. Teaching-learning-based optimization (TLBO) algorithm is employed to optimize the parameters of the FOPID controller. The superiority of the proposed TLBO-based FOPID controller has been demonstrated by comparing the results with recently published optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). Simulations are conducted using MATLAB/Simulink software under different operating conditions for the purpose of verifying the effectiveness of the proposed control strategy. Results show that the performance of the proposed approach provides better dynamic responses and it outperforms the other control techniques.http://dx.doi.org/10.1155/2019/4325648
collection DOAJ
language English
format Article
sources DOAJ
author Afef Badis
Mohamed Nejib Mansouri
Mohamed Habib Boujmil
spellingShingle Afef Badis
Mohamed Nejib Mansouri
Mohamed Habib Boujmil
Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID
International Journal of Photoenergy
author_facet Afef Badis
Mohamed Nejib Mansouri
Mohamed Habib Boujmil
author_sort Afef Badis
title Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID
title_short Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID
title_full Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID
title_fullStr Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID
title_full_unstemmed Cascade Control of Grid-Connected PV Systems Using TLBO-Based Fractional-Order PID
title_sort cascade control of grid-connected pv systems using tlbo-based fractional-order pid
publisher Hindawi Limited
series International Journal of Photoenergy
issn 1110-662X
1687-529X
publishDate 2019-01-01
description Cascade control is one of the most efficient systems for improving the performance of the conventional single-loop control, especially in the case of disturbances. Usually, controller parameters in the inner and the outer loops are identified in a strict sequence. This paper presents a novel cascade control strategy for grid-connected photovoltaic (PV) systems based on fractional-order PID (FOPID). Here, simultaneous tuning of the inner and the outer loop controllers is proposed. Teaching-learning-based optimization (TLBO) algorithm is employed to optimize the parameters of the FOPID controller. The superiority of the proposed TLBO-based FOPID controller has been demonstrated by comparing the results with recently published optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). Simulations are conducted using MATLAB/Simulink software under different operating conditions for the purpose of verifying the effectiveness of the proposed control strategy. Results show that the performance of the proposed approach provides better dynamic responses and it outperforms the other control techniques.
url http://dx.doi.org/10.1155/2019/4325648
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