Comparison of two regulators for phtovoltaic Systems: The PID regulator and sliding mode control

Kanzari, Zaineb; Khediri, Jalel; Zaafrane, Wajdi; Jemli, Mohamed

doi:10.1109/cistem.2016.8066771

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Figure 3, displays the electrical circuit of a boost converter [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]: This type of converter can be used as an adapter Sourceload when the load needs a higher voltage than that of the photovoltaic system generator [9-31-39]. Through changing the duty cycle D, it is possible to adapt the source to the load [16].…”

Section: Dc/dc Converter [39]mentioning

confidence: 99%

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Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

Maarouf

Ksentini

Azzag

et al. 2022

The Scientific Bulletin of Electrical Engineering Faculty

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Photovoltaic (PVS) generators’ nonlinear electrical characteristics allow for greater performance and efficiency when they are forced to operate at their peak power (MPP). This article suggests an adaptive method for maximizing power point tracking that makes use of artificial neural network (ANN) techniques (MPPT). A step-up converter powered by a separate solar generator is under the control of an ANN controller built on a neural network training database (PVS). The results show that ANN-MPPT has good control performance and is near to the maximum power point of PVS when compared to conventional MPPT methods like perturb and observe and incremental conductance.

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Section: Dc/dc Converter [39]mentioning

confidence: 99%

“…A specific type of artificial intelligence method is called an ANN [25]. AI applications offer advantages over traditional approaches [7].…”

Section: Mppt Based On Artificial Neural Networkmentioning

confidence: 99%

Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

Maarouf

Ksentini

Azzag

et al. 2022

The Scientific Bulletin of Electrical Engineering Faculty

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Fuzzy logic MPPT and battery charging control for photovoltaic system under real weather conditions

Zerouali

Ougli

Tidhaf

et al. 2020

2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS)

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Comparison of two regulators for phtovoltaic Systems: The PID regulator and sliding mode control

Cited by 2 publications

References 4 publications

Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

Improved Artificial Neural Network Design for MPPT Grid-Connected Photovoltaic Systems

Fuzzy logic MPPT and battery charging control for photovoltaic system under real weather conditions

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