A Machine Learning-Based Communication-Free PV Controller for Voltage Regulation

Due to the recent advancements in the manufacturing process of solar photovoltaics (PVs) and electronic converters, solar PVs has emerged as a viable investment option for energy trading. However, distribution system with large-scale integration of rooftop PVs, would be subjected to voltage upper li...

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Autores principales:	Shabib Shahid, Saifullah Shafiq, Bilal Khan, Ali T. Al-Awami, Muhammad Omair Butt
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	photovoltaic autonomous control electric vehicles Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/104200ad52144bd085ff8b7401c196b7
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spelling	oai:doaj.org-article:104200ad52144bd085ff8b7401c196b72021-11-11T19:47:56ZA Machine Learning-Based Communication-Free PV Controller for Voltage Regulation10.3390/su1321122082071-1050https://doaj.org/article/104200ad52144bd085ff8b7401c196b72021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12208https://doaj.org/toc/2071-1050Due to the recent advancements in the manufacturing process of solar photovoltaics (PVs) and electronic converters, solar PVs has emerged as a viable investment option for energy trading. However, distribution system with large-scale integration of rooftop PVs, would be subjected to voltage upper limit violations, unless properly controlled. Most of the traditional solutions introduced to address this problem do not ensure fairness amongst the on-line energy sources. In addition, other schemes assume the presence of communication linkages between these energy sources. This paper proposes a control scheme to mitigate the over-voltages in the distribution system without any communication between the distributed energy sources. The proposed approach is based on artificial neural networks that can utilize two locally obtainable inputs, namely, the nodal voltage and node voltage sensitivity and control the PV power. The controller is trained using extensive data generated for various loading conditions to include daily load variations. The control scheme was implemented and tested on a 12.47 kV feeder with 85 households connected on the 220 V distribution system. The results demonstrate the fair control of all the rooftop solar PVs mounted on various houses to ensure the system voltage are maintained within the allowed limits as defined by the ANSI C84.1-2016 standard. Furthermore, to verify the robustness of the proposed PV controller, it is tested during cloudy weather condition and the impact of integration of electric vehicles on the proposed controller is also analyzed. The results prove the efficacy of the proposed controller.Shabib ShahidSaifullah ShafiqBilal KhanAli T. Al-AwamiMuhammad Omair ButtMDPI AGarticlephotovoltaicautonomous controlelectric vehiclesEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12208, p 12208 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	photovoltaic autonomous control electric vehicles Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	photovoltaic autonomous control electric vehicles Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Shabib Shahid Saifullah Shafiq Bilal Khan Ali T. Al-Awami Muhammad Omair Butt A Machine Learning-Based Communication-Free PV Controller for Voltage Regulation
description	Due to the recent advancements in the manufacturing process of solar photovoltaics (PVs) and electronic converters, solar PVs has emerged as a viable investment option for energy trading. However, distribution system with large-scale integration of rooftop PVs, would be subjected to voltage upper limit violations, unless properly controlled. Most of the traditional solutions introduced to address this problem do not ensure fairness amongst the on-line energy sources. In addition, other schemes assume the presence of communication linkages between these energy sources. This paper proposes a control scheme to mitigate the over-voltages in the distribution system without any communication between the distributed energy sources. The proposed approach is based on artificial neural networks that can utilize two locally obtainable inputs, namely, the nodal voltage and node voltage sensitivity and control the PV power. The controller is trained using extensive data generated for various loading conditions to include daily load variations. The control scheme was implemented and tested on a 12.47 kV feeder with 85 households connected on the 220 V distribution system. The results demonstrate the fair control of all the rooftop solar PVs mounted on various houses to ensure the system voltage are maintained within the allowed limits as defined by the ANSI C84.1-2016 standard. Furthermore, to verify the robustness of the proposed PV controller, it is tested during cloudy weather condition and the impact of integration of electric vehicles on the proposed controller is also analyzed. The results prove the efficacy of the proposed controller.
format	article
author	Shabib Shahid Saifullah Shafiq Bilal Khan Ali T. Al-Awami Muhammad Omair Butt
author_facet	Shabib Shahid Saifullah Shafiq Bilal Khan Ali T. Al-Awami Muhammad Omair Butt
author_sort	Shabib Shahid
title	A Machine Learning-Based Communication-Free PV Controller for Voltage Regulation
title_short	A Machine Learning-Based Communication-Free PV Controller for Voltage Regulation
title_full	A Machine Learning-Based Communication-Free PV Controller for Voltage Regulation
title_fullStr	A Machine Learning-Based Communication-Free PV Controller for Voltage Regulation
title_full_unstemmed	A Machine Learning-Based Communication-Free PV Controller for Voltage Regulation
title_sort	machine learning-based communication-free pv controller for voltage regulation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/104200ad52144bd085ff8b7401c196b7
work_keys_str_mv	AT shabibshahid amachinelearningbasedcommunicationfreepvcontrollerforvoltageregulation AT saifullahshafiq amachinelearningbasedcommunicationfreepvcontrollerforvoltageregulation AT bilalkhan amachinelearningbasedcommunicationfreepvcontrollerforvoltageregulation AT alitalawami amachinelearningbasedcommunicationfreepvcontrollerforvoltageregulation AT muhammadomairbutt amachinelearningbasedcommunicationfreepvcontrollerforvoltageregulation AT shabibshahid machinelearningbasedcommunicationfreepvcontrollerforvoltageregulation AT saifullahshafiq machinelearningbasedcommunicationfreepvcontrollerforvoltageregulation AT bilalkhan machinelearningbasedcommunicationfreepvcontrollerforvoltageregulation AT alitalawami machinelearningbasedcommunicationfreepvcontrollerforvoltageregulation AT muhammadomairbutt machinelearningbasedcommunicationfreepvcontrollerforvoltageregulation
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A Machine Learning-Based Communication-Free PV Controller for Voltage Regulation

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