Automatic Fault identification in Grid Connected Photovoltaic System using Neural Network Controller

R. Sureshkumar; S. U. Prabha; M. Senthil Arumugam

doi:10.1109/ICCIKE47802.2019.9004425

Automatic Fault identification in Grid Connected Photovoltaic System using Neural Network Controller

R. Sureshkumar, S. U. Prabha, M. Senthil Arumugam

School of Engineering & Physical Sciences

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

3 Citations (Scopus)

Abstract

A significant reason for performance loss is fault occurring in the Photovoltaic (PV) system. Nowadays, with the availability of intelligent control techniques the faults can be identified easily. To identify the fault, Artificial Neural network controller is designed. The power injected to the grid by the photovoltaic system is decreased due to the faults occurring in the system. Usually the fault occurs in Photovoltaic panel, Maximum Power Point Tracking controller (MPPT), Boost converter, inverter and grid. The simulation is carried out on 50 kW PV system by means of Matlab to identify the fault.

Original language	English
Title of host publication	2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)
Publisher	IEEE
Pages	78-80
Number of pages	3
ISBN (Electronic)	9781728137780
DOIs	https://doi.org/10.1109/ICCIKE47802.2019.9004425
Publication status	Published - 20 Feb 2020
Event	2019 International Conference on Computational Intelligence and Knowledge Economy - Dubai, United Arab Emirates Duration: 11 Dec 2019 → 12 Dec 2019

Conference

Conference	2019 International Conference on Computational Intelligence and Knowledge Economy
Abbreviated title	ICCIKE 2019
Country/Territory	United Arab Emirates
City	Dubai
Period	11/12/19 → 12/12/19

Keywords

Fault Detection
Grid Connected PV System
Multi Layer Neural Network
Neural Network

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications

UN SDGs

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

Access to Document

10.1109/ICCIKE47802.2019.9004425

Cite this

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title = "Automatic Fault identification in Grid Connected Photovoltaic System using Neural Network Controller",

abstract = "A significant reason for performance loss is fault occurring in the Photovoltaic (PV) system. Nowadays, with the availability of intelligent control techniques the faults can be identified easily. To identify the fault, Artificial Neural network controller is designed. The power injected to the grid by the photovoltaic system is decreased due to the faults occurring in the system. Usually the fault occurs in Photovoltaic panel, Maximum Power Point Tracking controller (MPPT), Boost converter, inverter and grid. The simulation is carried out on 50 kW PV system by means of Matlab to identify the fault.",

keywords = "Fault Detection, Grid Connected PV System, Multi Layer Neural Network, Neural Network",

author = "R. Sureshkumar and Prabha, {S. U.} and {Senthil Arumugam}, M.",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2019 ; Conference date: 11-12-2019 Through 12-12-2019",

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language = "English",

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booktitle = "2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)",

publisher = "IEEE",

address = "United States",

}

Sureshkumar, R, Prabha, SU & Senthil Arumugam, M 2020, Automatic Fault identification in Grid Connected Photovoltaic System using Neural Network Controller. in 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). IEEE, pp. 78-80, 2019 International Conference on Computational Intelligence and Knowledge Economy, Dubai, United Arab Emirates, 11/12/19. https://doi.org/10.1109/ICCIKE47802.2019.9004425

TY - GEN

T1 - Automatic Fault identification in Grid Connected Photovoltaic System using Neural Network Controller

AU - Sureshkumar, R.

AU - Prabha, S. U.

AU - Senthil Arumugam, M.

PY - 2020/2/20

Y1 - 2020/2/20

N2 - A significant reason for performance loss is fault occurring in the Photovoltaic (PV) system. Nowadays, with the availability of intelligent control techniques the faults can be identified easily. To identify the fault, Artificial Neural network controller is designed. The power injected to the grid by the photovoltaic system is decreased due to the faults occurring in the system. Usually the fault occurs in Photovoltaic panel, Maximum Power Point Tracking controller (MPPT), Boost converter, inverter and grid. The simulation is carried out on 50 kW PV system by means of Matlab to identify the fault.

AB - A significant reason for performance loss is fault occurring in the Photovoltaic (PV) system. Nowadays, with the availability of intelligent control techniques the faults can be identified easily. To identify the fault, Artificial Neural network controller is designed. The power injected to the grid by the photovoltaic system is decreased due to the faults occurring in the system. Usually the fault occurs in Photovoltaic panel, Maximum Power Point Tracking controller (MPPT), Boost converter, inverter and grid. The simulation is carried out on 50 kW PV system by means of Matlab to identify the fault.

KW - Fault Detection

KW - Grid Connected PV System

KW - Multi Layer Neural Network

KW - Neural Network

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U2 - 10.1109/ICCIKE47802.2019.9004425

DO - 10.1109/ICCIKE47802.2019.9004425

M3 - Conference contribution

AN - SCOPUS:85080901900

SP - 78

EP - 80

BT - 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)

PB - IEEE

T2 - 2019 International Conference on Computational Intelligence and Knowledge Economy

Y2 - 11 December 2019 through 12 December 2019

ER -

Automatic Fault identification in Grid Connected Photovoltaic System using Neural Network Controller

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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