Islanding Detection System for Grid Connected Photovoltaic System under Different Fault Condition Using Intelligent Detection Method (IDM)

Abstract

A distributed power generating system can run in an off-grid or off grid mode and can be powered by either sustainable or nonrenewable energy sources. The widespread use of renewable energy sources, like grid-connected solar systems, presents distribution networks with additional technological difficulties, such as accidental islanding. Utility employees who are islanding may not be aware that a circuit is still energized, which might be harmful. On islanding detection in gird-connected PV systems, several proposed solutions were considered. In order to examine their current limits, this study aims to develop an islanding detection system for grid connected solar systems under various fault conditions using intelligent detection method (IDM). To use and monitor the many grid-connected PV system characteristics in this context, numerous sensors and controllers were proposed. The controller will carry out the islanding detection based on the sensor data’s categorization of defect. The circuit breaker will do the islanding, and an intelligent technique has been proposed to identify the islanding condition under different fault condition. For real-time realization, a 1 kW grid-connected solar system had been used, and an artificial neural network (ANN) was employed as a smart approach to identify the islanding mode under various fault conditions.

Keywords:

• distribution generation
• photovoltaic system
• islanding detection
• fault detection
• circuit breaker
• artificial neural network (ANN)

ACKNOWLEDGMENT

There is no acknowledgement involved in this work.

Funding

No funding is involved in this work.

Authorship contributions

There is no authorship contribution

Ethics approval and consent to participate

No participation of humans takes place in this implementation process.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Human and animal rights

No violation of Human and Animal Rights is involved.

Additional information

Notes on contributors

R. Jai Ganesh

R. Jai Ganesh received B.E degree in EEE from Mepco Schlenk Engineering College, Sivakasi, affiliated to Anna University, Chennai, Tamilnadu in 2012. M.E Degree in EEE from Mepco Schlenk Engineering College, Sivakasi, affiliated to Anna University, Chennai, Tamilnadu in 2016. He is currently working toward the Ph.D. degree at the Department of Electrical Engineering, Mepco Schlenk Engineering College, Sivakasi, affiliated to Anna University, Chennai, Tamilnadu, India. He has published 15 papers in referred National and International Journals and 10 papers in National and International Conferences. He has published five patents and received grant from TNSCST student’s project scheme. He has organized many workshops, seminars. He is an active member in IFERP and International Association of Academic plus Corporate Society. His research interests include soft computing application in power system engineering, renewable energy resources and its protection schemes.

S. Muralidharan

S. Muralidharan received B.E degree in EEE from M.K. University, Madurai, Tamilnadu in 1994. MS Degree in Software Systems from BITS, Pilani, Rajasthan, in 1997. PhD degree from Sastra Deemed University, Thanjavur, Tamilnadu, India, in 2009. He has published 59 papers in referred National and International Journals and 41 papers in National and International Conferences. He has published 7 books and delivered guest lecturer for many reputed institutions. He has guided 9 PhD Scholars, among 5 has been awarded. He has published four patents and received grant from AICTE-RPS. He has organized many workshops, seminars. He is an active member in FIE, IACSIT, IAENG, ISTE, MSySI. His research interests include power system, renewable energy power generation, and application of evolutionary algorithm to electrical engineering.