NDLI: Distributed ANNs in a layered architecture for energy management and maintenance scheduling of renewable energy HPS microgrids

Content Provider	IEEE Xplore Digital Library
Author	Jaganmohan Reddy, Y. Pavan Kumar, Y.V. Sunil Kumar, V. Padma Raju, K.
Copyright Year	2012
Description	Author affiliation: Engineering Test Services-Aerospace, Honeywell Technology Solutions Lab (Pvt) Ltd, Hyderabad, India (Pavan Kumar, Y.V.) \|\| Process Solutions-Automation & Control Solutions, Honeywell Technology Solutions Lab (Pvt) Ltd, Hyderabad, India (Jaganmohan Reddy, Y.) \|\| Department of Electronics and Communication Engineering Jawaharlal Nehru Technological University Kakinada (JNTUK), India (Sunil Kumar, V.; Padma Raju, K.)
Abstract	With increasing research on the field of alternative energy resources for sustainable development, more renewable energy sources are getting penetrated to the distributed network to form “microgrid”. This leads to more complexity in the distribution network in terms of energy management and realtime control. The objective of this paper is to design a system that forecasts the short (daily), medium (seasonal) and long term (yearly) load demand and the availability of energy resources at the microgrids. For scheduling the storage and transaction of electrical energy between neighboring microgrids, an energy management system (EMS) is designed. The EMS makes use of the forecasted data and real time data all together for managing an array of interconnected microgrids. The seasonal and yearly forecaster for a geographical boundary helps in maintenance scheduling and long term infrastructure development plan respectively. Recently, Artificial Neural Network (ANN) is found as a promising tool for statistical forecasting in real time applications. Hence, this paper makes use of ANN feature to forecast both load and availability of energy resources at microgrids in different scenarios like daily, seasonal, and yearly. The layered ANNs architecture is developed and trained with Levenberg-Marqurardt Back Propagation Algorithm. The entire design is simulated in MATLAB/Simulink. The proposed concept can be used in today's real time energy infrastructure to prevent future energy crises with improved reliability and smooth coordination among microgrids located at different areas.
Starting Page	1
Ending Page	6
File Size	482586
Page Count	6
File Format	PDF
ISBN	9781467320429
e-ISBN	9781467320436
DOI	10.1109/APCET.2012.6302067
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-02
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load Forecasting Artificial neural networks Microgrids Maintenance engineering Hybrid Power System (HPS) Microgrid controller (MC) Wind forecasting Energy Management System (EMS) Wind speed Artificial Neural Networks (ANN) Renewble energy sources Solar radiation Energy management
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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