Grey-Box Identification for Photovoltaic Power Systems Via Particle-Swarm Algorithm

Al-Messabi, N., Goh, C. S. and Li, Y. (2015) Grey-Box Identification for Photovoltaic Power Systems Via Particle-Swarm Algorithm. In: 21st International Conference on Automation & Computing (ICAC 2015), Glasgow, Scotland, 11-12 Sep 2015, pp. 1-7. (doi: 10.1109/IConAC.2015.7313980)

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Amongst renewable generators, photovoltaics (PV) are becoming more popular as the appropriate low cost solution to meet increasing energy demands. However, the integration of renewable energy sources to the electricity grid possesses many challenges. The intermittency of these non-conventional sources often requires accurate forecast, planning and optimal management. Many attempts have been made to tackle these challenges; nonetheless, existing methods fail to accurately capture the underlying characteristics of the system. There exists scope to improve present PV yield forecasting models and methods. This paper explores the use of apriori knowledge of PV systems to build clear box models and identify uncertain parameters via heuristic algorithms. The model is further enhanced by incorporating black box models to account for unmodeled uncertainties in a novel grey-box forecasting and modeling of PV systems.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Goh, Dr Cindy Sf and Li, Professor Yun
Authors: Al-Messabi, N., Goh, C. S., and Li, Y.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Copyright Holders:Copyright © 2015 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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