Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS
|dc.identifier.citation||Islam, F. and Al-Durra, A. and Muyeen, S.M. 2013. Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS. IEEE Transactions on Sustainable Energy. 4 (4): pp. 925-933.|
This paper presents a supervisory control unit (SCU) combined with short-term ahead wind speed prediction for proper and effective management of the stored energy in a small capacity flywheel energy storage system (FESS) which is used to mitigate the output power fluctuations of an aggregated wind farm. Wind speed prediction is critical for a wind energy conversion system since it may greatly influence the issues related to effective energy management, dynamic control of wind turbine, and improvement of the overall efficiency of the power generation system. In this study, a wind speed prediction model is developed by artificial neural network (ANN) which has advantages over the conventional prediction schemes including data error tolerance and ease in adaptability. The proposed SCU-based control would help to reduce the size of the energy storage system for minimizing wind power fluctuation taking the advantage of prediction scheme. The model for prediction using ANN is developed in MATLAB/Simulink and interfaced with PSCAD/EMTDC. Effectiveness of the proposed control system is illustrated using real wind speed data in various operating conditions.
|dc.publisher||The Institute of Electrical and Electronic Engineers (IEEE)|
|dc.title||Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS|
|dcterms.source.title||IEEE Transactions on Sustainable Energy|
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|curtin.department||Department of Electrical and Computer Engineering|