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dc.contributor.author Narayana, M
dc.contributor.author Sunderland, KM
dc.contributor.author Putrus, G
dc.contributor.author Conlon, MF
dc.date.accessioned 2023-03-17T08:02:20Z
dc.date.available 2023-03-17T08:02:20Z
dc.date.issued 2017
dc.identifier.citation Narayana, M., Sunderland, K. M., Putrus, G., & Conlon, M. F. (2017). Adaptive linear prediction for optimal control of wind turbines. Renewable Energy, 113, 895–906. https://doi.org/10.1016/j.renene.2017.06.041 en_US
dc.identifier.issn 0960-1481 en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/20767
dc.description.abstract In order to obtain maximum power output of a Wind Energy Conversion System (WECS), the rotor speed needs to be optimised for a particular wind speed. However, due to inherent inertia, the rotor of a WECS cannot react instantaneously according to wind speed variations. As a consequence, the performance of the system and consequently the wind energy conversion capability of the rotor are negatively affected. This study considers the use of a time series Adaptive Linear Prediction (ALP) technique as a means to improve the performance and conversion efficiency of wind4 turbines. The ALP technique is introduced as a real time control reference to improve optimal control of wind turbines. In this study, a wind turbine emulator is developed to evaluate the performance of the predictive control strategy. In this regard, the ALP reference control method was applied as a means to control the torque/speed of the emulator. The results show that the employment of a predictive technique increases energy yield by almost 5%. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Wind energy conversion systems en_US
dc.subject Wind turbine en_US
dc.subject Linear adaptive prediction en_US
dc.subject Power mapping technique en_US
dc.subject Wind speed sensor technique en_US
dc.subject Wind speed estimation en_US
dc.title Adaptive linear prediction for optimal control of wind turbines en_US
dc.type Article-Full-text en_US
dc.identifier.year 2017 en_US
dc.identifier.journal Renewable Energy en_US
dc.identifier.volume 113 en_US
dc.identifier.database ScienceDirect en_US
dc.identifier.pgnos 895-906 en_US
dc.identifier.doi 10.1016/j.renene.2017.06.041 en_US


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