Faculty Publications
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Item Comparison of modeling methods for wind power prediction: a critical study(Higher Education Press Limited Company, 2020) Shetty, R.P.; Sathyabhama, A.; Pai, P.S.Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods. © 2018, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature.Item An efficient online sequential extreme learning machine model based on feature selection and parameter optimization using cuckoo search algorithm for multi-step wind speed forecasting(Springer Science and Business Media Deutschland GmbH, 2021) Shetty, R.P.; Sathyabhama, A.; Pai, P.S.Accurate wind speed forecasting (WSF) has become increasingly important to overcome the adverse effects of stochastic nature of the wind on wind power generation. This paper proposes a multi-step hybrid online WSF model by combining online sequential extreme learning machine (OSELM), optimized variational mode decomposition (OVMD) and cuckoo search optimization algorithm (CSO). OVMD decomposes the wind speed series into subseries, and CSO selects the input features for each subseries. Multi-step forecasting for each subseries is performed using OSELM model optimized by CSO. Finally, the forecasting results are obtained by the aggregate calculations. The proposed model has been examined by using 10-min average wind speed data collected in monsoon and winter seasons from a supervisory control and data acquisition system of a 1.5 MW wind turbine situated in central dry zone of Karnataka, India. The results reveal that the model proposed captures the nonlinear characteristics of the wind speed in a better manner in comparison with the batch learning approach, giving accurate wind speed forecasts. This can help wind farms to estimate the wind power in a location efficiently. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.