, 44:163 | Cite as

Performance monitoring of wind turbines using advanced statistical methods

  • Anil Kumar KushwahEmail author
  • Rajesh Wadhvani


Estimation of wind power generation for grid interface helps in calculation of the annual energy production, which maintains the balance between electricity production and its consumption. For this purpose, accurate wind speed forecasting plays an important role. In this paper, linear statistical predictive models such as autoregressive integrated moving average (ARIMA), generalized autoregressive score (GAS) model and a GAS model with exogenous variable x (GASX) have been applied for accurate wind speed forecasting. Along with this, a non-linear statistical predictive modelling technique called non-linear GASX (NLGASX) has been proposed and applied to model non-linear time-series data. Furthermore, the proposed NLGASX model is optimized using modelling techniques based on neural networks, namely Sigmoid, TANH, Softmax and RELU. The proposed optimized NLGASX model performs far better as compared with other models. Wind speed is also used as an input to wind power curve model for predicting the wind power. According to the predicted wind power the annual energy has been calculated.


Statistical models wind power curve models TANH softmax RELU sigmoid annual energy production 


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Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMaulana Azad National Institute of TechnologyBhopalIndia

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