Error-Based Wind Power Prediction Technique Based on Generalized Factors Analysis with Improved Power System Reliability

As a green energy source, the use of wind has been rapidly growing in recent years. Whereas wind has complex and stochastic nature hence precise wind power predictions are essential for economic operation of the wind energy systems. For utilities, the rapid variations in wind power can generate serious problem of reliability reduction. The forecasting of wind power changes allows a utility to plan the connection and disconnection of wind power generation based on forecasting wind power generation and predicted load. In this paper, an environment friendly wind power prediction technique of variable-speed wind power system is proposed. The technique is employed from the prediction algorithm to create a prediction model to get accurate power. It is authenticated on the producer power curve of the variable-speed wind system. Additionally, the technique is used in average monthly wind power prediction and the outcomes show a huge improvement in prediction accuracy using the proposed method. Further, the likely value of rated wind speed for installed wind power system in Vishakhapatnam, Bhopal, Ahmedabad, Thiruvananthapuram, Bangalore, India, are also discussed. The empirical outcomes are compared with different wind forecast models and based on the root mean square error (RMSE), the proposed model gives the perfection in prediction accuracy compared to Gaussian Processes and Numerical Weather Prediction, Wind power prediction without adjustment, Wind power prediction with adjustment, support vector machine methods. Further, the developed model is used to evaluate the annual reliability indices by convolving the predicted generation with predicted load in the selected station.