Machine Intelligent Techniques for Ramp Event Prediction in Offshore and Onshore Wind Farms
Predicting ramp events is crucial for wind farm operators to mitigate potential damage to infrastructure and ensure grid stability, especially for large and rapid power fluctuations.
This paper addresses the problem of predicting ramp events in onshore and offshore wind farms to prevent damage to the utility grid. Using hybrid machine intelligent techniques, the study found that Support Vector Regression (SVR) models provided the best forecasting performance among the tested methods.
Globally, wind energy has lessened the burden on conventional fossil fuel based power generation. Wind resource assessment for onshore and offshore wind farms aids in accurate forecasting and analyzing nature of ramp events. From an industrial point of view, a large ramp event in a short time duration is likely to cause damage to the wind farm connected to the utility grid. In this manuscript, ramp events are predicted using hybrid machine intelligent techniques such as Support vector regression (SVR) and its variants, random forest regression and gradient boosted machines for onshore and offshore wind farm sites. Wavelet transform based signal processing technique is used to extract features from wind speed. Results reveal that SVR based prediction models gives the best forecasting performance out of all models. In addition, gradient boosted machines (GBM) predicts ramp events closer to Twin support vector regression (TSVR) model. Furthermore, the randomness in ramp power is evaluated for onshore and offshore wind farms by calculating log energy entropy of features obtained from wavelet decomposition and empirical model decomposition.