论文标题
机器智能技术用于海上和陆上风电场的坡道事件预测
Machine Intelligent Techniques for Ramp Event Prediction in Offshore and Onshore Wind Farms
论文作者
论文摘要
在全球范围内，风能减轻了基于化石燃料的发电的负担。对陆上和近海风电场的风力资源评估有助于准确预测和分析坡道事件的性质。从工业的角度来看，在短时间内的大型坡道事件可能会损坏与公用电网相连的风电场。在此手稿中，使用混合机智能技术（例如支持向量回归（SVR）及其变体，随机森林回归和梯度增强机器，用于陆上和近海风电场站点的机器）预测坡道事件。基于小波变换的信号处理技术用于从风速中提取特征。结果表明，基于SVR的预测模型为所有模型提供了最佳的预测性能。此外，梯度提升机（GBM）预测坡道事件接近双支持向量回归（TSVR）模型。此外，通过计算从小波的分解和经验模型分解的特征的对数能量熵来评估坡道功率的随机性。
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.
