深远海海域情况复杂,海面风速极易受海洋中尺度事件影响.所造成的异常数据点和Bump事件将导致爬坡检测准确率下降,影响深远海风电功率短期预测精度.因此,提出了一种同时考虑爬坡事件以及深远海气象因素的深远海风电功率短期预测方法.首先,设计基于状态标记和滑动窗口改进的参数和分辨率自适应算法(parameter and resolution adaptive algorithm,PRAA)实现爬坡事件检测并完成特征量提取;其次,分析深远海风速、风向及温度等多因素关联关系,扩充深远海气象因素特征样本维度,并通过主成分分析法(principal component analysis,PCA)深度挖掘潜在特征;最后,基于某海上风电场的实测数据,采用考虑爬坡和深远海气象因素的轻量梯度提升机(light gradient boosting machine,LightGBM)算法完成深远海风电功率的短期预测,仿真结果验证了所提方法的有效性.
Short-time prediction of long-distance offshore wind power based on ramp characteristics and improved PRAA
The conditions in long-distance offshore areas are complex,and surface wind speeds are highly susceptible to the influence of mesoscale oceanic events. The resulting anomalous data points and bump events will decrease the accuracy of ramp-up detection,affecting the short-term forecasting precision of offshore wind power in long-distance sea areas. Therefore,a short-term forecasting method for offshore wind power in long-distance sea areas is proposed,which simultaneously considers ramp-up events and long-distance sea meteorological factors. Firstly,an improved parameter and resolution adaptive algorithm (PRAA) based on state marker and sliding window is designed to detect ramp-up events and extract features. Secondly,the correlation of multiple factors such as wind speed,wind direction and temperature in the long-distance offshore is analyzed to expand the dimension of the feature samples of the meteorological factors,and the potential features are deeply explored by principal component analysis (PCA). Finally,based on the measured data of a domestic offshore wind farm,the light gradient boosting machine (LightGBM)considering ramp-up and meteorological factors in long-distance sea areas is used to complete the short-term prediction of long-distance offshore wind power. Simulation results verify the effectiveness of the proposed method.
long-distance offshore wind powerwind power ramp-up eventsPRAAramp-up characteristic quantitiesshort term prediction of wind power
万方数据
维普
