1. 国网冀北电力有限公司科学研究院
2. 华北电力大学电气与电子工程学院
纸质出版:2026
移动端阅览
杨伟新, 赵洪山, 杨铎, 等. 考虑机组健康状态的风电机组出力概率预测[J]. 华北电力大学学报(自然科学版), 2026,53(2):99-109.
杨伟新, 赵洪山, 杨铎, et al. 考虑机组健康状态的风电机组出力概率预测[J]. 2026, 53(2): 99-109.
风电机组健康状态会影响功率预测精度,为了提高短期风功率预测的准确度,促进可再生能源应用,提出了一种考虑机组健康状态的风电机组出力概率预测方法。首先构建基于自适应最大均值差异算法(AMMD)的风电机组健康状态评估模型;其次对风速等特征向量进行斯皮尔曼相关系数的重要性评估,将影响权重较大的因素与风电机组健康状态评估结果作为影响因素输入到随机森林概率预测模型中预测风电出力概率;最后采用某风电场SCADA数据进行预测区间准确度实例验证。实验结果表明,使用的方法相比于传统随机森林方法,平均绝对误差(MAE)降低了3.521
R
2
提高了12%;相比于其他对比算法预测效果也均有不同程度提高。研究成果可为低健康度的风电功率预测工作提供参考。
张仕良.基于深度神经网络的语音识别模型研究[D].中国科学技术大学,2017(09).
刘蕾.基于谱分析的大规模风电场功率超短期概率区间预测研究[D].东北电力大学,2019(07).
于群,霍筱东,何剑,李琳,张建新,冯煜尧.基于斯皮尔曼相关系数和系统惯量的中国电网停电事故趋势预测[J].中国电机工程学报,2023(14).
吴瑊,米增强,杨玉新,栾福明,李程,刘玉成.大型风机海上风电场集电系统拓扑优化[J].华北电力大学学报(自然科学版),2023(06).
张国珍,王其乐,叶天泽,杨锡运.基于随机森林和LSTM-自编码算法的风机高温降容状态智能检测方法[J].华北电力大学学报(自然科学版),2021(03).
李姣,郭鹏.基于ARGAN表面阴影预处理与迁移学习风电机组叶片故障识别[J].华北电力大学学报(自然科学版),2021(02).
闫家铭,毕天姝,胥国毅,刘方蕾,王凡.海上风电经VSC-HVDC并网改进频率控制策略[J].华北电力大学学报(自然科学版),2021(02).
史光宇,徐健,杨强.基于卷积神经网络的风电机组轴承机械故障智能诊断方法[J].华北电力大学学报(自然科学版),2020(04).
程启明,陈路,程尹曼,张强,高杰.基于EEMD和LS-SVM模型的风电功率短期预测方法[J].电力自动化设备,2018(05).
郭鹏,姜漫利,李航涛.基于运行数据和高斯过程回归的风电机组发电性能分析与监测[J].电力自动化设备,2016(08).
孔波利,崔丽艳,丁钊,李现伟,王以笑.基于风光混合模型的短期功率预测方法研究[J].电力系统保护与控制,2015(18).
王爽心,李涛,孙东旭,刘如九.基于小世界优化的支持向量机风电功率预测[J].太阳能学报,2015(03).
王丽婕,冬雷,高爽.基于多位置NWP与主成分分析的风电功率短期预测[J].电工技术学报,2015(05).
王贺,胡志坚,仉梦林.基于模糊信息粒化和最小二乘支持向量机的风电功率波动范围组合预测模型[J].电工技术学报,2014(12).
王焱,汪震,黄民翔,蔡祯祺,杨濛濛.基于OS-ELM和Bootstrap方法的超短期风电功率预测[J].电力系统自动化,2014(06).
董雷,周文萍,张沛,刘广一,李伟迪.基于动态贝叶斯网络的光伏发电短期概率预测[J].中国电机工程学报,2013(S1).
李莉,刘永前,杨勇平,韩爽.基于CFD流场预计算的短期风速预测方法[J].中国电机工程学报,2013(07).
陈道君,龚庆武,金朝意,张静,王定美.基于自适应扰动量子粒子群算法参数优化的支持向量回归机短期风电功率预测[J].电网技术,2013(04).
杨静,张楠男,李建,刘延明,梁美红.决策树算法的研究与应用[J].计算机技术与发展,2010(02).
Pengbo Li,Xiangwen Wang,Junjie Yang.Short-term wind power forecasting based on two-stage attention mechanism[J].IET Renewable Power Generation,2020.
Yan Ke,Wang Xiuying,Kim Jinman,Feng Dagan.A New Aggregation of DNN Sparse and Dense Labeling for Saliency Detection.[J].IEEE transactions on cybernetics,2020.
Mucun Sun,Cong Feng,Erol Kevin Chartan,Bri-Mathias Hodge,Jie Zhang.A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization[J].Applied Energy,2019.
Hui Liu,Zhu Duan,Yanfei Li,Haibo Lu.A novel ensemble model of different mother wavelets for wind speed multi-step forecasting[J].Applied Energy,2018.
NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, University of Cincinnati, PO Box 210072, Cincinnati, OH 45221-0072, USA,NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, University of Cincinnati, PO Box 210072, Cincinnati, OH 45221-0072, USA,NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, University of Cincinnati, PO Box 210072, Cincinnati, OH 45221-0072, USA,NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, University of Cincinnati, PO Box 210072, Cincinnati, OH 45221-0072, USA,NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, University of Cincinnati, PO Box 210072, Cincinnati, OH 45221-0072, USA,NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, University of Cincinnati, PO Box 210072, Cincinnati, OH 45221-0072, USA.A deviation based assessment methodology for multiple machine health patterns classification and fault detection[J].Mechanical Systems and Signal Processing,2018.
H.P. Simão,W.B. Powell,C.L. Archer,W. Kempton.The challenge of integrating offshore wind power in the U.S. electric grid. Part II: Simulation of electricity market operations[J].Renewable Energy,2017.
Mian Du,Jun Yi,Peyman Mazidi,Lin Cheng,Jianbo Guo.A Parameter Selection Method for Wind Turbine Health Management through SCADA Data[J].Energies,2017.
Wan, C.,Xu, Z.,Pinson, P.,Dong, Z.Y..Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine[J].IEEE Transactions on Power Systems: A Publication of the Power Engineering Society,2014(3).
Lee, D.,Baldick, R..Short-Term Wind Power Ensemble Prediction Based on Gaussian Processes and Neural Networks[J].IEEE transactions on smart grid,2014.
Hanieh Borhan Azad,Saad Mekhilef,Vellapa Gounder Ganapathy.Long-Term Wind Speed Forecasting and General Pattern Recognition Using Neural Networks[J].IEEE transactions on sustainable energy,2014.
Guillermo Mendez,Sharon Lohr.Estimating residual variance in random forest regression[J].Computational Statistics and Data Analysis,2011.
Andrew Kusiak,Wenyan Li.Short-term prediction of wind power with a clustering approach[J].Renewable Energy,2010.
T. H. M. El-Fouly,E. F. El-Saadany,M. M. A. Salama.Grey Predictor for Wind Energy Conversion Systems Output Power Prediction[J].IEEE Transactions on Power Systems: A Publication of the Power Engineering Society,2006.
0
浏览量
579
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621