电工技术学报  2021, Vol. 36 Issue (9): 1782-1790    DOI: 10.19595/j.cnki.1000-6753.tces.200226
新能源电力系统及装备 |
基于机联网-空间相关性权重的风电机组风速预测研究
沈小军1, 周冲成1,2, 付雪娇1
1.同济大学电子与信息工程学院 上海 200092;
2.国网上海市电力公司嘉定供电公司 上海 201800
Wind Speed Prediction of Wind Turbine Based on the Internet of Machines and Spatial Correlation Weight
Shen Xiaojun1, Zhou Chongcheng1,2, Fu Xuejiao1
1. College of Electronic and Information Engineering Tongji University Shanghai 200092 China;
2. Jiading Power Supply Company State Grid Shanghai Electric Power Company Shanghai 201800 China
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摘要 风速实时预测,可有效提高风电机组的控制性能和发电量,实现风能的高效利用。该文在分析风电场风速时空传播特性的基础上,提出基于机联网-空间相关性权重的风电机组风速实时预测架构,建立一种风速实时预测模型与流程,并基于卡尔曼滤波算法开展了与持续预测法、传统空间相关性预测法的算例对比。算例分析结果表明:提出的风速预测方法小时间尺度预测精度比持续预测法好,预测的容错性和稳健性优于传统空间相关性法;提出的风速预测理论框架可行有效,具备分钟级和超短期等多时间尺度风电机组风速实时预测能力。研究成果可为风电机组风速的实时精确感知提供一种新思路。
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沈小军
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付雪娇
关键词 风电机组机联网空间相关性风速预测    
Abstract:Real-time wind speed prediction can effectively improve the control performance and power generation of wind turbine, and realize the efficient use of wind energy. On the basis of analyzing the temporal-spatial propagation characteristics of wind speed in wind farms, a real-time prediction framework based on Internet of machines and the weight of spatial correlation is proposed in this paper. The prediction model and process is established, a Kalman filter algorithm is developed and comparison study is carried out with the persistent model and traditional spatial correlation prediction method. Case study result shows that on small time scale, the proposed method has better prediction accuracy than the persistent model, and the fault tolerance and robustness are superior to the traditional spatial correlation method. The feasibility and effectiveness of the proposed method is verified, and the prediction framework is capable of multi-time scale prediction such as minute level and ultra-short term wind speed forecasting. The research results can provide new insights for accurate real-time wind speed perception of wind turbines.
Key wordsWind turbines    internet of machines    spatial correlation    wind speed real prediction   
收稿日期: 2020-02-26     
PACS: TM614  
基金资助:国家重点研发计划资助项目(2018YFB1503100)
通讯作者: 沈小军 男,1979年生,博士,教授,研究方向为新能源高效利用与储能技术、输变电场景三维重构及其数字孪生技术、电力设备状态感知与智能诊断等。E-mail:xjshen79@163.com   
作者简介: 周冲成 男,1992年生,硕士,研究方向为风电场风参数感知技术。E-mail:chongchengzhou@163.com
引用本文:   
沈小军, 周冲成, 付雪娇. 基于机联网-空间相关性权重的风电机组风速预测研究[J]. 电工技术学报, 2021, 36(9): 1782-1790. Shen Xiaojun, Zhou Chongcheng, Fu Xuejiao. Wind Speed Prediction of Wind Turbine Based on the Internet of Machines and Spatial Correlation Weight. Transactions of China Electrotechnical Society, 2021, 36(9): 1782-1790.
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