Ultra-Short-Term Wind Power Prediction Method Based on Neural Network for Jiuquan Wind Power Base

doi:10.3969/j.issn.1000-7229.2013.09.001

Abstract

Abstract:

The randomness and volatility of wind power bring difficulties for power system dispatching and operation. Taking Jiuquan wind power base the first million kilowatt wind power base in China as an example, the ultra-short-term wind power prediction method based on neural network was studied, which analyzes and processes the real-time data of wind speed and wind power. On this basis, the modeling process of ultra-short-term prediction was analyzed based on the neural network algorithm and Bayes rule. Finally, the prediction model was validated through prediction results. The results show that the prediction model is reasonable, and has a high prediction accuracy, which can also provide a reference for dispatchers.

Key words: wind power prediction, ultra-short-term prediction, neural network, prediction model

MA Yanhong，WANG Ningbo，MA Ming，LIU Guangtu,ZHAO Long. Ultra-Short-Term Wind Power Prediction Method Based on Neural Network for Jiuquan Wind Power Base[J]. ELECTRIC POWER CONSTRUCTION, 2013, 34(9): 1-5.

References

［1］肖创英. 欧美风电发展的经验与启示［M］. 北京：中国电力出版社，2010：292-298.

［2］马彦宏，汪宁渤，刘福潮，等. 甘肃酒泉风电基地风电预测预报系统［J］. 电力系统自动化，2009，33(16)：88-90.

［3］汪宁渤. 大规模风电送出与消纳［M］. 北京：中国电力出版社，2012.

［4］汪宁渤. 甘肃酒泉千万kW风电基地面临的挑战与应对措施［J］. 电网与清洁能源，2009，30(7)：43-47.

［5］肖创英，汪宁渤，丁坤，等. 甘肃酒泉风电功率调节方式研究［J］. 中国电机工程学报，2010，30(10)：1-7.

［6］肖创英，汪宁渤，陟晶，等. 甘肃酒泉风电出力特性分析［J］. 电力系统自动化，2010，34(17)：64-67.

［7］汪宁渤，马彦宏，丁坤，等. 酒泉风电基地脱网事故频发的原因分析［J］. 电力系统自动化，2012，36(19)：1-5.

［8］Kariniotakis G，Mayer D，Moussafir J. ANEMOS：development of a next generation wind power forecasting system for the large-scale integration of onshore & offshore wind farms［C］//European Wind Energy Conference & Exhibition，Madrid，Spain，2003：1661-1667.

［9］Giebel G，Landberg L，Kariniotakis G. State-of-the-art on methods and software tools for short-term，prediction of wind energy production［C］//European Wind Energy Conference & Exhibition，Madrid，Spain，2003：1345-1352.

［10］Waldl H，Giebe G. The quality of a 48-hours wind power forecast using the german and danish weather prediction model［C］//Wind Power for the 21st Century，EUWEC Special Topic Conference，Kassel，Germany，2000：87-92.

［11］孙春顺，王耀南，李欣然. 小时风速的向量自回归模型及应用［J］. 中国电机工程学报，2008，28(14)：112-117.

［12］杜颖，卢继平，李青，等. 基于最小二乘支持向量机的风电场短期风速预测［J］. 电网技术，2008，32(15)：62-66.

［13］潘迪夫，刘辉，李燕飞. 基于时间序列分析和卡尔曼滤波算法的风电场风速预测优化模型［J］. 电网技术，2008，32(7)：82-86.

［14］杨秀媛，肖洋，陈树勇. 风电场风速和发电功率预测研究［J］. 中国电机工程学报，2005，25(11)：1-5.

［15］范高锋，王伟胜，刘纯，等. 基于人工神经网络的风电功率预测［J］. 中国电机工程学报，2008，28(34)：118-123.

［16］Focken U，Lange M，Waldl P. Previento-a wind power prediction system with an innovative upscaling algorithm［C］//2001 European Wind Energy Association Conference EWEC’01，Copenhagen，Denmark，2001：56-60.

［17］Martí Perez I. Wind forecasting activities［C］//The First IEA Joint Action Symposium on Wind Forecasting Techniques，Norrkoping，Sweden，2002：284-289.

［18］管胜利. 基于局域波分解及时间序列的风电场风速预测研究［J］. 华北电力技术，2009(1)：10-13.

［19］洪翠，林维明，温步瀛. 风电场风速及风电功率预测方法研究综述［J］. 电网与清洁能源，2011，27(1)：60-66.

［20］傅蓉，王维庆，何桂雄. 基于气象因子的BP神经网络风电场风速预测［J］. 可再生能源，2009，27(5)：86-89.

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