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1.中国农业大学信息与电气工程学院,北京市 100083
2.国家电网有限公司,北京市 100032
3.中国电力科学研究院有限公司,北京市 100192
Received:12 November 2019,
Revised:2020-02-18,
Published Online:02 June 2020,
Published:25 February 2021
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叶林,赵金龙,路朋等.考虑气象特征与波动过程关联的短期风电功率组合预测[J].电力系统自动化,2021,45(4):54-62.
YE Lin,ZHAO Jinlong,LU Peng,et al.Combined Prediction of Short-term Wind Power Considering Correlation of Meteorological Features and Fluctuation Process[J].Automation of Electric Power Systems,2021,45(4):54-62.
叶林,赵金龙,路朋等.考虑气象特征与波动过程关联的短期风电功率组合预测[J].电力系统自动化,2021,45(4):54-62. DOI: 10.7500/AEPS20191112002.
YE Lin,ZHAO Jinlong,LU Peng,et al.Combined Prediction of Short-term Wind Power Considering Correlation of Meteorological Features and Fluctuation Process[J].Automation of Electric Power Systems,2021,45(4):54-62. DOI: 10.7500/AEPS20191112002.
为解决短期风电功率预测关键气象因素提取难、天气波动过程与功率波动过程匹配性差的问题,提出了一种考虑气象特征与波动过程关联的短期风电功率组合预测方法。首先,通过最大相关-最小冗余原则得到数值天气预报的气象特征因素来划分天气波动过程。其次,考虑天气波动过程与功率波动过程的关联关系,建立了以气象特征因素为输入、以风电功率为输出的波动过程关联的短期组合预测模型。最后,将不同天气波动过程下的风电功率预测值在时序上进行重新组合,以得到波动过程为输出的短期风电功率预测结果。实际算例表明,采用气象特征因素作为输入以及面向波动过程关联的组合预测方法能够明显地提高短期风电功率的预测精度。
To cope with the problems of difficulties in extraction of the critical meteorological factors and the weak relevance between the weather and power fluctuation processes
a combined prediction method of short-term wind power considering correlation of meteorological features and fluctuation processes is proposed. At first
the meteorological characteristic factors of numerical weather prediction (NWP) are obtained by the rule of minimal redundancy maximal relevance (mRMR) to classify the weather fluctuation processes. Further
considering the relationship between weather and power fluctuation processes
a short-term combined prediction model is constrcuted
which takes meteorological characteristic factors as inputs and wind power as output. Finally
the prediction values of wind power associated with different weather processes are combined in time series to get the prediction results of short-term wind power
which takes fluctuation processes as the output. Case study shows that the combined prediction method with correlations of fluctuation processes which takes meteorological characteristic factors as inputs can significantly improve the prediction accuracy of the short-term wind power.
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