| 基于隐马尔可夫模型的日内风电功率预测误差区间滚动估计 |
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| 引用本文： | 周玮,钟佳成,孙辉,李国锋,孔剑虹,张富宏.基于隐马尔可夫模型的日内风电功率预测误差区间滚动估计[J].电力系统自动化,2018,42(21):90-95. |
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| 作者姓名： | 周玮 钟佳成 孙辉 李国锋 孔剑虹 张富宏 |
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| 作者单位： | 大连理工大学电气工程学院, 辽宁省大连市 116024,大连理工大学电气工程学院, 辽宁省大连市 116024,大连理工大学电气工程学院, 辽宁省大连市 116024,大连理工大学电气工程学院, 辽宁省大连市 116024,国网大连供电公司, 辽宁省大连市 116001,国网辽宁省电力有限公司, 辽宁省沈阳市 110004 |
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| 基金项目： | 国家电网公司科技项目(2017YF-27) |
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| 摘 要： | 风电的波动性和不确定性给大规模风电并网带来了挑战，估计风电场上报风电的预测功率误差范围，能够为含风电电力系统的运行调度提供重要信息。因此，提出基于隐马尔可夫模型的日内风电功率预测误差区间滚动估计方法。通过建立隐马尔可夫模型实现一定置信水平下对风电功率误差波动区间的快速估计，并利用局部加权回归散点平滑法对误差区间进行处理。以实际数据为例分析，结果表明所提方法能够给出风电功率预测误差的波动范围，为电力系统的调度与控制、备用容量的配置、风险评估等方面提供更全面的信息。
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| 关 键 词： | 风力发电 隐马尔可夫模型 误差区间估计 功率预测 |
| 收稿时间： | 2017/10/3 0:00:00 |
| 修稿时间： | 2018/9/13 0:00:00 |
An Interval Rolling Estimation Method for Daily Wind Power Forecast Errors Based on Hidden Markov Model |
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| ZHOU Wei,ZHONG Jiacheng,SUN Hui,LI Guofeng,KONG Jianhong and ZHANG Fuhong.An Interval Rolling Estimation Method for Daily Wind Power Forecast Errors Based on Hidden Markov Model[J].Automation of Electric Power Systems,2018,42(21):90-95. |
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| Authors: | ZHOU Wei ZHONG Jiacheng SUN Hui LI Guofeng KONG Jianhong and ZHANG Fuhong |
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| Affiliation: | School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China,School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China,School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China,School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China,State Grid Dalian Power Supply Company, Dalian 116001, China and State Grid Liaoning Electric Power Co. Ltd., Shenyang 110004, China |
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| Abstract: | The randomness and fluctuation of wind power bring challenges to the integration of wind power to the power grid. The error range estimation of predicted power for wind farms can provide important information for the operation and the scheduling of power system with wind power. So, an interval rolling estimation method for daily wind power forecast errors is proposed, which is based on hidden Markov model. The hidden Markov model can be built to achieve fast estimation of wind power error fluctuation range under the certain confidence level, and the local weighted regression smoothing method is used to deal with the error interval. Based on the actual data, the simulation results show that this method can give the fluctuation range of the wind power prediction error and provide more comprehensive information for the power system scheduling and control, the allocation of backup capacity, the risk assessment and so on. |
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| Keywords: | wind power generation hidden Markov model error interval estimation power prediction |
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