| 引用本文: | 肖逸,李程煌,刘若平,左剑,李银红.基于风速局部爬坡误差校正的风电功率优化预测[J].电力自动化设备,2019,39(3): |
| XIAO Yi,LI Chenghuang,LIU Ruoping,ZUO Jian,LI Yinhong.Optimal wind power prediction based on local ramp error correction of wind speed[J].Electric Power Automation Equipment,2019,39(3): |
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| 基于风速局部爬坡误差校正的风电功率优化预测 |
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肖逸1,2, 李程煌3, 刘若平1,2, 左剑4, 李银红1,2
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1.华中科技大学 电气与电子工程学院 强电磁工程与新技术国家重点实验室，湖北 武汉430074;2.华中科技大学 电气与电子工程学院 电力安全与高效湖北省重点实验室，湖北 武汉430074;3.长江勘测规划设计研究有限责任公司，湖北 武汉430010;4.广东电网有限责任公司电力调度控制中心，广东 广州510600
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| 摘要: |
| 准确的风电功率预测对于电力系统安全稳定运行具有重要意义，滞后性是产生风电功率预测误差的主要原因，尤其是风速变化较快时，滞后性引起的预测误差较大。考虑到风速波动与风电功率的变化息息相关，提出一种基于风速局部爬坡(LR)误差校正的方法来改善预测风速的滞后性，并将校正后的预测风速及历史功率数据作为输入进行风电功率预测。提出利用灰狼优化(GWO)算法对最小二乘支持向量机(LSSVM)的参数进行优化，以提高风电功率预测的准确性。算例结果表明，所提方法能够有效提高风电功率预测精度。 |
| 关键词: 风电功率预测 预测风速 滞后性 局部爬坡误差校正 最小二乘支持向量机 灰狼优化 |
| DOI：10.16081/j.issn.1006-6047.2019.03.029 |
| 分类号:TM761 |
| 基金项目: |
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| Optimal wind power prediction based on local ramp error correction of wind speed |
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XIAO Yi1,2, LI Chenghuang3, LIU Ruoping1,2, ZUO Jian4, LI Yinhong1,2
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1.State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2.Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;3.Changjiang Institute of Survey, Planning, Design and Research, Wuhan 430010, China;4.Guangdong Electric Power Dispatch Center, Guangzhou 510600, China
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| Abstract: |
| Accurate wind power prediction is significant for secure and stable operation of power system, and the lag is the main reason of wind power prediction error, especially when wind speed changes rapidly, the lag will result in big error. Considering strong relationship between wind speed and wind power, an error correction method based on LR(Local Ramp) of wind speed is proposed to improve the lag, and the predicted wind speed after correction and historical wind power are taken as input for wind power prediction. The parameters of LSSVM(Least Square Support Vector Machine) are optimized by using GWO(Grey Wolf Optimization) algorithm to improve the accuracy of wind power prediction. Case results show that the proposed method can effectively improve the accuracy of wind power prediction. |
| Key words: wind power prediction predicted wind speed lagging quality local ramp error correction least square support vector machine grey wolf optimization |
