[1]李昂儒,郑伟彦,赵京虎,等.基于分形理论的风电功率预测算法研究[J].计算机技术与发展,2021,31(03):191-195.[doi:10. 3969 / j. issn. 1673-629X. 2021. 03. 033]
　LI Ang-ru,ZHENG Wei-yan,ZHAO Jing-hu,et al.Research on Wind Power Prediction Algorithm Based on Fractal Theory[J].,2021,31(03):191-195.[doi:10. 3969 / j. issn. 1673-629X. 2021. 03. 033]
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基于分形理论的风电功率预测算法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年03期
页码:
191-195
栏目:
应用前沿与综合
出版日期:
2021-03-10

文章信息/Info

Title:
Research on Wind Power Prediction Algorithm Based on Fractal Theory
文章编号:
1673-629X(2021)03-0191-05
作者:
李昂儒1; 2; 郑伟彦3; 赵京虎1; 2; 杨 勇4; 王辉东5; 汪李忠5; 邢海青5
1. 南瑞集团有限公司(国网电力科学研究院有限公司),江苏 南京 211106;
2. 南瑞研究院西安研发中心,陕西 西安 710000;
3. 国网浙江省电力有限公司杭州供电公司,浙江 杭州 310007;
4. 国网浙江省电力有限公司,浙江 杭州 310012;
5. 国网浙江杭州市余杭区供电有限公司,浙江 杭州 311100
Author(s):
LI Ang-ru1; 2; ZHENG Wei-yan3; ZHAO Jing-hu1; 2; YANG Yong4; WANG Hui-dong5; WANG Li-zhong5; XING Hai-qing5
1. NARI Group Corporation (State Grid Electric Power Research Institute),Nanjing 211106,China;
2. NARI Research Institute Xi’an R & D Center,Xi’an 710000,China;
3. State Grid Zhejiang Hangzhou Power Supply Co. ,Ltd. ,Hangzhou 310007,China;
4. State Grid Zhejiang Power Supply Co. ,Ltd. ,Hangzhou 310012,China;
5. State Grid Zhejiang Hangzhou Yuhang District Power Supply Co. ,Ltd. ,Hangzhou 311100,China
关键词:
分形理论; 风电功率预测; 机器学习; K 近邻算法; 风电场数据
Keywords:
fractal theory; wind power prediction; machine learning; KNN; wind farm data
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 03. 033
摘要:
近些年,风能成为世界上装机容量较大的可再生能源之一,风力发电的并网容量不断增加,给电网稳定运行带来不小挑战,风力输出功率预测精度的提升能够有效地减轻风电并网时对电网的冲击,同时为电网的调度和安全运营提供保障。 为进一步提升的风电功率预测精度,借鉴分形理论并将其融合到风电功率预测模型中,同时结合自定义 K 最近邻算法(K-nearest-neighbor,KNN)。 采用分形理论的基本思想,考虑基准功率曲线问题和气象特征值,利用分形插值可有效地获取相邻样本的局部信息,从而使得插值曲线更好地保留原采样信息的大部分特征,最后使用多评价指标维度对预测效果进行评估。 以某风电场实测数据为例,与梯度提升决策树、随机森林、支持向量机预测模型进行比较,验证了提出的预测算法的有效性。
Abstract:
In recent years,wind energy has become one of the renewable energies with large installed capacity in the world. The gridconnected capacity of wind power generation has been gradually increasing,which brings great challenges to the stable operation of the power grid. The improvement of the prediction accuracy of wind power output can effectively reduce the impact of wind power grid. In order to achieve a better wind power prediction method,we study the application of fractal theory to wind power generation,apply the fractal theory to the wind power prediction model and combine the wind power prediction method of the custom KNN. Considering the problem of reference power curve and meteorological characteristic value,fractal interpolation can effectively obtain the local information of adjacent samples,so that the interpolation curve can better retain most of the features of the original sampling information and finally apply multiple evaluation indicators. The prediction results are evaluated and compared with the prediction models RFR, SVM and GBDT. The measured data of a wind farm is taken as an example to verify the effectiveness of the prediction model proposed.

相似文献/References:

[1]贾丽会 张修如.分形理论及在信号处理中的应用[J].计算机技术与发展,2007,(09):203.
　JIA Li-hui,ZHANG Xiu-ru.Fractal Theory and Its Application in Signal Processing[J].,2007,(03):203.
[2]王萍[],倪丽萍[],倪洋[]. 基于分形插值的我国旱灾数据分析研究[J].计算机技术与发展,2015,25(08):199.
　WANG Ping[],NI Li-ping[],NI Yang[]. Research on Analysis of Chinese Drought Data Based on Fractal Interpolation[J].,2015,25(03):199.
[3]侯增选,郑栓柱,郭超,等. 中国书画中干笔飞白仿真方法概述[J].计算机技术与发展,2015,25(11):145.
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更新日期/Last Update: 2020-03-10