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2025年8月11日 5:57 星期一
首页 > 过刊浏览>2021年第30卷第8期 >305-310. DOI:10.15888/j.cnki.csa.008033
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基于差分进化和规则约简的二型模糊方法在风电预测中的应用
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Application of Type-2 Fuzzy Method Based on Differential Evolution and Rule Reduction in Wind Power Prediction
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    摘要:

    当前, 风力发电量占比不断增加, 对风电预测的要求越来越高, 但由于风能本身存在的间歇性和不确定性等问题, 风电预测精度并不能达到理想的效果. 为了降低预测模型复杂性, 并提高风电预测精准度, 本文提出了一种基于差分进化和规则约简的二型模糊方法. 该模型给出了一种剪枝策略进行二型模糊规则的约简, 在此基础上, 采用差分进化算法进行二型模糊系统全部参数的优化学习. 最后, 通过与一型模糊方法和支持向量回归方法进行了对比, 证明了文中所提出的模型具有更好的预测精度.

    Abstract:

    Nowadays, with a higher proportion of wind power generation, wind power prediction is increasingly demanding. It is, however, not satisfyingly accurate due to the intermittent and uncertain nature of wind energy. In order to reduce the complexity of the prediction model and improve accuracy, we propose a type-2 fuzzy system based on differential evolution and rule reduction in this study. This model provides a pruning strategy for the reduction in type-2 fuzzy rules, based on which and the differential evolution algorithm is adopted to optimize the parameters of the reduced type-2 fuzzy system. Finally, the prediction accuracy of our method is higher than that of the type-1 fuzzy system and the support vector regressor, according to comparison.

    参考文献
    [1] Deng X, Shao HJ, Hu CL, et al. Wind power forecasting methods based on deep learning:A survey. Computer Modeling in Engineering & Sciences, 2020, 122(1):273-301
    [2] Wang KJ, Qi XX, Liu HD, et al. Deep belief network based k-means cluster approach for short-term wind power forecasting. Energy, 2018, 165:840-852.[doi:10.1016/j.energy.2018.09.118
    [3] Foley AM, Leahy PG, Marvuglia A, et al. Current methods and advances in forecasting of wind power generation. Renewable Energy, 2012, 37(1):1-8.[doi:10.1016/j.renene.2011.05.033
    [4] Khalid M, Savkin AV. A method for short-term wind power prediction with multiple observation points. IEEE Transactions on Power Systems, 2012, 27(2):579-586.[doi:10.1109/TPWRS.2011.2160295
    [5] Wang Z, Wang WS, Wang B. Regional wind power forecasting model with NWP grid data optimized. Frontiers in Energy, 2017, 11(2):175-183.[doi:10.1007/s11708-017-0471-9
    [6] Gomes P, Castro R. Wind speed and wind power forecasting using statistical models:AutoRegressive Moving Average (ARMA) and Artificial Neural Networks (ANN). International Journal of Sustainable Energy Development, 2012, 1(2):41-50.[doi:10.20533/ijsed.2046.3707.2012.0007
    [7] 惠小健, 王震, 张善文, 等. 基于ARMA的风电功率预测. 现代电子技术, 2016, 39(7):145-148, 153
    [8] Wang YR, Wang DC, Tang Y. Clustered hybrid wind power prediction model based on ARMA, PSO-SVM, and clustering methods. IEEE Access, 2020, 8:17071-17079.[doi:10.1109/ACCESS.2020.2968390
    [9] 李军, 李大超. 基于优化核极限学习机的风电功率时间序列预测? 物理学报, 2016, 65(13):130501.
    [10] Zeng JW, Qiao W. Short-term wind power prediction using a wavelet support vector machine. IEEE Transactions on Sustainable Energy, 2012, 3(2):255-264.[doi:10.1109/TSTE.2011.2180029
    [11] Jiao RH, Huang XJ, Ma XH, et al. A model combining stacked auto encoder and back propagation algorithm for short-term wind power forecasting. IEEE Access, 2018, 6:17851-17858.[doi:10.1109/ACCESS.2018.2818108
    [12] 王宁, 叶林, 陈盛, 等. 支持向量机向量维数对短期风电功率预测精度的影响. 电力系统保护与控制, 2012, 40(15):63-69.[doi:10.7667/j.issn.1674-3415.2012.15.012
    [13] 徐正华, 刘三明, 王致杰. 基于kNN-SVR模型的风电功率预测. 电力学报, 2019, 34(5):411-416
    [14] 王艳艳, 尹少平, 王灵梅, 等. 基于ANFIS模型的风功率超短期预测研究. 自动化技术与应用, 2016, 35(1):17-21
    [15] 高骞, 程霄, 沙宇恒, 等. 基于ANFIS的超短期风电出力预测模型及仿真. 沈阳工业大学学报, 2020, 42(1):29-33.[doi:10.7688/j.issn.1000-1646.2020.01.06
    [16] 李军, 王星辉. 基于区间二型FLS的短期风电功率多步预测. 控制工程, 2019, 26(2):215-222
    [17] Zadeh LA. Fuzzy sets. Information and Control, 1965, 8:338-353.[doi:10.1016/S0019-9958(65)90241-X
    [18] Mamdani EH. Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers, 1974, 121(12):1585-1588.[doi:10.1049/piee.1974.0328
    [19] Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences, 1975, 8(3):199-249.[doi:10.1016/0020-0255(75)90036-5
    [20] Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning-II. Information Sciences, 1975, 8(4):301-357.[doi:10.1016/0020-0255(75)90046-8
    [21] Zadeh LA. The concept of a linguistic variable and its application to approximate reasoning-III. Information Sciences, 1975, 9(1):43-80.[doi:10.1016/0020-0255(75)90017-1
    [22] Mendel JM. Uncertain rule-based fuzzy logic systems:Introduction and new directions. Upper Saddle River, NJ:Prentice Hall PTR, 2001.
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李银萍,李文峰,申存骁,张金萍,江永清.基于差分进化和规则约简的二型模糊方法在风电预测中的应用.计算机系统应用,2021,30(8):305-310

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  • 收稿日期:2020-11-11
  • 最后修改日期:2020-12-21
  • 在线发布日期: 2021-08-03
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