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    Suzhou Electric Appliance Research Institute
    期刊號: CN32-1800/TM| ISSN1007-3175

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    基于CGM-IPSO-LSSVM的短期風電功率預測

    來源:電工電氣發布時間:2023-07-01 09:01 瀏覽次數:248

    基于CGM-IPSO-LSSVM的短期風電功率預測

    康義1,羅利偉2
    (1 華北水利水電大學 電氣工程學院,河南 鄭州 450045;
    2 鄭州博努力計算機科技有限公司,河南 鄭州 450001)
     
        摘 要:為了電網的安全運行,應充分考慮氣象等相關因素對風電的影響程度來預測短期風電功率。提出采用改進灰色模型 (CGM)、改進粒子群算法 (IPSO) 和最小二乘支持向量機 (LSSVM) 混合的預測方法。CGM-IPSO-LSSVM 方法采用灰色模型的關聯性分析不同時刻的氣象等相關因素的數據,根據分析所得的氣象等相關因素數據來確定風參量的權重,再根據權重運用最小二乘支持向量機對風向量進行估計,并以風向量的估計值為依據,以收斂性更好的改進粒子群算法對 CGM 模型進行優化,求解出最終預測結果,對預測結果出現的誤差,采用傅里葉殘差序列進行補償。實驗結果表明,提出的 CGM-IPSO-LSSVM 預測方法考慮了多因素影響和克服了參數選擇優化的問題,其預測精度在要求的范圍內大幅提高,為風電并網的調度提供了有力依據,降低了棄風率。
        關鍵詞: 短期風電功率預測;改進灰色模型;改進粒子群算法;最小二乘支持向量機;融合預測
        中圖分類號:TM614 ;TM715     文獻標識碼:A     文章編號:1007-3175(2023)06-0022-05
     
    Short-Term Wind Power Prediction Based on CGM-IPSO-LSSVM
     
    KANG Yi1, LUO Li-wei2
    (1 School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China;
    2 Zhengzhou Bonuli Computer Technology Co., Ltd, Zhengzhou 450001, China)
     
        Abstract: In order to ensure the safe operation of the power grid, the influence of meteorological and other related factors on wind power should be fully considered to predict short-term wind power. Therefore, this paper proposes a hybrid prediction method using improved Cotes Grey Model(CGM), Improved Particle Swarm Optimization(IPSO) and Least Squares Support Vector Machine(LSSVM). The CGMIPSO-LSSVM method first uses the correlation of grey model to analyze the meteorological data and other related factors at different time.Then, according to the above data, the weight of wind parameters is determined. Third, based on the above weight, the least squares support vector machine is used to estimate the wind vector. Fourth, the improved particle swarm optimization with better convergence is adopted to optimize the CGM model to obtain the final prediction result on the basis of estimated values of the wind vector. Finally, the error of the prediction result is compensated by the Fourier residual sequence. The experiment results show that the CGM-IPSO-LSSVM prediction method takes the influence of multiple factors into consideration and overcomes the problem of parameter selection optimization. It not only greatly improves prediction accuracy within the required range to provide strong basis for the scheduling of wind power integration, but also reduces the abandoned wind rate.
        Key words: short-term wind power prediction; improved cotes grey model; improved particle swarm optimization; least squares support vector machine; fusion prediction
     
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