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中国电机工程学报 2008, 28(31) 108-113 DOI:
ISSN: 0258-8013 CN: 11-2107/TM |
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| 高电压技术 |
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应用新型多方法组合预测模型估计变压器油中溶解气体浓度 |
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杨廷方 刘沛 李浙 曾祥君 |
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华中科技大学电气与电子工程学院 华中科技大学电气与电子工程学院 湖南省超高压管理局 长沙理工大学电气与信息工程学院 |
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摘要：
为提高变压器故障的预测能力，提出了采用最优加权组合预测模型，将灰色理论、BP神经网络、遗传算法和卡尔曼预测算法这4种单项预测算法综合起来，对油中溶解气体的浓度及发展趋势进行精确预测。首先对根据各种单项预测方法的误差，按照预测误差平方和最小的原则计算各自的权系数，然后加权建立综合组合预测模型，并最终求解出变压器油中溶解气体的浓度。组合预测法能很好地综合各种单项预测方法的优势，更大限度地作出正确的预测。预测实例分析也证明了组合预测方法的可靠性和有效性。该方法不仅可以有效降低单项预测算法的预测误差，提高预测模型的预报能力，同时还为电力系统中其它领域的预测提供了新的思路。 |
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关键词：
灰色理论
BP神经网络
遗传算法
卡尔曼预测
油中溶解气体
浓度预测
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A New Combination Forecasting Model for Concentration Prediction of Dissolved Gases in Transformer Oil |
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YANG Ting-fang LIU Pei LI Zhe ZENG Xiang-jun |
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Abstract:
To improve fault prediction for power transformer, a new model of combination forecasting with optimal weights was proposed. Four existing methods namely Gray theory, BP neural network, Genetic Algorithm and Kalman filtering arithmetic were adopted synthetically to forecast the concentration and development trend of dissolved gases in transformer oil. Each optimal weight of the four methods was calculated firstly according to the principle of least error sum of square and their prediction error ratios. Then, the optimal combination forecasting model was formed based on the optimal weights. In terms of the model, concentration of dissolved gases in transformer oil would be obtained. In this way, the advantages of the four methods were concentrated and a maximum forecasting precision will be gained. Simulation results show that the proposed forecasting algorithm is feasible and dependable. Besides decreasing prediction error and improving forecasting precision greatly, it also provides a new way to solve other data forecasting problems in power system. |
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Keywords:
gray theory
BP neural network
genetic algorithm
Kalman filtering
dissolved gases in oil
concentration prediction
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收稿日期 2007-07-05 修回日期 1900-01-01 网络版发布日期 |
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DOI: |
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基金项目:
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通讯作者: 杨廷方 |
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作者简介: |
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作者Email: yangtingfang@126.com |
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| Copyright by 中国电机工程学报 |
