| 基于负荷实际暂态特性的配电网负荷模型的研究 |
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| 引用本文： | 康忠健,田爱娜,王 平,訾淑伟.基于负荷实际暂态特性的配电网负荷模型的研究[J].电网与水力发电进展,2010,26(12):1-7. |
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| 作者姓名： | 康忠健 田爱娜 王 平 訾淑伟 |
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| 作者单位： | 中国石油大学（华东） 电气工程系，山东 东营 257061;中国石油大学（华东） 电气工程系，山东 东营 257061；;承德石油高等专科学校 电气与电子工程系，河北 承德 067000;中国石油大学（华东） 电气工程系，山东 东营 257061； |
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| 基金项目： | 国家自然科学基金资助项目（60971077）；山东省自然科学基金资助项目（ZR2009FM061) |
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| 摘 要： | 针对现场实际负荷成分的时变性、随机性、复杂性、多样性和非线性的特点,在考虑实际负荷暂态特性的基础上,提出一种基于神经网络的配电网受控电流源负荷模型的方法。该方法根据负荷群在稳态运行条件下的电压和电流暂态特性,通过神经网络学习负荷群的电压电流特性,用受控电流源代替实际的负荷群,受控电流源的电流大小受神经网络控制,并利用Matlab/Simlink对所提出的负荷模型建立方法进行仿真验证。仿真结果表明所建立的负荷模型在单相短路、两相短路和三相短路条件下均具有良好的稳定性和准确性。
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| 关 键 词： | 负荷模型 暂态特性 神经网络 受控电流源 |
Load Model Based on Transient Characteristics of Load in the Distribution Network |
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| Authors: | KANG Zhong-jian TIAN Ai-n WANG Ping and ZI Shu-wei |
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| Affiliation: | Electric Engineering Department, China University of Petroleum, Dongying 257061, Shandong Province, China;Electric Engineering Department, China University of Petroleum, Dongying 257061, Shandong Province, China;College of Electrical and Electronic Engineering, Chengde Petroleum College, Chengde 067000, Hebei Province, China;Electric Engineering Department, China University of Petroleum, Dongying 257061, Shandong Province, China |
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| Abstract: | Considering the characteristics of time-variables,randomness,complexity,diversity and nonlinearity in actual load,a new load model with a controlled current source based on the transient characteristics of the load in distribution network is proposed.The transient voltage and current characteristics of the loads in the steady-state operation are studied by a BP neural network according to the proposed method.The actual loads are replaced by a controlled current source model,which the output current is controlled by the BP neural network.The proposed load model is simulated in the Matlab/Simlink.The simulation results suggest that the proposed load model be quite stable and accurate under the single-phase short faulty,two-phase short faulty and three-phase short faulty. |
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| Keywords: | load model transient characteristics neural network controlled current source |
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