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The K value determination research of advanced breaking current weighted cumulative method for VCB electrical endurance detection

机译:用于VCB电气耐久性检测的先进断流加权累加法K值确定研究。

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摘要

The integral action coefficient value K about the advanced breaking current weighted cumulative method (BCWC) used for the VCBs' contact system electrical endurance monitoring is studied. At first, the general selecting method of integral current coefficient K value is discussed, and the existent problem in the K value selecting, calculating process and potential improving room are analyzed. Considering the distribution, characteristics and factors which impact K value, and according to the analysis results the LM-BP neural network algorithm is adopted to research how dynamically select the K value based on the engineering application. The construct process, establishing skill, selecting method and determination criteria of the hidden layer node of researched K LM-BP neural network are significantly emphasized. In order to verify the feasibility and accuracy of the proposed LM-BP neural network the K value predict process was carried out by the researched neural network and the result indicate that the net has a good network optimization ability and forecast precision. Thus the feasibility of provided method can be achieved of this work The LM-BP neural network algorithm introduced can promise the K value selected more approximate to practical engineering, which can ensure and effectively improve the predicting accuracy.
机译:研究了用于VCB的接触系统电气耐久性监测的先进分断电流加权累积法(BCWC)的积分作用系数值K。首先讨论了积分电流系数K值的一般选择方法,分析了K值选择,计算过程和电位提高空间中存在的问题。考虑到影响K值的分布,特征和因素,根据分析结果,采用LM-BP神经网络算法研究如何根据工程应用动态选择K值。着重强调了研究的K LM-BP神经网络隐层节点的构建过程,建立技巧,选择方法和确定标准。为了验证所提出的LM-BP神经网络的可行性和准确性,通过研究的神经网络对K值进行了预测,结果表明该网络具有良好的网络优化能力和预测精度。因此,所提供方法的可行性得以实现。引入的LM-BP神经网络算法可以保证所选择的K值更接近实际工程,可以保证并有效地提高预测精度。

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