At present, an automatic-mechanic contact tap-changer is widely used in power system, but it can not frequently operate. In addition, arc will occur when the switch changes. In order to solve these two problems, this ...At present, an automatic-mechanic contact tap-changer is widely used in power system, but it can not frequently operate. In addition, arc will occur when the switch changes. In order to solve these two problems, this paper presented an automatic on-load voltage-regulating distributing transformer which employed non-contact solid-state relay as tap-changer, and mainly introduced its structure, basic principal, design method of each key link and experimental results. Laboratory simulation experiments informed that the scheme was feasible. It was a smooth and effective experiment device, which was practical in application.展开更多
为进一步提高变压器有载分接开关(on-loadtap changer,OLTC)机械故障的识别精度,从OLTC振动信号的混沌动力学特性出发,提出基于Bayes估计相空间融合与改进支持向量数据描述(support vector data description,SVDD)的OLTC机械故障诊断方...为进一步提高变压器有载分接开关(on-loadtap changer,OLTC)机械故障的识别精度,从OLTC振动信号的混沌动力学特性出发,提出基于Bayes估计相空间融合与改进支持向量数据描述(support vector data description,SVDD)的OLTC机械故障诊断方法。首先,利用多变量相空间重构(multivariate phase space reconstruction,MPSR)对OLTC切换时的多通道振动信号进行高维重构,然后基于Bayes估计与奇异值分解提取高维融合相空间相点的特征指标,据此建立以轮廓域图(contourmap,CM)为决策判据的改进SVDD识别模型,用于OLTC机械故障的有效诊断。对某OLTC模型正常与典型机械故障下切换时振动信号的分析结果表明:基于Bayes估计的OLTC振动信号高维相空间融合方法能获取更为完整的OLTC机械状态信息。相对于现有故障诊断方法,所提出的CM-SVDD诊断模型具有更优的识别精度和较高的计算效率。研究成果可为基于振动分析法的OLTC机械状态诊断技术提供重要依据。展开更多
变压器有载分接开关(on-load top changer,OLTC)的主要故障类型是机械故障,现有大多数研究仅诊断切换开关故障,难以辨识影响换档全过程的传动机构故障。为准确诊断切换开关与传动机构故障,该文提出一种基于换档全过程振动强度的OLTC机...变压器有载分接开关(on-load top changer,OLTC)的主要故障类型是机械故障,现有大多数研究仅诊断切换开关故障,难以辨识影响换档全过程的传动机构故障。为准确诊断切换开关与传动机构故障,该文提出一种基于换档全过程振动强度的OLTC机械故障诊断方法。首先,将多通道切换开关振动爆发数据转换为时域波形图输入改进的卷积神经网络(convolutional neural network,CNN),以获取池化层特征。然后,提出换档全过程振动强度特征,将换档全过程振动信号划分为多个区间,统计各区间中幅值超过阈值的点数,以表征各时间段平均振动强度。最后,提出一种新的特征处理方法改变以上两种特征的相对大小,并融合两种特征训练分类器诊断机械故障类型。实例分析表明:相比于现有OLTC机械故障诊断方法,所提方法能有效辨识传动机构故障,进一步提升对切换开关故障的诊断精度,具有较强的鲁棒性与泛用性,可为OLTC机械故障诊断研究提供新的思路。展开更多
文摘At present, an automatic-mechanic contact tap-changer is widely used in power system, but it can not frequently operate. In addition, arc will occur when the switch changes. In order to solve these two problems, this paper presented an automatic on-load voltage-regulating distributing transformer which employed non-contact solid-state relay as tap-changer, and mainly introduced its structure, basic principal, design method of each key link and experimental results. Laboratory simulation experiments informed that the scheme was feasible. It was a smooth and effective experiment device, which was practical in application.
文摘为进一步提高变压器有载分接开关(on-loadtap changer,OLTC)机械故障的识别精度,从OLTC振动信号的混沌动力学特性出发,提出基于Bayes估计相空间融合与改进支持向量数据描述(support vector data description,SVDD)的OLTC机械故障诊断方法。首先,利用多变量相空间重构(multivariate phase space reconstruction,MPSR)对OLTC切换时的多通道振动信号进行高维重构,然后基于Bayes估计与奇异值分解提取高维融合相空间相点的特征指标,据此建立以轮廓域图(contourmap,CM)为决策判据的改进SVDD识别模型,用于OLTC机械故障的有效诊断。对某OLTC模型正常与典型机械故障下切换时振动信号的分析结果表明:基于Bayes估计的OLTC振动信号高维相空间融合方法能获取更为完整的OLTC机械状态信息。相对于现有故障诊断方法,所提出的CM-SVDD诊断模型具有更优的识别精度和较高的计算效率。研究成果可为基于振动分析法的OLTC机械状态诊断技术提供重要依据。
文摘变压器有载分接开关(on-load top changer,OLTC)的主要故障类型是机械故障,现有大多数研究仅诊断切换开关故障,难以辨识影响换档全过程的传动机构故障。为准确诊断切换开关与传动机构故障,该文提出一种基于换档全过程振动强度的OLTC机械故障诊断方法。首先,将多通道切换开关振动爆发数据转换为时域波形图输入改进的卷积神经网络(convolutional neural network,CNN),以获取池化层特征。然后,提出换档全过程振动强度特征,将换档全过程振动信号划分为多个区间,统计各区间中幅值超过阈值的点数,以表征各时间段平均振动强度。最后,提出一种新的特征处理方法改变以上两种特征的相对大小,并融合两种特征训练分类器诊断机械故障类型。实例分析表明:相比于现有OLTC机械故障诊断方法,所提方法能有效辨识传动机构故障,进一步提升对切换开关故障的诊断精度,具有较强的鲁棒性与泛用性,可为OLTC机械故障诊断研究提供新的思路。
