基于栈式降噪自编码器的输变电设备状态数据清洗方法
发布时间：2018-10-14 18:56
【摘要】：针对当前输变电设备状态监测数据清洗过程繁琐,易造成信息丢失等问题,利用栈式降噪自编码器对"脏"数据的还原解析能力及异常状态特征提取能力,提出了一种基于栈式降噪自编码器的数据清洗方法。对设备正常工况及异常运行状态数据分别利用栈式降噪自编码器进行训练学习,获取损失函数向量,形成奇异点、缺失数据修复模型和设备异常运行状态数据降噪模型。通过核密度估计确定训练样本损失函数上限和容限时窗,根据测试数据重构误差和异常数据时长与损失函数上限和容限时窗间的关系,对"脏"数据进行分类处理。对某变压器油色谱中总烃含量及某导线温度数据进行清洗,结果表明所提方法能有效辨识奇异点、缺失信息及异常运行状态数据,并对奇异点、缺失值进行修复重构。在设备异常运行时刻,可以有效过滤干扰数据。
[Abstract]:Aiming at the problems of the current status monitoring data cleaning process of power transmission and transformation equipment, such as tedious cleaning process and easy to cause information loss, the ability of reducing and analyzing dirty data and extracting abnormal state features of stack noise reduction self-encoder are used. A data cleaning method based on stack noise reduction self-encoder is proposed. The data of normal working condition and abnormal operation state of equipment are trained and studied by stack noise reduction self-encoder to obtain loss function vector and form singularity data repair model and abnormal operation state data de-noising model. The upper limit and tolerance window of the loss function of training samples are determined by kernel density estimation. According to the relationship between the error of reconstruction of test data and the time of abnormal data and the upper limit and tolerance window of loss function, the dirty data are classified and processed. The results show that the proposed method can effectively identify the singularity point, the missing information and the abnormal operation state data, and repair and reconstruct the singular point and the missing value. Interference data can be filtered effectively at the abnormal operation time of the device.
【作者单位】： 上海交通大学电子信息与电气工程学院;国网山东省电力公司电力科学研究院;
【基金】：国家自然科学基金资助项目(51477100) 国家高技术研究发展计划(863计划)资助项目(2015AA050204) 国家电网公司科技项目(520626150032)~~
【分类号】：TM507
本文编号：2271346
[Abstract]:Aiming at the problems of the current status monitoring data cleaning process of power transmission and transformation equipment, such as tedious cleaning process and easy to cause information loss, the ability of reducing and analyzing dirty data and extracting abnormal state features of stack noise reduction self-encoder are used. A data cleaning method based on stack noise reduction self-encoder is proposed. The data of normal working condition and abnormal operation state of equipment are trained and studied by stack noise reduction self-encoder to obtain loss function vector and form singularity data repair model and abnormal operation state data de-noising model. The upper limit and tolerance window of the loss function of training samples are determined by kernel density estimation. According to the relationship between the error of reconstruction of test data and the time of abnormal data and the upper limit and tolerance window of loss function, the dirty data are classified and processed. The results show that the proposed method can effectively identify the singularity point, the missing information and the abnormal operation state data, and repair and reconstruct the singular point and the missing value. Interference data can be filtered effectively at the abnormal operation time of the device.
【作者单位】： 上海交通大学电子信息与电气工程学院;国网山东省电力公司电力科学研究院;
【基金】：国家自然科学基金资助项目(51477100) 国家高技术研究发展计划(863计划)资助项目(2015AA050204) 国家电网公司科技项目(520626150032)~~
【分类号】：TM507
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1 朱六璋;;调度信息系统的数据清洗应用[J];电力信息化;2007年04期
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