The condition monitoring data cleaning of power transmission and transformation equipment has some problems at present

such as loss of information

inaccuracy and complicated procedure. A data cleaning method based on stacked denoising autoencoders is proposed

which has the advantages of reconstructing noisy information and extracting features of abnormal status. The monitoring data of the equipment under normal and abnormal conditions is trained by the stacked denoising autoencoder to obtain the loss function vectors and build the outlier

missing data restoration model and abnormal status data denoising model. The upper limit of loss function and tolerant window are determined by means of kernel density estimation. By comparing reconstruction error and the lasting time of abnormal data with the upper limit of loss function and tolerant window

the dirty data is classified and cleaned by different models. The method is tested on total hydrocarbon concentration of oil chromatography data and conductor temperature data. An analysis of the examples shows that the method is able to automatically identify different types of abnormal data. Singular points and missing data are directly repaired by reconstruction. In the abnormal working condition

the denoising model can extract the actual effective information and eliminate the interference. This work is supported by National Natural Science Foundation of China(No. 51477100)

National High Technology Research and Development Program of China(863 Program)(No. 2015AA050204)

and State Grid Corporation of China(No. 520626150032).