当下电力设备状态大数据呈现爆炸式增长，设备故障、数据传输以及人为操作失误等原因都会导致问题数据的出现，影响数据质量以及后续的分析结果，因此数据清洗具有重要意义。目前大多数研究着力于识别出异常数据并直接剔除，破坏了数据的完整性。针对此问题，提出一种基于改进堆叠降噪自编码器的数据清洗方法，首先采用粒子群算法对堆叠降噪自编码器中的超参数进行优化，再利用堆叠降噪自编码器提取、还原数据特征的特点来进行数据清洗，实现对孤立点的修复和对空缺数据的填补，以有效提升电力设备状态数据的质量。所提方法简单高效，可以同时提高数据集的准确性和完整性。以电力设备的历史运行数据为例进行测试，算例结果表明本文所提方法相比于其他现有经典方法，数据清洗效果更好，且针对不同异常程度、不同运行状态的数据集都有良好的清洗效果，能够用于提高电力设备状态数据的质量。

At present, big data of power equipment condition shows explosive growth. Equipment failures and personnel errors may result in dirty data, affecting data quality and subsequent analysis results. Therefore, data cleaning is of great significance. Most researches focus on identifying and eliminating abnormal data directly, which destroys the integrity of data. In order to solve this problem, a data cleaning method based on improved stack noise reduction autoencoder is proposed in this paper. Firstly, particle swarm optimization is used to optimize the hyperparameters in the stack noise reduction autoencoder. Then the characteristics of the stack noise reduction autoencoder is used to extract and restore the data features to clean the data. Data quality is improved by repairing isolated data and filling in missing data. The proposed method is simple and efficient, improving both accuracy and integrity of data. Finally, the historical operation data of power equipment is taken as an example. The results show that the proposed method has better data cleaning effect than other existing classical methods, and it has good cleaning effect for data sets with different abnormal degree in different running states. The method proposed in this paper can be used to improve the quality of power equipment status data effectively.