电力变压器油中溶解气体离群值识别和数据重构

doi:10.19595/j.cnki.1000-6753.tces.231033

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摘要高质量传感数据是驱动新型电力系统数字化和智能化发展的基础,而由于传感器性能退化、传输中断或其他干扰因素,数据时常出现错误和异常值,造成数据利用率低等问题。针对在役电力变压器油中溶解气体在线监测数据,该文提出了基于COPOD、孤立森林（IForest）与Grubbs的联合方法提升油中溶解气体数据的价值。首先,通过COPOD和IForest筛选出包含离群点的数据集,再采用Grubbs对其进行检验,有效识别离群值。进一步地,采用掩码方式优化训练Transformer神经网络模型,填补空缺值重构油中溶解气体数据序列。在相同气体数据序列上,所提算法正确识别点数、正确识别离群点数和受试者工作特征曲线平均面积相比于传统K-近邻算法分别提升了3.5%、29.4%和5.0%。对于数据填补,对比双向缩放算法,填补后的数据与实际数据的方均根误差均值为7.29 μL/L,平均绝对误差均值为2.7 μL/L,性能分别提升了9.7%和9.2%,有效地提高了数据的质量和利用率。最后,通过11台500 kV变压器油中溶解气体数据分析,有力支撑了变压器状态评价和设备数字化管理。

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关键词 ：电力变压器, 油中溶解气体, 联合检测方法, 离群点检测, 数据重构技术

Abstract：Transformer plays a vital role of power transmission and voltage conversion in power system, and its health is of great significance to the safe operation and reliability of the whole power grid. Dissolved gas analysis (DGA) in oil is an important indicator for transformer condition evaluation and health evaluation, especially in the process of power equipment condition maintenance and the promotion of digital power grid. Its data quality and data accumulation are of great value for transformer health index analysis, equipment management and even smart grid development. However, due to the degradation of performance, transmission interruption or other interference factor during the operation of the on-line sensors, the monitoring data occur errors and outliers, which greatly affects the use of data, and results in a waste of resources.
Considering that Copula based outlier detection (COPOD) algorithm could handle multidimensional, high and large-scale data sets, and isolation forest (IForest) algorithm had a better detection effect on dense data sets, the two algorithms were combined to mark more outliers by setting a higher detection threshold as preliminary test. To reduce the influence of both ends of data on the detection algorithm caused by the trend of data, the method of sliding window was used to detect outliers by the two algorithms respectively, and the intersection of the results was taken as the outlier data set. Due to the high threshold, the suspect outlier data set were mixed with some normal DGA data. Further, Grubbs test with high sensitivity, wide applicability and suitable for single detection was introduced, and its performance is better in small samples. Then the suspect outlier data was tested twice through Grubbs method for improving the accuracy of outlier identification, and the misidentified normal data was excluded from the detection results. After the outliers are removed, Transformer neural network is used to fill in the outliers. Mask filling and observation reconstruction were adopted to optimize the loss function and train the network structure. Finally, the predicted values using neural network were used to fill in the data to construct DGA data.
Simulation data and actual online monitoring data of 11 500 kV oil-immersed transformers in operation were used to verify the performance of the algorithm. Results show that, the number of correct identification points, the number of correct identification outlier points and the average area of the receiver operating characteristic (ROC) curve of the proposed algorithm are improved by 3.5%, 29.4% and 5.0% on the same gas series compared with the traditional K-nearest neighbors (KNN). For data filling, compared with the bidirectional scaling (BiScaler) algorithm, the mean of root mean square error (RMSE) is 7.29 μL/L, and the mean of mean absolute error (MAE) is 2.66 μL/L. The performance is improved by 9.7% and 9.2% respectively, effectively improving the quality and utilization of data.
The following conclusions can be drawn from the simulation analysis: (1) The combined algorithm of COPOD, IForest and Grubbs, which is used to identify outliers in transformer DGA online data, is more accurate than the traditional algorithm KNN, the misidentification of outliers is also reduced. (2) By optimizing the loss function using mask filling and observation reconstruction, the Transformer neural network is trained to improve the prediction performance and effectively complete the data filling task. (3) After the transformer DGA data is filled, the data is more concentrated and the quality is significantly improved without changing the overall data distribution. This method has moderate complexity and high reliability,and it is suitable for large-scale data preprocessing in practical applications.

Key words： Power transformer dissolved gases in oil joint detection method outlier detection data reconstruction technique

收稿日期: 2023-07-03

PACS:

TM411

基金资助:国家自然科学基金（52177150）和南京航空航天大学科研与实践创新计划（xcxjh20220314）资助项目

通讯作者: 江军男,1988年生,博士,研究员,研究方向为面向电力设备及航空航天电气装备的状态监测与故障诊断。E-mail: jiangjun0628@163.com

作者简介: 张文乾男,1997年生,硕士研究生,研究方向为电气设备状态检测与故障诊断。E-mail: hnzzzwq123@163.com

引用本文:

江军, 张文乾, 李波, 李晓涵, 范利东. 电力变压器油中溶解气体离群值识别和数据重构[J]. 电工技术学报, 2024, 39(17): 5521-5533. Jiang Jun, Zhang Wenqian, Li Bo, Li Xiaohan, Fan Lidong. Outlier Detection and Data Reconstruction of Dissolved Gas in Oil for Power Transformers. Transactions of China Electrotechnical Society, 2024, 39(17): 5521-5533.

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