Online Measurement Error Detection for the ElectronicTransformer in a Smart Grid

Gu Xiong, Krzysztof Przystupa, Yao Teng, Wang Xue, Wang Huan, Zhou Feng, Xiang Qiong, Chunzhi Wang, Mikołaj Skowron, Orest Kochan, Mykola Beshley
2021 Energies  
With the development of smart power grids, electronic transformers have been widely used to monitor the online status of power grids. However, electronic transformers have the drawback of poor long-term stability, leading to a requirement for frequent measurement. Aiming to monitor the online status frequently and conveniently, we proposed an attention mechanism-optimized Seq2Seq network to predict the error state of transformers, which combines an attention mechanism, Seq2Seq network, and
more » ... ectional long short-term memory networks to mine the sequential information from online monitoring data of electronic transformers. We implemented the proposed method on the monitoring data of electronic transformers in a certain electric field. Experiments showed that our proposed attention mechanism-optimized Seq2Seq network has high accuracy in the aspect of error prediction.
doi:10.3390/en14123551 fatcat:cnpsjhe6b5d7nlklkep4jqkyoy