Online Measurement Error Detection for the ElectronicTransformer in a Smart Grid

• Main Authors: Gu Xiong, Krzysztof Przystupa, Yao Teng, Wang Xue, Wang Huan, Zhou Feng, Xiang Qiong, Chunzhi Wang, Mikołaj Skowron, Orest Kochan, Mykola Beshley
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: Energies
• Subjects: smart grid; transformer error prediction; attention mechanism; long short-term memory network; Seq2Seq network
• Online Access: https://www.mdpi.com/1996-1073/14/12/3551

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 bidirectional 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.