Online Measurement Error Detection for the ElectronicTransformer in a Smart Grid

被引:10
|
作者
Xiong, Gu [1 ]
Przystupa, Krzysztof [2 ]
Teng, Yao [1 ]
Xue, Wang [1 ]
Huan, Wang [1 ]
Feng, Zhou [3 ]
Qiong, Xiang [1 ]
Wang, Chunzhi [4 ]
Skowron, Mikolaj [5 ]
Kochan, Orest [4 ,6 ]
Beshley, Mykola [6 ]
机构
[1] China Elect Power Res Inst, Wuhan 430000, Peoples R China
[2] Lublin Univ Technol, Dept Automat, Nadbystrzycka 36, PL-20618 Lublin, Poland
[3] State Grid Chongqing Elect Power Co Mkt Serv Ctr, Chongqing 400015, Peoples R China
[4] Hubei Univ Technol, Sch Comp Sci, Wuhan 430000, Peoples R China
[5] AGH Univ Sci & Technol, Dept Elect & Power Engn, A Mickiewicza 30, PL-30059 Krakow, Poland
[6] Lviv Polytech Natl Univ, Dept Telecommun, Bandery 12, UA-79013 Lvov, Ukraine
基金
中国国家自然科学基金;
关键词
smart grid; transformer error prediction; attention mechanism; long short-term memory network; Seq2Seq network; SYSTEM; TRANSFORMERS; CALIBRATION; NETWORKS; INTERNET;
D O I
10.3390/en14123551
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:18
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