Article: An operation status prediction of transformer calibration instrument using PSO-attention-LSTM algorithm Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.9 pp.133 - 154 Abstract: To solve the problems of low sampling completeness, low accuracy, and long time consumption in traditional methods, a new operation status prediction of transformer calibration instrument using PSO-Attention-LSTM algorithm is proposed. Firstly, collect multidimensional temporal data through a remote calibration system. Secondly, the SAT-GAN model based on AEGAN is introduced to clean and repair abnormal data. Finally, the PSO-Attention-LSTM model dynamically assigns weights to each time step in the input sequence through attention mechanism, highlights key state information, and captures long-term dependencies of temporal features with the help of LSTM's gating mechanism, thereby achieving prediction of the operating status of the transformer calibrator. In experiments, the proposed technique attains a peak sampling integrity of 97.45%, accuracy rate of 98.60%, and a maximum time consumption of less than 1.19s, which verifies the engineering practicality of this method in predicting the operating status of the calibration instrument. Inderscience Publishers - linking academia, business and industry through research

Title: An operation status prediction of transformer calibration instrument using PSO-attention-LSTM algorithm

Authors: Quan Wang; Jun Zhang; Xia Han; Cong Qi; Yushu Cheng; Jiliang Fu; Jiachuan Long

Addresses: China Electric Power Research Institute, National Center for High Voltage Measurement, Key Laboratory of Measurement and Test of High Voltage and Heavy Current, State Administration for Market Regulation, Wuhan, 430074, China ' China Electric Power Research Institute, National Center for High Voltage Measurement, Key Laboratory of Measurement and Test of High Voltage and Heavy Current, State Administration for Market Regulation, Wuhan, 430074, China ' State Grid Shanxi Marketing Service Center, Taiyuan, 03002, China ' China Electric Power Research Institute, National Center for High Voltage Measurement, Key Laboratory of Measurement and Test of High Voltage and Heavy Current, State Administration for Market Regulation, Wuhan, 430074, China ' State Grid Shanxi Marketing Service Center, Taiyuan, 03002, China ' China Electric Power Research Institute, National Center for High Voltage Measurement, Key Laboratory of Measurement and Test of High Voltage and Heavy Current, State Administration for Market Regulation, Wuhan, 430074, China ' School of Electronics and Information Engineering, Wuhan Donghu College, Wuhan, 430212, China

Abstract: To solve the problems of low sampling completeness, low accuracy, and long time consumption in traditional methods, a new operation status prediction of transformer calibration instrument using PSO-Attention-LSTM algorithm is proposed. Firstly, collect multidimensional temporal data through a remote calibration system. Secondly, the SAT-GAN model based on AEGAN is introduced to clean and repair abnormal data. Finally, the PSO-Attention-LSTM model dynamically assigns weights to each time step in the input sequence through attention mechanism, highlights key state information, and captures long-term dependencies of temporal features with the help of LSTM's gating mechanism, thereby achieving prediction of the operating status of the transformer calibrator. In experiments, the proposed technique attains a peak sampling integrity of 97.45%, accuracy rate of 98.60%, and a maximum time consumption of less than 1.19s, which verifies the engineering practicality of this method in predicting the operating status of the calibration instrument.

Keywords: PSO-Attention-LSTM algorithm; transformer calibrator; operation status prediction; AEGAN; SAT-GAN model.

DOI: 10.1504/IJBIDM.2026.154230

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.9, pp.133 - 154

Received: 14 Nov 2025
Accepted: 13 Mar 2026
Published online: 17 Jun 2026 *

Title: An operation status prediction of transformer calibration instrument using PSO-attention-LSTM algorithm

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