TransFNN: A Novel Overtemperature Prediction Method for HVDC Converter Valves Based on an Improved Transformer and the F-NN Algorithm

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-04, Vol.23 (8), p.4110
• Main Authors: Zhou, Sihan, Qin, Liang, Sun, Hui, Peng, Bo, Ruan, Jiangjun, Wang, Jing, Tang, Xu, Wang, Xiaole, Liu, Kaipei
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; clustering algorithm; Computational linguistics; converter valve; Cooling; cooling system; Direct current; Electric current converters; Electric power transmission; Electric transformers; Heat; HVDC; Language processing; Mathematical models; Methods; Natural language interfaces; Neural networks; Power; Time series; time series prediction; Transformer; Valves; Water temperature
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23084110

Appropriate cooling of the converter valve in a high-voltage direct current (HVDC) transmission system is highly significant for the safety, stability, and economical operation of a power grid. The proper adjustment of cooling measures is based on the accurate perception of the valve's future overtemperature state, which is characterized by the valve's cooling water temperature. However, very few previous studies have focused on this need, and the existing Transformer model, which excels in time-series predictions, cannot be directly applied to forecast the valve overtemperature state. In this study, we modified the Transformer and present a hybrid Transformer-FCM-NN (TransFNN) model to predict the future overtemperature state of the converter valve. The TransFNN model decouples the forecast process into two stages: (i) The modified Transformer is used to obtain the future values of the independent parameters; (ii) the relation between the valve cooling water temperature and the six independent operating parameters is fit, and the output of the Transformer is used to calculate the future values of the cooling water temperature. The results of the quantitative experiments showed that the proposed TransFNN model outperformed other models with which it was compared; with TransFNN being applied to predict the overtemperature state of the converter valves, the forecast accuracy was 91.81%, which was improved by 6.85% compared with that of the original Transformer model. Our work provides a novel approach to predicting the valve overtemperature state and acts as a data-driven tool for operation and maintenance personnel to use to adjust valve cooling measures punctually, effectively, and economically.