Prediction of Dissolved Gas in Transformer Oil Based on Variational Mode Decomposition Integrated with Long Short-Term Memory

• Published in: Processes Vol. 13; no. 5; p. 1446
• Main Authors: Chen, Guoping, Li, Jianhong, Li, Yong, Hu, Xinming, Wang, Jian, Li, Tao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 09.05.2025
• Subjects: Accuracy; Algorithms; Decomposition; Dissolved gases; Electric transformers; Entropy; Fault diagnosis; Forecasts and trends; Gases; Lagrange multiplier; Long short-term memory; Mathematical optimization; Mean; Neural networks; Oils & fats; Optimization; Optimization algorithms; Prediction models; Run time (computers); Search algorithms; Time series; Trends; China
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr13051446

To address the nonlinear and non-stationary characteristics of dissolved gas concentration data in transformer oil, this paper proposes a hybrid prediction model (VMD-SSA-LSTM-SE) that integrates Variational Mode Decomposition (VMD), the Whale Optimization Algorithm (WOA), the Sparrow Search Algorithm (SSA), Long Short-Term Memory (LSTM), and the Squeeze-and-Excitation (SE) attention mechanism. First, WOA dynamically optimizes VMD parameters (mode number k and penalty factor α to effectively separate noise and valid signals, avoiding modal aliasing). Then, SSA globally searches for optimal LSTM hyperparameters (hidden layer nodes, learning rate, etc.) to enhance feature mining for non-continuous data. The SE attention mechanism recalibrates channel-wise feature weights to capture critical time-series patterns. Experimental validation using real transformer oil data demonstrates that the model outperforms existing methods in prediction accuracy and computational efficiency. For instance, the CH4 test set achieves a Mean Absolute Error (MAE) of 0.17996 μL/L, a Mean Absolute Percentage Error (MAPE) of 1.4423%, and an average runtime of 82.7 s, making it significantly faster than CEEMDAN-based models. These results provide robust technical support for transformer fault prediction and condition-based maintenance, highlighting the model’s effectiveness in handling non-stationary time-series data.