Acoustic-Based Online Monitoring of Cooling Fan Malfunction in Air-Forced Transformers Using Learning Techniques

Abstract

Cooling fans are one of the critical components of air-forced dry-type transformers for regulating internal temperatures. Therefore, effective malfunction detection is crucial to maintain the transformer temperature within an acceptable range and prevent overheating. Current malfunction detection of cooling fans in certain types of transformers relies on complementary indicators, such as top-oil temperature, oil convection, dissolved gas, and oil quality. However, these conventional indicators are not directly applicable to air-forced transformers, which primarily use cooling fans as their cooling system. To overcome this challenge, this study utilizes cooling fan audio records as indicators. The audio signals are classified into normal and malfunctioning classes using advanced learning algorithms, including convolutional neural networks and random forests. Learning algorithms require transforming recorded audio data into proper formats. Accordingly, convolutional neural networks are trained based on spectrogram images derived from audio signals. For random forests, various time-frequency feature extraction methods are used to derive meaningful representations from audio signals. Besides, multiple data augmentation techniques are employed to enhance the dataset size and diversity. Algorithmic performance is optimized through hyperparameter tuning and classifier threshold adjustment. To further validate the model, a test is conducted on another dataset to evaluate the fitted learning model applicability in real-world applications. Simulations reveal that convolutional neural networks outperform random forests, whereas the latter provides superior interpretability of acoustic features compared to the former.