LAUSR

Dissolved gas analysis (DGA) in transformer oil is pivotal for early fault diagnosis of oil-immersed transformers, and photoacoustic spectroscopy (PAS) has emerged as an effective technique for this purpose. However, conventional PAS systems suffer from complexity, high costs, and susceptibility to environmental interference due to the inclusion of mechanical choppers and filter discs. To address these limitations, this study proposes a simplified PAS-based sensor system that employs an electrically modulated infrared light source and a single filter, eliminating the need for mechanical components. The core innovation lies in establishing ternary first-order calibration equations to enable simultaneous detection of three key fault gases: methane (CH4), ethylene (C2H4), and ethane (C2H6). By leveraging the nonlinear relationship between photoacoustic signals and modulation frequencies, the excitation frequency is optimized to enhance detection specificity and sensitivity. Experimental results demonstrate that the system achieves a minimum detection limit of 39.09 µl l−1 for the mixture of the three gases, with a relative error of less than 20%. This simplified design significantly reduces system complexity and cost while improving stability, making it highly suitable for real-time on-site monitoring of transformer health. The proposed approach represents a notable advancement in DGA technology, offering a practical solution to support timely fault diagnosis and preventive maintenance of transformers.