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Reliable monitoring of power system equipment is essential for ensuring operational stability, minimizing unexpected outages, and improving grid reliability. Among various condition monitoring techniques, optical sensing technologies have attracted significant attention due to their high sensitivity, electromagnetic interference immunity, and suitability for harsh electrical environments. This paper presents the design and application of a photonic crystal fiber (PCF)-based sensing system for real-time monitoring in power systems, with particular emphasis on dissolved gas detection in oil-immersed transformers. The proposed sensing approach employs hollow-core photonic crystal fiber (HC-PCF) as an optical absorption chamber, enabling enhanced light–gas interaction while maintaining a compact and flexible sensor configuration. Based on infrared absorption spectroscopy and Beer–Lambert theory, the system is designed to achieve high-sensitivity detection of characteristic fault gases generated during transformer insulation degradation. The diffusion characteristics of gases inside the HC-PCF are theoretically analyzed and experimentally verified to evaluate sensor response performance. Experimental investigations demonstrate that the proposed PCF-based sensing system provides excellent linearity, strong selectivity, and improved detection sensitivity for low-concentration acetylene monitoring. Allan variance analysis indicates that the optimal signal-to-noise ratio is achieved with a 29 s averaging time, resulting in a minimum detection limit of 4.5 ppm. Furthermore, the compact structure and extended optical interaction length offered by the HC-PCF significantly improve the practicality of online transformer condition monitoring. The results confirm that photonic crystal fiber-based sensing technology offers a promising solution for next-generation real-time power system monitoring applications. Owing to its high sensitivity, compactness, and capability for continuous online operation, the proposed system demonstrates strong potential for deployment in intelligent grid monitoring and predictive maintenance of high-voltage electrical equipment.