Sensor and Partial Discharge Calibration for Diagnosis and Condition Monitoring of High Voltage Equipment: A Critical Analysis
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The crucial challenge in Condition Monitoring (CM) and Predictive Maintenance of high-voltage (HV) equipment is achieving reliable detection and precise localization of the Partial Discharge (PD) source. PD is a vital indicator of insulation degradation. This challenge is compounded by the complex and non-homogeneous operational environment, where internal structures like transformer windings and cores significantly distort and attenuate signals, while simultaneously creating the phenomenon of acoustic multipath. This critical analysis examines the convergence of advancements in sensor technology, calibration techniques, and sophisticated algorithms in the effort to overcome these PD localization challenges. The review highlights significant progress across various sensor types, including Acoustic Emission (AE) sensors optimized with the KLM Model to enhance sensitivity, UHF sensors that offer superior noise immunity, and innovative pressure-balanced fiber-optic acoustic sensors specifically designed for detecting dual PDs. To achieve higher localization accuracy, signal processing techniques have evolved beyond the basic Time Difference of Arrival (TDOA) method. Currently, robust algorithms are applied, such as Generalized Cross-Correlation with Phase Transformation (GCC-PHAT), which effectively suppresses noise and reflections, and Particle-Swarm-Optimization Route-Searching (PSORS) to intelligently model the acoustic signal propagation paths around internal obstructing structures. Alternative approaches also include online localization based on electrical Transfer Function analysis. The integration of highly sensitive sensor technology with intelligent pathfinding algorithms is key to realizing accurate multi-method diagnosis, thereby supporting more reliable and efficient operation of HV equipment
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