Application of Enhanced Optimal-Detection of Time–Frequency Domain Reflectometry on HTS Cable With High-Resolution

Abstract

Time–frequency domain reflectometry (TFDR) has been used for sensing various types of cables including high temperature superconducting (HTS) cables. Recently, the optimal-detection of time–frequency domain reflectometry (OD-TFDR) has been proposed and has better detection and localization performance than conventional TFDR. Additionally, OD-TFDR has provided a threshold that can distinguish detected signals from Gaussian noise. However, OD-TFDR does not provide a criterion for classifying the detected signals into fault signals and artifacts. Furthermore, the diagnosis result of OD-TFDR is not intuitive because an additional tool is needed to locate each fault. Thus, this paper proposes a new diagnosis algorithm that can provides a criterion for distinguishing between artifacts and faults by considering the propagation direction of the incident signal in the join time–frequency domain while preserving the detection and localization performance of OD-TFDR. Through a template matching process of generalized time–frequency cross-correlation (GTFCC), the core algorithm of OD-TFDR, a modified–generalized time–frequency cross-correlation (M–GTFCC) function is derived. Next, a modified–time–frequency cross-correlation (M–TFCC) function is derived by slicing the M–GTFCC function considering the propagation direction of signal. Finally, M–TFCC function is proposed as a new intuitive diagnosis result with artifact-free and high-resolution. The performance of the proposed algorithm is tested via experimental setup for a real-world single phase HTS cable with an emulated local quench by heating element, and the efficacy of the proposed algorithm is verified based on three performances: fault detection, locating accuracy, and resolution.