Advanced Preliminary Screening for PD Detection for Overhead Distribution Lines with Covered Conductors

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This study proposes a novel approach to optimize partial discharge detection in overhead lines with covered conductors. The approach uses 2D Convolutional Neural Networks and HW neural network accelerator Edge TPU. The critical advancement of this research lies in exploring two distinct 2D classification methods - 2D histograms and spectrogram - and adjusting neural network thresholds to selectively identify potential positive PD samples with enhanced accuracy. The proposed approach addresses the prevalent challenge of costly and limited remote data transmission in PD detection. It significantly reduces the need for extensive data transfer by focusing on potentially positive samples. This evaluation of two approaches for detection of partial discharges, coupled with the strategic threshold adjustment, presents a novel solution in the realm of PD detection, offering increased efficiency and cost-effectiveness. By evaluating and comparing these strategies of 2D classification and threshold optimization, this research contributes to the field of partial discharges detection. It proposes a method that not only minimizes operational costs but also aligns with environmental sustainability goals, paving the way for more advanced maintenance practices in power transmission systems.