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TBMF Framework: A Transformer-Based Multilevel Filtering Framework for PD Detection
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DOI：10.1109/TIE.2023.3274881.png)
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Abstract
En 中文
Partial discharge (PD) of overhead lines is an indication of imminent dielectric breakdown and a cause of insulation degradation. Efficient PD detection is the significant foundation of electrical system maintenance. This article proposes a transformer-based multilevel filtering (TBMF) framework for PD detection. It creates the multilevel filtering mechanism to be robust to large-scale industrial measurements contaminated with a variety of background noises and plenty of invalid information. The primary filtering innovatively creates the principle of possible PD measurements to replace feature extraction and reduce manual intervention. For the first time, multiple transformer-based algorithms are introduced to the PD detection field to process the possible PD measurements without relying on the sequence order. The secondary filtering then refines the segmentation-level results from the primary filtering and outputs the overall detection results. Multiple numerical algorithms, artificial intelligence models, and intelligent metaheuristic optimization have been adopted as methodologies of the secondary filtering. The TBMF framework is experimentally verified by extensive field trial data of medium-voltage overhead power lines. Its detection accuracy reaches 96.1$\%$, which outperforms other techniques in the literature. It provides an economic and complete PD detection solution to maintain the economical and safe operation of power systems.
Keywords:
Artificial intelligence (AI)
long-term online monitoring
medium-voltage (MV) overhead power line
metaheuristic optimization
multilevel filtering
partial discharge (PD) detection
signal processing
transformer
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