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- Title
Enhanced fault localization in multi-terminal transmission lines using novel machine learning.
- Authors
Wang, Yingyun; Qi, Xiaoxia; Chen, Yang
- Abstract
Accurate fault location on transmission lines is paramount for ensuring the reliable and efficient operation of the electricity grid, which underpins every aspect of modern society. Existing fault localization methods for transmission lines often face shortcomings, particularly in scenarios involving multi-terminal transmission lines, where complexities arise due to dispersed generations and intricate network configurations. Traditional approaches may struggle to provide accurate fault localization, impacting the reliability and efficiency of the electricity grid. Research provide a unique fault localization technique in this article depends on Phasor Measurement Units (PMUs) and Bidirectional Gradient Boost Random Forest (BDGB-RF) machine learning technique to address these challenges. The proposed method offers several advantages over traditional methods, including enhanced accuracy and efficiency. By leveraging PMU data and BDGB-RF, the method provides a two-phase fault localization strategy, incorporating fault line selection based on nodal current imbalance and subsequent fault distance determination. Simulation results demonstrate the effectiveness of the proposed approach, achieving up to 97% accuracy in the majority of studied scenarios, even under different tapping configurations. The adoption of this approach could significantly impact the practices of experts in the field, facilitating more reliable fault detection and localization in complex transmission line networks. This, in turn, can contribute to the resilience and stability of power systems, ultimately improving grid reliability and minimizing downtime.
- Subjects
PHASOR measurement; FAULT location (Engineering); ELECTRIC lines; RANDOM forest algorithms; MODERN society; ELECTRIC fault location
- Publication
International Journal for Simulation & Multidisciplinary Design Optimization, 2024, Vol 15, p1
- ISSN
1779-627X
- Publication type
Academic Journal
- DOI
10.1051/smdo/2024010