Operational Data Set, Cleaning, and Evaluation of AI Models for Predicting Faults in Medium-Voltage Reclosers 核心 · 已核验

doi:10.21227/4ydb-s273

"This study presents a time-series dataset collected from nine SEL three-phase reclosers installed in medium-voltage distribution networks of a utility in the Puno region, Peru, between January 2024 and June 2025. The dataset integrates two types of records: load measurements with a 15-minute resolution (156,023 observations) and protection system events (1,191 three-phase and single-phase faults). The electrical variables include phase and line-to-line voltages, currents, active and reactive power, power factor, frequency, accumulated energy, and sequence components.Data curation involved correcting date format inconsistencies, noise filtering, and temporal synchronization to ensure integrity and continuity. The dataset\u2019s uniqueness lies in its origin: real measurements from a Latin American utility, a region underrepresented in open power system datasets, and the careful selection of nine authorized reclosers from over 90 devices, based on data availability.This dataset enables the development and evaluation of machine learning methods for fault prediction, predictive maintenance, and time-series analysis in distribution networks, providing a valuable resource for both the data science and power systems communities."

落地页

https://ieee-dataport.org/competitions/operational-data-set-cleaning-and-evaluation-ai-models-predicting-faults-medium

许可证

CC-BY-4.0 (判读置信:inferred)

国内可访问性

国内直连:需登录 (2026-07-11 检测) 代理通道:需登录 (2026-07-11 检测)
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PHM 任务

fault_detection

故障工况