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| 适应高比例分布式能源接入的电能表接线错误智能识别 |
| Intelligent identification of meter wiring errors adapted to high-proportion distributed energy |
| 投稿时间：2024-12-03 修订日期：2025-05-31 |
| DOI： |
| 中文关键词: 分布式能源 接线错误识别 XGBoost 智能电表 |
| 英文关键词: Distributed Energy key word 2 Wiring Error Identifi-cation XGBoost Smart Meter |
| 基金项目:中国南方电网有限责任公司科技项目（030500KC23070002 (GDKJXM20230935)） |
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| 中文摘要: |
| 针对高比例分布式能源接入导致轻负载和无功过补偿场景下电能表接线错误误判问题，提出了一种基于XGBoost的智能识别方法。随着分布式能源接入，电网负荷特性发生变化，特别是在光伏、风电等可再生能源波动较大的情况下，电网可能出现轻负载现象。该方法通过分析轻负载和无功过补偿场景中的电流、功率因数等特征，结合实际采集数据和生成数据建立分类模型，有效区分正常运行状态与接线错误。在验证中，模型通过多种评价指标（包括准确率、精度、召回率和F1值），在检测接线错误方面表现出98%以上的高准确率，显著减少了误判率。研究结果表明，该方法能够有效应对由于高比例分布式能源接入导致的复杂电力负荷场景，提供一种高效、可靠的电力系统接线错误智能识别解决方案。 |
| 英文摘要: |
| To address the issue of misjudgment in meter wiring errors caused by light loads and reactive power overcompensation un-der high-proportion distributed energy integration, an intelligent identification method based on XGBoost is proposed. The inte-gration of distributed energy resources significantly alters grid load characteristics, especially under conditions of fluctuating renewable energy sources such as photovoltaics and wind pow-er, leading to light load scenarios. This method analyzes features such as current and power factor in light load and reactive power overcompensation scenar-ios, combining actual collected data with generated data to build a classification model. The model effectively distin-guishes normal operating conditions from wir-ing errors. Validation results demonstrate that the model achieves over 98% accuracy in detecting wiring errors, as measured by metrics such as accuracy, precision, recall, and F1-score, sig-nificantly reducing misjudgment rates. The findings show that this approach can effectively adapt to the complex power load scenarios caused by high-proportion distributed energy integration, providing an efficient and reliable solu-tion for intel-ligent identification of wiring errors in power systems. |
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