| 黄小奇,郑惠哲,曾崇立,黄朝凯,陈建熹,张孝顺.适应高比例分布式能源接入的电能表接线错误智能识别[J].电力需求侧管理,2026,28(3):125-130 |
| 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：2026-01-10 修订日期：2026-03-02 |
| DOI：10.3969/j.issn.1009-1831.2026.03.018 |
| 中文关键词: 高比例分布式能源 接线错误识别 智能电能表 |
| 英文关键词: high-proportion distributed energy wiring error identification smart meter |
| 基金项目:中国南方电网有限责任公司科技项目(030500KK23070002(GDKJXM20230935)) |
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| 中文摘要: |
| 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。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。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: |
| With the integration of distributed energy resources, the load characteristics of power grids have changed. In particular, under conditions where renewable energy sources such as photovoltaic and wind power exhibit large fluctuations, the grid may experience light-load conditions. To address the misjudgment of wiring errors in electric energy meters under scenarios of light load and reactive power over-compensation caused by a high penetration of distributed energy resources, an intelligent identification method based on XGBoost is proposed. By analyzing features such as current and power factor in light-load and reactive power overcompensation scenarios, and by combining actual collected data with generated data to build a classification model, the method effectively distinguishes normal operating states from wiring errors. In validation tests, the model achieved an accuracy of over 98% in detecting wiring errors, as demonstrated by multiple evaluation metrics, including accuracy, precision, recall, and F1-score, significantly reducing the false judgment rate. The results show that this method can effectively handle complex power load scenarios caused by the high penetration of distributed energy resources, providing an efficient and reliable intelligent solution for identifying wiring errors in power systems. |
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