混合CNN-变压器网络用于智能电网中的电力盗窃检测
Yu Bai1, Haitong Sun1, Lili Zhang1
1School of Electronical and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
这项研究介绍了一种混合CNN-变压器模型,用于先进的电力盗窃检测. 这种新的方法改善了电力消耗数据中的异常检测,提高了准确性和稳定性.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 电力盗窃是全球电力损失的主要原因之一.
- 现有的用于电力盗窃检测 (ETD) 的神经网络模型在深度特征提取方面扎.
- 精确检测电力消耗数据中的异常仍然是一个挑战.
研究的目的:
- 开发一种结合卷积神经网络 (CNN) 和变压器网络的新型混合模型,以改进电力盗窃检测.
- 提高从功耗数据中提取深度特征的能力,以获得更可靠的ETD.
- 解决现有方法在捕获多尺度和时间依赖性方面的局限性.
主要方法:
- 一种混合模型,将CNN与双尺度双分支 (DSDB) 结构集成在一起,用于浅层特征提取.
- 使用具有高斯加权重 (GWT) 的变压器模块进行深层次的时间依赖提取.
- 应用该模型来分析功耗数据以检测异常.
主要成果:
- 与现有方法相比,拟议的混合模型展示了优越的特征提取能力.
- 该模型获得了高的F1分数和曲线下的面积 (AUC) 值,表明有效的电力盗窃检测.
- 该方法在识别电力消耗模式中的异常方面表现出显著的稳定性.
结论:
- 混合CNN-GWT模型为电力盗窃检测提供了强大而稳健的解决方案.
- 这种方法在能源消耗数据中异常检测领域取得了重大进展.
- 这些发现表明,减少由于电力盗窃造成的电力损失是一个有希望的方向.
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