一种新的数据平衡方法和一个深层分形网络与光梯度增强方法用于智能电网中的盗窃检测
Afrah Naeem1, Nadeem Javaid2, Zeeshan Aslam1
1Department of Computer Science, Bahria University, Islamabad 44000, Pakistan.
Heliyon
|September 8, 2023
概括
本研究引入了一种使用智能电表数据和混合采样技术 (HOUBC) 来检测电力盗窃的新方法,以解决不平衡的数据集. 这种新的方法显著提高了电力公用事业的盗窃检测准确度.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 网络安全 网络安全
背景情况:
- 电力盗窃造成了巨大的收入损失和全球电力公用事业的安全风险.
- 传统的盗窃检测方法往往是低效的,监督学习与不平衡的数据集作斗争.
- 智能电网的进步,特别是先进计量基础设施 (AMI),使更复杂的电力盗窃检测成为可能.
研究的目的:
- 开发一种新且有效的模型,使用智能电表数据检测电力盗窃.
- 为了应对在监督学习中不平衡数据集的挑战,用于盗窃检测.
- 提出混合采样技术和深度学习模型,用于增强盗窃识别.
主要方法:
- 使用两类 (HOUBC) 的混合过量采样和不足采样技术被开发出来,以平衡训练数据集.
- 为了从特征提取和从电力消耗数据中学习,采用了与光梯度增强方法相结合的分形网络.
- 该模型使用中国国家电网公司的现实智能电表数据进行了训练和验证.
主要成果:
- 拟议的HOUBC技术有效地平衡数据集,提高模型性能.
- 与传统方法相比,混合深度学习模型展示了优越的电力盗窃检测能力.
- 该模型依赖智能电表数据,这有助于快速和广泛的采用.
结论:
- 开发的模型在检测电力盗窃,减轻收入损失和提高电网安全方面取得了重大进展.
- 对于机器学习应用中的不平衡数据挑战,HOUBC技术提供了一个强大的解决方案.
- 分形网络和梯度增强的集成显示出对分析各种应用的智能电网数据的前景.
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