基于RNN-BiLSTM-CRF的合并深度学习模型用于电力盗窃检测,以确保智能电网的安全
Aqsa Khalid1, Ghulam Mustafa2, Muhammad Rizwan Rashid Rana2
1Department of Computer Science, COMSATS University, Islamabad, Pakistan.
PeerJ. Computer science
|March 4, 2024
概括
本研究引入了一种先进的深度学习模型,用于打击电力盗窃,在智能电网中检测非技术损失 (NTLs) 时达到93.05%的准确性. 这种新的方法通过分析1D和2D电力数据来增强检测.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 电力盗窃在电网中造成重大非技术损失 (NTL),影响电网稳定性和电力供应质量.
- 传统的电力盗窃检测方法仅分析一维 (1D) 数据,对于复杂的智能电网缺乏足够的准确性.
- 智能电网能够实现先进的解决方案,用于检测和减轻由于双向数据流导致的电力盗窃等问题.
研究的目的:
- 开发一个强大的,基于深度学习的模型,用于精确检测智能电网中的电力盗窃.
- 克服现有的1D数据分析方法的局限性,以识别非技术性损失.
- 通过改进的盗窃检测机制,提高电源的安全性和可靠性.
主要方法:
- 提出了一个集体深度学习模型,即反复神经网络双向长期短期记忆条件随机场 (RNN-BiLSTM-CRF).
- 整合了单维 (1D) 和二维 (2D) 的电力消耗数据,以进行增强分析.
- 利用RNN和BiLSTM架构的优势,在消费数据中实现复杂的模式识别.
主要成果:
- 拟议的RNN-BiLSTM-CRF模型在检测电力盗窃时实现了93.05%的高准确率.
- 与主要依赖1D数据分析的现有方法相比,该模型表现出更高的性能.
- 一维和二维数据的合并显著提高了盗窃检测过程的有效性.
结论:
- 基于深度学习的RNN-BiLSTM-CRF模型提供了一个高度有效的解决方案,用于检测智能电网中的电力盗窃.
- 利用多维电力消耗数据对于提高非技术性损失检测的准确性至关重要.
- 开发的模型有助于保护智能电网并确保更可靠的电力供应.
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