基于人工智能的安全电网协议,用于智慧城市
Adel Sulaiman1, Bharathiraja Nagu2, Gaganpreet Kaur2
1Department of Computer Science, College of Computer Science, and Information Systems, Najran University, Najran 61441, Saudi Arabia.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了一种基于人工智能的方法,使用长短期内存 (LSTM) 和循环神经网络 (RNN) 来提高智能电网的安全性和效率. 该方法有效地检测电网中的网络威胁,改善数据管理和隐私.
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:
- 现代电力系统越来越复杂,集成了众多智能电网组件,产生大量数据.
- 传统计算与智能电网数据作斗争,需要人工智能驱动的解决方案来有效管理和安全.
- 网络攻击对智能电网的稳定性和效率构成重大威胁.
研究的目的:
- 使用LSTM和RNN开发基于AI的模型,以增强智能电网的动态特性.
- 区分正常的系统变化和实时网络威胁,包括来自修订编码方案 (RES) 的网络威胁.
- 为安全和高效的消费者电力数据共享提出联合学习战略.
主要方法:
- 开发LSTM和RNN模型,以适应性地捕捉时间变化的能源系统属性.
- 实施联合学习策略,支持边缘云,以共享消费者数据,优先考虑隐私和通信效率.
- 为能源数据所有者 (EDO) 和能源服务运营设计优化问题,考虑非独立和相同分布的 (IID) 数据效应.
主要成果:
- 拟议的LSTM基于RNN的结构有效地识别了假数据注入攻击 (FDIA) 和其他网络威胁.
- 模拟表明,该方法成功地鼓励EDO使用高质量的本地模型,提高能源服务提供商的支付并减少延迟.
- 与其他测试模型相比,LSTM模型的训练时间更长,训练损失更高,但实现了有效的威胁检测.
结论:
- 开发的基于人工智能的方法,特别是使用LSTM RNN,为检测智能电网中复杂的网络威胁提供了强大的解决方案.
- 联合学习策略确保了消费者隐私,同时实现了高效的数据利用,以改善电网运行.
- 该方法提高了智能电网的安全性,效率和数据管理能力,以应对不断变化的威胁和数据复杂性.
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