Sulaiman Adel, Nagu Bharathiraja, Kaur Gaganpreet, Karuppaiah Pradeepa, Alshahrani Hani, Reshan Mana Saleh Al, AlYami Sultan, Shaikh Asadullah
Department of Computer Science, College of Computer Science, and Information Systems, Najran University, Najran 61441, Saudi Arabia.
Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India.
Sensors (Basel). 2023 Sep 22;23(19):8016. doi: 10.3390/s23198016.
Due to the modern power system's rapid development, more scattered smart grid components are securely linked into the power system by encircling a wide electrical power network with the underpinning communication system. By enabling a wide range of applications, such as distributed energy management, system state forecasting, and cyberattack security, these components generate vast amounts of data that automate and improve the efficiency of the smart grid. Due to traditional computer technologies' inability to handle the massive amount of data that smart grid systems generate, AI-based alternatives have received a lot of interest. Long Short-Term Memory (LSTM) and recurrent Neural Networks (RNN) will be specifically developed in this study to address this issue by incorporating the adaptively time-developing energy system's attributes to enhance the model of the dynamic properties of contemporary Smart Grid (SG) that are impacted by Revised Encoding Scheme (RES) or system reconfiguration to differentiate LSTM changes & real-time threats. More specifically, we provide a federated instructional strategy for consumer sharing of power data to Power Grid (PG) that is supported by edge clouds, protects consumer privacy, and is communication-efficient. They then design two optimization problems for Energy Data Owners (EDO) and energy service operations, as well as a local information assessment method in Federated Learning (FL) by taking non-independent and identically distributed (IID) effects into consideration. The test results revealed that LSTM had a longer training duration, four hidden levels, and higher training loss than other models. The provided method works incredibly well in several situations to identify FDIA. The suggested approach may successfully induce EDOs to employ high-quality local models, increase the payout of the ESP, and decrease task latencies, according to extensive simulations, which are the last points. According to the verification results, every assault sample could be effectively recognized utilizing the current detection methods and the LSTM RNN-based structure created by Smart.
由于现代电力系统的快速发展，通过通信系统将更多分散的智能电网组件安全地连接到电力系统中，这些通信系统环绕着广阔的电力网络。通过实现分布式能源管理、系统状态预测和网络攻击安全等广泛应用，这些组件生成了大量数据，从而实现智能电网的自动化并提高其效率。由于传统计算机技术无法处理智能电网系统产生的大量数据，基于人工智能的替代方案受到了广泛关注。本研究将专门开发长短期记忆(LSTM)和递归神经网络(RNN)，通过纳入自适应时间发展的能源系统属性来解决这一问题，以增强受修订编码方案(RES)或系统重新配置影响的当代智能电网(SG)动态特性模型，以区分LSTM变化和实时威胁。更具体地说，我们为消费者向电网(PG)共享电力数据提供了一种联合指导策略，该策略由边缘云支持，保护消费者隐私，并且通信效率高。然后，他们针对能源数据所有者(EDO)和能源服务运营设计了两个优化问题，以及一种考虑非独立同分布(IID)效应的联邦学习(FL)中的局部信息评估方法。测试结果表明，LSTM的训练时间更长，有四个隐藏层，训练损失比其他模型更高。所提供的方法在几种情况下都能很好地识别FDIA。根据大量模拟结果显示，建议的方法可以成功地促使EDO采用高质量的局部模型，提高ESP的收益，并减少任务延迟，这是最后几点。根据验证结果，利用当前的检测方法和智能创建的基于LSTM RNN的结构，可以有效地识别每个攻击样本。
