Alsulami Abdulaziz A, Abu Al-Haija Qasem, Alturki Badraddin, Alqahtani Ali, Binzagr Faisal, Alghamdi Bandar, Alsemmeari Rayan A
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
Department of Cybersecurity, Faculty of Computer & Information Technology, Jordan University of Science and Technology, PO Box 3030, Irbid, 22110, Jordan.
Sci Rep. 2024 Jul 6;14(1):15591. doi: 10.1038/s41598-024-66387-1.
Microgrids are small-scale energy system that supplies power to homes, businesses, and industries. Microgrids can be considered as a trending technology in energy fields due to their power to supply reliable and sustainable energy. Microgrids have a mode called the island, in this mode, microgrids are disconnected from the major grid and keep providing energy in the situation of an energy outage. Therefore, they help the main grid during peak energy demand times. The microgrids can be connected to the network, which is called networked microgrids. It is possible to have flexible energy resources by using their enhanced energy management systems. However, connection microgrid systems to the communication network introduces various challenges, including increased in systems complicity and noise interference. Integrating network communication into a microgrid system causes the system to be susceptible to noise, potentially disrupting the critical control signals that ensure smooth operation. Therefore, there is a need for predicting noise caused by communication network to ensure the operation stability of microgrids. In addition, there is a need for a simulation model that includes communication network and can generate noise to simulate real scenarios. This paper proposes a classifying model named Noise Classification Simulation Model (NCSM) that exploits the potential of deep learning to predict noise levels by classifying the values of signal-to-noise ratio (SNR) in real-time network traffic of microgrid system. This is accomplished by initially applying Gaussian white noise into the data that is generated by microgrid model. Then, the data has noise and data without noise is transmitted through serial communication to simulate real world scenario. At the end, a Gated Recurrent Unit (GRU) model is implemented to predict SNR values for the network traffic data. Our findings show that the proposed model produced promising results in predicting noise. In addition, the classification performance of the proposed model is compared with well-known machine learning models and according to the experimental results, our proposed model has noticeable performance, which achieved 99.96% classification accuracy.
微电网是向家庭、企业和工业供电的小规模能源系统。由于微电网能够提供可靠且可持续的能源,因此可被视为能源领域的一项热门技术。微电网有一种名为孤岛模式的运行模式,在这种模式下,微电网与主电网断开连接,并在停电情况下持续供电。因此,它们在能源需求高峰期对主电网起到辅助作用。微电网可以连接到网络,这被称为联网微电网。通过使用其增强的能源管理系统,可以拥有灵活的能源资源。然而,将微电网系统连接到通信网络会带来各种挑战,包括系统复杂性增加和噪声干扰。将网络通信集成到微电网系统中会使系统容易受到噪声影响,可能会干扰确保平稳运行的关键控制信号。因此,需要预测通信网络产生的噪声,以确保微电网的运行稳定性。此外,还需要一个包含通信网络并能产生噪声以模拟真实场景的仿真模型。本文提出了一种名为噪声分类仿真模型(NCSM)的分类模型,该模型利用深度学习的潜力,通过对微电网系统实时网络流量中的信噪比(SNR)值进行分类来预测噪声水平。这是通过首先将高斯白噪声应用于微电网模型生成的数据来实现的。然后,有噪声的数据和无噪声的数据通过串行通信进行传输,以模拟现实世界的场景。最后,实现了一个门控循环单元(GRU)模型来预测网络流量数据的SNR值。我们的研究结果表明,所提出的模型在预测噪声方面产生了有前景的结果。此外,将所提出模型的分类性能与知名机器学习模型进行了比较,根据实验结果,我们提出的模型具有显著的性能,分类准确率达到了99.96%。