Intelligent Wireless Monitoring Technology for 10kV Overhead lines in Smart Grid Networks

INTRODUCTION: In scalable information systems, edge computing can help to overcome the challenges of latency, bandwidth, and connectivity in large-scale networks by reducing the amount of data that needs to be transmitted over the network. OBJECTIVES: The edge devices, such as sensors, cameras, gateways, routers, switches, multiplexers, integrated access devices, etc., can perform initial data processing and filtering, reducing the data volume sent to the central system. METHODS: This special issue aims to provide the recent progress of intelligent edge caching and computing for scalable information systems. RESULTS: The guest editors received more than 30 submissions, and finally, ten papers were accepted. CONCLUSION: This special issue has contributed to advancing academic research and practice in intelligent edge caching and computing for scalable information systems.

Section: Eai Endorsed Transactionsmentioning

confidence: 99%

Intelligent Edge Caching and Computing for Scalable Information Systems

Zhang

2023

“…As a typical form of Internet of Things (IoT) networks [1][2][3], the power grid network is a critical infrastructure that provides electricity to consumers across various regions. Research on power grid networks focuses on improving the efficiency, reliability, and resilience of the network while also ensuring the safety of the users and the environment.…”

Section: Introductionmentioning

confidence: 99%

Research on the Performance of Text Mining and Processing in Power Grid Networks

Zhou

Lin

et al. 2023

This paper employs deep learning technique to perform the research of text mining for power grid networks, focusing on fundamental elements such as loss and activation functions. Through some analysis and formulas, we explain how these functions contribute to deep learning. We also introduce major deep learning training models, including CNN and RNN, and provide visual aids to aid understanding. To demonstrate the impact of various factors on deep learning training, we employ control variable experiments to analyze the influence of factors such as learning rate, batch size, and data noise on model training trends. While the influence of hyperparameters and data noise are covered in this paper, other factors such as CPU and memory frequency, as well as GPU performance, also play a crucial role in deep learning training. Therefore, continuous adjustments to various factors are necessary to achieve optimal training results for deep learning models in power grid networks.

“…Motivated by the development of IoT networks, the use of digital technologies has enabled the smart grid to become a modernized electricity grid that enhances reliability, efficiency, and flexibility [12][13][14]. It comprises an interconnected network of electricity generation, transmission, and distribution systems that communicate with each other and electricity consumers in real-time.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis and Research of Knowledge Sharing System for Power Grid Networks

Zhou

Shi

Yang

et al. 2023

Knowledge sharing is a critical aspect of machine learning and knowledge management, which also plays an important role in regulating the power grid networks. Hence, it is important to investigate the performance of knowledge sharing in the power grid networks. Motivated by this, we firstly investigate a typical power grid network with a knowledge sharing node, where the transmit power of users is constrained by the knowledge sharing node. We then measure the system performance by evaluating the system outage probability (OP), where the analytical expression of OP is derived in detail. Finally, we present some simulation and numerical results on the OP for the considered power grid networks with knowledge sharing, in order to verify the proposed studies on the OP. In particular, these results show that the knowledge sharing can help enhance the system OP performance efficiently.