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Edge Computing for Smart Grid Cyber-Physical System
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Edge Computing for Smart Grid Cyber-Physical System

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 10322

Special Issue Editors

Dr. Peng Zeng

E-Mail Website
Guest Editor
Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang 110016, China
Interests: smart grid; cyber-physical system
Dr. Ning Zhang

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada
Interests: mobile edge computing; wireless networking; smart grid; connected vehicles
Dr. Lei Liu

E-Mail Website
Guest Editor
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: intelligent transportation systems; internet of vehicles; distributed computing
Special Issues, Collections and Topics in MDPI journals
Dr. Chunhe Song

E-Mail Website
Guest Editor
Shenyang Institute of Automation, Chinese Academy of Science, Shenyang 110016, China
Interests: smart grid; edge computing

Special Issue Information

Dear Colleagues,

A smart grid cyber–physical system is a highly intelligent system that is used for comprehensive data collection, state perception, and the system control of a smart grid system. With the continuous expansion of the scale of smart grids and the increasing complexity of the equipment, the data generated by smart grid systems present an explosive growth, which poses a great challenge to the real-time reliability of systems. Edge computing is used to provide users with nearby services with near-edge infrastructure or a large number of data terminals, so as to improve data processing efficiency and reduce the system delay and load of cloud computing. This Special Issue aims to collect original papers on the recent progress of edge computing for smart grid cyber–physical systems. Potential topics include, but are not limited to, the following:

  • Edge computing for the signal processing of smart grid cyber–physical systems.
  • Edge computing for the fault detection of smart grid cyber–physical systems.
  • Edge computing for the resource allocation and task scheduling of smart grid cyber–physical systems.
  • Edge computing for the terminal key management and access authentication technology for smart grid cyber–physical systems.
  • Edge computing for the topology identification and line loss rate calculation of smart grid cyber–physical systems.
  • Edge computing for the user behavior analysis of smart grid cyber–physical systems.
  • Edge computing for the noninvasive load monitoring of the household electrical equipment of smart grid cyber–physical systems.
  • Edge computing for the demand response of smart grid cyber–physical systems.
  • Edge computing for the optimal control of micro-grid cyber–physical systems with distributed energy.
  • Edge computing for the distributed cooperative optimal scheduling of multi-micro-grid cyber–physical systems.
  • Edge computing for the distributed intelligent transaction decision of multi-micro-grid cyber–physical systems.

Dr. Peng Zeng
Dr. Ning Zhang
Dr. Lei Liu
Dr. Chunhe Song
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart grid
  • cyber–physical system
  • edge computing
  • micro-grid
  • power
  • Internet of things
  • fault detection
  • load analysis and forecasting
  • security and privacy

Published Papers (4 papers)

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Research

20 pages, 2842 KiB  
Article
Research on Data Poisoning Attack against Smart Grid Cyber–Physical System Based on Edge Computing
by Yanxu Zhu, Hong Wen, Runhui Zhao, Yixin Jiang, Qiang Liu and Peng Zhang
Sensors 2023, 23(9), 4509; https://doi.org/10.3390/s23094509 - 05 May 2023
Cited by 2 | Viewed by 1863
Abstract
Data poisoning attack is a well-known attack against machine learning models, where malicious attackers contaminate the training data to manipulate critical models and predictive outcomes by masquerading as terminal devices. As this type of attack can be fatal to the operation of a [...] Read more.
Data poisoning attack is a well-known attack against machine learning models, where malicious attackers contaminate the training data to manipulate critical models and predictive outcomes by masquerading as terminal devices. As this type of attack can be fatal to the operation of a smart grid, addressing data poisoning is of utmost importance. However, this attack requires solving an expensive two-level optimization problem, which can be challenging to implement in resource-constrained edge environments of the smart grid. To mitigate this issue, it is crucial to enhance efficiency and reduce the costs of the attack. This paper proposes an online data poisoning attack framework based on the online regression task model. The framework achieves the goal of manipulating the model by polluting the sample data stream that arrives at the cache incrementally. Furthermore, a point selection strategy based on sample loss is proposed in this framework. Compared to the traditional random point selection strategy, this strategy makes the attack more targeted, thereby enhancing the attack’s efficiency. Additionally, a batch-polluting strategy is proposed in this paper, which synchronously updates the poisoning points based on the direction of gradient ascent. This strategy reduces the number of iterations required for inner optimization and thus reduces the time overhead. Finally, multiple experiments are conducted to compare the proposed method with the baseline method, and the evaluation index of loss over time is proposed to demonstrate the effectiveness of the method. The results show that the proposed method outperforms the existing baseline method in both attack effectiveness and overhead. Full article
(This article belongs to the Special Issue Edge Computing for Smart Grid Cyber-Physical System)
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15 pages, 2806 KiB  
Article
A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems
by Bin Tang, Yan Lu, Qi Li, Yueying Bai, Jie Yu and Xu Yu
Sensors 2023, 23(3), 1141; https://doi.org/10.3390/s23031141 - 19 Jan 2023
Cited by 3 | Viewed by 4477
Abstract
Industrial Cyber-Physical Systems (ICPS) connect intelligent manufacturing equipment equipped with sensors, wireless and RFID communication technologies through data interaction, which makes the interior of the factory, even between factories, become a whole. However, intelligent factories will suffer information leakage and equipment damage when [...] Read more.
Industrial Cyber-Physical Systems (ICPS) connect intelligent manufacturing equipment equipped with sensors, wireless and RFID communication technologies through data interaction, which makes the interior of the factory, even between factories, become a whole. However, intelligent factories will suffer information leakage and equipment damage when being attacked by ICPS intrusion. Therefore, the network security of ICPS cannot be ignored, and researchers have conducted in-depth research on network intrusion detection for ICPS. Though machine learning and deep learning methods are often used for network intrusion detection, the problem of data imbalance can cause the model to pay attention to the misclassification cost of the prevalent class, but ignore that of the rare class, which seriously affects the classification performance of network intrusion detection models. Considering the powerful generative power of the diffusion model, we propose an ICPS Intrusion Detection system based on the Diffusion model (IDD). Firstly, data corresponding to the rare class is generated by the diffusion model, which makes the training dataset of different classes balanced. Then, the improved BiLSTM classification network is trained on the balanced training set. Extensive experiments are conducted to show that the IDD method outperforms the existing baseline method on several available datasets. Full article
(This article belongs to the Special Issue Edge Computing for Smart Grid Cyber-Physical System)
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17 pages, 5297 KiB  
Article
A Low-Latency RDP-CORDIC Algorithm for Real-Time Signal Processing of Edge Computing Devices in Smart Grid Cyber-Physical Systems
by Mingwei Qin, Tong Liu, Baolin Hou, Yongxiang Gao, Yuancheng Yao and Haifeng Sun
Sensors 2022, 22(19), 7489; https://doi.org/10.3390/s22197489 - 02 Oct 2022
Cited by 4 | Viewed by 1832
Abstract
Smart grids are being expanded in scale with the increasing complexity of the equipment. Edge computing is gradually replacing conventional cloud computing due to its low latency, low power consumption, and high reliability. The CORDIC algorithm has the characteristics of high-speed real-time processing [...] Read more.
Smart grids are being expanded in scale with the increasing complexity of the equipment. Edge computing is gradually replacing conventional cloud computing due to its low latency, low power consumption, and high reliability. The CORDIC algorithm has the characteristics of high-speed real-time processing and is very suitable for hardware accelerators in edge computing devices. The iterative calculation method of the CORDIC algorithm yet leads to problems such as complex structure and high consumption of hardware resource. In this paper, we propose an RDP-CORDIC algorithm which pre-computes all micro-rotation directions and transforms the conventional single-stage iterative structure into a three-stage and multi-stage combined iterative structure, thereby enabling it to solve the problems of the conventional CORDIC algorithm with many iterations and high consumption. An accuracy compensation algorithm for the direction prediction constant is also proposed to solve the problem of high ROM consumption in the high precision implementation of the RDP-CORDIC algorithm. The experimental results showed that the RDP-CORDIC algorithm had faster computation speed and lower resource consumption with higher guaranteed accuracy than other CORDIC algorithms. Therefore, the RDP-CORDIC algorithm proposed in this paper may effectively increase computation performance while reducing the power and resource consumption of edge computing devices in smart grid systems. Full article
(This article belongs to the Special Issue Edge Computing for Smart Grid Cyber-Physical System)
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21 pages, 4968 KiB  
Article
Power Equipment Fault Diagnosis Method Based on Energy Spectrogram and Deep Learning
by Yiyang Liu, Fei Li, Qingbo Guan, Yang Zhao and Shuaihua Yan
Sensors 2022, 22(19), 7330; https://doi.org/10.3390/s22197330 - 27 Sep 2022
Cited by 2 | Viewed by 1481
Abstract
With the development of industrial manufacturing intelligence, the role of rotating machinery in industrial production and life is more and more important. Aiming at the problems of the complex and changeable working environment of rolling bearings and limited computing ability, fault feature information [...] Read more.
With the development of industrial manufacturing intelligence, the role of rotating machinery in industrial production and life is more and more important. Aiming at the problems of the complex and changeable working environment of rolling bearings and limited computing ability, fault feature information cannot be effectively extracted, and the current deep learning model is difficult to be compatible with lightweight and high efficiency. Therefore, this paper proposes a fault detection method for power equipment based on an energy spectrum diagram and deep learning. Firstly, a novel two-dimensional time-frequency feature representation method and energy spectrum feature map based on wavelet packet transform is proposed, and an energy spectrum feature map dataset is made for subsequent diagnosis. This method can realize multi-resolution analysis, fully extract the feature information contained in the fault signal, and accelerate the convergence of the subsequent diagnosis model. Secondly, a lightweight residual dense convolutional neural network model (LR-DenseNet) is proposed. This model combines the advantages of residual learning and a dense connection, and can not only extract deep features more easily, but can also effectively use shallow features. Then, based on the lightweight residual dense convolutional neural network model, an LR-DenseSENet model is proposed. By introducing the transfer learning strategy and adding the channel domain, an attention mechanism is added to the channel feature fusion layer, with the accuracy of detection up to 99.4%, and the amount of parameter calculation greatly reduced to one-fifth of that of VGG. Finally, through an experimental analysis, it is verified that the fault detection model designed in this paper based on the combination of an energy spectrum feature map and LR-DenseSENet achieves a satisfactory detection effect. Full article
(This article belongs to the Special Issue Edge Computing for Smart Grid Cyber-Physical System)
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