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Applied Sciences
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Research Progress on Cyber-Physical Distribution System
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Research Progress on Cyber-Physical Distribution System

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 8918

Special Issue Editors

Dr. Meng Tian

E-Mail Website
Guest Editor
Electronic Information School, Wuhan University, Wuhan 430072, China
Interests: cyber-physical resilience; game theory applications in smart grid
Dr. Ying Zhang

E-Mail Website
Guest Editor
Electrical and Computer Engineering, Montana State University, Bozeman, MT 59717, USA
Interests: electric distribution system situational awareness; artificial intelligence applications in smart grid; grid resilience
Dr. Zhengcheng Dong

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Interests: cyber-physical resilience; vulnerability analysis of interdependent networks

Special Issue Information

Dear Colleagues,

Modern distribution systems are facing increasing challenges, such as the volatility of renewable energy resources, the vulnerability of network topologies and the uncertainty of load demands. Distribution systems are tightly integrated with information and communication technologies, which have evolved into cyber-physical distribution systems (CPDS). Even though these technologies advance and optimize the operation of the electric power grid significantly, distribution systems, subject to complex cyber-physical interdependencies, are highly vulnerable to various risks. These risks caused by cyber systems can affect and even degrade system performance in terms of efficiency, security, safety, stability, and privacy. Hence, it should be emphasized that cyber systems and physical distribution networks should be equally important in CPDS, while traditional distribution systems are more concerned about physical distribution grids.

This Special Issue focuses on the advances in CPDS. We encourage the contribution of original papers on risk assessment, cyber-physical modeling and design, system planning, cybersecurity and privacy, cyberattack mitigation, stability analysis, and resilience in CPDS. We also welcome original research on innovative technologies and interdisciplinary study, e.g., artificial intelligence, new applications and new viewpoints in CPDS.

Dr. Meng Tian
Dr. Ying Zhang
Dr. Zhengcheng Dong
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. Applied Sciences 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 2400 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

  • cyber-physical distribution system
  • risk assessment
  • cyber-physical modeling
  • system planning
  • cybersecurity
  • privacy
  • stability analysis
  • resilience

Published Papers (5 papers)

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Research

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19 pages, 4674 KiB  
Article
Security-Oriented Cyber-Physical Risk Assessment for Cyberattacks on Distribution System
by Yuhang Zhang and Ming Ni
Appl. Sci. 2023, 13(20), 11569; https://doi.org/10.3390/app132011569 - 23 Oct 2023
Viewed by 839
Abstract
With the increasing deployment of advanced sensing and measurement devices, the modern distribution system is evolved into a cyber-physical power distribution system (CPPDS). Due to the extensive application of information and communication technology, CPPDS is prevalently exposed to a wide range of cybersecurity [...] Read more.
With the increasing deployment of advanced sensing and measurement devices, the modern distribution system is evolved into a cyber-physical power distribution system (CPPDS). Due to the extensive application of information and communication technology, CPPDS is prevalently exposed to a wide range of cybersecurity threats. In this paper, a novel security-oriented cyber-physical risk assessment method for CPPDS is proposed. Based on the information model composed of logical nodes, an attack graph of privilege promotion by exploiting the cyber vulnerabilities is constructed. The physical consequence caused by cyberattack is analyzed in detail. By using the Markov decision process (MDP) theory, the cyber-physical risk index (CPRI) is calculated. Furthermore, considering the allocation of the finite defense resource, the modified MDP approach with an attack–defense game is presented. The effectiveness of the proposed method is demonstrated with case studies on a four-feeder IEEE-RTBS test system. Full article
(This article belongs to the Special Issue Research Progress on Cyber-Physical Distribution System)
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23 pages, 2445 KiB  
Article
The Detection of False Data Injection Attack for Cyber–Physical Power Systems Considering a Multi-Attack Mode
by Buxiang Zhou, Xuan Li, Tianlei Zang, Yating Cai, Jiale Wu and Shijun Wang
Appl. Sci. 2023, 13(19), 10596; https://doi.org/10.3390/app131910596 - 22 Sep 2023
Cited by 1 | Viewed by 1188
Abstract
Amidst the evolving communication technology landscape, conventional distribution networks have gradually metamorphosed into cyber–physical power systems (CPPSs). Within this transformative milieu, the cyber infrastructure not only bolsters grid security but also introduces a novel security peril—the false data injection attack (FDIA). Owing to [...] Read more.
Amidst the evolving communication technology landscape, conventional distribution networks have gradually metamorphosed into cyber–physical power systems (CPPSs). Within this transformative milieu, the cyber infrastructure not only bolsters grid security but also introduces a novel security peril—the false data injection attack (FDIA). Owing to the variable knowledge held by cyber assailants regarding the system’s network structure, current achievements exhibit deficiencies in accommodating the detection of FDIA across diverse attacker profiles. To address the historical data imbalances encountered during practical FDIA detection, we propose a dataset balancing model based on generating adversarial network-gated recurrent units (GAN-GRU) in conjunction with an FDIA detection model based on the Transformer neural network. Harnessing the temporal data extraction capabilities of gated recurrent units, we construct a GRU neural network system as the GAN’s generator and discriminator, aimed at data balance. After preprocessing, the balanced data are fed into the Transformer neural network for training and output classification to discern distinct FDIA attack types. This model enables precise classification amidst varying FDIA scenarios. Validation involves testing the model on load data from the IEEE 118-bus system and affirming its high accuracy and effectiveness in detecting power systems after multiple attacks. Full article
(This article belongs to the Special Issue Research Progress on Cyber-Physical Distribution System)
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17 pages, 2503 KiB  
Article
Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization
by Sepideh Radhoush, Trevor Vannoy, Kaveen Liyanage, Bradley M. Whitaker and Hashem Nehrir
Appl. Sci. 2023, 13(12), 6938; https://doi.org/10.3390/app13126938 - 08 Jun 2023
Viewed by 1164
Abstract
Distribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE [...] Read more.
Distribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE results for dynamically changing ADNs. Because of this, smart measurement devices, which are synchronous at high frame rates, have recently been introduced to enhance the monitoring and control of ADNs in modern power networks. However, replacing all traditional measurement devices with smart measurements is not feasible over a short time. Thus, an essential part of the grid modernization process is to use both traditional and advanced measurements to improve DSSE results. In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. After a period of time, a network is modified by the installation of advanced measurement devices, such as Phasor Measurement Units (PMUs), to facilitate ADN monitoring and control with a desired performance. Our work proposes a method for taking advantage of all available measurements to improve DSSE results. First, a machine-learning-based regression model was trained from DSSE results obtained using only the traditional measurements available before the installation of smart measurement devices. After smart measurement devices were added to the network, the model predicted traditional measurements when those measurements were not available to enable synchronization between the traditional and smart sensors, despite their different refresh rates. We show that the regression model had improved performance under the condition that it continued to be updated regularly as more data were collected from the measurement devices. In this way, the training model became robust and improved the DSSE performance, even in the presence of more Distributed Generations (DGs). The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. We present the DSSE results in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for all approaches. The effectiveness of the proposed method was validated using two case studies in the presence of DGs: one using a modified IEEE 33-bus distribution system that considered loads and DGs based on a Monte Carlo simulation and the other using a modified IEEE 69-bus system that considered actual data for loads and DGs. The DSSE results illustrate that the proposed method is better than the sample-and-hold method. Full article
(This article belongs to the Special Issue Research Progress on Cyber-Physical Distribution System)
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14 pages, 544 KiB  
Article
A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
by Yunfa Li, Di Zhang, Zetian Wang and Guanxu Liu
Appl. Sci. 2023, 13(11), 6831; https://doi.org/10.3390/app13116831 - 05 Jun 2023
Cited by 3 | Viewed by 964
Abstract
With the advancement of smart devices, the operation and communication of smart grids have become increasingly efficient. Many smart devices such as smart meters, smart transformers, and smart grid controllers are already widely used in smart grids. Thus, a series of complex architectures [...] Read more.
With the advancement of smart devices, the operation and communication of smart grids have become increasingly efficient. Many smart devices such as smart meters, smart transformers, and smart grid controllers are already widely used in smart grids. Thus, a series of complex architectures and a series of communication modes have been formed. However, these smart devices will be exposed to various cyber attacks such as distributed denial of service (DDoS) attack and replay attack. This is because they are open and dynamic. Therefore, there are serious security problems in the complex architectures and the communication modes. In this paper, we propose a multi-domain authentication mechanism based on blockchain cooperation to maintain the security of smart devices. In this mechanism, we propose a series of methods and algorithms, which include initialization method based on blockchain cooperative authentication, dynamic change method of intelligent devices and information, cross-domain authentication algorithm, and cross-domain key cooperative algorithm. To demonstrate the security and effectiveness of our proposed mechanism, we analysed its security and conducted a series of simulation experiments. The analysis and simulation experiments show that our proposed approach is secure and effective. Full article
(This article belongs to the Special Issue Research Progress on Cyber-Physical Distribution System)
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Review

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16 pages, 1166 KiB  
Review
Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect
by Sungjoo Chung and Ying Zhang
Appl. Sci. 2023, 13(12), 6937; https://doi.org/10.3390/app13126937 - 08 Jun 2023
Cited by 4 | Viewed by 3620
Abstract
Advances in machine learning and artificial intelligence (AI) techniques bring new opportunities to numerous intractable tasks for operation and control in modern electric distribution systems. Nevertheless, AI applications for such grids as cyber-physical systems encounter multifaceted challenges, e.g., high requirements for the quality [...] Read more.
Advances in machine learning and artificial intelligence (AI) techniques bring new opportunities to numerous intractable tasks for operation and control in modern electric distribution systems. Nevertheless, AI applications for such grids as cyber-physical systems encounter multifaceted challenges, e.g., high requirements for the quality and quantity of training data, data efficiency, physical inconsistency, interpretability, and privacy concerns. This paper provides a systematic overview of the state-of-the-art AI methodologies in the post-pandemic era, represented by transfer learning, deep attention mechanism, graph learning, and their combination with reinforcement learning and physics-guided neural networks. Dedicated research efforts on harnessing such recent advances, including power flow, state estimation, voltage control, topology identification, and line parameter calibration, are categorized and investigated in detail. Revolving around the characteristics of distribution system operation and integration of distributed energy resources, this paper also illuminates prospects and challenges typified by the privacy, explainability, and interpretability of such AI applications in smart grids. Finally, this paper attempts to shed light on the deeper and broader prospects in the realm of smart distribution grids by interoperating them with smart building and transportation electrification Full article
(This article belongs to the Special Issue Research Progress on Cyber-Physical Distribution System)
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