A Hybrid Cyber Physical Digital Twin Approach for Smart Grid Fault Prediction

Tzanis, Nikolaos; Andriopoulos, Nikolaos; Magklaras, Aris; Mylonas, Eleftherios; Birbas, Michael; Birbas, Alexios

doi:10.1109/icps48405.2020.9274723

Cited by 31 publications

(16 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are a number of research efforts on DT for the smart grid [38,39,[68][69][70][71]. For example, General Electric (GE) proposed two models related to the use of DT in wind farms [68,69].…”

Section: Smart Gridmentioning

confidence: 99%

“…This architecture can also mitigate distributed denial of service (DDoS) attacks and false injection attacks. Likewise, Nikolaos et al [38] used DT to manage a large number of devices in the smart grid system. To be specific, they used the spike neural network (SNN) deployed on the smart meter in the grid to detect fault nodes.…”

Section: Smart Gridmentioning

confidence: 99%

“…For example, there are several existing surveys focusing on inspecting the framework of DT in industrial systems [30,[34][35][36]. Additionally, some studies have focused on categorizing different DTs in the power grid [37][38][39][40]. Nonetheless, since IoT is widely applied to different physical systems, it is necessary to conduct a comprehensive investigation of extending DTs to IoT-based smart systems driven by data science and engineering.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions

et al. 2022

View full text Add to dashboard Cite

The Internet of Things (IoT) connects massive smart devices to collect big data and carry out the monitoring and control of numerous things in cyber-physical systems (CPS). By leveraging machine learning (ML) and deep learning (DL) techniques to analyze the collected data, physical systems can be monitored and controlled effectively. Along with the development of IoT and data analysis technologies, a number of CPS (smart grid, smart transportation, smart manufacturing, smart cities, etc.) adopt IoT and data analysis technologies to improve their performance and operations. Nonetheless, directly manipulating or updating the real system has inherent risks. Thus, creating a digital clone of a real physical system, denoted as a Digital Twin (DT), is a viable strategy. Generally speaking, a DT is a data-driven software and hardware emulation platform, which is a cyber replica of physical systems. Meanwhile, a DT describes a specific physical system and tends to achieve the functions and use cases of physical systems. Since DT is a complex digital system, finding a way to effectively represent a variety of things in timely and efficient manner poses numerous challenges to the networking, computing, and data analytics for IoT. Furthermore, the design of a DT for IoT systems must consider numerous exceptional requirements (e.g., latency, reliability, safety, scalability, security, and privacy). To address such challenges, the thoughtful design of DTs offers opportunities for novel and interdisciplinary research efforts. To address the aforementioned problems and issues, in this paper, we first review the architectures of DTs, data representation, and communication protocols. We then review existing efforts on applying DT into IoT data-driven smart systems, including the smart grid, smart transportation, smart manufacturing, and smart cities. Further, we summarize the existing challenges from CPS, data science, optimization, and security and privacy perspectives. Finally, we outline possible future research directions from the perspectives of performance, new DT-driven services, model and learning, and security and privacy.

show abstract

Section: Smart Gridmentioning

confidence: 99%

Section: Smart Gridmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Similar concepts and methodology are introduced in [7] to predict and mitigate the sources of privacy leakage in smart automotive systems. A DT framework employing a data-driven ML model of the smart grid and model-based state estimator has been proposed in [25] for fault prediction in near real-time and taking preventive actuation.…”

Section: Role Of Dt Technologymentioning

confidence: 99%

Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks

Jagannath

Ramezanpour

Jagannath

2022

Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning

View full text Add to dashboard Cite

Digital twin (DT) technologies have emerged as a solution for realtime data-driven modeling of cyber physical systems (CPS) using the vast amount of data available by Internet of Things (IoT) networks. In this position paper, we elucidate unique characteristics and capabilities of a DT framework that enables realization of such promises as online learning of a physical environment, real-time monitoring of assets, Monte Carlo heuristic search for predictive prevention, on-policy, and off-policy reinforcement learning in real-time. We establish a conceptual layered architecture for a DT framework with decentralized implementation on cloud computing and enabled by artificial intelligence (AI) services for modeling and decision-making processes. The DT framework separates the control functions, deployed as a system of logically centralized process, from the physical devices under control, much like software-defined networking (SDN) in fifth generation (5G) wireless networks. To clarify the significance of DT in lowering the risk of development and deployment of innovative technologies on existing system, we discuss the application of implementing zero trust architecture (ZTA) as a necessary security framework in future data-driven communication networks.

show abstract

“…In the final list multiple articles highlight applications of DT in smart grids [68,[123][124][125][126][127][128][129][130]. In fact, smart grid is an energy infrastructure.…”

Section: Rq1_applications and Impactmentioning

confidence: 99%

Digital Twin (DT) in Smart Energy Systems - Systematic Literature Review of DT as a growing solution for Energy Internet of the Things (EIoT)

2021

View full text Add to dashboard Cite

In the industry 4.0 era, the Smart Energy System (SES) should be able to address the emerging challenges of digitization and socioeconomic/ecologic transition along with other critical entities of the society. However, because of the complexity of this system, both researchers and practitioners are seeking an agile and smart solution. The main motive of this review is to investigate the applications and implementation of Digital Twin (DT) in the provision of energy services. Research Questions (RQ) of this study include: RQ1: What are the applications of DT in SES and how effective is DT in that use case of EIoT? RQ2: Which issues of an SES can be addressed efficiently by using DT? Through answering the mentioned questions, the current study is heading to following objectives (O), O1: Describe the state of the art of DT in SES. O2: Develop a direction for energy 4.0 management through listing the applications, challenges and important factors of implementing DTs. O3: Provide a list of various approaches in employing DT in the scope of SES. The current study is a systematic literature review (SLR), based on SCOPUS, WOS and IEEE digital libraries. Two keywords (namely “Digital Twin” and “Energy Systems”) have been first used. To achieve the final list of articles, 2 levels of screening have been conducted. The first Screening was based on the relevance of the results concerning research objectives. The second screening was an abstract study. The exclusion/inclusion criteria in the abstract study were based on the research questions. The papers that have the potential of answering one of the research questions have been included. Since the implementation of DT is a rather new topic, both backward snowballing and forward snowballing strategies are implemented to finalize the article selection phase. 60 articles identified by searching through scientific databases and 11 articles have been appended to the list during the snowballing process. The results of the current review provide a managerial guideline for practitioners that are heading to utilize DT, along with an anthology of DT within SES scope to feed possible future studies.

show abstract

A Hybrid Cyber Physical Digital Twin Approach for Smart Grid Fault Prediction

Cited by 31 publications

References 10 publications

Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions

Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions

Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G Networks

Digital Twin (DT) in Smart Energy Systems - Systematic Literature Review of DT as a growing solution for Energy Internet of the Things (EIoT)

Contact Info

Product

Resources

About