Machine Learning for Digital Twins to Predict Responsiveness of Cyber-Physical Energy Systems

Snijders, Ron; Pileggi, Paolo; Broekhuijsen, Jeroen; Verriet, Jacques; Wiering, Marco A.; Kok, Koen

doi:10.1109/mscpes49613.2020.9133695

Cited by 24 publications

(14 citation statements)

References 16 publications

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“…This real time monitoring cycle facilitates by digital thread, which represents data from sensors, information from system and the flow of this information between physical and virtual entities. This aspect is very important for constructing efficient EIoT [117]. On the other hand DT provides online monitoring possibility which supports the EIoT concept in practice in SES [122].…”

Section: Rq1_applications and Impactmentioning

confidence: 80%

“…Anomaly detection is the most sought-after application of the DT. Monitoring the system operation is crucial to evaluate the systems performance [117]. System health monitoring is utilized in wind turbines [118], buildings [119], and batteries [120].…”

Section: Rq1_applications and Impactmentioning

confidence: 99%

“…Finally, it will provide a DT based monitoring system in control rooms [121]. To be more precise, even if the articles directly speak about system monitoring, the main goal of the dynamic monitoring is also anomaly detection [117]. Dynamic monitoring observes the system continuously in real time by receiving and analyzing data from sensors.…”

Section: Rq1_applications and Impactmentioning

confidence: 99%

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

2021

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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.

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Section: Rq1_applications and Impactmentioning

confidence: 80%

Section: Rq1_applications and Impactmentioning

confidence: 99%

Section: Rq1_applications and Impactmentioning

confidence: 99%

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

2021

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“…In the energy domain, many works have introduced DT to coordinate and support specific system goals, such as energy management [24,25], energy optimization [26] and monitoring the behaviour of the system, etc [27]. A recent research has proposed a DT that combines multi-physical and data-driven models for the multi-physics prediction of a PEMFC [28].…”

Section: Introductionmentioning

confidence: 99%

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Meraghni

Terrissa

Yue

et al. 2021

International Journal of Hydrogen Energy

112

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Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.

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“…For health monitoring and anomaly detection, such architectures can identify intrusions or anomalies that fall outside normal operation. Existing literature has explored this for industrial applications using similar techniques to those above, including [13], [14], and [15] who use digital-twins to detect anomalies in manufacturing and energy systems using machine learning. The concepts and benefits realised should equally apply to UAVs.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Security of Unmanned Aerial Systems using Digital-Twin Technology and Intrusion Detection

Fraser

Al‐Rubaye

Aslam

et al. 2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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In this paper the general susceptibilities of Unmanned Aerial Vehicles (UAVs) against modern-cyber threats are explored and potential solutions proposed. This is achieved by applying digital-twin architectures and datadriven methods to UAVs to facilitate identification of real-time intrusions and anomalies. These concepts are validated by performing novelty detection on open access UAV flight data with GPS spoofing attacks, which represents a typical system use-case. Multiple machine learning models are trained to demonstrate the feasibility of detecting modern cyberintrusions and anomalies using the digital-twin architecture. This includes both classical and deep learning techniques to help identify the most suitable model types for the proposed design. The overall results are positive and help highlight the potential of digital-twin architectures for the UAV contexts.

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Machine Learning for Digital Twins to Predict Responsiveness of Cyber-Physical Energy Systems

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Cited by 24 publications

References 16 publications

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

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

A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Enhancing the Security of Unmanned Aerial Systems using Digital-Twin Technology and Intrusion Detection

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