CROM Digital Twins and IoT

Digital Twin Microgrids
and IoT
Center for Research on Microgrids
www.crom.et.aau.dk

WWW.CROM.ET.AAU.DK 2
 Digital Twin Applications
 Industry 4.0 And Digital Twin
 DT Concept And Definitions
 Digital Modelling, Shadows & Twinning
 Early Adopters Of DT
 The Digital Twin From A Temporal View Point
 DT Enabling Technologies:
 Blockchain
 IoT
 AI
 Edge Computing
 DT Standardization
 Digital Twin For Microgrids
 DT Process and Its Services in Microgrids
 DT Modelling
 Use Case 1: DT For Microgrid Resilient Operation
 Use Case 2: Maritime DT Microgrids
OUTLINE

 Automotive industry
 Vehicle driving assistant system
 Manufacturing and Oil Industry
 Autonomous and smart manufacturing
 Aviation Industry
 Training during flight preparation
 Healthcare systems
 Developing DT of a human
 Predictive maintenance medical Equipment
 Education
 Modular courses
 Personalizing and customizing the education
Digital Twin ApplicaitonsDigital Twin Applications

Digital TwinDigital Twin
ENERGY Applications
 Maritime Microgrids
 Offshore Wind power plants
 Off-grid Energy Solutions
 Transportation and e-Mobility

Industry 4.0 and Digital Twin
According to the industrial revolution
paradigm or Industry 4.0, the next-
generation systems are the outcome
of the evolution and convergence of
new technologies such as onboard
computation, controllers, big data,
machine learning (ML), and IoT.
IoT
Digital Twin
AI
Big
data
CPS
Sensor
5G

Origin of Twin Concept:
NASA’s Apollo program: Two identical space
vehicles were built, allowing the engineers to
mirror the conditions of the space vehicle
during the mission. The vehicle remaining on
earth was being called the twin.
Every kind of prototype which is used to mirror the real operating conditions of a
real-time behavior, can be seen as a twin.
Concept of Twins
Boschert S., Rosen R. (2016) Digital Twin—The Simulation Aspect. In: Hehenberger P., Bradley D. (eds) Mechatronic Futures.
Springer, Cham

Digital Twin Definitions
• A set of virtual information that fully describes a potential or actual physical
production from the micro atomic level to the macro geometrical level. At its
optimum, any information that could be inspected from a physical
manufactured product can be obtained from its DT [2].
• The virtual image of the physical object, which makes the provided data
usable for various purposes in the control center. Thus, DT can serve as an
assist for making safe and efficient decisions [3].
[1] C. Brosinsky, D. Westermann, R. Krebs, "Recent and prospective developments in power system control centers: Adapting the
digital twin technology for application in power system control centers," In 2018 IEEE International Energy Conference (ENERGYCON),
2018 Jun 3 (pp. 1-6). IEEE.
[2] M. Grieves, J. Vickers, "Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems," In
Transdisciplinary perspectives on complex systems, Springer, Cham, pp 85–113, 2017.
[3] C. Brosinsky, X. Song, D. Westermann, "Digital Twin-Concept of a Continuously Adaptive Power System Mirror", In International
ETG-Congress 2019; ETG Symposium, 2019 May 8 (pp. 1-6). VDE.
• Software-based abstractions of complex physical systems (objects) which are
connected to the real system via a communication link to continuously
exchange data flow from the real environment and establishing a dynamic
digital mirror as a constantly running modelling engine [1].

W. Kritzinger, M. Karner, G. Traar, J. Henjes, W. Sihn, "Digital Twin in manufacturing: A categorical literature review and
classification," IFAC-Papers OnLine, 51(11): 1016-1022, 2018.
Digital Model
• A digital representation of an existing or planned physical object.
• It does not use any form of automated data exchange between the physical object and the digital
object.
Digital Shadow
• There exists an automated one-way data flow between the state of an existing physical object and
a digital object.
Digital Twin
• The data flows between an existing physical object and a digital object are fully integrated in both
directions
Digital Model, Shadow & Twin

 With the advent of DT, utilization of the simulation model is expanded over the
entire lifetime of the system (process).
Simulation-based System Design
Simulation allows a systemic approach to multi-
level and multi-disciplinary systems with
enhanced range of applications, e.g. model based
systems engineering.
Individual application
Simulation is limited to very
specific topics by experts, e.g.
mechanics.
Simulation tools
Simulation is a standard tool to answer
specific design and engineering
questions, e.g. fluid dynamics.
Digital Twin Concept
Simulation is a core functionality
of systems by means of seamless
assistance along entire life cycle,
e.g. supporting operation and
service with direct linkage to
operation data.
1960+
1985+
2000+
2015+
The Digital Twin From Simulation View Point

The Digital Twin from a temporal view point
Digital Twin is beneficial during the whole life cycle of a System/Asset from
planning and designing, to operation, maintenance, and expanding stages.
Design Operation Monitoring Maintenance Expansion

 US Air Force
 Applies the DT to predict aircraft structural life to improve its safety and
reliability.
 Tesla
 Developing a DT for every car it produces, allowing for synchronous data
transmission between cars and its factory.
Y. Zheng, S. Yang, H. Cheng, "An application framework of digital twin and its case study," Journal of Ambient Intelligence
and Humanized Computing, 10(3): 1141-1153, 2019.
 General electric
 A DT interface for managing wind farms (Two patents for windfarms).
 GE applied the DT for locomotive and healthcare.
A. M. Lund, K. Mochel, J. W. Lin, R. Onetto, J. Srinivasan, P. Gregg, J. E. Bergman, K.D. Hartling, A. Ahmed, S. Chotai, General
Electric Co., "Digital twin interface for operating wind farms," U.S. Patent 9,995,278, 2018 Jun 12.
Early Adopters of DT
 Danish Maritime Authority

A. Rasheed, O. San, T. Kvamsdal, "Digital twin: Values, challenges and enablers from a modeling perspective," IEEE Access,
8: 21980-22012, 2020.
DT Enabling Technologies and Challenges

DT Enabling Technologies: Block chain
 Blockchain (BC), first proposed in 2008.
 A BC is a type of distributed ledger technology that is used to
connect a large number of anonymous nodes without the
need for a central controlling agent.
 BC Utilizes a consensus mechanism to ensure security of the
network and allows participants to store and share data in a
secure and verifiable manner, even when the identity and
trustworthiness of other peers is unknown.
 All information is updated synchronously to the entire network
so that each peer keeps a record of the same ledger.
Blockchain and DT Cyber Security
https://www.gartner.com/en/newsroom/press-releases/2018-10-15-gartner-identifies-the-top-10-strategic-technology-
trends-for-2019
Liang G, Weller SR, Luo F, Zhao J, Dong ZY. Distributed blockchain-based data protection framework for modern power
systems against cyber attacks. IEEE Transactions on Smart Grid. 2018 Mar 27;10(3):3162-73.
 Improving opportunities for tracking and tracing physical
and digital assets.
BC
Criticism
BC
Advantages

DT Enabling Technologies: IoT
IoT-based Connectivity and DT
 Running of AI models with high accuracy with little to no missing data.
A. Fuller, Z. Fan, C. Day, "Digital Twin: Enabling Technology, Challenges and Open Research," arXiv preprint arXiv:1911.01276.
2019 Oct 29.
Key Requirements:
 Standardization (models, interfaces, sensor naming, …)
 Full Connectivity
 Sensor quality and reliability
IoT Platforms examples:
 ThingWorx
 IBM Watson IoT Platform

DT Enabling Technologies: AI
Artificial Intelligence and Data-driven Modelling, Learning, and Forecasting
 Producing valuable insights from data.
 Facilitate Automatic Learning.
AI methodologies:
 Decision tree
 Artificial Neural Network
 Fuzzy systems
 Deep Learning
 Reinforcement Learning

 Privacy and security
 Scalability
 Reliability
 Speed
 Efficiency
 Distributing the logic in different
network nodes.
 Designing the optimal configuration.
Advantages of proximity of the analytical resources to the end users
Edge Computing Challenges
DT Enabling Technologies: Edge Computing
Fog computing is a distributed computing
paradigm, that empowers the network devices
at different hierarchical levels with various
degrees of computational and storage
capability.
Preden, J., Kaugerand, J., Suurjaak, E., et al.: ‘Data to decision: pushing situational information needs to the edge of the
network’. IEEE Int. Inter-Disciplinary Conf. on Cognitive Methods in Situation Awareness and Decision Support, Orlando, USA,
March 2015, pp. 158–164.
Sarkar S, Misra S. Theoretical modelling of fog computing: a green computing paradigm to support IoT applications. Iet
Networks. 2016 Mar 1;5(2):23-9.

Digital Twins for Industrial Applications Definition, Business Values, Design Aspects, Standards And Use Cases An
Industrial Internet Consortium White Paper Version 1.0 2020-02-18
Standardization of Digital Twins
 IEC 62832: digital factory framework with digital factory assets
 ISO/IEC JTC1: technology trend report on Emerging Technology and Innovation (JETI).
 ISO/TC 184: Started 2019.
 IEEE P2806: Started 2019 by IEEE Standards.
 ISO TC 184/SC4/WG15: plug and play for twin elements, mainly on DT functions
 IEC PAS 63088: implementation of the digital twin for smart manufacturing.
 ISO TS 18101-1: requirements and specifications with focus on oil and gas interoperability.
 W3C Web of Things (WOT): Work on specifications of digital representation of things.

Virtual SystemPhysical System
DT-driven Services
Design and
Development
Predictive
Maintenance
Security Analysis Policy Making
Resilience
Operation
PredictionOperator
Training
Operation
Optimization
Data Stream Analysis
 Data Collection
 Data Cleaning
 Data Fusion
 Data Analysis
 Data Visualization
 Data Storage
DT Management
 Model Optimization
 Model Validation
 Model Adaptation
Information Management
 Data/Model Security
 Data/Model Sharing
DT-driven Services
DT Process and Its Services in Microgrids

Digital Twin Applications In Microgrids
• Design-Oriented DTs
• Closing the loop from operation and maintenance to design
Design and Development
• Producing data for normal, abnormal, and failure situations
• Fault signature identification
Data and scenario generation
• Analyzing possible undesired scenarios, consequences,
and required responsive action without putting the real
asset at risk
What/if analysis and Risk Assessment
• An advanced platform for training the power systems
human and automatic operators in a low cost and risk
environment
Operator Training
• Realizing full autonomous systems or with human in the
loop
System Autonomy
• Making timely, informed and reliable decisions using the
high-fidelity virtual models integrated with the optimization
algorithms
Control and Operation Management

The Digital Twin Applications In Microgrids
• Accurate predictions using real-time data and simulation modelsForecasting
• Offshore wind turbines, rural MGs, assets with complex installationsRemote Monitoring
• Perception of a system and associated subsystems in relation to its
environment and projection of its states in the near future [1], [2]
Condition Monitoring and Situation
Awareness
• Detect any malfunctioning of the different components, controllers,
sensors, etc. or faulty operation of the system by comparing it with
the available reference behavior.
Fault Diagnosis and Prognosis
• Detect anomalies in the asset performance and call for decisive
actions.
State of Health Monitoring and Predictive
Maintenance, Predict Remaining Useful Life
• Using DT, both the cyber and the physical layers could be
protected against potential threats such as natural disasters or
malicious attacks.
Security and Resiliency
• Compatibility analysis of the long-term outcomes of the new
policies with the expected desired purposes.
• Predict the system’s response to different future scenarios in
desired time horizons.
Expansion Planning and Policy-making
[1] X. He, Q. Ai, RC. Qiu, D. Zhang, "Preliminary Exploration on Digital Twin for Power Systems: Challenges, Framework, and
Applications," Preprint, 2019.
[2] M. R. Endsley, "Designing for situation awareness: An approach to user-centered design," CRC Press, 2011.

Establishing
Digital Twin
Modelling
Physical Process
Real-time Data
Connection
Model Evolution and
Adaptation
Physics-based
Models
Data-driven
Models
Hybrid Models
Communication
Systems
Sensor
Networks
Artificial
Intelligence
Automatic
Learning
Data Analytics
How to establish a Digital Twin?
 Developing High-fidelity models
 Reliable and low latency communication links between the digital and physical twins
 Systematic ways for adapting the digital twin using real-time data

Modelling
Model
Establishment
Model
Verification
Model
Updating
Model sharing
Modelling Aspects of Digital Twinning

CASE 1: DT for Microgrids Resilient operation
Pre-disturbance
state
Post-restoration
state
Disturbance
progress
Post-
disturbance
Restoration
state
Timet0 t1 t2 t3 T
s1
s2
L1
t1' t2' t3' T'
S1' S2'
L1'
PerformanceLevel
Anticipation
Recover
Adapt
Timely activation of preventive actions can
prevent or postpone the degradation of
system performance
The level of performance degradation of the
MG and the speed of performance
degradation can be reduced by timely
triggering the required functions.
The restoration time can be reduced by assisting the operator with re-
energizing the MGs based on the accurate information of event location and its
severity, the knowledge acquired through training, and projecting the system
response to restoration actions.
DT-based Approach
Conventional Approach

Real-Time
System Monitoring
Updating
threat models
Optimization and Adaptation
(offline)
Restoration Management
(Post event)
Anomaly Detection
Data Analytic model
Switching
(Automatic/Manually) to
emergency control mode
Identification of Event
Type, Location, Severity
and potential cascading
events
Activate responsive
actions
(During event)
Running
Simulations
Training
Optimizing
responsive
actions
Optimizimg
restorative
actions
Updating
threat list
DT-driven Resilience Enhancement
Model
Case 1: DT for Microgrids Resilient operation

Case 2: Maritime DT Microgrids
Ships DT Definition
 A Ship DT is a virtual representation of its physical counterpart which is connected to
the real system via communication links to exchange real-time data with the real
environment and establish a dynamic digital mirror.
 The Ship DT will reflect the behaviour of all physical assets and the system processes
using full available data and information from different sources.

Case 2: Maritime DT Microgrids
Ships DT Added values
 Improving system visualization
 Presenting a comprehensive overview of the system from component-level to
system-level
 Increasing ship systems understanding
 Improving system design through analysing the long effects of design alternatives
 Improving the ship performance
 Remote supervision and control assistance
 Realizing ships self-healing
 Situational awareness
 Increase degree of autonomy
 Enhancing Asset management, Degradation Monitoring, Fatigue analysis, Minimizing
down-time intervals, maximizing ships Safety, and Extending system life time
 Training the crews for efficient control of the ship especially under adverse operating
conditions

CROM Digital Twins and IoT

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to CROM Digital Twins and IoT

Similar to CROM Digital Twins and IoT (20)

More from Juan C. Vasquez

More from Juan C. Vasquez (20)

Recently uploaded

Recently uploaded (20)

CROM Digital Twins and IoT