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7th Iran Wind Energy Conference

Invited Topics

 

 

Inivted Topic:

Payam Teimourzadeh Baboli

 

Offis, Oldeburg, Germany

A Digital Twin Approach for Condition Monitoring System of Wind Turbines

 

About: Dr. Payam Teimourzadeh Baboli

 

Payam Teimourzadeh Baboli (S'08-M'15-SM'20) received his B.Sc. degree in electrical engineering from the University of Mazandaran (UMZ), Babolsar, Iran in 2007, and his M.Sc. and Ph.D. degrees in electrical engineering specializing in power systems both from the Tarbiat Modares University (TMU), Tehran, Iran in 2009 and 2014, respectively. He has been with UMZ, as an Assistant Professor in Electrical Engineering from Feb. 2015 to Jul. 2019. Since Aug. 2019, he is with OFFIS which is an institute for information technology as Principal Scientist. Since 2008, he also acts as a Senior Researcher with the Iranian Power System Engineering Research Center (IPSERC), Tehran, Iran. His research interests includes Data Science Applications in Smart Grids, Condition Monitoring and Predictive Maintenance Planning of Renewable Energy Sources, Integration of Renewable Energy Sources in Smart Grids, Energy Management and Demand Response, Power System Reliability and Resilience Analysis, Multi-Objective Optimization and Decision Making.

 

 

Lecture Summary: A Digital Twin Approach for Condition Monitoring System of Wind Turbines

 

This seminar discusses the modeling and simulation paradigm of a condition monitoring system for wind turbines (WTs), which is able to handle the large amount of real-time data from WT's sensors that are transferred using  Digital Twin (DT) modeling. Furthermore, the implementation simulation problem of DT is addressed by applying the proposed condition based monitoring system on the actual offshore WT data, which underlies lots of real-world DT challenges and increase the practical aspects. It should be noted that this seminar uses the result of an industrial project with real data. With the increasing demand for efficiency and performance from WTs due to the competitive conditions of the electricity market in smart grids, the challenges related to maintenance planning are increasing. For proper planning of WT maintenance, an integrated condition monitoring system is vital. In this seminar, a condition monitoring system for WTs based on data-driven models is proposed. First, the normal operating point of the WT key components is estimated using a tailor-made artificial neural network. Then, the deviation of the IoT-based real-time measurement data from the estimated values is calculated, indicating abnormal conditions. Another aspect which should be underlined is proposing an DT-based optimization problem to maximize accuracy in detecting abnormal conditions, which could lead to the failure of a WT. It has been shown that, using the DT model of the WTs and simulating the probable scenarios during abnormal conditions of a WT, an alarm could be triggered and a risk indicator could be calculated which represents the corresponding failure probability in that condition. Finally discussions on the results of implementing DT model on the real-data of an offshore wind farm in Germany would be provided.
 
 
Outline:
  • Motivation and research questions
  • Fundamentals of Digital Twin
  • Scope definition and modeling challenges
  • Framework for Condition Monitoring System
  • Health evaluation and anomaly detection paradigm of wind turbines
  • Discussion on results
  • Conclusion and future works
 
 

 

 
 
 
 
 
 
 
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