Track 8: Advanced Power Management and Fault Diagnosis in Renewable Energy and Transportation Electrification

Organizers
Li Liu, School of Electrical Engineering, Guangxi University, Assistant Professor
Lidan Chen, Faculty of Marine Engineering, Guangzhou Maritme University, Associate Professor
 

Abstract
This track delves into critical areas such as power quality management, electrification of transportation, fault diagnosis in photovoltaic systems and EV charging stations. Through advanced control technologies and their applications, the aim is to enhance the power quality of the grid. Additionally, novel power electronics technologies and their application in electric transportation systems will be introduced. The report also focuses on analyzing the fundamental principles, challenges, and importance of effective fault diagnosis in these systems, showcasing various methods and techniques for detecting and localizing faults. The role of data analytic, artificial intelligence, and machine learning in fault diagnosis and predictive maintenance will be thoroughly discussed, along with strategies and technologies for remote monitoring and condition assessment of photovoltaic systems and EV charging stations. Industry experts will share their experiences, case studies, and best practices in diagnosing and resolving faults in these systems. Finally, the report addresses the integration challenges, system design considerations, and implications for fault diagnosis when combining photostatic and EV charging infrastructure.


Content
This track concentrates on comprehensive management of power system quality, the latest advancements in transportation electrification, and innovative solutions for fault diagnosis in photovoltaic systems and EV charging stations. Insights into advanced control technologies that significantly improve the power quality of the grid will be provided, along with an introduction to new power electronics technologies that play a key role in promoting electrification of transportation. The basic theories of fault diagnosis will be analyzed, exploring different approaches to fault detection and localization in photovoltaic systems and EV charging stations. Data-driven analytical methods and machine learning algorithms' application in predictive maintenance and fault diagnosis will be examined in detail to enhance system reliability and efficiency. The track also discusses how to use remote monitoring technologies and real-time data analysis to assess system conditions, guiding future engineering decisions through case studies and best practices. Ultimately, the track emphasizes the unique challenges faced when integrating photovoltaic systems with EV charging stations, including system design and fault diagnosis issues.

Invited Speakers：
Professor Jingrui Zhang, Accociate Professor, School of Aerospace Engineering, Xiamen University
 

Topics
We seek original completed and unpublished work not currently under review by any other journal/magazine/conference.

Topics of interest include, but are not limited to:
*Power Quality Comprehensive Management: advanced control technology and applications to improve the grid power quality;
*Transportation Electrification: novel power electronics technology and application in electrical transportation system
*Photovoltaic Systems and EV Charging Station Fault Diagnosis: analyse fundamentals, challenges, and importance of effective fault diagnosis in these systems.
*Fault Detection and Localization Techniques: presentations and discussions on various methods and algorithms for detecting and localizing faults in photovoltaic systems and EV charging stations.
*Data Analytic and Machine Learning for Fault Diagnosis: exploring the role of data analytic, artificial intelligence, and machine learning in fault diagnosis and predictive maintenance.
*Remote Monitoring and Condition Assessment: strategies and technologies for remote monitoring, real-time data analysis, and condition assessment of photovoltaic systems and EV charging stations.
*Case Studies and Best Practices: industry experts sharing their experiences, case studies, and best practices in diagnosing and resolving faults in these systems.
*Integration of Photostatic and EV Charging Stations: discussion on the integration challenges, system design considerations, and fault diagnosis implications when combining photostatic and EV charging infrastructure.
 

Not limited to the above topics, all kinds of papers including power quality regulation and power electronic device applications are welcome to submit


 

 

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