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