Achieve reliable cascaded multilevel converter performance through advanced modulation and diagnostics

Cascaded multilevel converters demand sophisticated modulation and fault management strategies. Delivering coverage of these techniques, the book progresses from CMC-based industrial application backgrounds to DC-link voltage balancing and convolutional neural networks. Each technique receives analysis of advantages, disadvantages, and application scenarios, supported by MATLAB, Simulink, Mathematica, and Python code.

Readers will also find:

• Integration of artificial intelligence techniques with existing methods to enhance industrial applications of cascaded multilevel converters
• MATLAB and Simulink guidance for building simulations of multilevel converters covering both modulation and control implementations
• Counter-based diagnostic algorithms that use counters to represent switching states for systematic fault identification in converter systems
• Condition monitoring approaches for switching devices employing digital twin models paired with convolutional neural network architectures
• Accompanying code in MATLAB, Mathematica, and Python for formula deductions, neural network construction, and practical programming exercises

Designed for researchers, industry engineers, and graduate students in electric power engineering, this book provides a unified treatment of modulation, control, diagnostics, and fault tolerance for cascaded multilevel converters. It equips readers with the analytical tools and code resources needed to enhance CMC performance and reliability.

Hongjian Lin, PhD, is a Full Professor at the College of Electrical and Information Engineering, Hunan University, China. He serves as an Associate Editor for the IEEE Transactions on Industrial Informatics and received the Best Poster Award at IPEMC 2024-ECCE Asia. He has authored over 60 technical papers.

Dong Xie, PhD, is an Assistant Professor at the School of Integrated Circuit Science and Engineering, Southwest Jiaotong University, China. He received Best Paper and Best Poster Awards at the International Conference on Electrical Machines and Systems and IPEMC 2024-ECCE Asia.

Henry Shu-Hung Chung, PhD, is Dean of Students and Chair Professor at City University of Hong Kong, directing the Centre for Smart Energy Conversion and Utilization Research. A recipient of the 2021 IEEE PELS R. David Middlebrook Achievement Award, he has authored over 500 technical papers and holds 80 patents.

Jiangbiao He, PhD, is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He previously served as Lead Engineer at GE Global Research and held positions at Eaton Corporate Research and Rockwell Automation.

Xinglai Ge, PhD, is a Full Professor at the School of Electrical Engineering, Southwest Jiaotong University, and Vice Director of the Department of Power Electronics and Power Drive. He has authored and coauthored more than 80 technical papers in power electronics.

Wensheng Song, PhD, is a Full Professor at the School of Integrated Circuit Science and Engineering, Southwest Jiaotong University, China. He was previously a Visiting Scholar at the University of California at Irvine and the University of Alberta.