Special sessions > Special session 12

 Special Sessions 12 (SS12): 

Title: Recent Trends in Artificial Intelligence for Power Electronics 

• Minh Tu PHAM (primary contact person), INSA Lyon, Ampère, Villeurbanne, France, minh-tu.pham@insa-lyon.fr 
• Xuefang LIN SHI, INSA Lyon, Ampère, Villeurbanne, France, xuefang.shi@insa-lyon.fr 
• Guy CLERC, Université Claude Bernard Lyon 1, Ampère, Villeurbanne, France, guy.clerc@univ-lyon1.fr 

Special session theme: 

The emerging technology of Artificial Intelligence, or AI, intersects with several techniques simulating human cognitive processes. Although existing for several decades, research in this field has recently developed to the point of increasing the number of applications: medical diagnostics, robotics, video games, autonomous cars, algorithmic for finance, image classification, voice recognition, etc. The explosion of computing power in computers made AI switch, in the 2010s, from science fiction to an increasingly close reality, which has become a major scientific issue. 

Neural network algorithms, deep-learning, fuzzy logic, metaheuristic methods or quantum computers are all techniques and components that can potentially improve the performance of power electronics systems [1]. The many applications of AI in this area include prediction of remaining useful life for supercapacitors [2], optimization of the power module heatsink design [3], power point tracking control maximum for wind power conversion systems [4], fault detection for inverter [5], intelligent multi-color light emitting diode (LED) control [6]. 

[1] S. Zhao, F. Blaabjerg and H. Wang, "An Overview of Artificial Intelligence Applications for Power Electronics," in IEEE Transactions on Power Electronics, vol. 36, no. 4, pp. 4633-4658, April 2021, doi: 10.1109/TPEL.2020.3024914. 

[2] A. E. Mejdoubi, H. Chaoui, J. Sabor, and H. Gualous, “Remaining useful life prognosis of supercapacitors under temperature and voltage aging conditions,” IEEE Trans. Ind. Electron., vol. 65, no. 5, pp. 4357–4367, May 2018.  

[3] T. Wu, Z. Wang, B. Ozpineci, M. Chinthavali, and S. Campbell, “Automated heatsink optimization for air-cooled power semiconductor modules,” IEEE Trans. Power Electron., vol. 34, no. 6, pp. 5027–5031, Jun. 2019.  

[4] C. Wei, Z. Zhang, W. Qiao, and L. Y. Qu, “An adaptive network-based reinforcement learning method for MPPT control of PMSG wind energy conversion systems,” IEEE Trans. Power Electron., vol. 31, no. 11, pp. 7837–7848, Nov. 2016.  

[5] I. Bandyopadhyay, P. Purkait, and C. Koley, “Performance of a classifier based on time-domain features for incipient fault detection in inverter drives,” IEEE Trans. Ind. Informat., vol. 15, no. 1, pp. 3–14, Jan. 2019.  

[6] X. Zhan, W. Wang, and H. Chung, “Aneural-network-basedcolorcontrol method for multi-color LED systems,” IEEE Trans. Power Electron., vol. 34, no. 8, pp. 7900–7913, Aug. 2019. 

Topics of interest include, but are not limited to: 

• Advanced optimization for power electronic systems efficiency and energy management 
• Fault diagnosis of power electronic circuits 
• Applications of AI methods for parameter identification, data processing, digital twin and data mining in power electronics 
• State variable observation, flux estimation, motor speed estimation, remaining useful life prediction of power electronic systems 
• Data-driven approaches for power converter control