Special Issue "Modeling of Artificial Intelligence controller for Microgrid and SmartGrid application"

Submission Deadline: 31 December 2021 (closed)
Guest Editors
Dr. Venkateshkumar M, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai Campus, India. Email: m_venkateshkumar@ch.amrita.edu
Dr. Cheng Siong Chin, Newcastle University, Singapore. Email: cheng.chin@newcastle.ac.uk


The main objective of this special issue is to promote the important role of artificial intelligence controllers for Microgrid and their applications. Over the past few years, conventional controllers have been used for microgrid applications and face numerous problems of system stability as well as reliability. The main focus of this special issue is the modeling and design of various artificial intelligence algorithms (ANN, Fuzzy, ANFIS, GA, PSO, Deep Learning, and Firefly) for renewable energy and microgrid integration applications. The following key issues are addressed in this section. 


 AI-based MPPT controller design for Renewable Energy sources

• AI-based energy management system of microgrid

• AI-based Power quality improvement of microgrid 

• AI-based Smart inverters

• Smart Electrical Vehicle to Grid and Grid to Electrical Vehicle 

• Smart grid applications 

• Predictive Maintenance for Renewable Energy System 

• AI-based battery management system 

Microgrid, Smart grid, Renewable Energy, Energy Management, Power Quality, Smart Inverters, ANN, Fuzzy, ANFIS, GA, PSO, Deep Learning, Firefly algorithms, Electrical Vehicle

Published Papers
  • Experimental Performance Analysis of a Corrugation Type Solar Air Heater (CTSAH)
  • Abstract This paper explains the experimental performance evaluation of a Corrugated Type Solar Air Heater (CTSAH) for understanding its performance in a humid tropical climatic condition in Puducherry, India. This helps in understanding its effectiveness in using it for drying application of products like seafood, etc. Experiments were conducted at different mass flow rates and their effect on the heat gain, efficiency, friction factor heat transfer, etc., was analyzed. Experiments were carried out at different mass flow rates, i.e., M1 = 0.06 kg/s, M2 = 0.14 kg/s, M3 = 0.17 kg/s, M4 = 0.25 kg/s, M5 = 0.3 kg/s, and were conducted from 11:00… More
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