Performance Evaluation of RSM and ANFIS in Modelling Nano Fluids-based Mixed Insulating Fluids

Abstract

Transformer liquid dielectric is seen as a critical quality characteristic which must be very strong to withstand the voltage level in an abnormal state. In this research, prediction of dielectric strength, fire point and viscosity through response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) is developed to improve liquid dielectric life in the automated environment. An experimental design with RSM-based box–behnken method was planned and experiments with three distinct input parameters (Wt.% CNT, Wt.% ZrO₂ and Stirring temperature) were carried out. The set of data has been used to train and analyse using hybrid and back-propagation optimization methods. Model values of ANFIS were compared with experimental and RSM values. The comparison results showed that the developed ANFIS model is a powerful tool for modelling the nano fluids-based mixed insulating fluids.

KEYWORDS:

• ANFIS
• Box Behnken design
• Dielectric strength
• Pongamia pinnata
• Transformer oil

Additional information

Notes on contributors

S. S. Kumaresh

S S Kumaresh was born in Tamil Nadu, India in 1988. He received BE in electrical engineering and ME in Power electronics from Anna University in 2010 and 2012, respectively. He is working as teaching faculty in University College of Engineering Kancheepuram, Tamil Nadu, India. His main research area is power transformer insulating oil, photo voltaic panel efficiency improvement, modelling of experimental design, soft computing techniques.

M. Malleswaran

M Malleswaran received BE degree in ECE and ME degree in communication systems. He has completed PhD in navigation systems. He is currently working as professor in the Department of ECE in University College of Engineering Kancheepuram, Tamilnadu, India. He has 24 years of teaching and research experience. His main research area is communication systems, soft computing techniques. E-mail: [email protected]