Open Access

ARTICLE

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

by Ravi Samikannu^1,*, K. Vinoth², Narasimha Rao Dasari³, Senthil Kumar Subburaj⁴

1 Department of Electrical Computer and Telecommunications Engineering, Botswana International University of Science and Technology, Botswana
2 Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
3 Department of Electrical Power Engineering, College of Engineering, Defence University, Ethiopia
4 Department Electrical and Electronics Engineering, New Prince Shri Bhavani College of Engineering and Technology, Sembakkam, Tamil Nadu, India

* Corresponding Author: Ravi Samikannu. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 983-995. https://doi.org/10.32604/iasc.2023.026069

Received 14 December 2021; Accepted 14 February 2022; Issue published 06 June 2022

Abstract

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit. Renewable energy sources are playing a significant role in the modern energy system with rapid development. In renewable sources like fuel combustion and solar energy, the generated voltages change due to their environmental changes. To develop energy resources, electric power generation involved huge awareness. The power and output voltages are plays important role in our work but it not considered in the existing system. For considering the power and voltage, Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier (GPIC-MDCNNC) Model is introduced for the grid-connected renewable energy system. The input information is collected from two input sources. After that, input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model. Hidden layer 1 is transferred to hidden layer 2. Gaussian activation is employed for determining the output voltage with help of the controller. At last, the output layer offers the last value in GPIC-MDCNNC Model. The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages. GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works. The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

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Keywords

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