PALONN: Parallel Ant Lion Optimizer and Artificial Neural Network for Power Flow Control of the Micro Grid-Connected System

Abstract

In this paper, parallel ant-lion optimizer (ALO) and artificial neural network (ANN) for the micro-grid (MG) connected system with AC load is proposed. The proposed algorithm is the combined performance of ALO type I and II algorithms. The aim of the proposed algorithm is to increase the power transfer capability between the source and load sides and to offer several benefits like enhanced predicting capability, degradation in complexity, and randomization. ALO type I is utilized for active power controller parameter optimization with enhanced searching behaviour like crossover and mutation. Using the ALO type II, the reactive power controller parameters’ optimization is performed with online predictor ANN. The exact control signals of the power controller are determined based on the system power deviations using the parallel operation of the algorithms. With the performance of the proposed controller, the power exchange is enhanced and ensured between the sources to load. The implementation is done by the MATLAB/Simulink platform. The performance of the proposed method is analysed using comparison analysis with the existing techniques.

KEYWORDS:

• AC load
• ALO type I
• ALO type II
• ANN
• Crossover
• MG
• Mutation
• Power flow control

Additional information

Notes on contributors

Raju Manuel

Raju Manuel was born in Kollam, Kerala, India, in April 1973. He received the BTech degree in electrical & electronics engineering, MTech degree in power systems engineering from University of Kerala in 1994 and 2012, respectively and currently doing PhD (part time) in electrical engineering at Karpagam Academy of Higher Education, Karpagam University, Coimbatore. His current research interest includes smart grid, behaviour of smart grid during the unbalanced condition, and the effect of energy storage in the behaviour of smart grid. He is currently assistant professor in electrical engineering, College of Engineering Karunagappally, Kerala. He is a life member of the Indian Society for Technical Education, IEEE, and IEEE-PES Society. Corresponding author. Email: [email protected]

G. Emayavaramban

G Emayavaramban received the BE and ME degrees from Anna University, and the PhD degree in EEE from the Karpagam Academy of Higher Education, Coimbatore, India. He has four years of excellence in research. He is currently an assistant professor with the Karpagam Academy of Higher Education. He has published several papers in referred journals and conferences. His research interests include biosignal processing, artificial intelligence, and human–computer interface. He had received silver and bronze medals in national and international exhibitions for his research. Email: [email protected]