Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Computer science & Cloud computing. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

A review on microgrid central controller

Abstract: The microgrid central controller has most important role for satisfactory automated operation and control of microgrid while working in grid connected and islanded modes. The central controller has several features for proper coordination of distributed energy resources as per their power generation capacity to serve the critical and non-critical loads. It also initiates the protection techniques at the time of occurrence of severe short circuit fault at the grid end or in microgrid thus ensuring stability and reliability. This paper presents a comprehensive literature review on microgrid central controller. The evolution and advancement of microgrid central controller technology is explored and presented in a compact form. The classification of microgrid central controllers is proposed based on the outcomes found in the process of review. The role of central controller in the domains of microgrid protection, stability and power quality are also explored and summarized. This literature review may be considered as an initial platform for research work on microgrid central controller.

Multi-objective spotted hyena optimizer: A Multi-objective optimization algorithm for engineering problems

Abstract: This paper proposes a multi-objective version of recently developed Spotted Hyena Optimizer (SHO) called Multi-objective Spotted Hyena Optimizer (MOSHO). It is used to optimize the multiple objectives problems. In the proposed algorithm, a fixed-sized archive is employed for storing the non-dominated Pareto optimal solutions. The roulette wheel mechanism is used to select the effective solutions from archive to simulate the social and hunting behaviors of spotted hyenas. The proposed algorithm is tested on 24 benchmark test functions and compared with six recently developed metaheuristic algorithms. The proposed algorithm is then applied on six constrained engineering design problems to demonstrate its applicability on real-life problems. The experimental results reveal that the proposed algorithm performs better than the others and produces the Pareto optimal solutions with high convergence.