Modified teaching learning based optimization for selective harmonic elimination in multilevel inverters
Muhammad Tayyab Yaqoob, Mohd Khairil Rahmat, Siti Marwangi Mohamad Maharum
Abstract
Renewable Energy sources are becoming an important source of energy generation. Battery energy storages systems are used to supply the power when there is variation in power generation due to climate change. Power stored in batteries need to be converted from Direct current (DC) into Alternating current (AC) current to integrate it with the grids. This paper presents the modification in a recently emerged teaching learning-based optimization (TLBO) to be implemented in multilevel inverters. The results obtained from proposed modified TLBO has been compared with existing TLBO, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Imperialistic Competitive Algorithm (ICA). Furthermore, the proposed algorithm has been tested for four well-known mathematical functions like Greiwangks, Ackley’s Dejon’s and Drop wave and find out that the proposed algorithm has a better convergence rate than normal TLBO. Finally, the optimized switching angles obtained from the proposed algorithm has been tested in SIMULINK by designing an 11-level cascaded H-bridge multilevel inverter and observe the magnitude of individual harmonics. The results obtained from the proposed algorithm shows the better harmonic reduction while comparing with other algorithms.