Artificial intelligence based grid connected inverters for power quality improvement in smart grid applications

The Smart Grid (SG) is treated as the next level of modern power system which uses the bilateral flow of power and information. The ability of the smart grid for two-way communication amid the utility and consumers makes the grid smart. For proper functioning, all the elements and parameters associated with it should work effectively and efficiently. Power Quality (PQ) is an important issue related to a modern power system. In this paper, more focus is given on PQ improvement in the microgrid (MG) system (which is a part of SG) using shunt hybrid filters (SHF). The performance of SHF is investigated using an improved and advanced controlling technique, i.e., Adaptive Fuzzy-Neural-Network (AFNN) Control for achieving an efficient SG operating under different scenarios of loads and supply voltages. The proposed controller is compared with the other controlling techniques like adaptive fuzzy sliding (AFS) control and adaptive fuzzy back stepping (AFBS). The analysis is performed with the MATLAB/Simulink tool.