Artificial intelligence based grid connected inverters for power quality improvement in smart grid applications. (July 2021) - Das, Soumya Ranjan; Ray, Prakash K.; Sahoo, Arun K.; Singh, Krishna Kant; Dhiman, Gaurav; Singh, Akansha
Highlights: Power Quality improvement is analysed in smart grid (micro-grid) integrated shunt hybrid filter system. The performance of shunt hybrid filter is performed with Adaptive Fuzzy-Neural-Network Control (AFNN) for achieving an efficient smart grid operation under different power system scenarios. For validating the working of the proposed technique, AFNN is compared with adaptive fuzzy sliding (AFS) control and adaptive fuzzy back stepping (AFBS) techniques. Non-singular terminal SMC (NTSMC) is designed to solve singularity point problem and provide very high precision and finite-time control for shunt hybrid filter. Abstract: 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. Graphical abstract: Image, graphical abstract