TY  - EJOU
AU  - Vasudevan, Nisha 
AU  - Venkatraman, Vasudevan 
AU  - Ramkumar, A. 
AU  - Muthukumar, T. 
AU  - Sheela, A. 
AU  - Vetrivel, M. 
AU  - Saraswathi, R. J. Vijaya 
AU  - Josh, F. T. 

TI  - Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality
T2  - Energy Engineering

PY  - 2023
VL  - 120
IS  - 8
SN  - 1546-0118

AB  - MigroGrid (MG) has emerged to resolve the growing demand for energy. But because of its inconsistent output, it can result in various power quality (PQ) issues. PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources. Similarly, the employment of nonlinear loads will introduce harmonics into the system and, as a result, cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system. Thus, this research focuses on power quality enhancement in the MG using hybrid shunt filters. However, the performance of the filter mainly depends upon the design, and stability of the controller. The efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network (AFNN) controller. The performance of the proposed topology is examined in a MATLAB/Simulink environment, and experimental findings are provided to validate the effectiveness of this approach. Further, the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping (AFBS) and Adaptive Fuzzy Sliding (AFS) to prove its superiority over power quality improvement in MG. From the analysis, it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%, which is less than the acceptable limit standard.
KW  - Artificial intelligence; resistive inductive load; shunt hybrid filter; smart grid; adaptive fuzzy back-stepping; power factor

DO  - 10.32604/ee.2023.027821