Performance Enhancement of Droop Controlled DC Microgrid Using Solar Fed Hybrid BIFRED Converter with RBFNN

Abstract

Electric power system’s structure is shifting from a generator to a converter-based, posing the risk of reduced system inertia. This transition is due to the increased penetration of power electronics-based renewable energy sources (RESs) and Energy Storage Systems (ESS). The microgrid concept supports their accommodation into electric networks; however, it requires appropriate control to solve the voltage stability issues created by reduced inertia. The proposed DC microgrid aims to maintain voltage stability in DC-link comprising wind, solar, and ESS, irrespective of changes in operating conditions. An improved Z source- Boost Integrated Fly Back Rectifier/Energy Storage DC–DC (BIFRED) converter is proposed to increase the output voltage of photovoltaic (PV) system to achieve better DC-link voltage stability. Radial basis function neural network (RBFNN) approach is used to harvest the maximum power possible from the PV panel and to improve the conversion efficiency. The proposed DC microgrid also includes Wind Energy Conversion System (WECS) powered by a Doubly Fed Induction Generator (DFIG). It integrates droop control with virtual inertia and damping control and effectively handles the voltage stability issues brought out by RESs and loads. Artificial neural network (ANN) optimized droop-controlled bidirectional converter (BDC) is implemented to integrate ESS and resolve the imbalance created by ESS. The work is validated by MATLAB simulation model and a laboratory prototype.