Adaptive Damping Control to Enhance Small-Signal Stability of DC Microgrids

Abstract

This article proposes an adaptive active control approach for damping the low-frequency oscillations in a dc microgrid (DC-MG). The DC-MG is comprised of hybrid power sources (HPSs) formed by a parallel set of supercapacitor modules and photovoltaic systems. The HPS controller includes a multiloop voltage controller for adjusting the DC-MG voltage and a virtual impedance loop for damping current oscillations. The virtual impedance loop is augmented to the inner loop of the voltage controller. An adaptive tuning strategy is developed to adjust the damping coefficient of the virtual impedance loop optimally. In the tuning process, a small-signal analysis is used to determine an initial adjustment for the damping coefficient. Subsequently, an approach based on intelligent neural network is intended to provide accurate online correction of the damping coefficient, which passes the dependence of the converter control system on the operating point conditions and accommodates different operation conditions. A sensitivity analysis is also conducted to investigate the effects of the system parameters on the HPS stability. Moreover, a mesh analysis is carried out to examine the stability of low-frequency modes of the whole DC-MG using the proposed control scheme. Case studies are conducted to demonstrate the performance of the proposed control strategy, and the analysis results are verified by hardware-in-the-loop (HIL) setup using OPAL-RT (OP5600) and dSPACE (DS1202) simulators.