Advanced Voltage Stability Assessment in Renewable-Powered Islanded Microgrids Using Machine Learning Models

Abstract

The assessment of voltage stability within a microgrid is essential to ensure that all buses in the system can maintain the required voltage levels. Recent research has focused on developing modern voltage stability estimation equipment rather than identifying optimal locations for integrating inverter-based resources (IBRs) within the network. This study analyzes and evaluates voltage stability in power systems with increasing levels of IBRs using modal analysis methodologies that consider active power (PV) and reactive power (QV). It examines the impact of load flow when integrating IBRs into the weakest-and strongest-load buses. Additionally, this study introduces a support vector machine (SVM) approach to assessing voltage stability in a microgrid. The results indicate that the proposed SVM approach achieved an optimal accuracy of 95.10%. Using the IEEE 14-bus scheme, the methodology demonstrated the effective and precise determination of the voltage stability category of the system. Furthermore, the analysis was conducted using the modified DES power system. The core contribution of this research lies in evaluating and identifying the locations that are the most and least favorable for integrating IBRs within the simplified DES power system network, utilizing modal analysis for both QV and solar photovoltaics (SPVs). The results of the load flow analysis suggest that integrating IBR is significantly more beneficial in the most substantial bus, as it minimally impacts other load buses assessed as the least reliable bus within the system.