Reliability Assessment of Power Systems Integrated with High-Penetration of Power Converters

Abstract

Moving towards renewable and environmental-friendly energy resources has intensified the importance of power electronic converters in future power systems. The issue of reliability becomes more critical than ever before. This research proposes a hierarchical reliability framework to evaluate the electric power system reliability from the power electronic converter level to the overall system level. In the first stage, the reliability of each power converter is modeled in an accurate manner. Dynamic behaviors of various integrated semiconductor devices and the converter topology are considered. In the second stage, we calculate system-level reliability indicators such as expected energy not served (EENS) and loss of load expectation (LOLE) are estimated through a non-sequential Monte Carlo simulation. Machine learning regression models such as support vector regression (SVR) and random forests (RF) are implemented to bridge the nonlinear reliability relationship between two stages. Moreover, a variance-based global sensitivity analysis (GSA) is conducted to rank and identify the most influential converter uncertainties with respect to the variance of system EENS. Based on the GSA conclusions, system operators can take proactive actions to mitigate the potential risk of the system. Furthermore, Bayesian network (BN) structure learning and scoring algorithms are applied to visualize a converter-based BN structure. Reliability interdependencies among different nodes are quantified through information entropy theory such that reliability causal relations can be revealed. This dissertation also studies and discusses opportunities of various emerging technologies. Some improvements and suggestions of the proposed framework are included as well.