Abstract
With the proliferation of renewable energy and power electronic converters in power systems, the reliability issue has raised more research attention than ever before. This paper proposes a comprehensive framework to assess the reliability of a power system considering the effect from various power converter uncertainties. For the converter stage, we formulate a reliability model for each power converter based on several semiconductor devices, for which ambient uncertainties and converter topologies are considered. For the system stage, we estimate system reliability indicators through a non-sequential Monte Carlo simulation and calculate their variances. Afterward, we leverage machine learning regression algorithms between two stages to establish a nonlinear reliability relation. Moreover, a variance-based sensitivity analysis (SA) is conducted to rank and identify the most influential converter uncertainties with respect to the variance of system EENS. Based on the SA conclusions, system operators can take proactive actions to mitigate the potential risk of the system.
Highlights
T HE incorporation of renewable energy resources (RES) in power systems has brought several challenges to realizing a reliable power delivery
The system reliability indicators are calculated through a non-sequential Monte Carlo (MC) approach
A variance-based global SA (GSA) is applied in the second stage to conduct an importance ranking for different groups of power converters
Summary
T HE incorporation of renewable energy resources (RES) in power systems has brought several challenges to realizing a reliable power delivery. The second stage of the proposed framework presents for the first time the application of the variance-based GSA to identify the contribution of each converter uncertainty to the variance of the system reliability indicator This novel application is of critical importance as future power systems become increasingly implemented with RESs and power converters. The variance-based GSA method is applied to the proposed power system to provide instructive information to system operators and stakeholders such that better operational planning can be achieved This novel application is intended to promote the development of the implementation of more advanced SA methods in power systems such that interested researchers can further interpret the reliability behavior of future power systems.
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