Dimension Reduction Based Non-Parametric Disaggregation for Dependence Modeling in Composite System Reliability Evaluation

Abstract

The correlated variation of bus loads and wind powers has posed a challenge to power system security. How to construct an accurate dependence model becomes vital for power system reliability evaluation. Most currently used methods in estimating the multivariate probability density function (PDF) not only ignore the aggregation constraint but also encounter the curse of dimensionality. In this paper, a new non-parametric disaggregation (ND) technique is presented to disaggregate an aggregate variable (e.g. system load) into non-aggregate variables (e.g. bus loads) while meeting the aggregation constraint. An innovative hierarchical dimension reduction technique is proposed and integrated into the ND model to transform a high-dimensional conditional PDF estimation into some low-dimensional estimations, which effectively resolves the difficulty associated with high dimensionality. The above two techniques are incorporated into an effective three-stage conditional sampling method to form the proposed dimension reduction based ND model (DRND) for high-dimensional dependence modeling in composite system reliability evaluation. The validity of the proposed DRND is verified by three test systems, i.e. MRTS79, MRTS96, and the latest RTS-GMLC.