A new methodology for ultra-fast and accurate statistical EMT analysis in electric power-systems

• QMF-EMT, a novel method for statistical EMT studies, achieves superior accuracy and computational efficiency compared to conventional and commercial software. • Enhancements include support for sequential switching and the incorporation of nonlinear elements such as surge arresters, enabling comprehensive EMT analysis. • The method’s computational effort is independent of network size, using Kron network reduction to simplify models without accuracy loss or constraints. • Since the method does not involve rational approximations, it is free of passivity-violation problems. • Convolutions involved statistical studies in this model are and can be easily computed in a parallel hardware. • The proposed method can perform EMT simulations and statistical studies hundreds of times faster than other methodologies. • QMF-EMT offers practical solutions for selecting integration steps and simulation counts, addressing key gaps in EMT statistical studies. • The proposed methodology enables the execution of statistical studies on regular-scale networks, allowing for the computation of thousands of simulations on conventional computers. This paper introduces Quadrature Mirror Filter Electromagnetic Transient (QMF-EMT) methodology as a novel, precise, and ultra-fast method for statistical studies (SS) of EMTs in electrical networks. Building on a previously proposed approach for real-time EMT simulation, QMF-EMT incorporates nonlinear elements such as surge arresters, sequential switching operations, and a statistical switching model. The method is highly parallelizable and achieves superior accuracy and computational speed compared to conventional time-domain (TD) and frequency-domain (FD) techniques. The effectiveness of QMF-EMT is demonstrated through its application to the statistical analysis of two test networks. Validation is performed by comparing the results with established techniques, including PSCAD/EMTDC and the Numerical Laplace Transform (NLT) method. The case studies include the integration of surge arresters, controlled switching operations as overvoltage (OV) mitigation measures, and a large-scale network comprising 39 three-phase nodes. Notably, QMF-EMT's high computational speed on conventional CPUs enables efficient determination of the required number of events and integration steps for statistical analyses. This work underscores QMF-EMT's potential as a transformative tool for addressing computational challenges in EMT studies, particularly in large-scale power-systems.