This task‐force paper documents and summarizes the models of power transformers that have been proposed, used, and validated in the exercise of digital real‐time simulation. Power transformer is one of the most important equipment in power systems, and its modelling for electromagnetic transient simulation has evolved over time, especially in the area of real‐time simulation. The focus of the paper is to document and archive the models that have been well accepted and used for transient analysis in digital real‐time simulation so that readers can use it as a master document for transformer modelling in real‐time simulation studies. It includes both conventional and specialized models of power transformers that have been broadly acknowledged by the power engineering community. The models provided here come with detailed mathematical representation and their implementation techniques. A comparative study is also performed to illustrate the differences in their performances. In the end, an application guideline has been provided to guide the readers to select the appropriate model for their study.

This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.