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Contribution: This paper introduces the real-world limitations and non-technical aspects of power electronics (PEs) projects to students through innovative tutorial activities. Background: Many electrical engineering curricula offer a PE courses (PECs) for third- or fourth-year undergraduate students. Prior research on PEs education mainly focused on improving students’ experimental skills through developing practical experiments, laboratory activities, and problem/project-based learning. An instructional approach that instead employs real-world knowledge and skills is worth evaluating. Intended Outcomes: Students should be able to consider real-world technical and non-technical limitations when applying theory to design PE circuits and converters, and be able to select and carry out appropriate tests to troubleshoot circuits. Application Design: Prior research on engineering education emphasized the importance of introducing real-world limitations to the students as part of their curriculum. This paper suggests that the tutorial activities presented in a PEC can help students acquire skills in designing and troubleshooting a circuit or system according to desired technical aspects, real-world limitations, and available data. Findings: Evidence of the validity of this approach in a PEC at two Australian universities, over four academic years, is provided. Students receiving the new tutorial activities had percentage scores some 10–15 points higher than those who had traditional tutorials. Another evaluation reveals the students’ vibrant participation in the activities during the new tutorial sessions. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data driven framework for detecting topology transitions in a distribution network. The framework analyzes data from phasor measurement units (PMUs) and relies on the fact that changes in network topology results in changes in the structure and admittance of the network. Using voltage and current phasors recorded by PMUs, the proposed method approximates network parameters using an ensemble-based deep learning model and thus, it does not require any knowledge of network parameters and load models. Using the prediction error of the proposed model, a connectivity matrix which shows the status of switches is constructed. In contrast to other methods, this proposed framework does not require a library of voltage and current transients associated with possible network transitions. It can also detect simultaneous switching actions and is robust to noise and load variations. The model yields a lower error detection rate, and its performance is validated using a modified version of the IEEE 33 bus network and a real feeder located in Queensland, Australia, under full and partial observability conditions. The proposed model has also been compared with another data driven method in terms of inference time and error detection rates. [ABSTRACT FROM AUTHOR]

Operational performance in the low-voltage distribution network (LVDN) can be undermined by its inherent unbalances, which may become worse as the penetration of rooftop solar continuously increases. To address this issue, load balancing via phase-reconfiguration devices (PRDs), which can change phase positions of residential customers as required, provides a cost-efficient option. However, most reported approaches to control PRDs require that demands of all residential customers are available, which are not viable for many LVDNs without smart meters or advanced metering infrastructure (AMI) installed. To bridging the gap in this field, this paper proposes a novel method to control PRDs purely based on measurable data from PRDs, and its controller. Based on limited information, sensitivity analysis in the network with PRDs is studied, followed by the optimization model that comprehensively considers operational requirements in the network. Moreover, slack variables are introduced to the model, and penalized in the objective function to assure either a strategy that is secure or with minimized violations can always be provided. The model is a challenging mixed-integer non-convex programming (MINCP) problem, which is reformulated as an efficient solvable mixed-integer second-order cone programming (MISOCP) based on exact reformulations or accurate linear approximations. Simulations based on two modified IEEE systems, and a real system in Australia demonstrate that an efficient strategy can be provided to mitigate unbalances in the network. [ABSTRACT FROM AUTHOR]