Conference Agenda

In a multi-terminal direct current (MTdc) system based on a modular multilevel converter (MMC), high-speed and large interruption capability direct current circuit breakers (de CDs) are required for de fault interruption. However, the commercialisation of these breakers is challenging, especially offshore, due to the large footprint of the surge arrester.Hence, a supplementary control is required to limit the rate of current rise along with the fault current limiter.Furthermore, the operation of the de CB is not frequent, thus, it can lead to delays in fault interruption. This study proposes the indirect model predictive control (MPC)-based zero-current control. This control provides de fault current suppression by continuously controlling the zero-sequence current component using circulating current suppression control (CCSC), and by providing feedback to the outer voltage loop and inner current loop of MMCs. The proposed control is simulated for pole-to-pole and pole-to-ground faults at the critical fault location of an MTdc system. The simulation is performed in Real Time Digital Simulator (RTDS) environment, which shows that the predictive control reduces the rate of rise of the fault current, and in this way provides an additional 3 ms after the de fault occurrence for the de CB to clear the fault. Besides, the energy absorbed by the de CB's surge arrester during the pole-to-pole and pole-to-ground fault remains the same with the proposed control

This paper presents a tool developed in EMTP to automatically determine model parameters for matching existing field measurements. The tool uses the particle swarm optimization (PSO) algorithm to calibrate or update existing models. To enhance the performance of the tool, a technique used to improve PSO efficiency is also proposed. Two test cases are presented. The first case aims to determine the parameters of the reactive power control loop in a PV park controller model. The second case finds the unknown parameters in an exciter model of a synchronous machine connected to a grid.

Deep learning techniques have recently demonstrated outstanding success when used for Power Quality Disturbance (PQD) classification. However, a core obstacle is that deep neural networks (DNN)s are complex models, and their architecture is designed using trial and error processes. Accordingly, the problem of finding the optimal architecture can be considered as a problem that consists of high-dimensional solutions. Meanwhile, in the last couple of years, Neural Architecture Search (NAS) techniques have been developed to efficiently find the best possible performance architecture for a specific task. In this light, the goal of this research is to develop a method to find optimal PQD classifiers using the NAS technique, based on an evolutionary algorithm. This method can converge efficiently to an optimal DNN architecture. Thus, a classifier that achieves high accuracy for PQDs classification is provided using limited resources and with minimal human intervention. This idea is demonstrated on two different DNN typologies- convolutional neural networks (CNN) and Bi-directional long short-term memory (Bi-LSTM). By adopting this method, the results of the generated PQD classifiers are more accurate when compared to recently developed classifiers.

This paper develops a novel multi-rate, multi-solverco-simulation framework combining dynamic phasors, transientstability, base-frequency dynamic phasors for frequency-adaptivesimulation of transients, and electromagnetic transient (EMT)models. This framework subdivides a given power network intoseveral types of subsystems based on the connected devices,required accuracy in representing dynamic details, electricaldistance from perturbations, and the intended purpose of thestudy; as such, the paper describes methods and guidelines tosimulate each subsystem using the most appropriate solver andtime-step size to maximize simulation efficiency and accuracy.It also addresses the tasks of multiple interfacing and solverinteractions that are essential in coupling different solvers. Theproposed framework is built around an industrial-grade EMTsimulator, to which other solvers are interfaced, enabling accessto a variety of power system models and distinct features. Theaccuracy and efficiency of the framework are demonstratedthrough co-simulations carried out on a modified version of the118-bus network, which includes an MMC-HVDC system.

This paper analyzes the use of fitting techniques based on partial fraction expansions in the fitting of modal transmission line functions and the assumption of constant and real transformation matrix (constant T) in the transformation of modal functions into phase domain. The focus is on the fitting of the propagation function due to its complexity compared to the characteristic admittance function. It is demonstrated for the first time that using a constant T can intrinsically violate the passivity of the transmission line system depending on the choice of frequency point for assigning the constant T. Consequently, the final rational model violates passivity at certain frequency intervals. Second contribution is the evaluation of the fitting performance with a new solution strategy based on the recently introduced rational Krylov fitting (RKF). The case studies suggest that RKF results in accurate and less order models compared to the vector fitting (VF) algorithm which is the de facto method in electromagnetic transient-type models. Finally, the fitting accuracy of the legacy constant T model based on Bode fitting is presented in the phase frame giving a clear picture of its poor fitting performance compared to modern methods and explaining its inaccuracies in the time domain.

In this paper, an alternative approach to power loss calculation in a transformer supplying a nonlinear load is presented. The advantage of the proposed approach is that it relies on readily available transformer technical data in contrast to the data required by the methodology described in IEEE std. C57.110-2018. Experimental verification of the proposed approach was carried out using a 4.5kVA laboratory dry type power transformer. The results obtained experimentally show
high compatibility with theoretical model featuring an error margin smaller than 0.2%.