Synchronous machines form the principal source of electrical power in power systems. Modeling of the synchronous machines for transient analysis has always been an active topic of research. In this paper, a novel wound-field three-phase synchronous machine model is developed for the accurate and efficient simulation of multi-scale transients. The machine stator equations are expressed with analytic signals in the phase domain, thus providing direct interface between machine and external network models. Frequency shifting is applied to stator quantities to eliminate the ac carrier in the stator windings which enables the use of large time-step size. An artificial damper winding is introduced to eliminate the numerical saliency. To provide accurate and stable solutions with multiple time-step sizes, the artificial winding parameters setting algorithm is established. The proposed machine model is expressed in terms of a Norton equivalent with constant admittance matrix without introduction of prediction of any electrical quantity. The update of the admittance matrix at each time-step is avoided. The analysis of test cases demonstrates the effectiveness of the proposed multiscale synchronous machine model and the artificial winding parameters setting algorithm.

The Electromagnetic Transients Program (EMTP) is widely used for simulating electromagnetic transients in power systems. In recent years, applications of electrolyzers in electric power systems have attracted considerable attention because electrolyzers are among the most promising energy-conversion devices for realizing hydrogen production from renewable energy sources while enhancing system flexibility by functioning as controllable loads. With the growing interest in electrolyzers, it would be advantageous to broaden the application of EMTP to include multi-physics transients such as those observed in electrolyzers. This article details the implementation of such an extension for a proton exchange membrane electrolyzer (PEMEL). Analogies between electric, mass transfer, and thermal quantities are adopted to develop an electric circuit model that describes currents, voltages, mass transfer, pressure, heat transfer, and temperature. The PEMEL model considering the interactions of electric, mass transfer, and thermal transients can be readily implemented in EMTP-type programs using existing components from standard libraries. The proposed electric circuit model was validated through comparison with experimental data under both steady-state and transient conditions. The value of the proposed model was illustrated via application to a PEMELbased integrated electrical and gas system.

The paper presents a comprehensive analysis of losses in several battery energy storage system (BESS) converters using EMT simulations. The work is motivated by the critical need to determine converters’ semiconductor losses, particularly due to the high-frequency dc-dc converters that interface batteries. Two general classes, namely modular multilevel and two-level converter topologies, are considered. A simulation-based, computationally efficient, and accurate loss calculation method, which utilizes device data sheet loss curves, is used to estimate semiconductor losses based upon post-processing of EMT simulation results of the converters. The paper quantifies the impact of switching frequency and circulating current suppression controller on the losses. Comparative assessments of the merits of each topology are also presented.

This paper explores and evaluates various approaches to accelerate Electromagnetic Transient (EMT) simulations of power systems using Graphical Processing Units (GPUs). Existing EMT simulation methods face computational challenges in systems with extensive renewable energy sources due to the complexity and switching dynamics of the system. The paper focuses on simulation methods based upon specialized GPU solvers to handle simulations of large and complicated power systems (e.g., with extensive switching components) with computational efficiency. Results from benchmark systems show significant speedups, particularly for large networks with high-frequency switching events.

With increasing wind energy integration, grid codes require low-voltage ride-through (LVRT) compliance to ensure stability and prevent economic losses. This paper focuses on achieving LVRT in doubly fed induction generator (DFIG) based systems using positive- and negative-sequence component control. The delayed signal cancellation (DSC) method is compared with common techniques based on the second-order generalized integrator (SOGI), T/4 delay and low-pass or notch filter methods, demonstrating its superior dynamic performance. A novel approach for rotor-current decomposition is proposed, transforming mixed-frequency signals into general unbalanced signals for DSC processing. Sequence-component control schemes based on DSC decomposition are developed following LVRT requirements. Controller hardware-in-loop (HIL) tests of a 2.5-MW wind turbine validate the strategy’s effectiveness in mitigating grid faults and enhancing LVRT performance.

Parameter estimation is essential for optimal operation and control of electric machines in automotive applications. Machine learning (ML) has emerged as a powerful tool for estimation. However, ML performance strongly depends on the accurate availability of large datasets. In electric machines, experimental test rigs provide accurate data, but collecting large datasets is limited and labor intensive. Finite element analysis (FEA) can generate accurate data; however, incorporating inverter time-harmonic effects through voltage-source models is computationally demanding. To address both the accurate data requirement and the computational burden, this paper proposes a novel time-harmonic-aware physics-informed neural network (THA-PINN) model for estimation of the d- and q-axis flux linkages of permanent magnet-assisted synchronous reluctance machines (PMaSynRMs) which can be used for its optimal control. Time-harmonics are incorporated using the double Fourier integral (DFI) to generate FEA-based training data for an inverter-fed PMaSynRM. A single-input multi-output (SIMO) PINN framework employing the torque equation as a physicsbased residual is developed to train the THA-PINN model, which achieves higher accuracy with significant reductions in data and time compared to a state-of-the-art time-harmonic-aware artificial neural network (THA-ANN) model.

Synchronous reluctance motors (SynRMs) offer a sustainable alternative to electric vehicle (EV) traction drives by eliminating reliance on rare-earth materials and mitigating associated supply chain and geopolitical concerns. However, the absence of rare-earth magnets leads to reduced efficiency and power density. To address these limitations, ferrite magnets are incorporated into the rotor to realize a rare-earth-free permanent magnet-assisted SynRM (PMaSynRM), and a wide bandgap (WBG) device-based inverter is employed to further enhance efficiency and power density. In this paper, a novel loss modeling framework is developed to accurately predict the efficiency of the WBG-based PMaSynRM drive by incorporating both inverter losses and machine losses, including fundamental and pulse-width modulation (PWM)-induced components. The proposed approach leverages a double Fourier integral (DFI)-based formulation to account for switching harmonics and applies a golden section search to determine the optimal switching frequency that maximizes overall drive efficiency across different operating points. As an example, at 1200 rpm and $4 \mathrm{~N}. \mathrm{m}$, the optimal switching frequency of 64.89 kHz improves system efficiency from 83.3% at 1 kHz to 87.8%, demonstrating a performance gain unattainable with a fixed switching frequency strategy.

In this paper, the theory and application of dynamic phasors (DPs) to model and simulate electrical circuits are revisited. The paper reveals foundational conditions that must be in place so that DPs are able to offer computational benefits that are commonly, yet incorrectly, attributed to them as universal characteristics. Following a companion model-based approach using DPs, eigenvalue and steady-state analyses are conducted to assess the precision of EMT and DP modeling methods as a function of the simulation time step. Through a case study of the IEEE 9-bus system, the effects of large time-steps on simulation accuracy are illustrated. The findings demonstrate that while DP-based modeling can accurately represent steady-state behavior of circuits with large time-steps, its accuracy is limited during transients conditions, highlighting the importance of judicious time-step selection for accurate simulations.

The emergence of multi-energy networks, comprising electricity and natural gas (NG), presents novel and complex challenges to the comprehensive analysis of energy systems. Energy systems based on electricity and gas adhere to distinct physical laws and mathematical representations. As attention and interest in integrated electricity and gas systems (IEGS) grow, expanding the scope of applying electrical analogies to pneumatic quantities is advantageous. The objectives of this paper are to implement the extension of this analogy and to conduct a multi-rate simulation of IEGS. It shows how the NG pipeline and gas compressor station (GCS) can be modeled using basic electric elements for the simulation of pneumatic transients. The primary objective of devising the multi-rate algorithm is to attain greater efficiency during the computational procedure. The target system is partitioned into an electrical network subsystem (ENS) and a gas network subsystem (GNS). Different time steps are adopted in the simulation of these subsystems. A novel interface model based on gas turbines is proposed to represent the interactions between ENS and GNS. A comparatively large time-step size is used in the GNS for accelerated computations. The multi-rate simulation algorithm is accompanied by validation and application to demonstrate its effectiveness in enabling efficient simulation of IEGS.

This paper presents a comprehensive analysis of the functional response verification of an inverter operating as a Virtual Synchronous Machine (VSM) in grid-following mode. The proposed VSM control strategy addresses critical technical challenges associated with integrating Inverter-Based Resources (IBRs) into weak power grids. Modern grid codes outline essential performance requirements of IBRs, including voltage regulation, reactive power support, frequency control, and resilience during grid disturbances. An electromagnetic transient (EMT)-domain based testing procedure (generally referred to as Model Quality Tests – MQT) of a 50 MVA solar PV plant controlled as a VSM is presented. These tests ensure accurate model representation and IBR’s compliance with regulatory standards. Furthermore, the paper explores the adaptability of VSM controls in delivering essential functional features, such as inertial and fast frequency response, to enhance grid stability and operational efficiency. The test results highlight the ability of inverters operating as VSMs to meet essential grid code requirements, thereby enabling the reliable integration of renewable energy sources into power systems and supporting the transition toward sustainable energy.

A grid-forming (GFM) controller often consists of a layer emulating a (virtual) synchronous machine, and a current-limiting layer. While the former layer is responsible for controlling the converter as a voltage source, the protection of power electronic switches against overcurrent is enabled by the latter. Several current-limiting methods for GFM inverter applications are suggested in literature and may be broadly categorized as no-loop and multi-loop controllers. A comparative assessment is conducted between three major current-limiting methods. To effectively evaluate them, eigenvalue-based stability assessment and detailed modelling in PSCAD/EMTDC are conducted. Different bandwidths of interactions that are excited by different current controllers are revealed. Sensitivity analysis is then conducted to select optimal control parameter values. The fault-ride-through analysis shows the challenges in both balanced and unbalanced fault-ride-through capabilities.

This paper develops a detailed equivalent model for modular multilevel converters with partially-integrated battery energy storage. The proposed model gains computational efficiency in two ways. Firstly, it markedly reduces the large number of nodes in the conventional switching model of the converter, thereby shrinking the size of its admittance matrix. Secondly, it avoids computationally expensive re-triangularization of the admittance matrix during the normal operation of the converter and restricts it only to the rare occasions of converter blocking. Mathematical derivation of the model is carried out using differential equations of the converter. The computational efficiency and accuracy of the proposed model are confirmed by comparison of the results from its implementation in the PSCAD/EM TDC simulator against conventional detailed switching models and measurements from a single-phase scaled-down laboratory setup. This paper also shows a case study wherein a converter with partially-integrated batteries is included in the CIGRE B4-5 benchmark system.

The increasing integration of inverter-based resources (IBRs) presents significant challenges for Electromagnetic Transient (EMT) simulations. Frequent changes in the admittance matrix, driven by switching events in IBRs, require repeated factorization of the network equation, further increasing computational demands. Using general-purpose Graphical Processing Units (GPUs), EMT simulations can achieve substantial performance improvements. This paper introduces a fully functional GPU-based EMT simulator that incorporates the compensation method for enhanced efficiency. This approach is particularly advantageous in scenarios with high IBR penetration, as it facilitates the use of multirate simulation techniques. The proposed simulator demonstrates significant potential for addressing the computational challenges of modern power systems with significant IBR integration.

Power systems are rapidly being dominated with converter-based generation causing increased interactions. Reduced system strength tends to exacerbate the already weakened steady state and transient behaviour of such systems. Simulations carried out for such systems using conventional phasor-based and EMT modelling methods have limitations. This paper uses a dynamic phasor-based modelling method to analyse systems with grid-forming and grid-following converters under different network conditions. This method enables analysis of both small-signal and transient behaviours, eliminating the need to model systems in multiple platforms, and integrates the capabilities of EMT and conventional phasor methods. Quantitative analysis is provided on how controllers of grid-following converters are affected by the system strength. The supporting capabilities of grid-forming converters are demonstrated, while highlighting limitations in series-compensated network configuration.

The power modulations carried out by a grid-forming inverter are profoundly affected by the capability of the inverter's dc-side circuit to support such modulations. Although preliminary work on dc microgrids includes the dc-side dynamics, analysis of potential ac-side interactions in the presence of grid-forming inverters is lacking. This paper presents an in-depth study of the interactions in grid-forming inverter systems considering the critical dynamics contributed by the inverter's dc-side circuitry. The study in this paper is based upon dynamic phasor modeling and eigenvalue analysis techniques. The results presented for an exemplar system of a grid-forming inverter paralleled with a synchronous machine prove the significance of including dc-side dynamics to accurately capture the full range of interactions that may occur in such systems. Detailed electromagnetic transient simulation results from PSCAD/EMTDC are included to verify the validity of the predictions of the dynamic-phasor-based model.

Wide-bandgap (WBG) power devices, such as silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFET) and gallium nitride (GaN) high-electronmobility transistors (HEMTs), are gaining attention in electric drives due to their capability to reduce switching losses and achieve higher power density compared to silicon (Si) insulatedgate bipolar transistors (IGBTs). In parallel, the synchronous reluctance motors (SynRMs) are emerging as rare-earth-free alternatives to permanent magnet machines, which suffer from supply chain challenges and high costs. The WBG device-based SynRM drive has been rarely studied, despite its promising potential for efficiency gains and sustainable operation. To address this gap while ensuring accurate predictions, this paper integrates a finite-element analysis (FEA)-derived SynRM model into an electro-thermal simulation of a WBG device-based drive that simultaneously accounts for machine's nonlinearities, semiconductor losses and junction temperature limits. Switching frequencies are tuned to maintain full-load junction temperature within 70 – 80 °C. Results indicate GaN yields the highest efficiency, while SiC offers nearly equal efficiency with superior current quality in terms of total harmonic distortion (THD). The findings demonstrate the promise of WBG device-based converters for efficient and power-dense SynRM drive applications.

This paper describes modern methods that are pursued in order to enable EMT simulation of large power systems approaching sizes of thousands of nodes. The paper describes solutions such as (i) co-simulation, where multiple solvers are assigned to solve specific areas of a large network in different detail, (ii) multi-solver simulation (e.g., dynamic phasors and EMT), and (iii) network equivalents, wherein portions of a large network are replaced with a computationally efficient equivalent. The paper explains the merits as well as limitations of these methods and points out contemporary directions for further research and development. Large-scale EMT-type simulation using real-time platforms is also addressed.

Synchronous reluctance machines (SynRMs) and permanent magnet-assisted SynRMs (PMaSynRMs) are promising rare-earth-free alternatives to permanent magnet synchronous machines (PMSMs), which suffer from high cost and supply chain issues. PMaSynRMs may be operated with control strategies that optimize the operation in maximum torque per ampere (MTPA) and field weakening (FW) regions. This goal is achieved with accurate estimation of parameters such as d- and qaxis flux linkages and incorporating them in the control process. Finite element analysis (FEA) offers accurate parameter values but is computationally intensive, while artificial neural networks (ANN) demand large datasets for reliable accuracy. To tackle these challenges, this paper proposes and implements a state-of-the-art physics-informed neural network (PINN) framework to estimate the d- and q-axis flux linkages of PMaSynRMs. The study highlights the burden of obtaining FEA-based lookup tables and compares the proposed PINN framework with ANN framework. Results demonstrate that PINN can accurately estimate flux linkages despite the highly anisotropic structure of PMaSynRMs, while using a reduced number of data samples compared to ANN.

This paper presents a dynamic-phasor-based, average-value modeling method for power systems with extensive converter-tied subsystems. In the proposed approach, the overall system model is constructed using modular functions, interfacing both conventional and converter-tied resources. Model validation is performed against detailed Electro-Magnetic Transient (EMT) simulations. The analytical capabilities offered by the proposed modeling method are demonstrated on a modified IEEE 9-bus system. A Graphics Processing Unit (GPU)-based parallel computing approach for the solution of the resulting model is presented and exemplified on a modified IEEE 118-bus system, showing significant improvements in computing efficiency over EMT solvers. A co-simulation approach using a Central Processing Unit (CPU) and a GPU is also presented and exemplified using a modified version of the IEEE 118-bus system, demonstrating the model’s parallelization.

The main challenge of hybridizing ultracapacitors (UCs) with batteries in electric vehicles is their uncertain economic viability, besides their complexity and weight, which should be fully addressed. Therefore, this article determines the general condition for achieving a justified economic system, which is held when the average annual cost (AAC) of a battery‐UC system over a vehicle's useful life is lower than the annual cost of a sole‐battery for a specific system design, energy management strategy, vehicle type, and driving style. As such, the energy storage system is designed in a case study vehicle, and the optimal current distribution is found by dynamic programming (DP) under UDDS, HWFET, and US06 driving cycles. Then, by economic analysis, it is indicated that although adding an UC incurs additional costs, it saves the AAC by improving the battery health and prolonging its lifespan up to a maximum of 15‐year calendar life, which proves its economic justification. Investing in UCs is more economically viable for vehicles with severe driving cycles and high current stress. Finally, the DP optimal trajectory is implemented into an experimental setup under the US06 driving cycle to verify the evaluated strategy.