Real-time cosimulation of geographically dispersed laboratories enables extensive system simulations but faces significant challenges from communication delays, impacting accuracy and stability. This issue is crucial in real-time power system cosimulation, where delays can disrupt synchronism and hinder dynamic analyses. This article proposes model-free predictive delay compensation methods as viable alternative signal transformation-based methods. This study explores the frequency domain stability of a model-free framework for delay prediction and compensation at power system interfaces using the ideal transformer method as interface algorithm. Similarly, time-domain implementation reveals signal amplitude magnification, addressed by introducing a delay- and frequency-dependent normalizing factor. This framework adapts interface coupling signals, enabling real-time parameter tuning without complex processing or system models, enhancing co-simulation accuracy and stability for distributed power system analyses.

Future distribution grids are expected to face an increasing penetration of electric vehicles (EVs) and heterogeneous distributed energy resources (DERs). This demands a distributed energy management (EM) to manage power generation and delivery of energy sources to maintain power quality under the impact of EV charging, to save operating costs, and to enhance resiliency. However, the global optimality of the distributed EM’s optimization problem is still an issue in existing works because of the non-convex nature of the optimization problem. In this paper, a distributed EM strategy for grid-connected distribution networks is proposed. In particular, the EM strategy is composed of two steps. In the first step, some conditions of the EM optimization task are relaxed to apply an algorithm converging to the global optimality. The results of the first step are used to reconfigure constraints of the full optimization problem in Step 2. The proposed scheme is validated by implementing the real-time controller-hardware-in-the-loop (CHIL) experimentation on the IEEE 33 bus system. To study the impact of EV charging, EV data is collected from the market and the literature to generate realistic EV load profiles to demonstrate the effectiveness of the proposed strategy on saving operating costs and maintaining power quality.

Power hardware-in-the-loop (PHIL) simulation is a technique whereby actual power hardware is interfaced to a virtual surrounding system, simulated in real-time, through PHIL interfaces making use of power amplifiers and/or actuators. A number of seemingly disparate interface algorithms (IA) have been proposed in the literature for achieving the virtual coupling between the simulated and physical portions of the system, with each presenting different strengths and shortcomings. In this work, a framework based on an architecture for bilateral teleoperation systems is described, which is suitable for the formulation of linear PHIL IAs, encompassing the majority of the existing IAs proposed in the literature. Formulations of a number of existing PHIL IAs are given in the context of the described framework. Requirements for achieving transparency with the IAs are described, and several of the existing IAs are discussed in terms of the framework, adherence to the transparency requirements, and performance. As the architecture also lends itself to the development of flexible IA modules for real-time simulators, the implementation and application of an IA module reflecting this architecture is also described.

Recent efforts in the development of MVDC ship power systems have been focused on finding affordable and efficient ways to interface with high-power, pulsed loads presented by advanced weaponry. To achieve this goal, advanced controls are required to regulate the power sharing among distributed resources within the ship system and maintain the voltage stability during the varying loading conditions. This paper proposes an adaptive droop control method that is applied to the power management layer of a notional MVDC ship power system to achieve these goals when faced with the various loading conditions and pulsed loads. The algorithm is then applied in a real-time experiment to validate its performance under a notional battle scenario.

In this paper, we report on the utilization of advanced distributed control algorithms, deployed on physical controllers, for an Integrated Power and Energy System (IPES) energy management system. In addition, we provide an evaluation of the management system. Results from a hardware-in-the-Ioop experiment will be analyzed to show the effectiveness of the realtime control system. The goal of this paper is to provide valuable insights from a practical system controls the application of reasonable size and system complexity.

The advent of pulsed-power loads puts a large amount of stress on ship power systems. Naval applications of pulsed-power loads include energy weapons and radars which often demand more power than what is available through the local, dedicated generation. Incorporating loads and sources in a single power system proves beneficial for these systems; however, generators cannot be the only source due to the high-power ramp rates of some loads. Energy storage must be incorporated into the integrated power system. In this paper, a control strategy for meeting load demand is shown. A higher-level control layer deemed energy management takes on this task. The energy management control layer coordinates the generators and energy storage to ensure that load demand is met using a hybrid control approach of heuristics and model predictive control.

This paper develops a new two-stage approach for accurate modeling and prediction of tidal current. The proposed method makes use of a novel fuzzy feature selection to extract the most preferable features from the tidal current speed and direction data set. The selected features are further used to train a support vector regression for accurate prediction. The setting parameters of the proposed model are trained by a new optimization algorithm based on the harmony search algorithm to get to the most optimal training targets. The proposed optimization algorithm makes use of the crossover and mutation operators from genetic algorithm to escape from the local optima and find the global solutions. Experimental tidal data from Persian Gulf, Iran, are used to assess the accuracy and performance of the proposed model. The results show the appropriate performance and high precision of the proposed model in comparison with other famous methods.

Islanded DC microgrid architecture and control have become popular recently. One of the main control architectures is the DC bus voltage following type, where a main generator controls the DC bus voltage and the others follow the bus voltage and the power commands. Conventionally, the power regulation in the following generators is often employed with the droop control method. The main problem of the droop control is the non-precise power sharing characteristic. One of the improvement approaches is to use PI controllers for correcting the power share. However, PI controllers do not provide optimal control. Thus, in this paper a new power control method utilizing model predictive control is proposed and applied in the power control for islanded DC microgrids. The algorithm and method are presented and validated through simulation in MATLAB/Simulink and PLECS.

The concept of an all-electric ship, while offering unprecedented advantages from the point of view of efficiency and flexibility of operation, has introduced new challenges in terms of stability and power flow control. The advent of a full power electronics power system has raised new questions from the point of view of system dynamics, particularly when dealing with the new medium-voltage direct current distribution. The overall goal of guaranteeing a secure operation of the power system has brought researchers to consider two main approaches: reducing the dynamics of the large load to operate in a range of dynamics compatible with traditional generation systems, or making the generator set smarter through its power electronics interface. This paper compares these approaches to stable operation, focusing on the latter considered more in line with the progress of technology and in general more appealing.

The area of modeling and simulation is a critical aspect in the basic research to commercialization and instantiation cycle. This paper reports on modeling and simulation in the context of verification, validation, and experimentation of power and energy systems and associated electrical apparatus via the utilization of power hardware in the loop (PHIL)-based strategies. PHIL is a powerful technique for testing and demonstration of systems in a rigorous and dynamic manner that is not achievable with other methodologies; however, it must only be conducted with foreknowledge of the technique and its challenges in order to realize its significant benefits. This paper reports on the state of the art in PHIL and its challenges and presents sample case studies illustrating its impact.

The Switched Reluctance Machine (SRM) is known for being one of the oldest electric machine designs. Unfortunately, it is usually assumed that this implies that the machine is outdated. However with the advent of microprocessors, the SRM has become a suitable option for a number of applications because the shortcomings of the machine can be mitigated with control. Compared to other machines, the SRM is more rugged, has a simpler structure, and is less expensive to manufacture. The method presented is concerned with finding the turn-on and turn-off angles needed to generate maximum torque per ampere (MTPA). The method applies a particle swarm optimization (PSO) technique that searches for the angles that maximize the inductance of the SRM in order to achieve MTPA. The inductance function was obtained experimentally. The method was applied to a 4-phase 8/6 SRM. The proposed method was found to be effective at both low speeds (beneath the base speed) and high speeds (above the base speed), but MTPA could only be asserted for low speeds. The switched reluctance motor was modeled in Simulink.

Power hardware-in-the-loop (PHIL) is an important tool for testing and verifying various aspects of systems level research. It provides a realistic assessment of how a device will interact under various conditions and environments, allowing the testing process to truly vet the apparatuses before full scale deployment. Crucial to the PHIL concept, is the hardware-in-the-loop interface. Interfaces must be accurate, stable and able to pass through dynamics so that the hardware under test sees the most realistic view of the remainder to the system and vice versa. The damping impedance method (DIM) provides the appropriate characteristics for dynamic load applications but its philosophy requires some modification when in a system that has considerable harmonic content in either voltage and/or current. This paper will provide a modified DEVI (MDEVI) method for PHIL applications to meet this criteria. Specifically, it will investigate a power-electronic-based system with a load containing harmonic content and will compare the accuracy of the results against a traditional DEVI interface method.

This paper presents the application of Power Hardware-in-the-Loop (PHIL) experiments in electric vehicle development. The switched reluctance machine (SRM) is the subject of PHIL experiments that emulate operational conditions of an electrical vehicle, including battery discharging, mechanical loading cycles, and machine failure for the scenario in which an SRM is employed as the propulsion device. The test bed design is presented along with the experimental data of the test cases. The uniqueness and practicality of PHIL-based experiments in electric vehicles are emphasized and the versatility is demonstrated through several potential operational scenarios of SR.

Electrical systems utilizing electromagnetic devices, namely power systems of all electric ships, are subject to nonlinearities from regenerative loads, distributed energy storage systems, and onboard loads such as air handling and fluid pumps. Thus, accurate and timely electromagnetic device models are required in order to fully assess the impact of such transient and/or nonlinear activity. Specifically, by exploiting an often overlooked technique, i.e. the Magnetic Equivalent Circuit (MEC) technique, a solution of adequate granularity for the electromagnetic device may be attained while still obeying a faster time commitment when compared to the simulation standard for electromagnetic devices, the finite element analysis (FEA) technique. This paper proposes a benchmarking study considering MEC, FEA, and Fourier series based inductance approximation differential equation modeling methods. A switched reluctance machine (SRM) is used as the case study device due to its inherent nonlinearity and since it provides an ideal foundation for incorporating various characteristics of the MEC modeling technique. In order to highlight the potential accuracy and computational benefits attainable via MEC when compared to FEA and differential equation models of the same machine and system a notional Hardware-in-the-Loop (HIL) applications study is proposed. Such a study simulates the SRM MEC model driven via power electronics and loaded via a mathematical load profile. Ultimately, this work aims to motivate to the development of real-time MEC modeling (RT-MEC).

This paper investigates the impact of power quality criteria required by photovoltaic (PV) converters on legacy system transformers and its design procedure, since it is often argued that the use of traditional design for transformers in the PV integrated power systems is acceptable. The criteria studied in this paper are voltage amplitude variation (flicker), harmonics, frequency variation, power factor (PF) variation, DC bias, thermal cycling (loading), transients, inrush current, eddy current, and stray loss. Some of these problems will be simulated with a Voltage Source Inverter (VSI) and Current Source Inverter (CSI) which are frequently used in PV integrated power systems as Distributer Generation (DG) by decentralized controlled PV inverters. In this type of power system configuration, transformers may be utilized for isolation and/or filtering or as a part of the feeder facility. On the other hand, if the transformerless PV inverters are used in PV systems, then only the distribution transformers will be affected by the PV converters power quality problems. These issues can reveal or change the allowable penetration level at a specific bus location along the feeder. Furthermore, output voltage of VSI and output current of CSI of PV systems have a faster dynamic response and more distorted output than legacy power system voltage and current outputs. This means that the VSI and CSI need stiff controllers to ensure a suitable power exchange to the utility network, particularly to sensitive equipment like transformers that are designed for legacy unidirectional power transformations with a cleaner voltage and current than that provided by PV inverter based systems.

Load type can introduce at least one additional state variable in the state-space representation for dynamic equations of voltage source inverters (VSI) which may make controller design harder to solve. Additionally, the voltage reference term in state space realization demonstrates the VSI control as a tracking problem. This tracking problem can be transformed into a regulation problem using appropriate transformation; consequently, the linear quadratic regulator (LQR) technique, which is originally applicable to regulation problem, can be employed to design a linear controller including load current state for standalone VSIs. The load current is also virtually obtained by an Enhanced Phase Lock Loop (EPLL) in the control structure instead using of a current sensor in hardware. The controller is designed for a VSI unit and then simulation and experimental results are presented to confirm performance of the proposed controller.

This paper introduces a multi-platform system for real-time studies of an isolated permanent magnet synchronous generator (PMSG). The system consists of an Altera DE2 Development Board field-programmable gate-array (FPGA), a Texas Instruments (TI) TMS320F28335 digital signal processor (DSP), and a PC running dSPACE1103 Control Desk for real time interaction and data acquisition. This work demonstrates the real time simulation performance of the PMSG implemented at a 1μs time step on the Altera DE2 FPGA utilizing an explicit Euler numerical ordinary differential equation (ODE) solver. The model is then validated by comparing the results to the widely accepted MATLAB/SIMULINK/SimPowerSystems PMSG under the same parameters and external excitations. The work presents the system architecture as well as introduces a simple methodology of implementing the coupled differential equations of the PMSG in an FPGA for real time simulation. The use of the Altera DE2 FPGA eliminates the cost of using a real-time simulation platform and allows for a smaller achievable time step which is advantageous when simulating high speed machines to observe system transients. This work demonstrates a simple process that uses one clock and takes advantage of the IEEE arithmetic and numeric types libraries. The multi platform structure also provides potential future hardware-in-the-loop capabilities for more complex system integration.

A number of circuits have been proposed to supply constant current during EMRG (electromagnetic rail-gun) firing as well as recovery of the energy remaining in the inductive storage element after firing. In many ways, a switched inductor based pulsed power supply for EMRGs is an alternative to capacitive-based systems. However, using an inductor as the pulsed power supply has several issues. In this research effort a hybrid inductive-capacitive pulse forming network circuit is proposed to provide solutions to those problems.

This paper discusses the whole design process of a position control system of a single-phase reluctance machine under mechanical and electrical constraints. A MIMO Neural network is used to model the nonlinear properties of the machine. Based on it, a neural network based control scheme is developed to precisely control the rotor position of the designed single-phase reluctance machine. Simulation results show that a MIMO neural network model can effectively capture the nonlinear characteristics of the designed machine and the proposed neural network control scheme can control the rotor position precisely.