A High-Fidelity, Low-Latency, FPGA-Based, Real-Time Development Platform for Advanced Aircraft Power Systems

Future electrical power systems will be dominated by power electronic converters, which are deployed for the integration of renewable power plants, responsive demand, and different types of storage systems. The stability of such systems will strongly depend on the control strategies attached to the converters. In this context, laboratory-scale setups are becoming the key tools for prototyping and evaluating the performance and robustness of different converter technologies and control strategies. The performance evaluation of control strategies for dynamic frequency support using fast active power regulation (FAPR) requires the urgent development of a suitable power hardware-in-the-loop (PHIL) setup. In this paper, the most prominent emerging types of FAPR are selected and studied: droop-based FAPR, droop derivative-based FAPR, and virtual synchronous power (VSP)-based FAPR. A novel setup for PHIL-based performance evaluation of these strategies is proposed. The setup combines the advanced modeling and simulation functions of a real-time digital simulation platform (RTDS), an external programmable unit to implement the studied FAPR control strategies as digital controllers, and actual hardware. The hardware setup consists of a grid emulator to recreate the dynamic response as seen from the interface bus of the grid side converter of a power electronic-interfaced device (e.g., type-IV wind turbines), and a mockup voltage source converter (VSC, i.e., a device under test (DUT)). The DUT is virtually interfaced to one high-voltage bus of the electromagnetic transient (EMT) representation of a variant of the IEEE 9 bus test system, which has been modified to consider an operating condition with 52% of the total supply provided by wind power generation. The selected and programmed FAPR strategies are applied to the DUT, with the ultimate goal of ascertaining its feasibility and effectiveness with respect to the pure software-based EMT representation performed in real time. Particularly, the time-varying response of the active power injection by each FAPR control strategy and the impact on the instantaneous frequency excursions occurring in the frequency containment periods are analyzed. The performed tests show the degree of improvements on both the rate-of-change-of-frequency (RoCoF) and the maximum frequency excursion (e.g., nadir).

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Robust and high fidelity real-time hybrid substructuring

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2021.107720 ◽

2021 ◽

Vol 157 ◽

pp. 107720

Author(s):

Christina Insam ◽

Arian Kist ◽

Henri Schwalm ◽

Daniel J. Rixen

Keyword(s):

Real Time ◽

High Fidelity

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Online Learning Algorithms for the Real-Time Set-Point Tracking Problem

Applied Sciences ◽

10.3390/app11146620 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6620

Author(s):

Arman Alahyari ◽

David Pozo ◽

Meisam Farrokhifar

Keyword(s):

Decision Making ◽

Power Systems ◽

Real Time ◽

Demand Response ◽

Tracking Problem ◽

Smart Meters ◽

Set Point ◽

Point Tracking ◽

Power Set ◽

Decision Making Framework

With the recent advent of technology within the smart grid, many conventional concepts of power systems have undergone drastic changes. Owing to technological developments, even small customers can monitor their energy consumption and schedule household applications with the utilization of smart meters and mobile devices. In this paper, we address the power set-point tracking problem for an aggregator that participates in a real-time ancillary program. Fast communication of data and control signal is possible, and the end-user side can exploit the provided signals through demand response programs benefiting both customers and the power grid. However, the existing optimization approaches rely on heavy computation and future parameter predictions, making them ineffective regarding real-time decision-making. As an alternative to the fixed control rules and offline optimization models, we propose the use of an online optimization decision-making framework for the power set-point tracking problem. For the introduced decision-making framework, two types of online algorithms are investigated with and without projections. The former is based on the standard online gradient descent (OGD) algorithm, while the latter is based on the Online Frank–Wolfe (OFW) algorithm. The results demonstrated that both algorithms could achieve sub-linear regret where the OGD approach reached approximately 2.4-times lower average losses. However, the OFW-based demand response algorithm performed up to twenty-nine percent faster when the number of loads increased for each round of optimization.

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Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽

10.3390/s21113715 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3715

Author(s):

Ioan Ungurean ◽

Nicoleta Cristina Gaitan

Keyword(s):

Internet Of Things ◽

Real Time ◽

Development Process ◽

Fog Computing ◽

Low Latency ◽

Practical Implementation ◽

Industrial Internet Of Things ◽

Industrial Internet ◽

Starting Point ◽

Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

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Real‐time deep artifact suppression using recurrent U‐Nets for low‐latency cardiac MRI

Magnetic Resonance in Medicine ◽

10.1002/mrm.28834 ◽

2021 ◽

Author(s):

Olivier Jaubert ◽

Javier Montalt‐Tordera ◽

Dan Knight ◽

Gerry J. Coghlan ◽

Simon Arridge ◽

...

Keyword(s):

Real Time ◽

Cardiac Mri ◽

Low Latency ◽

Artifact Suppression

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On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures

Electronics ◽

10.3390/electronics10060689 ◽

2021 ◽

Vol 10 (6) ◽

pp. 689

Author(s):

Tom Springer ◽

Elia Eiroa-Lledo ◽

Elizabeth Stevens ◽

Erik Linstead

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Real Time ◽

Operating Systems ◽

System On Chip ◽

Low Latency ◽

Management Framework ◽

On Chip ◽

Specialized Hardware ◽

Deterministic Behavior

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems.

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