The behaviour of virtual synchronous machine (VSM) based converters in front of non-saturable faults

Power converter penetration has increased substantially in the last 20 years bringing new challenges from the system protection perspective. The power network is undergoing a major transformation as the major part of new installed power comes from non-synchronous sources such as wind or solar. These changes might lead to malfunction of the conventional protection schemes such as overcurrent protection or distance protection relays. At the same time, the reduction of the system inertia might cause the tripping of the Loss of Main protection due to a very aggressive Rate of Change of Frequency. To enhance the grid voltage source characteristic and mitigate the loss of inertia, a new set of converter controllers known as Grid forming Converter or Virtual Synchronous Machine has been suggested in recent years. The performance of VSM could provide a potential advantage compared to traditional power converter controllers when a large frequency deviation occurs helping to keep the system stable. This article quantifies and compares the performance of different converter control algorithms including Current Vector Control, Virtual Synchronous Machine and Power Synchronisation Control in front of different frequency events

Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as a case study, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.Comment: 10 pages, 10 figures. Previously as title: "Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games", Mar 201

An Equivalence Checking Framework for Agile Hardware Design

Agile hardware design enables designers to produce new design iterations efficiently. Equivalence checking is critical in ensuring that a new design iteration conforms to its specification. In this paper, we introduce an equivalence checking framework for hardware designs represented in HalideIR. HalideIR is a popular intermediate representation in software domains such as deep learning and image processing, and it is increasingly utilized in agile hardware design.We have developed a fully automatic equivalence checking workflow seamlessly integrated with HalideIR and several optimizations that leverage the incremental nature of agile hardware design to scale equivalence checking. Evaluations of two deep learning accelerator designs show our automatic equivalence checking framework scales to hardware designs of practical sizes and detects inconsistencies that manually crafted tests have missed

Shear response behavior of STF/kevlar composite fabric in picture frame test

The picture frame test was applied to compare Kevlar neat and STF/Kevlar composite fabrics. The digital image correlation markers method was applied to measure the shear deformation behavior of the fabric in real-time under three loading rates: 100, 500, and 1000 mm/min. A theoretical model was applied to evaluate the effect of STF on the shear deformation stiffness of the fabric and cells and on the energy absorption during shear deformation. The results show that the STF/Kevlar composite fabric has a larger load-carrying capacity than the neat fabric in the picture frame test, and has obvious loading rate dependence. The yarn cell of the fabric undergoes slip deformation and reaches a shear-locked state; the shear modulus and the cell spring torsion coefficient of the STF/Kevlar composite fabric are significantly higher than those of neat fabric. The shear thickening behavior of STF occurs at higher loading rates, and the composite fabric has the highest shear deformation stiffness and shear energy absorption level

DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models

Performing magnetic resonance imaging (MRI) reconstruction from under-sampled k-space data can accelerate the procedure to acquire MRI scans and reduce patients' discomfort. The reconstruction problem is usually formulated as a denoising task that removes the noise in under-sampled MRI image slices. Although previous GAN-based methods have achieved good performance in image denoising, they are difficult to train and require careful tuning of hyperparameters. In this paper, we propose a novel MRI denoising framework DiffCMR by leveraging conditional denoising diffusion probabilistic models. Specifically, DiffCMR perceives conditioning signals from the under-sampled MRI image slice and generates its corresponding fully-sampled MRI image slice. During inference, we adopt a multi-round ensembling strategy to stabilize the performance. We validate DiffCMR with cine reconstruction and T1/T2 mapping tasks on MICCAI 2023 Cardiac MRI Reconstruction Challenge (CMRxRecon) dataset. Results show that our method achieves state-of-the-art performance, exceeding previous methods by a significant margin. Code is available at https://github.com/xmed-lab/DiffCMR.Comment: MICCAI 2023 STACOM-CMRxReco

A negative sequence current injection (NSCI)-based active protection scheme for islanded microgrids

The growing penetration of converter interfaced generation creates unprecedented challenges to protection strategies at all voltage levels. This paper proposes a novel Negative Sequence Current Injection (NSCI)-based active protection scheme for islanded microgrids. The faulty section identification method based on the negative sequence current increment between the pre-injection and current generation steady state conditions enables the scheme to achieve an excellent High Impedance Fault (HIF) detection capability. The proposed NSCI control algorithm maintains the phase angle of the negative sequence current fixed during injection progress, thus providing a highly discriminative feature which facilitates the correct identification of the faulty section. As no form of communication is required the proposed protection scheme can be very cost-effective and flexible in practical applications. Following the detailed description of the principle of operation and the setting procedure, a systematic simulation-based validation is undertaken considering a variety of influencing factors such as fault type, resistance and position, as well as impact of load distribution under HIFs, and possible presence of Synchronous Generators (SGs). The results show that the scheme has an excellent detection and discrimination ability, especially during unbalanced faults, and is not affected by load distribution or behaviour of other sources, including synchronous machine

Towards A Correct-by-Construction FHE Model

This paper presents a correct-by-construction method of designing an FHE model based on the automated program verifier Dafny. We model FHE operations from the ground up, including fundamentals like GCD, coprimality, Montgomery multiplications, and polynomial operations, etc., and higher level optimizations such as Residue Number System (RNS) and Number Theoretic Transform (NTT). The fully formally verified FHE model serves as a reference design for both software stack development and hardware design, and verification efforts. Open-sourcing our FHE Dafny model with modular arithmetic libraries to GitHub is in progress

A multi-objective control strategy for three phase grid-connected inverter during unbalanced voltage sag

This paper presents a new multi-objective control strategy for inverter-interfaced distributed generation (IIDG) to ensure its safe and continuous operation under unbalanced voltage sags. The proposed control strategy can effectively improve the low voltage ride through (LVRT) capability, reduce active power oscillations, and limit overcurrent simultaneously, which are marked as the most important control objectives of IIDG during unbalanced voltage sags. The advanced voltage support scheme, which utilizes positive sequence component, is firstly proposed to maximize the LVRT capability of IIDG during unbalanced voltage sags. Then, to ensure the safety of IIDG, the active power oscillation suppression and current limitation algorithm are designed individually. Based on the control algorithms of such objectives, the multi-objective control method, including scenario classification and reference current determination, is then presented to achieve such three objectives under various system conditions simultaneously. Finally, case studies and evaluations based on MATLAB/Simulink are carried out to illustrate the effectiveness of the proposed method