The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …

This paper aims to introduce deep learning to the power quality community by reviewing the
latest applications and discussing the open challenges of this technology. Publications …

相比目前常用的跟网型变流器, 构网型变流器具有同步电压源特性, 可以有效提升电力电子化的
新型电力系统的稳定性, 因而近年来受到了广泛的关注. 为了给构网型变流器大规模工程应用 …

This paper introduces for the first time, to our knowledge, a framework for physics-informed
neural networks in power system applications. Exploiting the underlying physical laws …

This paper introduces, for the first time to our knowledge, physics-informed neural networks
to accurately estimate the AC-Optimal Power Flow (AC-OPF) result and delivers rigorous …

In this article, the fixed-time (FXT) synchronization of discontinuous competitive neural
networks (CNNs) involving time-varying delays is investigated. Firstly, two kinds of …

Permanently increasing penetration of converter-interfaced generation and renewable
energy sources (RESs) makes modern electrical power systems more vulnerable to low …

Power systems around the world are experiencing an energy revolution that substitutes
fossil fuels with renewable energy. Such a transition poses two significant challenges: highly …

Many real-life optimization problems frequently contain one or more constraints or objectives
for which there are no explicit formulae. If however data on feasible and/or infeasible states …

This paper introduces for the first time a framework to obtain provable worst-case
guarantees for neural network performance, using learning for optimal power flow (OPF) …