Showing papers by "Wilsun Xu published in 2023"
TL;DR: In this article , a model of the doubly-fed induction generator (DFIG) for harmonic power flow analysis is presented, which can be represented by a coupled impedance matrix, which includes the harmonic coupling between the RSC and the GSC and coupling between different harmonics.
2 citations
TL;DR: In this paper , an equivalent voltage index is proposed to represent the impact of harmonic voltages and their features (such as peak value) into an equivalent increase in the fundamental frequency voltage otherwise experienced by the capacitor.
Abstract: Harmonic distortions are known to affect the normal operation and life of power equipment. Shunt capacitor is one piece of equipment that is very sensitive to harmonics. Although limits have been established to limit the harmonic distortions experienced by a capacitor, methods to quantify the harmonic impact with easy-to-understand indices are still at large. This paper aims to address the above gap by proposing an equivalent voltage index. This index is to represent the impact of harmonic voltages and their features (such as peak value) into an equivalent increase in the fundamental frequency voltage otherwise experienced by the capacitor. The main advantage of such an equivalence is that PQ and non-PQ engineers can easily relate the impact to the general capacitor loading concept. The index is especially helpful to understand the impact of harmonics on capacitor and thus to justify harmonic mitigation projects.
20 Feb 2023
TL;DR: In this article , a refined model is developed to seek optimal energy allocation for edge computing with artificial intelligence (AI) /machine learning (ML) in current 5GAdvanced and future 6G wireless technologies.
Abstract: Edge computing (EC) with artificial intelligence (AI) /machine learning (ML) is a promising paradigm in current 5GAdvanced and future 6G wireless technologies. Energy optimization is a primary issue in most EC/ML systems. In this paper, a refined model is developed to seek optimal energy allocation. The present work introduces several features which were often omitted in existing studies, including fine-grained discrete optimization, non-singular CPU cycle allocation, longtailed data traffic, and non-singular pathloss terms. The energy optimization model is solved by the ML methodology and compared with a conventional solver. Simulations showed that the efficiency may be improved more than 90%.