Zhenghua XU | Searcharxiv

arXiv2026

Holistic Grid-Forming Control to Enhance the Frequency Support from HVDC-Connected Offshore Wind Power Plants

To address the frequency stability challenges posed by the rising penetration of power electronics in power systems, HVDC-connected offshore wind power plants (OWPPs) are increasingly expected to provide inertial response and frequency containment reserve (FCR). In this paper, an improved holistic grid-forming (GFM) control is proposed, aiming to enhance the frequency support by coordinating the GFM controls implemented at all AC and DC terminals of an HVDC-OWPP system, without requiring communication. Firstly, the model of a typical HVDC-OWPP system is developed for control design. Accordingly, the proposed controllers are formulated, followed by an analytical tuning method, where the upper bound of the bandwidth at each AC or DC terminal is identified. Finally, simulations are conducted to verify the functionality and compare the performance with that of representative control configurations. The results show that the proposed holistic GFM control achieves faster response and thus more effective frequency support, while the utilization of the inherent energy storage of each converter is minimized, thereby supporting a new design philosophy for converter control in converter-dominated systems.

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arXiv2020

Deep Learning in Computer-Aided Diagnosis and Treatment of Tumors: A Survey

Computer-Aided Diagnosis and Treatment of Tumors is a hot topic of deep learning in recent years, which constitutes a series of medical tasks, such as detection of tumor markers, the outline of tumor leisures, subtypes and stages of tumors, prediction of therapeutic effect, and drug development. Meanwhile, there are some deep learning models with precise positioning and excellent performance produced in mainstream task scenarios. Thus follow to introduce deep learning methods from task-orient, mainly focus on the improvements for medical tasks. Then to summarize the recent progress in four stages of tumor diagnosis and treatment, which named In-Vitro Diagnosis (IVD), Imaging Diagnosis (ID), Pathological Diagnosis (PD), and Treatment Planning (TP). According to the specific data types and medical tasks of each stage, we present the applications of deep learning in the Computer-Aided Diagnosis and Treatment of Tumors and analyzing the excellent works therein. This survey concludes by discussing research issues and suggesting challenges for future improvement.

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