Xiaojun Deng | Searcharxiv

arXiv2026

Hybrid Virtual Space Vector Modulation With Extended Power Factor Range for Coupled Eight-Switch Three-Phase Three-Level Inverter

With the development of photovoltaic (PV) and motor drive, the coupled eight-switch three-phase three-level inverter (TP-TLI) is receiving more attentions. However, the previous modulation strategies for coupled eight-switch TP-TLI are suitable for high power factor (PF), which cannot satisfy the requirement for PV and motor drive. Aiming at this, this paper proposes a hybrid virtual space vector modulation (HVSVM) for the coupled eight switch TP-TLI with extended PF range. The mechanism of PF limitation is analyzed in deep. Two virtual vectors are constructed in each sector, and three basic VSVMs with different PF range are proposed according to the number of virtual small vectors used. Combined with three VSVMs, the HVSVM is proposed by detecting the three-phase current polarities. The PF angle range of the HVSVM is extended from about [-30°, 30°] to [-90°, 90°]. Besides, an active neutral-point voltage control (NPVC) is also presented by using two coefficients to adjust the dwell times of the two pairs of redundant small vectors. This approach considers the NP current and the derivation of NPV, and thereby it is accurate to control the NPV. The correctness and feasibility of the proposed HVSVM and active NPVC are verified by experiments through a 3 kVA prototype.

eess.SY↗

arXiv2025

Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and collaboratively train models across multiple clients with different data distributions, model structures, task objectives, computational capabilities, and communication resources. This diversity leads to significant heterogeneity, which increases the complexity of model training. In this paper, we first outline the basic concepts of heterogeneous federated learning and summarize the research challenges in federated learning in terms of five aspects: data, model, task, device, and communication. In addition, we explore how existing state-of-the-art approaches cope with the heterogeneity of federated learning, and categorize and review these approaches at three different levels: data-level, model-level, and architecture-level. Subsequently, the paper extensively discusses privacy-preserving strategies in heterogeneous federated learning environments. Finally, the paper discusses current open issues and directions for future research, aiming to promote the further development of heterogeneous federated learning.

cs.LG↗