arXiv subjects

Shengbo Zhang

Publications and source records attributed to Shengbo Zhang.

3 recordsLinked to original sources

arXiv2026

An Enhanced Submodule for Modular Multilevel Converter with DC Fault Ride-Through Capability

Modular multilevel converter (MMC) has been successfully applied in various power electronic systems owing to its high efficiency, scalability, and superior output performance. Although the half-bridge submodule (HBSM) is widely used in MMCs for its structural simplicity, it is incapable of handling direct-current (DC) short-circuit faults. The diode-clamp submodule (DCSM) addresses this limitation by providing DC fault ride-through capability. However, because its two identical capacitors are connected in series, the equivalent capacitance is halved. To overcome this drawback, an enhanced SM is proposed in this paper. For the same energy storage capacity, the proposed SM reduces the total required capacitance by 75% compared with the DCSM. In addition, the proposed SM requires one fewer diode than the DCSM, thereby lowering the overall MMC capital cost. The topology and the operating modes of the proposed SM are described in detail, and its functionality is experimentally validated. The results demonstrate that the proposed SM can suppress DC fault currents and restore normal operation without additional external protection devices.

eess.SY↗

arXiv2023

MVMR-FS : Non-parametric feature selection algorithm based on Maximum inter-class Variation and Minimum Redundancy

How to accurately measure the relevance and redundancy of features is an age-old challenge in the field of feature selection. However, existing filter-based feature selection methods cannot directly measure redundancy for continuous data. In addition, most methods rely on manually specifying the number of features, which may introduce errors in the absence of expert knowledge. In this paper, we propose a non-parametric feature selection algorithm based on maximum inter-class variation and minimum redundancy, abbreviated as MVMR-FS. We first introduce supervised and unsupervised kernel density estimation on the features to capture their similarities and differences in inter-class and overall distributions. Subsequently, we present the criteria for maximum inter-class variation and minimum redundancy (MVMR), wherein the inter-class probability distributions are employed to reflect feature relevance and the distances between overall probability distributions are used to quantify redundancy. Finally, we employ an AGA to search for the feature subset that minimizes the MVMR. Compared with ten state-of-the-art methods, MVMR-FS achieves the highest average accuracy and improves the accuracy by 5% to 11%.

cs.LG↗

arXiv2023

Decentralized Local Updates with Dual-Slow Estimation and Momentum-based Variance-Reduction for Non-Convex Optimization

Decentralized learning (DL) has recently employed local updates to reduce the communication cost for general non-convex optimization problems. Specifically, local updates require each node to perform multiple update steps on the parameters of the local model before communicating with others. However, most existing methods could be highly sensitive to data heterogeneity (i.e., non-iid data distribution) and adversely affected by the stochastic gradient noise. In this paper, we propose DSE-MVR to address these problems.Specifically, DSE-MVR introduces a dual-slow estimation strategy that utilizes the gradient tracking technique to estimate the global accumulated update direction for handling the data heterogeneity problem; also for stochastic noise, the method uses the mini-batch momentum-based variance-reduction technique.We theoretically prove that DSE-MVR can achieve optimal convergence results for general non-convex optimization in both iid and non-iid data distribution settings. In particular, the leading terms in the convergence rates derived by DSE-MVR are independent of the stochastic noise for large-batches or large partial average intervals (i.e., the number of local update steps). Further, we put forward DSE-SGD and theoretically justify the importance of the dual-slow estimation strategy in the data heterogeneity setting. Finally, we conduct extensive experiments to show the superiority of DSE-MVR against other state-of-the-art approaches.

cs.DC↗