Recent Advances in Electrical & Electronic Engineering - Volume 18, Issue 8, 2025

The extensive utilization of the dual active bridge (DAB) topology in the domain of power electronics has led to the prevalence of DC/AC converters based on DAB that adopt two-stage conversion and phase-shifting control strategies in their design. This has resulted in an increase in losses and complexity in practice.

The objective of this study is to propose a parameter optimization method for a single-stage isolated bidirectional DC/AC converter, utilizing a hybrid control method of variable frequency and phase shift control to enhance the efficiency of the converter.

The use of a single-stage topology eliminates the DC link composed of decoupling capacitors in the middle. Subsequently, the equal power method was employed to facilitate frequency conversion and phase shift control. Furthermore, the LC parameters of the converter were optimized based on the multi-objective ant lion optimizer.

A simulation model was constructed based on a single-stage grid-connected system. In comparison to the multi-objective particle swarm optimization algorithm and the multi-objective sparrow algorithm, the LC parameters optimized by the multi-objective ant lion algorithm demonstrated a reduction in the THD of grid-connected current by 27% and 66%, respectively. The optimized parameters result in a reduction in the harmonic distortion rate of the grid-connected current to 1.3% and an improvement in efficiency to 90%.

The simulation results have demonstrated the efficacy and precision of the parameter optimization strategy, and the proposed optimization strategy has the potential to enhance the performance of grid-connected systems.

This study aimed to improve the electricity economy of data centers.

Data centers have been rapidly developed over the last decade to balance increasing computing workloads. Such a rapid development has been found to result in a significant increase in higher electricity expenses for data centers.

The objective of this study was to improve the operation efficiency of a data center by developing a data center energy management model considering shiftable data workloads, energy storage, and renewable energy.

First, a data center workload model has been established to describe its temporal shifting characteristics. Then, a data center power consumption model has been developed to describe the energy usage of IT equipment and cooling systems. Finally, the energy management model for the data center has been developed to reduce the electrical energy costs of the data center.

Case studies have been performed on a trial system to illustrate the validity of the proposed model, which has been found to meet the requirements.

Rationally re-scheduling the delay-tolerant workloads and making full use of energy storage can enhance the flexibility of the power system and reduce electricity costs by 19.06%.

Electrocardiogram-based facial emotion identification has been a widely spread field in the last few decades. Due to its non-linearity, non-stationary and noisy properties, it is a very difficult job to create a framework that is capable of recognizing emotions with a high recognition rate.

In this work, we introduce a new framework for facial emotion detection based on feature creation using a topographic representation of ECG signal properties. The feature map is created using deep learning techniques, and further, extricated features are then used for classification techniques to detect facial emotion recognition.

The recognition results are achieved on two publicly available facial expression datasets, i.e., Ascertain and Dreamer. We illustrated the usefulness of our framework by comparing results with other existing methods.

The recognition results prove that the introduced framework can enhance the identifying rate on various given datasets.