Author

Baolian Liu

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Open access Sep 2026

State-Triggered Adaptive Microgrid Dispatch for EV Charging: A Hybrid Deep Learning and Multi-Objective Optimization Framework

The rapid growth of electric vehicle (EV) charging demand requires accurate short-term load forecasts and dispatch strategies that can respond to changing microgrid operating conditions. This study proposes a hybrid framework that combines a VMD-CNN-ABiLSTM-IGCRA forecasting model with a state-triggered adaptive schedu...

Guan-Jian Zhu, Xin Ma, Yan-Jing Guo et al. · Information · 0 citations

Open access Aug 2026

T-HMM-Based Transformer Fault Diagnosis in Grid-Connected Renewable Energy Systems

Experimental results demonstrate that the proposed T-HMM accurately tracks state evolution trends and effectively identifies fault categories, achieving significantly superior state recognition accuracy and multi-step prediction hit rates compared to conventional HMM, with substantially reduced mean absolute error.

Wei Li, Shanyun Gu, Lei Shen et al. · Energies · 0 citations

Open access 2026

A Hybrid Deep Learning Framework with Adaptive Signal Decomposition for Enhanced Ultra-Short-Term Wind Power Forecasting in Power System Operations

A novel hybrid intelligent framework—integrating Improved Lotus Effect Algorithm, Variational Mode Decomposition, and ensemble deep learning—specifically designed for ultra-short-term wind power prediction in energy dispatch applications is engineers, which employs elite chaotic opposition-based learning to autonomousl...

Lei Shen, Qifeng Xiang, Q. Gao et al. · Energy Engineering · 0 citations