604 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Yang Li20 profiles78 · h 57
- Guoqing Li2 profiles15 · h 31
- Lei Kou2 profiles10 · h 23
- Yuanzheng Li2 profiles10 · h 26
- Y. Li9 · h 69
- Zhaoyang Qu3 profiles9 · h 14
- Zhen Yang9 · h 10
- Chen Chen3 profiles8 · h 45
- Dongbo Zhao2 profiles8 · h 28
- Jiazheng Li2 profiles6 · h 6
- Lei Wang3 profiles6 · h 9
- Meng Zhang3 profiles6 · h 7
- Huazhong University of Science and TechnologyCN11 papers
- State Grid Corporation of China (China)CN10 papers
- Argonne National LaboratoryUS9 papers
- North China Electric Power UniversityCN9 papers
- Changchun Institute of TechnologyCN6 papers
- Illinois Institute of TechnologyUS6 papers
- Electric Power UniversityVN5 papers
- Jilin Electric Power Research Institute (China)CN5 papers
- Nanjing University of Information Science and TechnologyCN5 papers
- Qilu University of TechnologyCN5 papers
- Shandong Academy of SciencesCN5 papers
- Xi'an Jiaotong UniversityCN5 papers
12 papers · 1 filter
ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation
Yang Li, Hanjie Wang, Yuanzheng Li +2
Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites. While federated learning enables privacy-preserving collaboration wi…
Extraction of Typical Operating Scenarios of New Power System Based on Deep Time Series Aggregation
Zhaoyang Qu, Zhenming Zhang, Nan Qu +5
Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system. This study proposed a novel deep time series aggregation…
PMU measurements based short-term voltage stability assessment of power systems via deep transfer learning
Yang Li, Shitu Zhang, Yuanzheng Li +2
Deep learning has emerged as an effective solution for addressing the challenges of short-term voltage stability assessment (STVSA) in power systems. However, existing deep learnin…
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach
Yang Li, Ruinong Wang, Yuanzheng Li +2
In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatilit…
Fault diagnosis for three-phase PWM rectifier based on deep feedforward network with transient synthetic features
Kou Lei, Liu Chuang, Cai Guo-Wei +3
Three-phase PWM rectifiers are adopted extensively in industry because of their excellent properties and potential advantages. However, while the IGBT has an open-circuit fault, th…
Fault diagnosis for open-circuit faults in NPC inverter based on knowledge-driven and data-driven approaches
Lei Kou, Chuang Liu, Guo-wei Cai +3
In this study, the open-circuit faults diagnosis and location issue of the neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis approach based on knowledge d…
Auto-Encoder-Extreme Learning Machine Model for Boiler NOx Emission Concentration Prediction
Zhenhao Tang, Shikui Wang, Xiangying Chai +3
An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm…
A Robust Approach for the Decomposition of High-Energy-Consuming Industrial Loads with Deep Learning
Jia Cui, Yonghui Jin, Renzhe Yu +4
The knowledge of the users' electricity consumption pattern is an important coordinating mechanism between the utility company and the electricity consumers in terms of key decisio…
A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems
Yang Li, Meng Zhang, Chen Chen
Facing the difficulty of expensive and trivial data collection and annotation, how to make a deep learning-based short-term voltage stability assessment (STVSA) model work well on…
Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning
Fengrui Liu, Yang Li, Baitong Li +2
The emerging cryptocurrency market has lately received great attention for asset allocation due to its decentralization uniqueness. However, its volatility and brand new trading mo…
Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach
Yang Li, Jiazheng Li, Yi Wang
Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables. Based on big historical data, a novel federated deep ge…
The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum Entropy
Guoliang Feng, Wei Lu, Witold Pedrycz +2
Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dyna…