Yi Luo

About

Yi Luo was born in Hubei, China in 1991. He received the B.E. degree and Ph.D. degree in School of Electrical and Electronic Engineering from Huazhong University of Science and Technology, Wuhan, China, in 2013, 2018, respectively. He is now working as a researcher in China Southern Power Grid, Electric Power Research Institute, Guangzhou, China. His current researches include the field of power quality analysis, modern signal process, machine learning and power transformers.

Employment

• China Southern Power Grid Co Ltd
  2018 - Present

Education

• Huazhong University of Science and Technology PhD
  2013 - 2018
• Huazhong University of Science and Technology Bachelor's degree
  2009 - 2013

Projects & Funding

Projects & funding information is unavailable.

Publications (15)

• Theory of Quantity Value Traceability of Effective Apparent Power and Evaluation Method of Uncertainty
  Energies 2025 DOI: 10.3390/en18123214
  +Save
• Evolutionary Game Theory-Based Analysis of Power Producers’ Carbon Emission Reduction Strategies and Multi-Group Bidding Dynamics in the Low-Carbon Electricity Market
  Processes 2025 DOI: 10.3390/pr13040952
  +Save
• A Practical Anonymous Voting Scheme Based on Blockchain for Internet of Energy
  Security and Communication Networks 2022 DOI: 10.1155/2022/4436824
  +Save
• DAKEs: Decentralized Authenticated Key Exchange Protocols via Blockchain for Smart City
  Wireless Communications and Mobile Computing 2022 DOI: 10.1155/2022/3314051
  +Save
• Method to Evaluate the Resistance–Capacitance Voltage Divider and Uncertainty Analysis
  Energies 2021 DOI: 10.3390/en14227744
  +Save
• Temperature Rise Characteristics and Error Analysis of a DC Voltage Divider
  Energies 2021 DOI: 10.3390/en14071914
  +Save
• Novel Method Based on Variational Mode Decomposition and a Random Discriminative Projection Extreme Learning Machine for Multiple Power Quality Disturbance Recognition
  IEEE Transactions on Industrial Informatics 2019 DOI: 10.1109/TII.2018.2871253
  +Save
• A highly accurate and fast power quality disturbances classification based on dictionary learning sparse decomposition
  Transactions of the Institute of Measurement and Control 2019 DOI: 10.1177/0142331218758886
  +Save
• Research on Key Test Methods of the Smart Meter Software Based on Failure Modes
  Journal of Physics Conference Series 2019 DOI: 10.1088/1742-6596/1325/1/012172
  +Save
• Three-Layer Bayesian Network for Classification of Complex Power Quality Disturbances
  IEEE Transactions on Industrial Informatics 2018 DOI: 10.1109/TII.2017.2785321
  +Save