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Master Tutor6-Ph.D Fu Wenlong

Clicks:209 Publisher:lxsb Updated:2018-07-05 15:08:43

Title: Lecturer

Research discipline:

 Electrical Engineering and Automation

Research directions:

power plant condition monitoring and fault diagnosis; power generation system modeling and simulation and optimization control; signal processing of power devices.

 

Researching experience:

participated in a number of science and technology projects including the National Natural Science Foundation of China, State Grid Hubei Power Company and State Grid Xinyuan Company.

 

Learning experience:

2007-2011, Huazhong University of Science and Technology, undergraduate, received a bachelor's degree in engineering

2011-2016, Huazhong University of Science and Technology, straight attack Bo, received a doctorate of engineering

 

Recent research directions:

new energy power generation system modeling, simulation and control of large data-driven fault diagnosis method

 

Recent projects:

1. complex non-linear dynamic modeling and diagnosis of hydropower units, National Natural Science Foundation of China, participation

2. Integrated Fault Diagnosis of Pumped Storage Unit and Nonlinear Predictive Control, National Natural Science Foundation of China, Participation

3. equivalent model identification of small and medium-sized hydro-power units based on PMU or fault recording , National Grid Hubei Power Company science and technology projects, participation

4. hydro-generator state diagnosis system, Nanjing NARI Hydropower Company, involved

 

Papers:

1.WenlongFu, J Zhou, Y Zhang, et al. A state tendency measurement for a hydro-turbine generating unit based on aggregated EEMD and SVR. Measurement Science and Technology, 2015, 26(12): 125008.(SCI)

2.Jianzhong Zhou, Wenlong Fu*, Yongchuan Zhang, et al. Fault diagnosis of generator unit based on a novel weighted support vector data description with fuzzy adaptive threshold decision. Transactions of the Institute of Measurement and Control, 2016.(SCI)

3.Wenlong Fu, J Zhou, Y Zhang. Fault diagnosis for rolling element bearings with VMD time-frequency analysis and SVM. IMCCC 2015. (EI)

4. Vibrant Fault Diagnosis for Hydro-Electric Generating Unit Based on Support Vector Data Description Improved With Fuzzy K Nearest Neighbor. (EI)

5. Vibration trend prediction of hydroelectric generating unit based on OVMD and SVR. (EI)

Email: fuwenlong_hust@163.com

Tel:13618680447

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