Educational Background:

• PhD, Power System and Its Automation, North China Electric Power University (2003)
• BA, Computer Science and Technology, North China Electric Power University, (1999)

  Research:

  Research Projects:

1. Research on Early Warning Method of Cascading Failure for Power Grid with Wind Power Integration Based on Brittleness Theory of Complex System
2. Research on Fault Diagnosis of Transformer based on Multi-source Information Fusion
3. Research on Fault Diagnosis Model of Power Equipment Based on CRB
4. Research on Power Grid Cascading Failure Early Warning Method Considering the Integration of Wind Power

 Journal Articles

1. Gang Ma, Linru Jiang, Kaihua Zhou, Guchao Xu. A Method of Line Fault Location Based on Traveling Wave Theory . International Journal of Control and Automation, 2016, 9(2).
2. Gang Ma, Linru Jiang, Guchao Xu, Jianyong Zheng. A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR . Mathematical Problems in Engineering, 2015, 2015(203083): 1-9.
3. Gang Ma, Guchao Xu, Ju Rong, Tiantian Wu. Study on Optimal Configuration of the Grid-connected Windsolarbattery Hybrid Power System. International Journal of Sustainable Energy, 2015-9-3.
4. Gang Ma, Guchao Xu, Linru Jiang, Qian Tao. Study on Anti-electricity Theft Based on Electricity Comparative Method. American Journal of Electrical and Electronic Engineering, 2015, 3(4): 107-111.
5. Gang Ma, Guchao Xu, Linru Jiang, Fu Mingxing, Ju Rong. The Optimization Method of Wind Farm Storage Capacity Based on Energy Loss Rate. Renewable Energy, 2015, 33(12): 1825-1830.
6. Gang Ma, Guchao Xu, Shuai Chen, Jiale Guo, Hang Zhang. Research on Maximum Power Tracking Algorithm for Photovoltaic System [J]. Journal of Computing and Electronic Information Management, 2015, 2(2): 100-115.
7. Gang Ma, Bai Fan, Linru Jiang, Zheng Jianyong. The Maximum Power Tracking Algorithm for Photovoltaic System Based on Self Optimizing Duty Ratio [J]. Renewable Energy, 2015, 33(5): 719-724.
8. Gang Ma, Wu Kehe, Li Yi. A Method of Electrical Equipment State Diagnosing.