Hanif Livani

Hanif Livani

Hanif Livani

Assistant Professor He, him, his
Biography

Hanif Livani is an assistant professor at the University of Nevada, Reno with affiliation in the Department of Electrical and Biomedical Engineering. He received the B.S. and the M.S. degree in electrical engineering, both from the University of Mazandaran, Iran and the Ph.D. degree in electrical engineering from the Virginia Polytechnic and State University (Virginia Tech). He joined the faculty of the University of Nevada, Reno in Fall 2014.

Education
  • Ph.D., Electrical Engineering, Virginia Tech, 2014
  • M.Sc., Electrical Engineering, University of Mazandaran, Iran, 2009
  • B.Sc., Electrical Engineering, University of Mazandaran, Iran, 2006
Professional memberships
  • Institute of Electrical & Electronics Engineers (IEEE)
  • IEEE Power and Energy Society

Prospective graduate students

I am looking for graduate students to join my research group in the Department of Electrical and Biomedical Engineering at the University of Nevada, Reno. Students with good academic records and with machine learning and power engineering research interests are encouraged to apply.

Once you are admitted to the University, feel free to contact me for available funding. Please visit the Graduate School website and the Office of International Students and Scholars for further information on admission to the University.

Learn more about our graduate programs

Selected publications

Book/book chapters

  • Hanif Livani, Big Data Application in Power Systems, Reza Arghandeh & Yuxun Zhou (Eds.), Supervised Learning-Based Fault Location in Power Grids (pp. 303-319). Elsevier Science.

Refereed journal publications

  • Iman Niazazari, Reza J. Hamidi, Hanif Livani, and Reza Arghandeh, “Electromagnetics Transient Events Root Cause Analysis using Spatiotemporal Feature Learning,” (Under Review) International Journal of Electrical Power & Energy Systems, 2019.
  • Vahid Sarfi and Hanif Livani, “Multi-Objective Volt/VAR Control in Distribution Systems with Multiple DERs” (Under Review) IEEE Transaction on Power Systems, 2019.
  • Mohammad Jafari, Vahid Sarfi, Amir Ghasemkhani, Hanif Livani, Lei Yang, and Hao Xu, “Biologically-Inspired Adaptive Intelligent Secondary Control for Microgrids under Cyber Imperfections” (Accepted) IET, 2019.
  • Reza J. Hamidi, and Hanif Livani, “A Recursive Method for Traveling-Wave Arrival-Time Detection in Power Systems,” IEEE Transactions on Power Delivery, vol. 34, no. 2, pp. 710-719, April 2019.
  • Vahid Sarfi and Hanif Livani, “An Economic-Reliability Security-Constrained Optimal Dispatch for Microgrids” IEEE Transaction on Power Systems, vol. 33, no. 6, pp. 6777-6786, Nov. 2018.
  • Iman Niazazari and Hanif Livani, “A PMU-Data-Driven Disruptive Event Classification in Distribution Systems,” Electric Power System Research, vol. 157, pp. 251-260, April. 2018.
  • R. J Hamidi, and H. Livani, “Adaptive Single-Phase Auto-Reclosing Method using Power Line Carrier Signals,” International Journal of Electrical Power and Energy Systems, vol. 96, pp. 64-73, March, 2018. 

Refereed proceedings

  • M. Jafari, V. Sarfi, A. Ghasemkhani, H. Livani, L. Yang, and H. Xu, “Adaptive Neural Network Based Intelligent Secondary Control for Microgrids,” (Accepted) IEEE PES 2019.
  • Md Kamruzzaman, Mohammed Ben-Idris and Hanif Livani, “A Cost Effective Energy Exchange Strategy to Improve Reliability of Microgrids” (In Proc.) North American Power Symposium, Fargo, NC, September 2018.
  • M. Jafari, V. Sarfi, A. Ghasemkhani, H. Livani, L. Yang, and H. Xu, “Adaptive Neural Network Based Intelligent Secondary Control for Microgrids,” (In Proc.) IEEE Texas Power and Energy Conference (TPEC), 2018.

Research interests

  • Machine learning and data analytics
  • Cyber-physical energy systems
  • Power system state estimation
  • Fault location in transmission & distribution networks
  • Reliable integration of renewable energy systems and distribution generations to smart grid
  • Smart grid applications in power system monitoring and automation
  • Electric energy market
  • Application of signal processing to power system