Post Title

Mohammad Ramin Feizi

Current Position: Invited Researcher
Email: Faizi.m.r[at]gmail.com
Phone:
Link:

Education

  • BSc: (2012) Electrical Engineering, Sahand University of Technology, Tabriz, Iran
  • MSc: (2014) Electrical Engineering, University of Kurdistan, Sanandaj, Kurdistan, Iran
  • PhD:
  • Research interests

  • Microgrid Planning/Operation and Control
  • Robust/Intelligent control with application in power systems
  • Renewable Energy and Smart Grids
  • Short Biography

    Frequency stability in microgrids under islanded operation mode is one of the most important control problems in new power system design. MGs control in an islanded mode is more difficult than the grid-connected mode, because the main utility grid supports voltage and frequency regulation of the MGs in grid-connected mode. In the islanded mode, other MG resources should compensate the fluctuation in wind power and solar irradiation. For sake of these challenge, intelligent and robust control methods are designed. First, supervisory fuzzy logic controller is proposed with two main goals: holding of structure simplicity that is desirable in industrial environment and implementable capability without opening the existing conventional PI control loops. In addition, H∞ and µ-synthesis robust control techniques are used to develop the MG secondary frequency control loop. The DEG, MT and FC micro-sources are responsible units in secondary control loop to balance the MG’s load and power generation. Also a generalize method for energy management as well as frequency control of microgrid (MG) to determine set points optimal operation and cost minimization of micro-sources (MS). Artificial neural network (ANN) scheme through microgrid central controller (MGCC) is applied to obtain set points in an efficient economical way. It has the on-line capability to adjust the active reserve power generation, economically to demand in islanded mode. To improve the secondary frequency control issue based on optimized set points, an Adaptive Neuro Fuzzy Inference System (ANFIS) for PI parameters tuning is proposed.