This dissertation focuses on addressing the critical tasks of secondary control in microgrids, particularly in the islanding operating mode. The presence of noise in sensors and communication links necessitates the design of a noise-resilient secondary control approach. In response, this dissertation introduces the Nonlinear Generalized Minimum Variance (NGMV) control approach into the islanded AC and DC microgrid's secondary control system. The effectiveness of the proposed approach is demonstrated through MATLAB simulations. Additionally, the dissertation presents an adaptive, model-free, and data-driven control approach for secondary voltage control in AC microgrids, addressing uncertainties. By employing Unfalsified Adaptive Control (UAC), the approach selects the most suitable stabilizing controller from a set of pre-designed controllers while minimizing the knowledge required from the microgrid. MATLAB simulations validate the effectiveness of the proposed method under different scenarios, including load changes, droop coefficients change, and communication link failures. Overall, this research contributes to the development of robust and noise-resilient control techniques for microgrid secondary control in islanded modes.
Microgrid, Secondary Control, Nonlinear Generalized Minimum Variance, Volterra Model, Power system stability, Uncertainty
This dissertation is based upon work supported by the National Science Foundation under Awards OIA-1757207 and ECCS-2214441.
Level of Degree
Electrical and Computer Engineering
First Committee Member (Chair)
Second Committee Member
Third Committee Member
Fourth Committee Member
Habibi, Seyed Iman. "Noise-resilient and Adaptive Distributed Control Techniques for Microgrids." (2023). https://digitalrepository.unm.edu/ece_etds/609
Available for download on Friday, August 01, 2025