"Development and Optimization of Adaptive Voltage and Frequency Control" by Anjan Debnath
 

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical and Computer Engineering

First Advisor's Name

Arif Sarwat

First Advisor's Committee Title

committee chair

Second Advisor's Name

Nezih Pala

Second Advisor's Committee Title

committee member

Third Advisor's Name

Sumit Paudyal

Third Advisor's Committee Title

committee member

Fourth Advisor's Name

Norman Munroe

Fourth Advisor's Committee Title

committee member

Keywords

Voltage Regulation, Frequency Regulation, Optimization, Microgrid, Artificial Neural Network

Date of Defense

6-22-2023

Abstract

A microgrid, associated with Distributed Energy Resources (DERs), is subject to voltage and frequency fluctuations due to the dependency on weather parameters, besides voltage deviations due to load side variations which could create instability in both the AC and DC sides of AC-DC microgrids. In that regard, this dissertation is aimed at developing adaptive controllers to regulate the voltage and frequency of PV-based microgrids. For voltage regulation, the dissertation proposes a new control strategy to unify maximum power point tracking (MPPT) from photovoltaic (PV) arrays and voltage regulation (VR) in a load-adaptive way where PV has been utilized as a voltage source. An Artificial Neural Network (ANN) is used to determine the Maximum Power Point (MPP) of the PV system, and a DC-DC buck converter with the proposed control technique is used to regulate the DC bus voltage at the desired operating point. The effectiveness of the proposed control strategy is demonstrated by varying irradiance from the input side and load variations from the output side. For frequency regulation, this dissertation proposes a novel method of frequency regulation for power systems with high penetration of inverter-based renewable energy resources, such as Photovoltaic (PV) systems. The method generates virtual inertia (VI) based on the frequency dynamics of the system using the conventional swing equation and feeds an Artificial Neural Network (ANN) to determine the corresponding operating point for the PV. Simulation results show that the proposed control mechanism provides high accuracy, and a fast-tracking speed, and has the potential to significantly improve stability during frequency disturbance in PV-based power systems. In addition, the dissertation proposes a hierarchical control strategy for islanded microgrids that prioritizes PV as the primary control and battery as the secondary control for dynamic frequency regulation. The PV operating point on the P-V curve is determined based on frequency deviation, and the battery control is activated to avoid moving the PV outside a predefined region. The control parameters of the local controllers for PV and battery are optimized by the particle swarm optimization (PSO) algorithm and simulations show the proposed architecture reduces battery cycling, increases longevity, and reduces overall system cost. Moreover, the dissertation also explored and proposed a straight-line-based simple MPPT algorithm to extract maximum power from PV with high efficiency for rapidly changing weather parameters, a binary search algorithm for optimal sizing of photovoltaic and energy storage systems with annual zero load deficit constraint, and particle swarm optimization (PSO)-based PID controller design for converters and inverters with faster settling time and reduced transients- overshoots and undershoots of the response signals.

Identifier

FIDC011209

Available for download on Wednesday, July 23, 2025

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