Wind Hybrid Integration Platform (WHIP)

This research presents the development of distributed wind turbine generator (WTG) model developed in MATLAB/Simulink. The simulation models include detailed representations of the aerodynamic subsystem, permanent magnet synchronous generator (PMSG), power electronics, and control algorithms. This work introduces a robust method to estimate the power coefficient curve 𝐶𝑝 (𝜆) from field data corrected for turbulence and sensor noise, yielding a physically consistent lookup table for aerodynamic torque calculation. The electrical subsystem consists of a back-to back converter with 𝑑𝑞-frame current control. The generator-side controller implements maximum power point tracking (MPPT) through speed regulation at the optimal tip-speed ratio. The grid-side controller maintains the DC-link voltage and regulates both active and reactive power exchange with the grid. The model enables real-time dynamic simulations and supports integration studies for microgrids and off-grid applications. Simulation results validate the effectiveness of the proposed approach and demonstrate its capability to capture the key dynamics of distributed WTGs.

Resources:

Development and Validation of a Wind Turbine Generator Simulation Model

Field-Calibrated Aerodynamic and Electromechanical Dynamic Simulation of a Bergey Excel 15 Wind Turbine for Distributed Generation

As grid integration of distributed energy resources such as solar photovoltaics, wind turbine generators, and energy storage systems becomes more common, the need for effective voltage regulation increases. This project was designed and implemented in MATLAB/Simulink to perform voltage regulation for a wind turbine generator. Simulation results for the advanced controller demonstrated that the wind turbine generators could regulate to the desired target voltage under varying wind speeds and load conditions. Compared to traditional Volt-Var Curve (VVC) control results, the advanced control was able to regulate to the desired target voltage with little to no voltage deviation. 

Resources:

Model Characterization and Frequency Regulation in Wind-Diesel Hybrid Microgrids

The integration of distributed energy resources into the grid has become more common. However, the variability from these renewable energy resources creates voltage challenges for distribution systems. To prevent adverse effects on the system, utilities must ensure that system voltage is maintained within required operational limits. This project demonstrated the ability of a wind turbine generator and a photovoltaic inverter to jointly provide voltage regulation in a distribution system using volt-var curve control. The proposed models were connected to a large power distribution circuit model and simulated using MATLAB/Simulink, showing that the wind turbine generators and PV inverters could absorb and inject reactive power to regulate voltage effectively.

Resources:

Development of a Wind Turbine Generator Volt-Var Curve Control for Voltage Regulation Using Power Hardware-in-the-Loop

Development of a Wind Turbine Generator Volt-Var Curve Control for Voltage Regulation in Grid Connected Systems

Volt-Var Curve Control Using Wind Turbine Generators and Photovoltaic Inverters for Voltage Regulation

As distributed energy resources are increasingly integrated into the grid, distribution power systems are becoming more flexible. While bringing generation close to consumption offers many benefits, DERs may cause undesirable effects due to their variability. This project contributed to the design, training, and validation of a reinforcement learning agent for voltage regulation using reactive power in distributed wind turbine generators. The proposed controller can regulate voltage tightly around nominal levels, successfully avoiding violations of the ANSI C84.1 standard. Compared to other droop-based approaches, such as volt-var curve control, the proposed reinforcement learning agent is optimized for operating in this system, ensuring a response with greater precision. 

Resources:

Reactive Power Control for Fast-Acting Voltage Regulation of Distributed Wind Turbines Using Reinforcement Learning

Coordinated, Data-Driven Voltage Regulation in Wind-plus-Solar Power Distribution Systems with Energy Storage

Cooperative Multi-Agent Reinforcement Learning Method for Fast Voltage Regulation using Distributed Wind Turbine Generators

The integration of renewable energy resources has generated interest in using these devices to support grid activities. Good power quality depends on several factors, such as keeping voltages and frequency within acceptable ranges. This project focused on the participation of wind turbine generators in frequency regulation activities for wind-diesel hybrid microgrids. The deep deterministic policy gradient model was examined and shown to improve power quality by keeping frequency closer to nominal levels and significantly minimizing frequency excursions. Additionally, the DDPG model can increase wind penetration and reduce fuel consumption.

Resources:

Increasing the Penetration of Distributed Wind in an Isolated Microgrid Through Data-driven Frequency Regulation using Reinforcement Learning