An Estimation Model for Offshore Wind Power Generation with Characteristic Category Evaluation Plan

Authors

Jide LuFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical and Computer Engineering

First Advisor's Name

Jean Andrian

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Kang K. Yen

Second Advisor's Committee Title

committee member

Third Advisor's Name

Deng Pan

Third Advisor's Committee Title

committee member

Fourth Advisor's Name

Nezih Pala

Fourth Advisor's Committee Title

committee member

Keywords

power, energy

Date of Defense

3-23-2023

Abstract

Wind power is an economically viable and environmentally friendly renewable energy source. Offshore wind power is becoming increasingly important due to its stable conditions and proximity to load centers. However, the integration of wind turbines and wind power plants must be carefully managed to ensure power system stability. Accurate estimation of wind power production is critical for balancing the power system in real-time and avoiding power outages. This is a challenging task due to the stochastic and intermittent nature of wind power, as well as the complex relationship between wind speed and wind power. Accurate estimation can reduce the costs of installing wind farms and is essential for assessing their economic viability. The forecasting of wind power generation is a widely utilized method for assessing the unpredictability of wind power output. Recent studies have indicated that no one-size-fits-all estimation model can be deemed the most suitable or effective for wind power forecasting, as there are significant disparities among different wind categories, including velocity estimation, wind direction estimation, and the internal effects of turbines within wind farms. As a result, a data-driven modeling framework is required to avoid the reliance on a single model for power generation forecasting. Instead, a physics-based data processing system should be established to provide high-quality data inputs to the model. In wind power plants that operate in series,

variations in power output between turbines can result in violations of the operating limits of motors and power electronic converters. The main cause of these variations is the aerodynamic wake effect of the turbines. Thus, it is crucial to consider the wake model when analyzing the problem at the wind plant level and to develop a model that captures the interactions between different turbines. To address the challenges mentioned above, this study developed a wind power forecasting model for offshore wind power systems. The model takes into account the complexity of wind power generation, and employs category theory-based data analysis to preprocess the raw data. Multiple neural network models are integrated to perform wind power forecasting, while also accounting for the wake effects of wind turbines within the offshore wind farm. This comprehensive approach enables the model to forecast the wind power output of the entire wind farm, and provides a valuable tool for enhancing the efficiency and reliability of offshore wind power systems.

Identifier

FIDC011010

Recommended Citation

Lu, Jide, "An Estimation Model for Offshore Wind Power Generation with Characteristic Category Evaluation Plan" (2023). FIU Electronic Theses and Dissertations. 5313.
https://digitalcommons.fiu.edu/etd/5313