Applying AI to Wind Turbine Control System - ON-670Project type: Research
Desired discipline(s): Aerospace studies, Engineering, Engineering - computer / electrical, Engineering - mechanical, Engineering - other, Computer science, Mathematical Sciences, Mathematics, Statistics / Actuarial sciences
Company: Borrum Energy Solutions
Project Length: 6 months to 1 year
Preferred start date: As soon as possible.
Language requirement: English
Location(s): Waterloo, ON, Canada; Canada
No. of positions: 1
Desired education level: Master's
Open to applicants registered at an institution outside of Canada: No
About the company:
Borrum Energy Solutions is a Canadian high-end engineering firm that design, manufactures, and assemble the Anorra, a modular microgeneration wind turbine for rural residential power generation. Turbines and towers are designed to safely operate in the harsh Canadian weather. Most of the components are sourced or manufactured in Canada. Promoting clean renewable energy production, the turbines can offset greenhouse gas emissions from natural gas heating, reduce electricity costs, and increase energy availability. The lightweight design further enables convenient transportation to remote locations and easy installation. Additionally, the turbine kits are customized for the user’s location with both tower height and anchor styles. The applications include heating, water heating, lighting, water purification, and battery charging.
Describe the project.:
The project’s purpose is to:
- Assess the feasibility of adding Artificial intelligence ( AI) to the RPM controlling software
- Build a prototype – proof of concept.
Each microgeneration wind turbine targeted location has a unique wind profile. Furthermore, this wind profile changes over the seasons and over the years. One generic set of parameters for the RPM controlling application cannot optimize the electricity generation for each one of the microgeneration wind turbines.
The goal is for an AI-based addition to the RPM regulating software to improve the controlling algorithms through experience/learning. Various real-time data capture such as wind speed, RPM, power generation, and temperature would support the learning, etc. Research shows that electricity yield can increase by 25 % when such a system is implemented.
The BES's main goal is to increase the electricity yield of its microgeneration wind turbines by adding an AI component to its product offering. The end customer would gain from an increase in electricity and a better costs benefit ratio. Furthermore, an AI-based regulating component would add another difference to BES's product offering to help solidify its competitive position in Canada.
Our firm has no expertise in AI. Candidates might consider the following steps:
- Familiarize itself with wind turbine engineering and blade RPM control
- Familiarize with the two main Canadian standards to influence RPM control
- Familiarize with the current regulating RPM software
- Assess how AI can fulfill the stated goals
- Make recommendations including methodology
- Build and test a prototype
We are not familiar with the AI-based methodology and techniques.
BES has an RPM regulating prototype that can provide a framework for an AI prototype.
The candidate must have knowledge and experience applying AI techniques appropriate for this project.