Predictive Maintenance of Wind Generators based on Artificial Intelligence Enabled Big Data Analysis

该合作研究项目将专注于基于人工智能(AI)的风力发电机组预测性维护和**故障预测。风力涡轮机在过去20年里激增，这一趋势预计将继续下去。因此，确保风能发电设备的可靠性和可用性是最重要的，这需要适当的操作和维护(O&M)。因此，风电场运营商对通过改进他们的风力发电机维护和故障避免**计划来降低运营和维护成本非常感兴趣**该计划可以**利用尖端技术和工具的力量，包括基于人工智能的大数据分析，**用适当和及时的预测性维护和**故障预测来取代当前的定期维护计划，从而可以最大限度地减少风力发电设备的故障和停机时间。**然后该项目将专注于开发和测试大型**风电场的基于人工智能的预测性维护技术，基于从风电场运营商那里获得的大型数据集，目标是**加强可再生能源发电的电网整合，以获得更可靠、更清洁和可持续的**电力供应。该项目将帮助一家初创公司进入可再生能源的预测性维护**市场，目的是将项目成果商业化，为公司创造**经济效益，并为**加拿大的可再生能源部门创造社会和经济效益。

This collaborative research project will focus on Artificial Intelligence (AI)-based predictive maintenance and**failure prediction for wind farm generators. Wind turbines have proliferated over the past two decades, with the**trend expected to continue. Hence, ensuring that wind energy generation equipment is reliable and available is**paramount, which requires proper operation and maintenance (O&M). Thus, wind farm operators are quite**interested in decreasing their O&M costs by improving their wind generator maintenance and failure avoidance**programs, for which the power of cutting-edge techniques and tools, including AI-based big data analysis, can**be used to replace current periodic maintenance programs with proper and timely predictive maintenance and**failure prediction, so that wind generation equipment failures and down-times can be minimized.**The project will then focus on developing and testing AI-based predictive maintenance techniques for large**wind farms, based on large data sets received from a wind farm operator, with the goal of contributing to**enhance the grid integration of renewable energy generation for a more reliable, cleaner, and sustainable**electrical supply. The project will help a start-up company to enter into the market of predictive maintenance**for renewable energy sources, with the objective of commercializing the outcomes of the project to create**economic benefits for the company and social and economic benefits for the renewable energy sector in**Canada.