Machine Learning for Wind Turbine Blades Maintenance

Description

Wind power is a traditional and clean source of energy. Many traditional wind turbine systems are available, which are used in different applications. In recent years, the acceptance of wind turbines has been increased for producing electric energy. It is a green source of energy and is capable of producing sufficient level of energy for utilization. But, the production of energy is highly dependent on wind direction and speed. In addition, the appropriate functioning and efficient components of turbines make it more effective to produce energy according to the theoretical amount of power. Therefore, timely maintenance of the wind turbines is an essential task. In this project work, the following three objectives have been proposed to accomplish:

1. In the first phase, a review was carried out to find the recent techniques used for identifying the maintenance of the wind turbine. Thus, the literature has been collected and explored relevant to machine learning and wind power generation systems.
2. The second phase involves a machine learning technique for accurately predicting the wind power generation efficiency.
3.  An improved model has been proposed by using machine learning to estimate the maintenance of the wind turbine. The method incorporates the techniques of time series analysis and forecasting for designing the described approach.

The project is appropriate for P Ph.D. students who want to contribute to machine learning and sustainable sources of energy. In order to, accomplish the above objectives the following work will be included:

1. A review article of 8-10 pages
2. Two research papers with simulation implementation and results analysis (10-12 pages). both the papers are utilizing the common dataset for conducting experiments.
   1. The first research paper includes the method to predict wind turbine power generation
   2. The second paper includes a technique to use the prediction system for estimating the time of maintenance
3. thesis writing
4. plag correction