Proactive maintenance of small wind turbines using IoT and machine learning models

Wind is one of the most important natural resources from which we can generate power through wind turbines, Presently the wind turbines may be small or large but the output power generated is used for various purposes. There may be a possibility of wear and tear of the turbines which may lead to physical damage. There are no proper mechanisms available for monitoring the turbines from remote locations through wireless mode. The implementation of live remote monitoring and intelligent condition monitoring techniques reduces the downtime and would increase the lifetime of the turbines. This paper proposes a novel smart and proactive maintenance system that would aid in diagnosing the major faults with the wind turbines and a prediction analysis tool that would forecast the generation status of the small wind turbines. A Sensor based IoT system (SBIS) will help to monitor the significant parameters of the wind turbines which determine its working conditions like wind speed, vibration, temperature, and output power. A combined approach of Machine Learning techniques for SBIS have been used for proactive maintenance of small wind turbines. The diagnosis part of the system takes input from the sensors and uses a cloud platform for predictive analysis. The machine learning algorithms like Linear Regression (LR), Support Vector Machine (SVM), Optimized Artificial Neural Network (OANN), and XG Boost (XGB) are applied and the results are summarized for prediction. The results of the algorithms are compared for accuracy of the sensed data and it is observed that the OANN algorithm is better performing for proactive maintenance and power prediction of the small wind turbines.