Institutions have been rapidly redirecting their investments away from fossil fuels, creating a favorable scenario for the generation of clean energy. The wind industry, especially, has seen an exponential increase in recent years. Early fault detection creates an alternative for operation and maintenance (O&M), helping forgo costs before they reach a catastrophic stage since preventive methods are used to stop malfunctions before their occurrence, improving turbine reliability and allowing for maximum utilization and better control of the wind farms. In this document, deep learning methods such as Autoencoders-LSTM (Long short-term memory) are used on time series data produced from SCADA to reach the optimal results.

• Deep learning techniques and time series analytics are being used to detect faults, predict potential occurrence of faults in wind turbines before they occur in order to prevent them, and lastly to predict wind power generation within fixed time intervals for better control and maximum utilization of wind turbines.

• Creating a dataset that simulates local weather conditions and presents predictions for wind power generation that are accurate for Egypt.

• Obtaining general weather conditions that occurred during the collection of the dataset and incorporating the weather conditions as features to seek new accuracies.

The proposed system is a predictive model that aims to use dataset, mostly provided by the SCADA system, to detect possible faults in wind turbines.

A web-app that aims to use entered data and highlights the fault is expected.