Predicting Solar Performance Ratio Based on EncoderDecoder Neural Network Model Fault diagnosis of photovoltaic (PV) arrays is an important task for improving the reliability and safety of the overall photovoltaic system. In the traditional fault diagnosis, most of the past researches focus on the IV curve to determine the PV arrays state. However, most inverters as smart meters cannot obtain data in high frequency and high volume in order to calculate the IV curve, and the information usually cannot be retrieved conveniently. In this paper, we evaluate various Recurrent Neural Network Modelsxe2x80x99 from the recent advances in the deep learning field and their abilities in predicting PV arrays power generation. The results show the EncoderDecoder model using Convolutional Neural Network (CNN) as the decoder and Temporal Convolutional Network (TCN) are more effective in predicting the PV arrays solar output production and can be used as the foundation for fault diagnosis and prediction.

Timeseries Deep Learning Fault Detection with the Application of Wind Turbine Benchmark In this paper, a deep learning fault detection approach is proposed based on the convolutional neural network in order to cope with one class of faults in wind turbine systems. Fault detection is very vital in nowadays industries due to the fact that instantly detection can prevent waste of cost and time. Deep learning as one of the powerful approaches in machine learning is a promising method to identify and classify the intrigued problems, which are hard to solve by classical methods. In this case, less than 5% performance reduction in generator torque along with sensor noise, which is challenging to identify by an operator or classical diagnosis methods is studied. The proposed algorithm, which is evolved from convolutional neural network idea, is evaluated in simulation based on a 4.8 MW wind turbine benchmark and the accuracy of the results confirms the persuasive performance of the suggested approach.