Fault prediction model in wind turbines using deep learning structure with enhanced optimisation algorithm

AbstractDigital Twin (DT) is used for lifetime monitoring of the drive train and can be a costly option. This proposal adopts the predictive modelling of wind turbines by digital twins by deep learning strategies. Initially, the data is acquired from publicly available wind turbine datasets. Next, the deep features and statistical features are extracted, and the autoencoder is adapted to get the deep features. Then, the Enhanced Marine Predators Algorithm (EMPA) is to select the optimal weighted fused features, where the EMPA would tune the weights used for fusion and the features selection. Finally, the predictive modelling is done via a newly recommended Adaptive Deep Temporal Convolution Network with an Attention Mechanism (ADTCN-AM). It is tuned for precise outcomes with the help of EMPA for forecasting the wind speed and predicting the generated power. The comparative performance analysis of the recently used wind prediction system model shows better efficient results.KEYWORDS: Twin predictive model in wind turbinesfeature extractionenhanced marine predators algorithmadaptive deep temporal convolution network with attention mechanismoptimal weighted fused features Disclosure statementNo potential conflict of interest was reported by the author(s).Practical implicationThe real-time-based twin prediction model in wind turbines gives the computer-oriented solutions for next-generation. It is utilised to generate a digital copy of wind farms interconnected with the physical wind turbines for analysis and prediction process. It helps analyse and understand wind farms easily. It helps to deal with the issue of real-time control of the characteristics of UAV swarm. It includes context-awareness capabilities, which are utilised to identify cybersecurity problems in real time for smart grid deployments.Additional informationNotes on contributorsMahendra Bhatu GawaliMahendra Bhatu Gawali received his BE degree in 2008, M.E. degree in 2013 and Ph.D. degree in 2019 from University of Mumbai, MS, India. Currently he working as Professor in IT department of Sanjivani College of Engineering, Kopargaon, Savitribai Phule Pune University, Pune, MS, India. His area of interests is Digital Twin, Cognitive Intelligence, Artificial Intelligence, Cloud Computing, Optimisation.Swapnali Sunil GawaliSwapnali Sunil Gawali is working as assistant professor in the computer engineering department of Sanjivani College of Engineering, Kopargaon. She has completed her BE and ME from Savitribai Phule Pune University, Pune. Her area of interest is data mining, artificial intelligence.Megharani PatilMegharani Patil is an associate professor in the computer engineering department of Thakur College of Engineering and Technology, Mumbai, and head of the department of artificial intelligence and machine learning. She has received her PhD from the University of Mumbai. Her area of research interest is user experience design and intelligent systems. She has published her research papers in many national and international journals and conferences. A patent is registered in her name, and she has published two books.