TY - JOUR
T1 - IoT-Enabled Predictive Maintenance for Wind Turbines: Leveraging Machine Learning and Wireless Sensor Networks
AU - Lars Andersen
AU - Freya Nielsen
AU - Mette Sørensen
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2023
VL - 2
IS - 2
SP - 61
N2 - Wind power (WP) is a Renewable Energy Source (RES) that has experienced remarkable growth as society continues to seek environmentally friendly energy alternatives. This study primarily explores Predictive Maintenance (PM) of Wind Turbines (WT). It evaluates the potential advantages of Wind Energy (WE) when integrated with the Internet of Things (IoT) and Wireless Sensor Networks (WSN). The research proposes an IoT-WSN framework for PM, structured into three key stages: the initial stage involves data acquisition via sensors; the second stage involves data transmission via an internet connection; and the third stage involves data analytics performed in a cloud computing environment. For PM analytics, this work presents a Predictive Maintenance Convolutional Long Short-Term Memory (PM-C-LSTM) model that integrates the spatial feature-extraction strengths of a Convolutional Neural Network with the time-series modeling capabilities of LSTM networks. The PM-C-LSTM model combines a CNN for spatial pattern detection with LSTM networks for sequential data processing, without compromising the precision of WT-PM. Additionally, a Failure Sample Generator model is incorporated to quantify soft failure and hard failure elements, thereby enhancing the predictive performance of the Machine Learning (ML) approach. The model was deployed on a Wind Farm (WF) on the Qinghai-Tibet Plateau, using data collected over 16 months. Comparative evaluations against several conventional models, based on multiple performance metrics, demonstrate that the PM-C-LSTM model delivers superior PM capabilities. The experimental results suggest that the combined use of IoT and ML has strong potential to advance PM strategies for WT, ultimately contributing to greater efficiency and sustainability in WE production.
UR - https://isespub.com/k660845482
ER -