Performance Monitoring of Wind Turbines Gearbox Utilising Artificial Neural Networks — Steps toward Successful Implementation of Predictive Maintenance Strategy

Abstract

Manufacturing and energy sectors provide vast amounts of maintenance data and information which can be used proactively for performance monitoring and prognostic analysis which lead to improve maintenance planning and scheduling activities. This leads to reduced unplanned shutdowns, maintenance costs and any fatal events that could affect the operations of the overall system. Performance and condition monitoring are among the most used strategies for prognostic and health management (PHM), in which different methods and techniques can be implemented to analyse maintenance and online data. Offshore wind turbines (WTs) are complex systems increasingly needing maintenance. This study proposes a performance monitoring system to monitor the performance of the WT power generation process by exploiting artificial neural networks (ANN) composed of different network designs and training algorithms, using simulated supervisory control and data acquisition (SCADA) data. The performance monitoring is based on different operating modes of the same type of wind turbine. The degradation models were developed based on the generated active power resulting from different degradation levels of the gearbox, which is a critical component of the WTs. The deviations of the wind power curves for all operating modes over time are monitored in terms of the resulting power residuals and are modelled using ANN with a unique network architecture. The monitoring process uses the recursive form of the cumulative summation (CUSUM) change detection algorithm to detect the state change point in which the gearbox efficiency is degraded by evaluating the power residuals predicted by the ANN model. To increase the monitoring effectiveness, a second ANN model was developed to predict the gearbox efficiency to monitor any failure that could happen once the efficiency degrades below a threshold. The results show a high degree of accuracy in power and efficiency prediction in addition to monitoring the abnormal state or deviations of the power generation process resulting from the degraded gearbox efficiency and their corresponding time slots. The developed monitoring method can be a valuable tool to provide maintenance experts with alarms and insights into the general state of the power generation process, which can be used for further maintenance decision-making.

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

“…The deterministic approach uses physics-based models to estimate the life based on the degradation rate of the components such as the work done in [14]. The data-driven approach uses machine learning techniques to learn the degradation patterns from the sensor data [15]. Once the useful life probability is predicted, the maintenance schedule can be optimized.…”

Detección de fallos en aerogeneradores flotantes mediante redes neuronales usando OpenFAST

“…La energía eólica se encuentra firmemente en la lista de las energías renovables en creciente demanda, hallando en la tecnología flotante una clara avenida para la expansión tanto tecnológica como económica (Tomás-Rodríguez and Santos, 2019).…”

“…El empleo de redes neuronales para la detección de fallos en aerogeneradores ha sido explorado en (Cho et al, 2021) y (Yang et al, 2022), mientras que su aplicación en escenarios offshore es más escasa, destacando el trabajo de (Shaheen and Németh, 2023), que se centra en concreto en la detección y monitorización de fallos de la caja de engranajes.…”