Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data

Abstract

Rystad Energy's analysis shows that installed offshore wind capacity will rise to 27.5 GW in 2026 from 10.5 GW in 2020. This report indicates that increasingly complex maintenance needs must be met for wind turbines(WTs). IRENA report shows that offshore wind operation and maintenance (OM) costs typically constitute 16-25% of the cost of electricity for offshore wind farms deployed in the G20 countries. Data collection and analytics, predictive maintenance, and production output optimisation of WTs must be explored to increase operational reliability and reduce maintenance costs of WTs. Predictive maintenance in wind turbines can be achieved by analysing data obtained by sensors already equipped with the WT. This network of sensors forms part of a Supervisory Control and Data Acquisition (SCADA) system. We developed a method for monitoring and detecting anomalies in the WT critical components, such as the gearbox and the generator. The proposed approach is based on the historical SCADA data that is common in most wind farms. We developed models using extreme gradient boosting (XGBoost) and Long Short-Term Memory (LSTM) to build the characteristics behaviour of critical WT components, and Statistical Process Control (SPC) was used to evaluate its anomalous behaviour. The proposed method was tested on two real case studies regarding six different WT to determine its effectiveness and applicability.

Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated ondata from operational wind farms

“…If it is possible to clean the signal this should be considered, since it will improve the performance of the NBM model. This can be done for example by aggregating the data to a lower resolution (see (Chesterman et al, 2022), (Chesterman et al, 2021) and (Turnbull et al, 2021)), or cleaning or filtering the data using expert knowledge (see (Peter et al, 2022), (Takanashi et al, 2022), (Verma et al, 2022), (Turnbull et al, 2021), (Udo and Yar, 2021), (Beretta M. and J., 2020) and (Kusiak and Li, 2011)).…”

“…-10-minutes: (Bermúdez et al, 2022), (Black et al, 2022), (Campoverde et al, 2022), (Chesterman et al, 2022), (Maron et al, 2022), (Mazidi et al, 2017), (Miele et al, 2022), (Peter et al, 2022), (Takanashi et al, 2022), (Beretta et al, 2021), (Beretta M. and J., 2020), (Catellani et al, 2021), (Chen et al, 2021), (Chesterman et al, 2021), (Meyer, 2021), (Turnbull et al, 2021), (Udo and Yar, 2021), (Beretta M. and J., 2020), (Liu et al, 2020), (McKinnon et al, 2020, (Renström et al, 2020), (Zhao et al, 2018), (Bangalore et al, 2017), (Dienst and Beseler, 2016), (Bangalore and Tjernberg, 2015), https://doi.org/10.5194/wes-2022-120 Preprint. Discussion started: 21 February 2023 c Author(s) 2023.…”

“…However, they require in general more training data and training time. Examples of algorithms that are used in the current state-of-the-art are tree-based models like random forest (see (Chesterman et al, 2022), (Turnbull et al, 2021), (Kusiak and Li, 2011)) and gradient boosting (see (Chesterman et al, 2022), (Maron et al, 2022), (Beretta et al, 2021), (Udo and Yar, 2021), (Beretta M. and J., 2020), https://doi.org/10.5194/wes-2022-120 Preprint. Discussion started: 21 February 2023 c Author(s) 2023.…”

“…This model is particularly suitable for time series since it is able to model the time dependencies. This model is used in (Bermúdez et al, 2022) and (Udo and Yar, 2021). Two other models that have also been used, but to a much lesser extent are the Extreme Learning Machines (ELM) (see (Marti-Puig et al, 2021)) and the Generative Adversarial Network (GAN) (see (Peng et al, 2021)).…”

Condition Monitoring of Wind Turbine Generators Based on SCADA Data and Feature Transfer Learning

“…However, due to remote wind farm location, harsh working environment, and dynamic working conditions, wind farm often faces problems of frequent wind turbine failure, low power generation efficiency, and high maintenance costs [3], [4]. Therefore, condition monitoring of the wind turbines and their critical components is a main task [5], [6]. For example, the generator is one of the core subsystems of wind turbine.…”