Browsing by Author "Santos, Matilde"
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Item Open Access Accounting for environmental conditions in data-driven wind turbine power models(IEEE, 2022-09-05) Pandit, Ravi; Infield, David; Santos, MatildeContinuous assessment of wind turbine performance is a key to maximising power generation at a very low cost. A wind turbine power curve is a non-linear function between power output and wind speed and is widely used to approach numerous problems linked to turbine operation. According to the current IEC standard, power curves are determined by a data reduction method, called binning, where hub height, wind speed and air density are considered as appropriate input parameters. However, as turbine rotors have grown in size over recent years, the impact of variations in wind speed, and thus of power output, can no longer be overlooked. Two environmental variables, namely wind shear and turbulence intensity, have the greatest impact on power output. Therefore, taking account of these factors may improve the accuracy as well as reduce the uncertainty of data-driven power curve models, which could be helpful in performance monitoring applications. This paper aims to quantify and analyse the impact of these two environmental factors on wind turbine power curves. Gaussian process (GP) is a data-driven, nonparametric based approach to power curve modelling that can incorporate these two additional environmental factors. The proposed technique's effectiveness is trained and validated using historical 10-minute average supervisory control and data acquisition (SCADA) datasets from variable speed, pitch control, and wind turbines rated at 2.5 MW. The results suggest that (i) the inclusion of the additional environmental parameters increases GP model accuracy and reduces uncertainty in estimating the power curve; (ii) a comparative study reveals that turbulence intensity has a relatively greater impact on GP model accuracy, together with uncertainty as compared to blade pitch angle. These conclusions are confirmed using performance error metrics and uncertainty calculations. The results have practical beneficial consequences for O&M related activities such as early failure detection.Item Open Access SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends(Sage, 2022-09-19) Pandit, Ravi; Astolfi, Davide; Hong, Jiarong; Infield, David; Santos, MatildeThis paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.Item Open Access Use of state-of-art machine learning technologies for forecasting offshore wind speed, wave and misalignment to improve wind turbine performance(MDPI, 2022-07-08) Sacie, Montserrat; Santos, Matilde; López, Rafael; Pandit, RaviOne of the most promising solutions that stands out to mitigate climate change is floating offshore wind turbines (FOWTs). Although they are very efficient in producing clean energy, the harsh environmental conditions they are subjected to, mainly strong winds and waves, produce structural fatigue and may cause them to lose efficiency. Thus, it is imperative to develop models to facilitate their deployment while maximizing energy production and ensuring the structure’s safety. This work applies machine learning (ML) techniques to obtain predictive models of the most relevant metocean variables involved. Specifically, wind speed, significant wave height, and the misalignment between wind and waves have been analyzed, pre-processed and modeled based on actual data. Linear regression (LR), support vector machines regression (SVR), Gaussian process regression (GPR) and neural network (NN)-based solutions have been applied and compared. The results show that Nonlinear autoregressive with an exogenous input neural network (NARX) is the best algorithm for both wind speed and misalignment forecasting in the time domain (72% accuracy) and GPR for wave height (90.85% accuracy). In conclusion, these models are vital to deploying and installing FOWTs and making them profitable.