Performance Assessment of a Wind Turbine Using SCADA based Gaussian Process Model

Pandit, Ravi; Infield, David

doi:10.36001/ijphm.2018.v9i1.2702

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Wind farms are attached to the SCADA system to constantly collect data about wind turbines. This data can be utilized for different analysis including failure prediction [8], gearbox malfunction [7], assessment of the wind turbine performance [11], and windturbine wake effect [12][13][14]. There have been many research studies to forecast wind power according to the characteristics of wind farms.…”

Section: Related Workmentioning

confidence: 99%

Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System

Delgado

Fahim

2020

Energies

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The number of wind farms is increasing every year because many countries are turning their attention to renewable energy sources. Wind turbines are considered one of the best alternatives to produce clean energy. Most of the wind farms installed supervisory control and data acquisition (SCADA) system in their turbines to monitor wind turbines and logged the information as time-series data. It demands a powerful information extraction process for analysis and prediction. In this research, we present a data analysis framework to visualize the collected data from the SCADA system and recurrent neural network-based variant long short-term memory (LSTM) based prediction. The data analysis is presented in cartesian, polar, and cylindrical coordinates to understand the wind and energy generation relationship. The four features: wind speed, direction, generated active power, and theoretical power are predicted and compared with state-of-the-art methods. The obtained results confirm the applicability of our model in real-life scenarios that can assist the management team to manage the generated energy of wind turbines.

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Section: Related Workmentioning

confidence: 99%

Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System

Delgado

Fahim

2020

Energies

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Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracy

Pandit

Infield

2019

Renewable Energy

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Gaussian Process (GP) models are increasingly finding application in wind turbine condition monitoring and in particular early fault detection. GP model accuracy is greatly influenced by the choice and type of the covariance functions (used to describe the similarity between two given data points). Hence, the appropriate selection and composition of covariance functions is essential for accurate GP modelling. In this study, an in-depth analysis of commonly used stationary covariance functions is presented in which wind turbine power curve used where GP based power curve has been constructed using different stationary covariance functions, and after that, a comparative analysis has been carried out in order to identify the most effective covariance function. The commonly used squared exponential covariance function is taken as the benchmark, against which other covariance functions are assessed. The results show that the performance (in terms of model accuracy and uncertainty) of GP fitted power curve models based on rational quadratic covariance functions is almost the same as for the most commonly used squared exponential function. Thus, rational quadratic covariance functions can be used instead of squared exponential covariance functions. In this paper, strength and weakness of stationary covariance functions would be highlighted for effective condition monitoring.

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Performance Assessment of a Wind Turbine Using SCADA based Gaussian Process Model

Cited by 2 publications

References 22 publications

Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System

Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System

Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracy

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