Integrated Machine Learning and Enhanced Statistical Approach-Based Wind Power Forecasting in Australian Tasmania Wind Farm

Yao, Fang; Liu, Wei; Zhao, Xuhao; Li, Song

doi:10.1155/2020/9250937

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…However, TPID-NEW configuration gives the smallest settling time. Although external disturbances have not been considered in this experiment, the results of TAB-NEW and TAB-MW are not the same due to the effect of the internal disturbance (12).…”

Section: Simulation Resultsmentioning

confidence: 96%

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Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Sierra-García

Santos

2021

IEEE Access

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Data about wind are usually available from different databases, for different locations. In general, this information is the average of the wind speed over time. The wind reports are crucial for designing wind turbine controllers. But when working with floating offshore wind turbines (FOWT), two problems arise regarding the wind measurement. On the one hand, there are no buoys at deep sea, but near the coast where the wind is not so strong neither so stable; so the measurements do not fully correspond to reality. On the other hand, these floating devices are subjected to extreme environmental conditions (waves, currents, . . .) that produce disturbances and thus may distort wind measurements. To address this problem, this work presents a novel pitch neuro-control architecture based on neuro-estimators of the effective wind. The control system is composed of a proportional-integral-derivative (PID) controller, a lookup table, a neuro-estimator, and a virtual sensor. The neuro-estimator is used to estimate the effective wind in the FOWT and to forecast its future value. Both current and future wind signals are combined and power the controller. The virtual sensor also provides a measure of the effective wind based on other available signals related to the wind turbine, such as the pitch angle and the angular velocity of the generator. Neural networks are trained online to adapt to changes in the environment. Intensive simulations are carried out to validate the effectiveness of this neuro control approach. Controller performance is compared to a PID, obtaining better results. Indeed, an improvement of 16% for sinusoidal wind and an average improvement of 8% are observed.INDEX TERMS Floating offshore wind turbines (FOWTs), pitch control, neuro-estimator, neural network, virtual sensor, wind energy.

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Section: Simulation Resultsmentioning

confidence: 96%

“…A specific challenge of wind turbines in general, and of floating offshore wind turbines in particular, is the wind forecasting and the disturbance in the measurement of wind speed [11], [12]. As it is well known, the output power generated by a wind turbine is directly related to the wind speed.…”

Section: Related Workmentioning

confidence: 99%

Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Sierra-García

Santos

2021

IEEE Access

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“…Deep learning techniques have proven to be exceptionally proficient in wind power forecasting to reveal intricate patterns and correlations within extensive and intricate datasets, a challenge that conventional methods may find difficult to address [8]. Wind power prediction plays a vital role in various timeframes, encompassing very short-term intervals (few minutes to half an hour) for activities like regulation, real-time grid operations, turbine control, and market clearing.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network

Kanagarathinam,

Aruna,

Ravivarman

et al. 2023

Sustainability

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Integrating wind energy forecasting into urban city energy management systems offers significant potential for optimizing energy usage, reducing the carbon footprint, and improving overall energy efficiency. This article focuses on developing a wind power forecasting model using cutting-edge technologies to enhance urban city energy management systems. To effectively manage wind energy availability, a strategy is proposed to curtail energy consumption during periods of low wind energy availability and boost consumption during periods of high wind energy availability. For this purpose, an LSTM-based model is employed to forecast short-term wind power, leveraging a publicly available dataset. The LSTM model is trained with 27,310 instances and 10 wind energy system attributes, which were selected using the Pearson correlation feature selection method to identify crucial features. The evaluation of the LSTM-based forecasting model yields an impressive R2 score of 0.9107. The model’s performance metrics attest to its high accuracy, explaining a substantial proportion of the variance in the test data. This study not only contributes to advancing wind power forecasting, but also holds promise for sustainable urban energy management, enabling cities to make informed decisions in optimizing energy consumption and promoting a greener, more resilient future.

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