An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine

Mahmoud, Tawfek; Zhang, Dong; Ma, Jin

doi:10.1016/j.renene.2018.03.035

Cited by 83 publications

(26 citation statements)

References 51 publications

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“…Adopting data-driven solutions is therefore a good method to reduce the cost of the system, and it is desirable to design effective data-driven condition monitoring based on the temperature signal [4,15]. In previous research, artificial intelligence (AI) methods, such as artificial neural networks (ANNs) [16], have been utilized for renewable energy systems because AI methods can accommodate the random and non-stationary properties of the nonlinear models [17,18]. Generally, AI based on machine learning methods can be classified into two subsystems, namely conventional machine learning models and deep learning models.…”

Section: Introductionmentioning

confidence: 99%

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

et al. 2019

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Effective intelligent condition monitoring, as an effective technique to enhance the reliability of wind turbines and implement cost-effective maintenance, has been the object of extensive research and development to improve defect detection from supervisory control and data acquisition (SCADA) data, relying on perspective signal processing and statistical algorithms. The development of sophisticated machine learning now allows improvements in defect detection from historic data. This paper proposes a novel condition monitoring method for wind turbines based on Long Short-Term Memory (LSTM) algorithms. LSTM algorithms have the capability of capturing long-term dependencies hidden within a sequence of measurements, which can be exploited to increase the prediction accuracy. LSTM algorithms are therefore suitable for application in many diverse fields. The residual signal obtained by comparing the predicted values from a prediction model and the actual measurements from SCADA data can be used for condition monitoring. The effectiveness of the proposed method is validated in the case study. The proposed method can increase the economic benefits and reliability of wind farms.

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Section: Introductionmentioning

confidence: 99%

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

et al. 2019

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“…The approach based on Machine Learning (ML) could be applied in other cases studies, e.g. electrical grid by using extreme learning machines and self-adaptive evolutionary extreme learning machines to directly modelling prediction intervals [45]. ML in WTB has been employed in SHM [46].…”

Section: Recently Non-linear Models Such As Hierarchical Non-linearmentioning

confidence: 99%

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

et al. 2019

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The mass of ice on wind turbines blades is one of the main problems that energy companies have in cold climates. This paper presents a novel approach to detect and classify ice thickness based on pattern recognition through guided ultrasonic waves and Machine Learning. To successfully achieve a supervised classification, it is necessary to employ a method that allows the correct extraction and selection of features of the ultrasonic signal. The main novelty in this work is that the approach considers four feature extraction methods to validate the results, grouped by linear (AutoRegressive (AR) and Principal Component Analysis) and nonlinear (nonlinear-AR eXogenous and Hierarchical Non-Linear Principal Component Analysis), and feature selection is done by Neighbourhood Component Analysis. A supervised classification was performed through Machine Learning with twenty classifiers such as Decision tree, Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, and Ensemble Classifiers. Finally, an evaluation of the classifiers was done in single frequency and multi-frequency modes, obtaining accurate results.

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“…Renewable energy (specifically energy harvesting), acting as an alternative of fossil fuels, is one of the directions to fight against global warming. Typical sources are solar [16,17] and wind [18,19]. Focuses are basically on the reliability and efficiency of energy harvesting.…”

Section: Trends and Future Developmentmentioning

confidence: 99%

The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Lytras

Chui

2019

Energies

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Human beings share the same community in which the usage of energy by fossil fuels leads to deterioration in the environment, typically global warming. When the temperature rises to the critical point and triggers the continual melting of permafrost, it can wreak havoc on the life of animals and humans. Solutions could include optimizing existing devices, systems, and platforms, as well as utilizing green energy as a replacement of non-renewable energy. In this special issue "Artificial Intelligence for Smart and Sustainable Energy Systems and Applications", eleven (11) papers, including one review article, have been published as examples of recent developments. Guest editors also highlight other hot topics beyond the coverage of the published articles.

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An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine

Cited by 83 publications

References 51 publications

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

A Novel Condition Monitoring Method of Wind Turbines Based on Long Short-Term Memory Neural Network

Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis

The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

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