A survey of artificial neural network in wind energy systems

Marugán, Alberto Pliego; Márquez, Fausto Pedro García; Pérez, Jesús María Pinar; Ruiz-Hernández, Diego

doi:10.1016/j.apenergy.2018.07.084

Cited by 385 publications

(150 citation statements)

References 156 publications

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“…Within this category, an artificial neural network (ANN) is one of many promising choices, as it has been shown to serve as a universal approximator of any nonlinear relations [11,12]. Depending on its expected outcomes, different types of ANNs have been applied to a variety of disciplines, including those related to applied thermal engineering: from predicting solar radiation [13] and wind speed [14] to forecast of pressure drop in heat exchangers [15].…”

Section: Introductionmentioning

confidence: 99%

On the prediction of critical heat flux using a physics-informed machine learning-aided framework

Zhao

Shirvan

Salko

et al. 2020

Applied Thermal Engineering

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The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is essential to the design and safety of a two-phase flow boiling system. Despite the abundance of predictive tools available to the thermal engineering community, the path for an accurate, robust CHF model remains elusive due to lack of consensus on the DNB triggering mechanism. This work aims to apply a physics-informed, machine learning (ML)-aided hybrid framework to achieve superior predictive capabilities. Such a hybrid approach takes advantage of existing understanding in the field of interest (i.e., domain knowledge) and uses ML to capture undiscovered information from the mismatch between the actual and domain knowledge-predicted target. A detailed case study is carried out with an extensive DNB-specific CHF database to demonstrate (1) the improved performance of the hybrid approach as compared to traditional domain knowledge-based models, and (2) the hybrid model's superior generalization capabilities over standalone ML methods across a wide range of flow conditions. The hybrid framework could also readily extend its applicability domain and complexity on the fly, showing an elevated level of flexibility and robustness. Based on the case study conclusions, the window-type extrapolation mapping methodology is further proposed to better inform high-cost experimental work. Keywords: critical heat flux, departure from nucleate boiling, hybrid framework, machine learning, domain knowledge. API application programming interface CHF critical heat flux DK domain knowledge DNB departure from nucleate boiling EPRI Electric Power Research Institute LUT look-up table MAE mean absolute error ML machine learning MSE mean squared error NN (feed-forward) neural network PWR pressurized water reactor ReLU rectified linear unit RF random forest rRMSE relative root-mean-square error

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Section: Introductionmentioning

confidence: 99%

On the prediction of critical heat flux using a physics-informed machine learning-aided framework

Zhao

Shirvan

Salko

et al. 2020

Applied Thermal Engineering

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“…With the advantages of non-pollution, low costs and remarkable benefits of scale, wind power is considered as one of the most important sources of energy [206]. ANN have been widely employed for processing large amounts of data obtained from data acquisition systems of wind turbines [207]. In recent years, many approaches based on DL architectures have been proposed for the prediction of the power output of wind power systems.…”

Section: E Power Systemsmentioning

confidence: 99%

A review of deep learning with special emphasis on architectures, applications and recent trends

Sengupta

Basak

Saikia

et al. 2020

Knowledge-Based Systems

302

143

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Deep learning has solved a problem that as little as five years ago was thought by many to be intractable -the automatic recognition of patterns in data; and it can do so with an accuracy that often surpasses that of human beings. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners trying to make sense out of the flood of data that now inundates our society. As public awareness of the efficacy of deep learning increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge produced by experts in the field. Where does one start? How does one determine if a particular Deep Learning model is applicable to their problem? How does one train and deploy such a network? A primer on the subject can be a good place to start. With that in mind, we present an overview of some of the key multilayer artificial neural networks that comprise deep learning. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is becoming critical to many computer applications, we include a section on using neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology: anomalous behavior detection in financial applications or in financial time-series forecasting, predictive and prescriptive analytics, medical image processing and analysis and power systems research. The thrust of this review is to outline emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to use deep learning in their work for what it does best: statistical pattern recognition with unparalleled learning capacity with the ability to scale with information.

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“…With the advantages of non-pollution, low costs and remarkable benefits of scale, wind power is considered as one of the most important sources of energy [185]. ANN have been widely employed for processing large amounts of data obtained from data acquisition systems of wind turbines [186]. In recent years, many approaches based on DL architectures have been proposed for the prediction of the power output of wind power systems.…”

Section: F Power Systemsmentioning

confidence: 99%

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

Sengupta¹,

Basak²,

Saikia³

et al. 2019

Preprint

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Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perception about the efficacy of deep neural architectures in complex pattern recognition tasks grows, sequentially up-to-date primers on the current state of affairs must follow. In this review, we seek to present a refresher of the many different stacked, connectionist networks that make up the deep learning architectures followed by automatic architecture optimization protocols using multi-agent approaches. Further, since guaranteeing system uptime is fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale with information.

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A survey of artificial neural network in wind energy systems

Cited by 385 publications

References 156 publications

On the prediction of critical heat flux using a physics-informed machine learning-aided framework

On the prediction of critical heat flux using a physics-informed machine learning-aided framework

A review of deep learning with special emphasis on architectures, applications and recent trends

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

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