Wind Power Curve Modeling With Hybrid Copula and Grey Wolf Optimization

Wei, Danxiang; Wang, Jianzhou; Li, Zhiwu; Wang, Rui

doi:10.1109/tste.2021.3109044

Cited by 37 publications

(5 citation statements)

References 33 publications

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“…The Grey Wolf Optimizer (GWO) algorithm is used to obtain the optimum CNN architecture to achieve the best classification result. The GWO algorithm is used in solving optimization problems [20][21][22][23] and improving other metaheuristic optimization algorithms [24][25][26][27][28][29]. It is preferred for hyperparameter optimization of the CNN architecture.…”

Section: Structurementioning

confidence: 99%

A Hyperparameter Optimization for Galaxy Classification

Şenel¹

2023

Computers, Materials &Amp; Continua

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In this study, the morphological galaxy classification process was carried out with a hybrid approach. Since the Galaxy classification process may contain detailed information about the universe's formation, it remains the current research topic. Researchers divided more than 100 billion galaxies into ten different classes. It is not always possible to understand which class the galaxy types belong. However, Artificial Intelligence (AI) can be used for successful classification. There are studies on the automatic classification of galaxies into a small number of classes. As the number of classes increases, the success of the used methods decreases. Based on the literature, the classification using Convolutional Neural Network (CNN) is better. Three metaheuristic algorithms are used to obtain the optimum architecture of CNN. These are Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. A CNN architecture with nine hidden layers and two full connected layers was used. The number of neurons in the hidden layers and the fully connected layers, the learning coefficient and the batch size values were optimized. The classification accuracy of my model was 85%. The best results were obtained using GWO. Manual optimization of CNN is difficult. It was carried out with the help of the GWO meta-heuristic algorithm.

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Section: Structurementioning

confidence: 99%

A Hyperparameter Optimization for Galaxy Classification

Şenel¹

2023

Computers, Materials &Amp; Continua

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“…where F x and F y denote the marginal distributions and H denotes the full bivariate distribution. A delicate elaboration of this approach can be found in [55,78,104,105].…”

Section: (E) Artificial Neural Network (Ann)mentioning

confidence: 99%

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

et al. 2022

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In the wind energy industry, the power curve represents the relationship between the “wind speed” at the hub height and the corresponding “active power” to be generated. It is the most versatile condition indicator and of vital importance in several key applications, such as wind turbine selection, capacity factor estimation, wind energy assessment and forecasting, and condition monitoring, among others. Ensuring an effective implementation of the aforementioned applications mostly requires a modeling technique that best approximates the normal properties of an optimal wind turbines operation in a particular wind farm. This challenge has drawn the attention of wind farm operators and researchers towards the “state of the art” in wind energy technology. This paper provides an exhaustive and updated review on power curve based applications, the most common anomaly and fault types including their root-causes, along with data preprocessing and correction schemes (i.e., filtering, clustering, isolation, and others), and modeling techniques (i.e., parametric and non-parametric) which cover a wide range of algorithms. More than 100 references, for the most part selected from recently published journal articles, were carefully compiled to properly assess the past, present, and future research directions in this active domain.

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“…Moreover, wind power output has significant non-stationary, stochastic, and intermittent characteristics, which pose serious challenges to the safe and reliable operation of the power system (Jia et al, 2023). Therefore, the personnel of the system operation center will take "wind abandonment" measures to ensure the reliability and safety of the power system operation (Wei et al, 2022;Zhu et al, 2023). The measures can lead to energy waste, which is not conducive to improving new energy consumption and reducing the competitiveness of new power systems containing wind power (Liu et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Wind power output prediction: a comparative study of extreme learning machine

Wang,

Niu

2023

Front. Energy Res.

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This study aims to propose a wind power prediction method that achieves high accuracy in order to minimize the impact of wind power on the power system and reduce scheduling difficulties in systems incorporating wind power. The importance of developing renewable energy has been recognized by society due to the increasing severity of the energy crisis. Wind energy offers advantages such as efficiency, cleanliness, and ease of development. However, the random nature of wind energy poses challenges to power systems and complicates the scheduling process. Therefore, accurate wind power prediction is of utmost importance. A wind power prediction model was constructed based on an improved tunicate swarm algorithm–extreme learning machine (ITSA-ELM). The improved tunicate swarm algorithm (ITSA) optimizes the random parameters of extreme learning machine (ELM), resulting in the best prediction performance. ITSA is an enhancement of the tunicate swarm algorithm (TSA), which introduces a reverse learning mechanism, a non-linear self-learning factor, and a Cauchy mutation strategy to address the drawbacks of poor convergence and susceptibility to local optima in TSA. Two different scenarios were used to verify the effectiveness of ITSA-ELM. The results showed that ITSA-ELM has a decrease of 1.20% and 21.67% in MAPE, compared with TSA-ELM, in May and December, respectively. This study has significant implications for promoting the development of renewable energy and reducing scheduling difficulties in power systems.

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Wind Power Curve Modeling With Hybrid Copula and Grey Wolf Optimization

Cited by 37 publications

References 33 publications

A Hyperparameter Optimization for Galaxy Classification

A Hyperparameter Optimization for Galaxy Classification

Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review

Wind power output prediction: a comparative study of extreme learning machine

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