RETRACTED ARTICLE: Optimized deep learning neural network model for doubly fed induction generator in wind energy conversion systems

Rajasingam, N.; Rasi, D.; Deepa, S. N.

doi:10.1007/s00500-019-03947-y

Cited by 12 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mathematical modeling of a hybrid power system is presented in this research, and the controller design is realized using a new deep learning technique. The variablespeed power generator torque is governed using a PID controller within the proposed method [82]. The PID controller's gains are fine-tuned using a deep learning model.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

et al. 2022

View full text Add to dashboard Cite

Nowadays, learning-based modeling methods are utilized to build a precise forecast model for renewable power sources. Computational Intelligence (CI) techniques have been recognized as effective methods in generating and optimizing renewable tools. The complexity of this variety of energy depends on its coverage of large sizes of data and parameters, which have to be investigated thoroughly. This paper covered the most resent and important researchers in the domain of renewable problems using the learning-based methods. Various types of Deep Learning (DL) and Machine Learning (ML) algorithms employed in Solar and Wind energy supplies are given. The performance of the given methods in the literature is assessed by a new taxonomy. This paper focus on conducting comprehensive state-of-the-art methods heading to performance evaluation of the given techniques and discusses vital difficulties and possibilities for extensive research. Based on the results, variations in efficiency, robustness, accuracy values, and generalization capability are the most obvious difficulties for using the learning techniques. In the case of the big dataset, the effectiveness of the learning techniques is significantly better than the other computational methods. However, applying and producing hybrid learning techniques with other optimization methods to develop and optimize the construction of the techniques is optionally indicated. In all cases, hybrid learning methods have better achievement than a single method due to the fact that hybrid methods gain the benefit of two or more techniques for providing an accurate forecast. Therefore, it is suggested to utilize hybrid learning techniques in the future to deal with energy generation problems.

show abstract

Section: Deep Learning Techniquesmentioning

confidence: 99%

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

et al. 2022

View full text Add to dashboard Cite

show abstract

“…There are only three components of the HSI colour space that aren't important (I). The use of hue information in colour image segmentation has increased since it is more accurate and human vision is closer to the HSI colour space than RGB [12][13][14]. Segmentation precision is therefore critical to the performance of a higherclass dispensation scheme, where segmentation methods include extraction of regions and feature clusters.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

YOLO Based Deep Learning Model for Segmenting the Color Images

Rasi¹,

AntoBennet

Renjith

et al. 2023

IJEER

Self Cite

View full text Add to dashboard Cite

The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation. This study seeks to build a light-weight for object detector that uses a depth and colour image from a publically available dataset to identify objects in a scene. It's likely to output in the depth way by expanding the YOLO network's network architecture. Using Taylor based Cat Salp Swarm algorithm (TCSSA), the weight of the suggested model is modified to improve the accuracy of region extraction findings. It is possible to test the detector's efficacy by comparing it to various datasets. Testing showed that the suggested model is capable of segmenting input into multiple metrics using bounding boxes. The results shows that the proposed model achieved 0.20 of Global Consistency Error (GCE) and 1.85 of Variation of Information (VOI) on BSDS500 dataset, where existing techniques achieved nearly 1.96 to 1.86 of VOI and 0.25 to 0.22 of GCE for the same dataset.

show abstract

Regulated Jordan—Elman Neural Network-Based Controller Model for Grid-Connected Wind Energy Conversion Systems

Deepa

Rajasingam

2020

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

RETRACTED ARTICLE: Optimized deep learning neural network model for doubly fed induction generator in wind energy conversion systems

Cited by 12 publications

References 23 publications

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques

YOLO Based Deep Learning Model for Segmenting the Color Images

Regulated Jordan—Elman Neural Network-Based Controller Model for Grid-Connected Wind Energy Conversion Systems

Contact Info

Product

Resources

About