IDM based on image classification with CNN

Manikonda, Santhosh K G; Gaonkar, D. N.

doi:10.1049/joe.2019.0025

Cited by 28 publications

(17 citation statements)

References 38 publications

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“…On the other hand, CNN is a locally connected network [20]. CNN's algorithm has a convolution layer consisting of a set of filters that can be learned, often referred to as kernels [21]. In this study, we used the CNN algorithm to classify apple species.…”

Section: 𝑓(X) = X • W + 𝑏mentioning

confidence: 99%

Efficient Fruits Classification Using Convolutional Neural Network

Abidin¹,

Hamzah²,

Endah³

2021

ijicom

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Classification of fruits is a growing research topic in image processing. Various papers propose various techniques to deal with the classification of apples. However, some traditional classification methods remain drawbacks to producing an effective result with the big dataset. Inspired by deep learning in computer vision, we propose a novel learning method to construct a classification model, which can classify types of apples quickly and accurately. To conduct our experiment, we collect datasets, do preprocessing, train our model, tune parameter settings to get the highest accuracy results, then test the model using new data. Based on the experimental results, the classification model of green apples and red apples can obtain good accuracy with little loss. Therefore, the proposed model can be a promising solution to deal with apple classification.

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Section: 𝑓(X) = X • W + 𝑏mentioning

confidence: 99%

Efficient Fruits Classification Using Convolutional Neural Network

Abidin¹,

Hamzah²,

Endah³

2021

ijicom

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“…Islanding detection methods range from active [11] to passive [12] to intelligence-based methods [13][14][15]. Active methods are fast-acting and possess a lot of significant advantages but then degrade power quality.…”

Section: Introductionmentioning

confidence: 99%

An image classification based approach for islanding detection in a sustainable distributed generation system using convolutional neural network

Adenugba

Orovwode

Ademola

2022

IOP Conf. Ser.: Earth Environ. Sci.

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In today’s power system, distributed generation (DG) penetration level has increased to match the ever-increasing demand for energy. DG integration introduces peculiar challenges to the entire power system. Due to the increased DG penetration, these challenges have become technically and economically very important. Effective and fast islanding detection is necessary to prevent power quality degradation, equipment loss, and human life loss. In this study, an islanding detection model based on convolutional neural network (CNN) is proposed. The method utilizes the ability of CNN to perform accurate image classification. It identifies islanding by classifying scalogram images obtained by applying wavelet transform to the concatenated voltage waveforms for each event. The islanding detection model is trained with well-processed images to improve classification accuracy. Noise is incorporated into the data to investigate the susceptibility of the method to noise. The results obtained prove that the proposed islanding detection model can handle the problem of islanding detection.

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“…13,14 Under grid-connected mode (GCM) the response of the injected signal will be negligible, and the system operation will not get affected while under islanded mode (IM), this response will display a large change in operating parameters due to loss of mains. 15 Even though these techniques have a very small or almost zero NDZ region, 16–19 whenever applied to the system, the power quality gets degraded because of the signal introduced into the system. 19…”

Section: Introductionmentioning

confidence: 99%

“…15 Even though these techniques have a very small or almost zero NDZ region, 16–19 whenever applied to the system, the power quality gets degraded because of the signal introduced into the system. 19…”

Section: Introductionmentioning

confidence: 99%

Wide area monitoring, protection, and control application in islanding detection for grid integrated distributed generation: A review

Tshenyego

Samikannu

Mtengi

2021

Measurement and Control

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The assimilation of Distributed Generation (DG) into the electric power system (EPS) has become more attractive as the world is following a trend to reduce greenhouse gas emissions by introducing more renewable energy forms resulting in high penetration scenarios. This high penetration of DGs brings several challenges to the protection philosophy of the EPS which compromises its reliability, availability, and efficiency. Under high DG penetration scenarios, conventional islanding detection methods (Idms) fail to detect an island as the grid loses its inertia to leverage a significant frequency and voltage mismatch necessary for Idms to effectively detect an islanding event. This has given rise to the birth of Artificial Intelligent (AI) methods that are found to perform better in islanding detection. AI Idms are computationally intensive and require a lot of data to operate accurately. Because the computational burden of these methods requires fast computing hardware, the current trend of AI Idms are integrated with Wide Area Monitoring, Protection, and Control (WAMPAC) system. This paper aims at reviewing all these Idms and the WAMPAC’s system latency when hosting AI Idms which are currently the best in islanding detection. This is done to determine if the WAMPAC system latency plus Idms computational time meet the islanding detection time specified by the IEEE Standard 1547 framework.

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IDM based on image classification with CNN

Cited by 28 publications

References 38 publications

Efficient Fruits Classification Using Convolutional Neural Network

Efficient Fruits Classification Using Convolutional Neural Network

An image classification based approach for islanding detection in a sustainable distributed generation system using convolutional neural network

Wide area monitoring, protection, and control application in islanding detection for grid integrated distributed generation: A review

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