A power system restoration method using voltage source converter–high-voltage direct current technology, aided by time-series neural network with firefly algorithm

Shaik, Jan Bhasha; Ganesh, V.

doi:10.1007/s00500-019-04459-5

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…The function of the input layer is to transmit input messages to the hidden layer. The number of nodes is the same as the number of inputs [6]. The input and hidden link weights are 1.…”

Section: Structure Of Rbf Networkmentioning

confidence: 99%

Energy Efficiency Evaluation of HVDC Converter Station Based on Deep Neural Network Model

Rong

et al. 2023

Applied Mathematics and Nonlinear Sciences

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This paper introduces a new idea of energy efficiency evaluation of high voltage direct current (HVDC) converter based on deep neural networks. This paper analyzes the leading operating indicators and finds the main influencing factors in constructing the energy-saving hands of each converter table. Then this paper uses the deep neural network method to establish the converter table's energy efficiency model. The deep neural network for energy efficiency evaluation of the converter table is obtained. In this paper, PSCAD/EMTDC was used for simulation to evaluate the energy-saving effect. The research shows that the method of energy efficiency evaluation based on deep neural networks is feasible.

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“…The function of the input layer is to transmit input messages to the hidden layer. The number of nodes is the same as the number of inputs [6]. The input and hidden link weights are 1.…”

Section: Structure Of Rbf Networkmentioning

confidence: 99%

Energy Efficiency Evaluation of HVDC Converter Station Based on Deep Neural Network Model

Rong

et al. 2023

Applied Mathematics and Nonlinear Sciences

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“…Lagunes et al [91] used FA to optimize parameters of membership functions in type-2 fuzzy controllers. Neural networks combined with FA are adopted in [92] to restore a power network. FA integrated by bee colony algorithm is presented to find variables of a concrete damage model [93].…”

Section: Introductionmentioning

confidence: 99%

Development of a COVID-19 Patients’ Fatality Prediction System Using Swarm Intelligent Convolution Neural Network

Kareem

Odeniyi

Lawal

2023

AJRCoS

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Aims: This work aims to develop a system that can be used to accurately and timely predict the fatality of a positively tested COVID-19 patient through the use of a deep learning technique – a swarm intelligent convolutional neural network. Methodology: The dataset used in this study was acquired from the Kaggle repository database. The dataset contains the Lung Chest X-Ray images of COVID-19 patients. The images were pre- processed to obtain the desired image quality for further processing. This was followed by segmenting the pre-processed images. An Enhanced Firefly Algorithm (EFA) was formulated by applying the roulette wheel selection procedure to model the movement process of the firefly as a deterministic process to assist the standard Firefly Algorithm (FA) and application of Chaotic Sinusoidal Map Function to model the attractive process of the firefly which establishes a balance between exploration and exploitation in FA. The EFA was applied to optimize Convolution Neural Network (CNN) hyper-parameters (number of layers, number of filters per layer, filter size and batch size). The segmented result was subsequently presented to EFA-CNN feature extraction and prediction of COVID-19 patient fatality cases. The formulated deep learning models (EFA-CNN and CNN) were implemented using Matrix Laboratory 2020a software. The implemented models were evaluated using specificity, sensitivity, false positive rate, accuracy, and recognition time/rate to determine the performance of the developed models. Results: The findings revealed that the EFA-CNN model performs better in the prediction of COVID-19 patients’ fatality compared to the CNN model. It was also discovered that the formulated EFA applied to select optimal values of the hyper-parameters for the CNN architecture accounted for improved recognition accuracy and reduced recognition time of the developed COVID-19 Patients’ Fatality Prediction System. Conclusion: The system developed will assist both the government and healthcare workers in providing the needed computational capability for the prediction of the fatality level of a positively tested COVID-19 patient.

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Electrical and Electronics Metrology: From Quantum Standard to Applications in Industry and Strategic Sectors

Somkuwar

Singh

Siwach

et al. 2020

Metrology for Inclusive Growth of India

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A power system restoration method using voltage source converter–high-voltage direct current technology, aided by time-series neural network with firefly algorithm

Cited by 5 publications

References 33 publications

Energy Efficiency Evaluation of HVDC Converter Station Based on Deep Neural Network Model

Energy Efficiency Evaluation of HVDC Converter Station Based on Deep Neural Network Model

Development of a COVID-19 Patients’ Fatality Prediction System Using Swarm Intelligent Convolution Neural Network

Electrical and Electronics Metrology: From Quantum Standard to Applications in Industry and Strategic Sectors

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