A Novel Control Scheme for PV/WT/FC/Battery to Power Quality Enhancement in Micro Grid System: A Hybrid Technique

Rajesh, P.; Shajin, Francis H.; Umasankar, L.

doi:10.1080/15567036.2021.1943068

Cited by 44 publications

(41 citation statements)

References 27 publications

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“…55 The SOC of EV batteries may be modeled with logarithmic normal distribution and can be examined by utilizing logarithmic average (μ), SD (σ) of state of charge variables. 11 The parametric value E specifies the initialization of SOC EV batteries, which differ from 0 to 1. Figure 2 portrays the logarithmic normal distribution of state of charge utilized, where the parameters μ, σ are computed utilizing Equation (23).…”

Section: Electric Vehicle Demandmentioning

confidence: 99%

“…The SOC of EV batteries may be modeled with logarithmic normal distribution and can be examined by utilizing logarithmic average (

μ

), SD ( σ ) of state of charge variables 11 . The parametric value E specifies the initialization of SOC EV batteries, which differ from 0 to 1.…”

Section: Mathematical Modelling Of Ev Fast Charging Stationmentioning

confidence: 99%

“…The fast charging contains the demerits of huge power requirement along impact on the national power grid when compared to the sluggish charge. [10][11][12][13][14] To deal this issue, the wind, photovoltaic power, energy storage system (WPE) is utilized to offer safe and convenient charging service for EV in fast charging stations. 15 The utility for wind turbines (WT), photovoltaic (PV) energy technology and energy storage system (ESS) speedy electro voltaic station of charging cannot expenses and environmental pollution.…”

Section: Introductionmentioning

confidence: 99%

“…The fast EV charging stations are most required to help the EV industry development. The fast charging contains the demerits of huge power requirement along impact on the national power grid when compared to the sluggish charge 10‐14 . To deal this issue, the wind, photovoltaic power, energy storage system (WPE) is utilized to offer safe and convenient charging service for EV in fast charging stations 15 …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An optimization framework for capacity allocation and energy management of fast electric vehicle charging stations‐wind photovoltaic energy using artificial transgender longicorn algorithm

Arivalahan

Balaji

2022

Intl J of Energy Research

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Summary This paper proposes the optimal design for fast electric vehicle charging stations for the capacity configuration of components and the power scheduling strategy. The proposed approach is the artificial transgender longicorn algorithm, which is a simple and powerful controller. The optimal capacity of the system components and the optimal power scheduling approach are determined. First, an electric vehicle charging simulation model is built considering the demand response, which dynamically modified the charging expectation based on the electricity price at the time of use. Second, based on the system design, a multi‐objective optimization model is proposed with minimal objectives for the electricity cost and polluting emissions. Finally, a scenario analysis is carried out and it is concluded that renewable energy supplies, connection to the utility grid and demand response may help improve the performance of the optimization objectives. By then, the performance of the proposed technique is implemented in the MATLAB platform and compared with several existing approaches.

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Section: Electric Vehicle Demandmentioning

confidence: 99%

“…The SOC of EV batteries may be modeled with logarithmic normal distribution and can be examined by utilizing logarithmic average (

μ

), SD ( σ ) of state of charge variables 11 . The parametric value E specifies the initialization of SOC EV batteries, which differ from 0 to 1.…”

Section: Mathematical Modelling Of Ev Fast Charging Stationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An optimization framework for capacity allocation and energy management of fast electric vehicle charging stations‐wind photovoltaic energy using artificial transgender longicorn algorithm

Arivalahan

Balaji

2022

Intl J of Energy Research

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“…The indications of this AD diseases are severe in the last stage. If the AD diseases grow to its last stage, it destroys brain nerves systems 8–13 . When the brain functions are totally affected, it may leads to death.…”

Section: Introductionmentioning

confidence: 99%

Multiclass recognition of AD neurological diseases using a bag of deep reduced features coupled with gradient descent optimized twin support vector machine classifier for early diagnosis

Velliangiri

Pandiaraj

et al. 2022

Concurrency and Computation

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Alzheimer's disease (AD) is an advanced neurodegenerative disease of the brain that affects the nerve system of brain. Previously, several feature extraction and classification methods were discussed, but that methods provide high over fitting problem, which leads to minimization of detection accuracy. To overcome these issues, the multi class classification of AD diseases using bag of deep feature reduction technique and twin support vector machine classifier (TSVM) optimized with gradient decent optimizer is proposed in this manuscript for classifying the AD disease as severe AD, mild cognitive impairment, healthy control. At first, the input EEG signals are pre-processed. To decrease the execution time and processing time with feature size, a bag of deep features reduction technique is used. The reduced feature signals are classified by optimized TSVM. The simulation process is implemented in MAT-LAB environment. The proposed model achieves higher accuracy 33.84%, 28.93%, 33.03%, 27.93%, higher precision 22.87%, 16.97%, 16.97%, and 36.97%, compared with the existing methods, such as piecewise aggregate approximation support vector machine (MCC-EEG-PAA-SVM), convolutional neural network (MCC-EEG-CNN), conformal kernel-based fuzzy support vector machine (MCC-EEG-CKF-SVM), Pearson correlation coefficient-based feature selection strategy with linear discriminant analysis classifier (MCC-EEG-PCC-LDA). K E Y W O R D S bag of deep features (BoDrF) reduction, gradient decent optimizer, multi class classification, twin support vector machine classifier INTRODUCTIONAlzheimer's disease is a permanent neurodegenerative disease and the most predominant form of dementia. [1][2][3][4] The initial symptoms of the AD disease contain disremembering the current actions. When the disease grows, a person who affected by Alzheimer's disease also grows severe memory impairment and drops the capability to carry everyday jobs. [5][6][7] AD caused difficult to absorb as well as thinking. The indications of this AD diseases are severe in the last stage. If the AD diseases grow to its last stage, it destroys brain nerves systems. [8][9][10][11][12][13] When the brain functions are totally affected, it may leads to death. Presently, more than 46.8 million peoples affected dementia in the world, it is predictable to spread 74.7 million in 2030. [14][15][16]

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Energy Economics and Environment

Sanjeevikumar

Nasab

Zand

et al. 2023

Integrated Green Energy Solutions Volume 2

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A Novel Control Scheme for PV/WT/FC/Battery to Power Quality Enhancement in Micro Grid System: A Hybrid Technique

Cited by 44 publications

References 27 publications

An optimization framework for capacity allocation and energy management of fast electric vehicle charging stations‐wind photovoltaic energy using artificial transgender longicorn algorithm

An optimization framework for capacity allocation and energy management of fast electric vehicle charging stations‐wind photovoltaic energy using artificial transgender longicorn algorithm

Multiclass recognition of AD neurological diseases using a bag of deep reduced features coupled with gradient descent optimized twin support vector machine classifier for early diagnosis

Energy Economics and Environment

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