Multiobjective optimization-based energy management system considering renewable energy, energy storage systems, and electric vehicles

Kafazi, Ismail El; Bannari, Rachid; Azar, Ahmad Taher

doi:10.1016/b978-0-12-820004-9.00016-4

Cited by 5 publications

(5 citation statements)

References 67 publications

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“…Robustness is commonly characterised as the bare minimum that a control system must meet in order to be usable in a real situation. Once created, the controller's settings do not alter, and control performance is assured (Ahmed et al, 2023a(Ahmed et al, ,b, 2022aSergiyenko et al, 2023 ;Kengne et al, 2023a,b ;Hashim et al, 2023 ;Hameed et al, 2023 ;Fekik et al, 2023a,b ;Zhang et al, 2023 ;Wang et al, 2023 ;Dendani et al, 2023 ;Toumi et al, 2022 ;Mahdi et al 2022 ;Ali el al., 2022a,b ;Abdul-Kareem et al, 2022;Abed et al, 2022;Sekhar et al, 2022 ;Saidi et al, 2022;Al Mhdawi et al, 2022 ;Lajouad et al, 2021;El Kafazi et al, 2021 ;Sundaram et al, 2021 ;Tolba et al, 2017a,b ;Soliman et al, 2017Soliman et al, , 2020Wang et al, 2017;Humaidi et al 2022Humaidi et al , 2021Humaidi et al , 2020Djeddi et al, 2019;Shukla et al, 2018;Khettab et al, 2018;Vaidyanathan et al, 2021aVaidyanathan et al, ,b,c,d,e, 2019Vaidyanathan et al, , 2018aVaidyanathan et al, ,b,c,d, 2017aVaidyanathan et al, ,b,c, 2015aVaidyanathan and Azar, 2021, 2016a,b,c,d,e,f, 2015aSantoro et al, 2013).…”

Section: Robustness Analysismentioning

confidence: 99%

A Design and Implementation of a New Control Based on Petri Nets for Three Phase PWM-Rectifier

Fekik

Hamida

Kais

et al. 2023

International Journal of Service Science, Management, Engineering, and Technology

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This article introduces a novel and effective diagram based on direct instantaneous power control (DPC) of a PWM-controlled rectifier connected to the grid without a switching table. An optimum control vector of the PWM rectifier's input voltage, which depends on the switching states determined by a Petri nets controller, is adopted. This approach limits the instantaneous detection errors of reactive and active powers, maintains the DC bus voltage at a reference level, and ensures current close to a sinusoidal wave, guaranteeing operation at a unit power factor. The instantaneous tracking errors of active and reactive powers and the angular position of the voltage are used as input variables for the proposed controller, which then selects the best control vector for the converter based on the transition of a Petri net. The significant advantages of DPC based on Petri nets compared to traditional switching tables are that hysteresis comparators are not required, and the classical regulation of active and reactive powers is achieved in all sectors. Simulation and testing findings demonstrated excellent performance, supporting the viability of the suggested control approach using Petri nets.

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Section: Robustness Analysismentioning

confidence: 99%

A Design and Implementation of a New Control Based on Petri Nets for Three Phase PWM-Rectifier

Fekik

Hamida

Kais

et al. 2023

International Journal of Service Science, Management, Engineering, and Technology

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“…Deep learning technology, which evolved from Artificial Neural Networks (ANN), has become a major issue in the computer world and is widely used in fields such as healthcare, image identification, text analytics, cybersecurity, and many more (Dudekula et al, 2023;Fati et al, 2022;Boulmaiz et al, 2022, Zaidi et al 2022Ganesan et al, 2022;Abbas et al, 2022;Azar et al, 2021a,b;Ibrahim et al, 2020;Ramadan et al, 2022;Aslam et al, 2021). Machine Learning (ML) is an artificial intelligence subset that generates dynamic algorithms capable of making data-driven judgments (Hussain et al, 2023;Atteia et al, 2023;Salam et al, 2021Salam et al, , 2022Mathiyazhagan et al, 2022;Ashfaq et al, 2022a,b;Inbarani et al, 2022Inbarani et al, , 2020Inbarani et al, , 2018Inbarani et al, , 2015aInbarani et al, ,b, 2014aFekik et al, 2021Fekik et al, , 2018aEl Kafazi et al, 2021;Sundaram et al, 2021;Hussien et al, 2020;Mjahed et al, 2020 ;Sayed et al, 2019;Aboamer et al, 2019Aboamer et al, , 2014aSallam et al, 2020;Kumar et al, 2017Banu et al, 2017Banu et al, , 2014Ben Abdallah et al, 2016Fredj et al, 2016 ;Malek and Azar, 2016a,b;Malek et al, 2015a,b;…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Nivetha

Inbarani

2023

International Journal of Sociotechnology and Knowledge Development

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Coronavirus sickness (COVID-19) recently adversely disrupted the medical care system and the entire economy. Doctors, researchers, and specialists are working on new-fangled methods to detect COVID-19 relatively efficiently, such as constructing computerized COVID-19 detection systems. Medical imaging, such as Computed Tomography (CT), has a lot of opportunity as a solution to RT-PCR approaches for quantitative assessment and disease monitoring. COVID-19 diagnosis based on CT images can provide speedy and accurate results. A quantitative criterion for diagnosis is provided by an automated segmentation method of infection areas in the lungs. As an outcome, automatic image segmentation is in high demand as a clinical decision aid tool. To detect COVID-19, Computed Tomography images might be employed instead of the time-consuming RT-PCR assay. In this research, a unique technique is provided for segmenting infection areas in the lungs using CT scan images from COVID-19 patients. “Ground Glass Opacity (GGO)” regions were detected using Novel Adaptive Histogram Binning Based Lesion Segmentation (NAHBLS) method. Many metrics were also employed to evaluate the proposed method, including “Sorensen–Dice similarity”, “Sensitivity”, “Specificity”, “Precision”, and “Accuracy” measures. Experiments have shown that the proposed method can effectively separate the lung infections with good accuracy. The results show that the proposed Novel Adaptive Histogram Binning Based Lesion Segmentation based on automatic approach is effective at segmenting the lesion region of the image and calculated the Infection Rate (IR) over the lung region in Computed Tomography scan.

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“…Machine Learning (ML) is a subset of artificial intelligence that creates dynamic algorithms capable of generating data-driven decisions (Hussain et al, 2023;Atteia et al, 2023;Salam et al, 2021Salam et al, , 2022Mathiyazhagan et al, 2022;Ashfaq et al, 2022a,b;Fekik et al, 2021Fekik et al, , 2018aEl Kafazi et al, 2021;Bouakrif et al, 2019;Sundaram et al, 2021;Hussien et al, 2020;Mjahed et al, 2020 ;Sayed et al, 2019;Aboamer et al, 2019Aboamer et al, , 2014aSallam et al, 2020;Kumar et al, 2017Banu et al, 2017Banu et al, , 2014Ben Abdallah et al, 2016Fredj et al, 2016 ;Malek and Azar, 2016a,b;Vaidyanathan & Azar, 2016;Zhu & Azar, 2015;Malek et al, 2015a,b;Ding et al, 2015;Elshazly et al, 2013a,b,c).…”

Section: Deep Trs Architecture (Dtrsa)mentioning

confidence: 99%

Novel Architecture for Image Classification Based on Rough Set

Nivetha

Inbarani

2023

International Journal of Service Science, Management, Engineering, and Technology

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The Computed Tomography (CT) scan images classification problem is one of the most challenging problems in recent years. Different medical treatments have been developed based on the correctness of CT scan images classification. In this work, a novel deep learning architecture is proposed to correctly diagnose COVID-19 patients using CT scan images. In fact, a new classifier based on rough set theory is suggested. Extensive experiments showed that the novel deep learning architecture provides a significant improvement over well-known classifier. The new classifier produces 95% efficiency and a very low error rate on different metrics. The suggested deep learning architecture coupled with novel tolerance outperforms the other standard classification approaches for the detection of COVID-19 using CT-Scan images.

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Multiobjective optimization-based energy management system considering renewable energy, energy storage systems, and electric vehicles

Cited by 5 publications

References 67 publications

A Design and Implementation of a New Control Based on Petri Nets for Three Phase PWM-Rectifier

A Design and Implementation of a New Control Based on Petri Nets for Three Phase PWM-Rectifier

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Novel Architecture for Image Classification Based on Rough Set

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