Towards Latency Bypass and Scalability Maintain in Digital Substation Communication Domain with IEC 62439-3 Based Network Architecture

Tightiz, Lilia; Yoo, Joon

doi:10.3390/s22134916

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…In [40], a new energy storage system based on pumped hydro energy storage (PHES) is proposed for an integrated microgrid, and the accuracy and reliability of the LFC in the investigated system are calculated. Te authors of [41,42] discuss virtual inertia simulation based on energy storage system derivatives and its efect on power system frequency control. An efcient optimization strategy called the confict-based Volleyball Premier League (OVPL) algorithm optimizes the required energy storage system (ESS) and controller parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

Providing a Control System for Charging Electric Vehicles Using ANFIS

Mahdavi,

Samavat,

Javanmardi

et al. 2024

International Transactions on Electrical Energy Systems

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Frequency control, especially when incorporating distributed generation units such as wind and solar power plants, is crucial for maintaining grid stability. To address this issue, a study proposes a method for controlling the connection status of electric vehicles (EVs) to prevent frequency fluctuations. The method utilizes an adaptive neural-fuzzy inference system (ANFIS) and a whale optimization algorithm to regulate the charging or discharging of EV batteries based on frequency fluctuations. The objective is to minimize and adjust the frequency fluctuations to zero. The proposed method is evaluated using a real microgrid composed of a wind power plant, a solar power plant, a diesel generator, a large household load, an industrial load, and 711 electric vehicles. The ANFIS system serves as the primary controller, taking inputs such as electric vehicle and battery status and generating outputs that determine the charging or discharging of the electric vehicles. Several investigations are conducted to assess the effectiveness of this model, and the results obtained are compared with the normal state where electric vehicles only consume power. By implementing this method, it is expected that the connection status of electric vehicles can be optimized to help stabilize the grid and minimize frequency fluctuations caused by the integration of distributed renewable energy sources. This study highlights the importance of automatic frequency control in smart grids and offers a potential solution using ANFIS and the whale optimization algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Providing a Control System for Charging Electric Vehicles Using ANFIS

Mahdavi,

Samavat,

Javanmardi

et al. 2024

International Transactions on Electrical Energy Systems

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“…An important blot to the definition was made by economists. They indicated the presence in the material world of material, documentary evidence of the existence of an intangible asset (for example, in the form of a document), which is one of the criteria for defining an intangible object as an asset (Tightiz & Yoo, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Relevant Problems of Intangible Asset Impairment in The Russian IT Industry

Prodanova,

Tarasova,

Volobuev

et al. 2023

Br. J. Ed., Tech. Soc.

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Resumo: A indústria de TI está evoluindo rapidamente e começando a penetrar em todas as áreas da vida. As grandes empresas estão a formar ecossistemas que lhes permitem expandir e diversificar significativamente as suas fontes de rendimento. No caso da indústria de TI, o desenvolvimento da base de software para a implementação de tais ecossistemas depende diretamente de novos produtos de software, patentes, tecnologias e know-how, que na contabilidade moderna são maioritariamente classificados como ativos intangíveis. Assim, examinar o impacto de fatores externos e internos na imparidade deste tipo de ativos é uma questão de grande relevância na conjuntura atual. Este artigo se concentrará nos ativos intangíveis das principais empresas russas de TI, como Yandex e VK.Group. O estudo inclui uma análise da literatura relevante e do quadro regulamentar sobre imparidade de ativos intangíveis, bem como um exame da dinâmica do desempenho da demonstração da posição financeira do Yandex e do VK.Group relacionada com esta questão. A pesquisa pretende delinear as especificidades da redução ao valor recuperável de ativos intangíveis na indústria e as questões que surgem durante esse processo. Com base nos resultados, as empresas de TI ampliaram sua base de ativos devido à expansão ativa de serviços e fontes de renda. À medida que a Yandex se expandiu, a sua quota de ativos intangíveis aumentou, enquanto o VK.Group reduziu a sua quota de ativos intangíveis.

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“…To define this potential risk, it is assumed that additional information can be recovered from the training dataset if the pictures are altered in various ways. This is known as data augmentation and may replicate a broader representation of images of fruits and vegetables, preventing probable overfitting to the training dataset [ 36 ]. How does one determine which data augmentation approach to employ?…”

Section: Data Collection and Processingmentioning

confidence: 99%

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

Mukhiddinov

Muminov

2022

Sensors

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Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is challenging. This study presents a deep-learning system for multiclass fruit and vegetable categorization based on an improved YOLOv4 model that first recognizes the object type in an image before classifying it into one of two categories: fresh or rotten. The proposed system involves the development of an optimized YOLOv4 model, creating an image dataset of fruits and vegetables, data argumentation, and performance evaluation. Furthermore, the backbone of the proposed model was enhanced using the Mish activation function for more precise and rapid detection. Compared with the previous YOLO series, a complete experimental evaluation of the proposed method can obtain a higher average precision than the original YOLOv4 and YOLOv3 with 50.4%, 49.3%, and 41.7%, respectively. The proposed system has outstanding prospects for the construction of an autonomous and real-time fruit and vegetable classification system for the food industry and marketplaces and can also help visually impaired people to choose fresh food and avoid food poisoning.

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Towards Latency Bypass and Scalability Maintain in Digital Substation Communication Domain with IEC 62439-3 Based Network Architecture

Cited by 7 publications

References 23 publications

Providing a Control System for Charging Electric Vehicles Using ANFIS

Providing a Control System for Charging Electric Vehicles Using ANFIS

Relevant Problems of Intangible Asset Impairment in The Russian IT Industry

Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning

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