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Öğe Controlling and tracking the maximum active power point in a photovoltaic system connected to the grid using the fuzzy neural controller(Institute of Electrical and Electronics Engineers Inc., 2023) Yusupov, Z.; Yaghoubi, E.; Yaghoubi, E.In contemporary smart distribution microgrids, both AC and DC loads and sources are consistently accessible, often operating at varying voltage levels simultaneously. Consequently, the typical scenario in today's microgrids involves a hybrid microgrid setup, necessitating the integration of inverters to facilitate power sharing between the AC and DC sections. Hence, this paper introduces a solution for active power control within an integrated AC/DC microgrid incorporating decentralized photovoltaic sources. The proposed solution employs a fuzzy neural controller to manage power generation, including the complexities of tracking the maximum power point during partial shading conditions. This approach effectively addresses the challenges posed by the combined microgrid configuration. The simulation results provide clear evidence of the success of the proposed method in controlling the active power managed by the DC microgrid and transferring it to the AC section. © 2023 IEEE.Öğe Modeling and Control of Decentralized Microgrid Based on Renewable Energy and Electric Vehicle Charging Station(Springer Science and Business Media Deutschland GmbH, 2024) Yusupov, Z.; Almagrahi, N.; Yaghoubi, E.; Yaghoubi, E.; Habbal, A.; Kodirov, D.Energy scarcity, environmental pollution, and the exponential rise in demand for energy are all significant global problems. The integration of distributed energy resources (DER) into electric power systems (EPS) and the use of electric vehicles (EVs) due to low pollution has increased in recent decades. The operating reliability and efficiency of the power systems are affected when DERs and energy storage devices are integrated into a distribution network. In a different scenario, a vital step toward low-carbon mobility is the electrification of automobiles. To integrate the widespread adoption of electric vehicles into EPS, it is necessary to coordinate the combination of centralized and decentralized control of EPS at the EV grid infrastructure. In this paper, a microgrid (MG) with decentralized control of renewable energy and an EV charging station is designed and modeled for Karabuk University campus. In the proposed mathematical model, the Park transformer is applied based on injection current in the Point of Common Coupling (PCC) and using the PI controller to control active power. This technique can keep the frequency and power in both grid-connected and islanded modes, also significantly reducing the number of harmonics in the grid-connected mode. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Öğe Real-time techno-economical operation of preserving microgrids via optimal NLMPC considering uncertainties(Elsevier B.V., 2024) Yaghoubi, E.; Yaghoubi, E.; Yusupov, Z.; Rahebi, J.In the modern era, managing optimal real-time control of microgrids during the operation phase has been a significant challenge, requiring careful consideration of both technical and economic factors. This paper introduces a framework for the real-time control of islanded microgrids using a preserving network. This structure incorporates various distributed generation sources, including rotating and non-rotating resources, along with energy storage systems. The optimization function within model predictive control (MPC) manages essential network parameters, such as frequency and voltage, while addressing real-time economic and technical objectives. To enhance precision and account for uncertainties in generation and consumption parameters, the integration of continuous power flow and the preserving network model is employed. This approach aims to create a model that closely mirrors real-world conditions, ensuring a more accurate representation of microgrid dynamics. The proposed structure demonstrates significant improvements in both technical and economic performance compared to Standard MPC and Adaptive MPC, highlighting its potential for more efficient islanded microgrid management. The proposed framework achieves notable reductions in total voltage deviation of 85.87% and 87.62% compared to Standard MPC and Adaptive MPC, respectively. Additionally, it delivers impressive enhancements in frequency deviation of 99.46% and 96.62% compared to Standard MPC and Adaptive MPC, respectively. Economically, the proposed framework significantly outperforms both, reducing costs by 39.29% compared to Standard MPC and by 28.12% compared to Adaptive MPC. © 2024 THE AUTHORSÖğe A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering(Springer Science and Business Media Deutschland GmbH, 2024) Yaghoubi, E.; Yaghoubi, E.; Khamees, A.; Vakili, A.H.Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem. © The Author(s) 2024.
