Search

Your search keyword '"Yorino, Naoto"' showing total 440 results
Start Over Author "Yorino, Naoto"
440 results on '"Yorino, Naoto"'

2. Distributed Agent-Based Voltage Control Approach for Active Distribution Systems

Author

Bedawy, Ahmed, Yorino, Naoto, Sasaki, Yutaka, Zoka, Yoshifumi, Mumbere, Kihembo Samuel, Kubo, Ryuta, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Giri, Chandan, editor, Iizuka, Takahiro, editor, Rahaman, Hafizur, editor, and Bhattacharya, Bhargab B., editor

Published
2023
Full Text
View/download PDF

13. Guest Editorial: Artificial intelligence‐empowered reliable forecasting for energy sectors.

Author

Mahmoud, Karar, Guerrero, Josep M., Abdel‐Nasser, Mohamed, and Yorino, Naoto

Subjects
ENERGY industries, ARTIFICIAL neural networks, MACHINE learning, FORECASTING, QUANTILE regression, CONVOLUTIONAL neural networks, DEMAND forecasting
Abstract

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

Published
2024
Full Text
View/download PDF

29. Determination of Generator Capability Curve using Modified-Single Machine to Infinite Bus (M-SMIB) System Approach

Author

Rusilawati Rusilawati, Budi Sulistiawati Irrine, and Yorino Naoto

Subjects
maximum generation limit, operation generator limit, protection system setting, Environmental sciences, GE1-350
Abstract

The capability curve for each generator unit is usually provided by the generator manufacturer. But in practice, the generator can reach its maximum generation limit before reaching the maximum limit on the generator capability curve provided by the generator manufacturer. This might occur because of the load location is far from the generator or the varying of the loading value so that the maximum generation limit is smaller than the value given on the generator capability curve of the manufacturer. In this paper, the generator capability curve is determined using the Modified Single Machine to Infinite Bus (M-SMIB) system approach to determine the maximum generation limit every time there is a change in loading or change in the load location. After the maximum generation limit of each unit generator is known, the generator capability curve that is always in accordance with the real time situation can be formed. Thus, the operation limit of each generator can be recognized, determine the appropriate protection system setting and can prevent the electric power system disturbance. This method will be applied to generator units in the four bus IEEE system with two generators.

Published
2020
Full Text
View/download PDF

32. A novel design of single‐phase microgrid based on non‐interference core synchronous inverters for power system stabilization.

Author

Yorino, Naoto, Sekizaki, Shinya, Adachi, Kota, Sasaki, Yutaka, Zoka, Yoshifumi, Bedawy, Ahmed, Shimizu, Toshihisa, and Amimoto, Kazuya

Subjects
*MICROGRIDS, *ROOT-mean-squares, *SYSTEM analysis, *FREQUENCY stability
Abstract

This paper summarizes our recent work on Grid‐Forming Inverter (GFM) application to power systems. We have developed a novel design of GFM, a single‐phase synchronous inverter (SSI) for the conventional 100/200V distribution network based on the concept of "non‐interference core (NIC) dynamic model." This paper first explains the design concept of NIC‐SSI and a simulation model of SSI for power system analysis. Then the stabilization effect is investigated, where the installation of SSI on the single‐phase consumer side is assumed as a new concept. The improvement of frequency and transient stability are evaluated. The SSI model is verified by comparing the developed root mean square (RMS) simulation, hardware‐in‐the‐loop (HIL) simulation, and the experiment using SSI hardware. The simulation results show that the SSI has the considerable ability of grid stabilization. Singlephase micro‐grid (SMG) operations using SSIs are also presented. The effectiveness of SMG operations is shown based on laboratory experiments. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

34. Advanced Extreme Learning Machine for An Hour PV Forecast Using General Weather Data.

Author

Farid, Imam Wahyudi, Priyadi, Ardyono, Purnomo, Mauridhi Hery, Abdillah, Muhammad, Tjahjono, Anang, and Yorino, Naoto

Subjects
MACHINE learning, RENEWABLE energy sources, BACK propagation, ELECTRIC power distribution grids, SUPPORT vector machines
Abstract

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results