Zobrazeno 1 - 10
of 49
pro vyhledávání: '"mohammad pazoki"'
Publikováno v:
Electrica, Vol 24, Iss 3, Pp 601-615 (2024)
This paper presents an approach to address the critical challenge of load forecasting in the Indian state of Odisha. Motivated by the necessity for accurate predictions to support efficient planning and operation of the power system network, the work
Externí odkaz:
https://doaj.org/article/3aaded95c3f346d9b9156cefec0af5f9
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 5, Pp 899-910 (2024)
Abstract In this paper, Gaussian Process Regression (GPR)‐based models which use the Bayesian approach to regression analysis problem such as load forecasting (LF) are proposed. The GPR is a non‐parametric kernel‐based learning method having th
Externí odkaz:
https://doaj.org/article/04e2b46381994224b54c00388b262d12
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 5, Pp 952-962 (2024)
Abstract Load demand forecasting is very important for the management, designing and analysis of an electrical grid system. Load forecasting has progressively become a crucial component of the energy management system with the growth of the smart mic
Externí odkaz:
https://doaj.org/article/6613d40c43824068b5b73a56626da069
Publikováno v:
مجله مدل سازی در مهندسی, Vol 20, Iss 71, Pp 101-112 (2022)
In this paper, the effect of installing a hybrid superconducting fault current limiter on the apparent impedance seen by the distance relay in power systems compensated with STATCOM has been analyzed. The STATCOM is installed in the system to meet th
Externí odkaz:
https://doaj.org/article/b1a59a1f033349e6b58bfe93f5438ac7
Publikováno v:
MethodsX, Vol 10, Iss , Pp 102018- (2023)
This paper presents a Fuzzy Inference System (FIS)-based method to detect a fault, identify the faulty section, and recognize the faulty pole in the Voltage Source Converter (VSC)-based Multi-Terminal High Voltage Direct Current (MT-HVDC) system. The
Externí odkaz:
https://doaj.org/article/b049a26a5c1149208ffff14645af34e7
Autor:
Mohammad Pazoki
Publikováno v:
مجله مدل سازی در مهندسی, Vol 16, Iss 52, Pp 119-129 (2018)
In this paper, using pattern recognition method all fault type is classified. Firstly, feature vectors obtained from sequence components of current and/or voltage signals are normalized by efficient technique. Afterwards, the proposed supervising fun
Externí odkaz:
https://doaj.org/article/7c5adaf1f1c146f5a92473a0123b3852
Publikováno v:
IEEE Access, Vol 6, Pp 27373-27382 (2018)
This paper presents a novel intelligent fault classification scheme for fixed series capacitor compensated transmission line. The singular value decomposition principle is applied along with the fast discrete orthonormal S-transform (FDOST) and bagge
Externí odkaz:
https://doaj.org/article/45e00ceaaf7441df9e4a8f1c66787862
Publikováno v:
Electric Power Systems Research. 221:109434
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::db5b3ccc3de7c374d8f9ec8450ead04a
https://doi.org/10.1016/j.epsr.2023.109434
https://doi.org/10.1016/j.epsr.2023.109434
Publikováno v:
IEEE Transactions on Industrial Informatics. 17:5230-5238
This article presents a reliable islanding detection scheme for distributed generation (DG) to minimize the nondetection zone using a pattern-recognition method. A hybrid time–frequency signal decomposition along with a machine learning processes t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f81f8a3382f29da830c30d76ebbfeb7
https://doi.org/10.1109/tii.2020.3029675
https://doi.org/10.1109/tii.2020.3029675
Publikováno v:
Neural Computing and Applications. 33:8795-8820
In a dual-circuit transmission line, the location of evolving faults (EVFs) is more tiresome due to its multifaceted nature. In this paper, a novel data-mining-based scheme is proposed for location of EVFs by using an ensemble of regression trees, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ec7de724b78a64a90437fbe750375d0
https://doi.org/10.1007/s00521-020-05628-6
https://doi.org/10.1007/s00521-020-05628-6