Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Ebrahim Balouji"'
Publikováno v:
IEEE Transactions on Industry Applications. 58:4214-4224
In this research work, deep machine learning-based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics, and interharmonics originating from highly time-varying electric arc furnace (EAF) curren
Publikováno v:
IEEE Transactions on Dielectrics and Electrical Insulation. 29:287-294
Publikováno v:
IEEE Transactions on Industry Applications. 56:3250-3260
In this article, a new approach to compensate both the response and reaction times of active power filters (APF) for special cases of highly time-varying harmonics and interharmonics of electric arc furnace (EAF) currents is proposed. Instead of usin
Autor:
Navid Bayati, Ebrahim Balouji, Hamid Reza Baghaee, Amin Hajizadeh, Mohsen Soltani, Zhengyu Lin, Mehdi Savaghebi
Publikováno v:
Bayati, N, Balouji, E, Baghaee, H R, Hajizadeh, A, Soltani, M, Lin, Z & Savaghebi, M 2022, ' Locating high-impedance faults in DC microgrid clusters using support vector machines ', Applied Energy, vol. 308, 118338 . https://doi.org/10.1016/j.apenergy.2021.118338
Bayati, N, Balouji, E, Baghaee, H R, Hajizadeh, A, Soltani, M, Lin, Z & Savaghebi, M 2022, ' Locating High-Impedance Faults in DC Microgrid Clusters Using Support Vector Machines ', Applied Energy, vol. 308, 118338 . https://doi.org/10.1016/j.apenergy.2021.118338
Bayati, N, Balouji, E, Baghaee, H R, Hajizadeh, A, Soltani, M, Lin, Z & Savaghebi, M 2022, ' Locating High-Impedance Faults in DC Microgrid Clusters Using Support Vector Machines ', Applied Energy, vol. 308, 118338 . https://doi.org/10.1016/j.apenergy.2021.118338
With the increasing number of DC microgrids, DC microgrid clusters are emerging as a cost-effective solution. Therefore, due to the possible long distances between DC microgrids, once a fault occurs and is cleared, it should be located. Especially, l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd25a4e5ec188f6028fb8a9f715d331f
https://vbn.aau.dk/da/publications/b4816f80-1a49-42c7-8b02-1f531c07b6a5
https://vbn.aau.dk/da/publications/b4816f80-1a49-42c7-8b02-1f531c07b6a5
Autor:
Ebrahim Balouji
In this research work, deep machine learning based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics and interharmonics originating from highly time-varying electric arc furnace (EAF) current
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1977ecfe5d8f3ea674930d8c048ed692
https://doi.org/10.36227/techrxiv.17707166.v1
https://doi.org/10.36227/techrxiv.17707166.v1
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2021
Phantom Limb Pain (PLP) is a chronic condition frequent among individuals with acquired amputation. PLP has been often investigated with the use of functional MRI focusing on the changes that take place in the sensorimotor cortex after amputation. In
Publikováno v:
GlobalSIP
A study of machine learning (ML) methods for classification of data from partial discharges (PDs) is described. A novel set of features are suggested and tested using an extensive set of machine learning based algorithms. The aim is to classify PDs o
Publikováno v:
IAS
In this research work, time- and frequency-domain Deep Learning (DL) based methods have been developed to pre-detect harmonic and interharmonic components of a current waveform of an Electric Arc Furnace (EAF) application. In the time-domain DL based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e13f1dc8e335efe7a4a82e2f4f2336e
https://aperta.ulakbim.gov.tr/record/70537
https://aperta.ulakbim.gov.tr/record/70537
Publikováno v:
2018 18th International Conference on Harmonics and Quality of Power (ICHQP).
In this paper, a deep learning (DL)-based method for automatic feature extraction and classification of voltage dips is proposed. The method consists of a dedicated architecture of Long Short-Term Memory (LSTM), which is a special type of Recurrent N
This paper proposes a novel method for voltage dip classification using deep convolutional neural networks. The main contributions of this paper include: (a) to propose a new effective deep convolutional neural network architecture for automatically
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d72b654ebc10210a4da1f6b9f43eeae
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-70217
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-70217