Journal of Energy and Power Technology (JEPT) is an international peer-reviewed Open Access journal published quarterly online by LIDSEN Publishing Inc. This periodical is dedicated to providing a unique, peer-reviewed, multi-disciplinary platform for researchers, scientists and engineers in academia, research institutions, government agencies and industry. The journal is also of interest to technology developers, planners, policy makers and technical, economic and policy advisers to present their research results and findings.

Journal of Energy and Power Technology focuses on all aspects of energy and power. It publishes not only original research and review articles, but also various other types of articles from experts in these fields, such as Communication, Opinion, Comment, Conference Report, Technical Note, Book Review, and more, to promote intuitive understanding of the state-of-the-art and technology trends.

Main research areas include (but are not limited to):
Renewable energies (e.g. geothermal, solar, wind, hydro, tidal, wave, biomass) and grid connection impact
Energy harvesting devices
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Hydrogen energy 
Fuel cells
Nuclear energy
Energy economics and finance
Energy policy
Energy and environment
Energy conversion, conservation and management
Smart energy system

Power Generation - Conventional and Renewable
Power System Management
Power Transmission and Distribution
Smart Grid Technologies
Micro- and nano-energy systems and technologies
Power electronic
Biofuels and alternatives
High voltage and pulse power
Organic and inorganic photovoltaics
Batteries and supercapacitors

Publication Speed (median values for papers published in 2023): Submission to First Decision: 5.1 weeks; Submission to Acceptance: 11.6 weeks; Acceptance to Publication: 7 days (1-2 days of FREE language polishing included)

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This article has been retracted on 28 June 2023
Open Access Research Article

Transfer Learning for Fault Detection with Application to Wind Turbine SCADA Data

Silvio Simani 1,*, Saverio Farsoni 1, Paolo Castaldi 2

  1. Department of Engineering, University of Ferrara, Via Saragat 1E, Ferrara, 44122, FE, Italy

  2. Department of Electrical, Electronic and Information Engineering, University of Bologna, Via Fontanelle, Forl´ı, 47121, FC, Italy

Correspondence: Silvio Simani

Academic Editor: Andrés Elías Feijóo Lorenzo

Collection: Wind Energy

Received: February 02, 2023 | Accepted: March 15, 2023 | Published: March 21, 2023 | Retracted: June 28, 2023

Journal of Energy and Power Technology 2023, Volume 5, Issue 1, doi:10.21926/jept.2301011

Recommended citation: Simani S, Farsoni S, Castaldi P. Transfer Learning for Fault Detection with Application to Wind Turbine SCADA Data. Journal of Energy and Power Technology 2023; 5(1): 011; doi:10.21926/jept.2301011.

© 2023 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.


In agreement with the Editor-in-Chief, the editorial office, and the authors, this article has been removed and marked as retracted because its content and data contained plagiarism from an unpublished study. Please refer to the Removal Statement.

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