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 original research and review articles and also publishes Survey, Comments, Perspectives, Reviews, News & Views, Tutorial and Discussion Papers from experts in these fields 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
Energy storage
Hybrid/combined/integrated energy systems for multi-generation
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

Archiving: full-text archived in CLOCKSS.

Rapid publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 6.1 weeks after submission; acceptance to publication is undertaken in 8.9 days (median values for papers published in this journal in the second half of 2021, 1-2 days of FREE language polishing time is also included in this period).

Current Issue: 2023  Archive: 2022 2021 2020 2019

Special Issue

Fault Diagnosis and Health Status Assessment of Wind Turbine

Submission Deadline: January 31, 2024 (Open) Submit Now

Guest Editors

Xiaoan Yan, PhD, Associate Professor

Nanjing Forestry University, Nanjing, China

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Research interests: fault diagnosis; signal processing; condition monitoring; wind turbine

Minping Jia, PhD, Professor

Southeast University, Nanjing, China

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Research interests: machine monitoring and fault diagnosis; vibration measurement, analysis and control; dynamic signal analysis and processing

Zengtao Chen, PhD, Associate Professor

University of Alberta, Edmonton, Canada

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Research interests: mechanics of materials; materials modelling; damage and fracture mechanics

About This Topic

Wind turbine is a complex mechatronic equipment, which requires an efficient monitoring and maintenance system to ensure its safety, reliability and economy in service. Due to the complex service conditions, harsh environment and difficult maintenance of wind turbines, the traditional fault diagnosis andhealth status assessment theory cannot meet the needs of wind power industry for near-zero fault operation of wind turbines. Wind turbine fault diagnosis and health status assessment strategy based on machine learning, making full use of the wind turbine SCADA data information, which can achieve accurate fault detection and low-cost and efficient preventive maintenance. The most representative machine learning algorithm is the deep learning, which is also a hot research direction in the field of machine condition monitoring and fault diagnosis.

The aim of this Special Issue is to collect the recent results on adopting deep learning theory or some other advanced models for wind turbine fault diagnosis, health status assessment, and remaining life prediction.

Potential topics to be covered:

Machine learning;
Deep learning;
Transfer learning;
Data acquisition;
Condition monitoring;
Signal processing;
Feature extraction;
Fault diagnosis;
Pattern recognition;
Health status assessment;
Remaining life prediction.