Fault Diagnosis of Clean Energy Equipment

Dear Colleagues,

The growing reliance on clean energy has spurred a significant shift towards sustainable and low-carbon energy systems. However, the complexity and diversity of clean energy equipment, such as wind turbines, photovoltaic systems, energy storage devices, oil and gas pipelines, and nuclear power plant components, pose new challenges in reliability, maintenance, and operational efficiency. Fault diagnosis is crucial for ensuring the safe, stable, and efficient operation of these systems by enabling early detection, accurate localization, and effective mitigation of faults, thereby reducing downtime, maintenance costs, and potential environmental impacts.

This Special Issue aims to gather cutting-edge research contributions on fault diagnosis of clean energy equipment, with a focus on innovative methodologies, practical applications, and future trends. We invite submissions from experts in diverse fields, including fault diagnosis, electromagnetic precision measurement, electromagnetic field modeling, and artificial intelligence. Contributions may include original research articles, review papers, and case studies that address fault diagnosis techniques and their applications in various clean energy equipment.

1. Fault Diagnosis Techniques for Energy Infrastructure
   Focus on oil and gas pipelines and nuclear power plant components.
   Include advanced signal processing, real-time monitoring, and non-destructive testing.

2. Electromagnetic Field Modeling and Applications
   Use electromagnetic field modeling for fault detection in energy equipment.
   Explore simulation, innovative testing methods, and integration with AI.

3. Artificial Intelligence and Machine Learning in Fault Diagnosis
   Deep learning techniques, data-driven models, and intelligent diagnostic systems.
   Hybrid approaches combining traditional methods with AI.

4. Condition Monitoring and Safety Analysis
   Condition monitoring and fault detection in nuclear reactors.
   Safety and reliability analysis of critical energy components.

5. Interdisciplinary Approaches for Fault Diagnosis
   Integration of electromagnetic measurements and machine learning.
   Multi-sensor data fusion and practical applications.

6. Innovative Fault Detection and Mitigation Strategies
   Early warning systems, predictive maintenance, and case studies.
   Focus on reducing downtime and environmental impacts through advanced techniques.

We look forward to your submissions.

Dr. Qi Xiao
Dr. Senxiang Lu
Dr. Yu Yao
Dr. Wenhui Li
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electricity is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• fault diagnosis techniques for energy infrastructure
• electromagnetic field modeling and applications
• artificial intelligence and machine learning in fault diagnosis
• condition monitoring and safety analysis
• interdisciplinary approaches for fault diagnosis
• innovative fault detection and mitigation strategies