Utilization of Fault Diagnosis for Renewable and Sustainable Energies
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".
Deadline for manuscript submissions: 17 April 2025 | Viewed by 1303
Special Issue Editor
Interests: artificial neural networks; fault diagnosis; fault diagnose for energy system; health monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As the global energy landscape continues to undergo a transformative shift towards cleaner and more sustainable solutions, there is a growing recognition of the pivotal role played by artificial intelligence technologies in enhancing the reliability and efficiency of renewable energy systems. The increasing deployment of renewable energy sources, such as solar, wind, and hydropower, underscores the need for advanced monitoring and diagnostic tools to address operational challenges and ensure optimal performance. In this context, the integration of AI into the realm of fault diagnosis holds particular significance. AI technologies, including machine learning algorithms, deep learning models, and data analytics, offer unprecedented capabilities in processing vast amounts of data, identifying patterns, and predicting potential faults or anomalies in renewable energy infrastructure.
Considering the above, this Special Issue focuses on the use artificial intelligence and smart energy systems, with a specific focus on fault diagnosis in the context of renewable and sustainable energy sources.
The aims and topics of interest for publication include, but are not limited to the below:
Aims:
- AI Applications in Fault Diagnosis: Investigation of the application of AI techniques such as machine learning, deep learning, and data analytics for fault diagnosis in renewable energy systems.
- Smart Energy Systems: Exploration of the integration of AI into smart energy systems to enhance fault detection, localization, and mitigation strategies.
- Sustainability: Emphasizing the role of fault diagnosis in the promoting sustainability by optimizing the performance and lifespan of renewable energy assets.
- Interdisciplinary Perspectives: Encouraging contributions from researchers and practitioners working at the intersection of AI, energy engineering, and sustainability.
Topics of Interest:
- Machine learning and data-driven approaches for fault diagnosis in renewable energy systems;
- Smart-grid technologies and their role in fault detection and management;
- Case studies and real-world applications of AI in optimizing renewable energy performance;
- Integration of fault diagnosis into the design and operation of sustainable energy systems;
- Cross-disciplinary perspectives on AI and smart energy for sustainable development.
Dr. Marcel Luzar
Guest Editor
Manuscript Submission Information
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Keywords
- non-linear system modeling
- artificial intelligence
- system fault diagnosis
- renewable energy
- sustainable energy
- intelligent control
- industrial and software application
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