Control, Diagnostics and Protection for Electrical Machines, Power Electronics and Drives

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 1607

Special Issue Editors


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Guest Editor
Department of Automation, Electrical and Electronic Engineering and Industrial Informatics, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain
Interests: energy conversion; electric power generation; electric power transmission; electric power apparatus; electrical machines; electrical drives
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Automatic Control, Electrical and Electronic Engineering and Industrial Informatics, Universidad Politécnica de Madrid, 28006 Madrid, Spain
Interests: protection of electrical machines; condition monitoring of electrical machines; fault diagnosis of electrical machines; synchronous machines excitation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The modern era of electrification and energy efficiency relies heavily on advanced power conversion systems, including electrical machines, power electronics and drives. These technologies form the backbone of industrial automation, power generation, renewable energy integration, transportation electrification, and more. However, as their adoption increases, so too do challenges in ensuring optimal performance, reliability and fault tolerance. This Special Issue focuses on recent advancements in control strategies, diagnostic and protection methods for electrical machines, power electronics and drives.

Contributions to this collection will address state-of-the-art research on robust control techniques, including adaptive, predictive, and intelligent control strategies for electrical machines and drives. Papers are also invited on innovative diagnostic tools for condition monitoring, fault detection, protection mechanisms and predictive maintenance. The role of electronics in ensuring system reliability and safety will be a key focus, particularly in the development of protection strategies that can respond dynamically to system anomalies. Additionally, this Special Issue will highlight cutting-edge advancements in wide-bandgap semiconductors, high-efficiency power converters and their seamless integration into electrical machines and drive systems, showcasing the critical role of electronic components in advancing control, diagnostics and protection capabilities.

This Special Issue aims to serve as a platform for researchers and practitioners to share insights, propose novel approaches, and discuss emerging trends that will shape the future of power conversion technologies. We encourage submissions of theoretical studies, experimental validations, real-world applications and review works fostering a comprehensive understanding of the challenges and opportunities in this domain. By showcasing groundbreaking research and practical solutions, this Special Issue seeks to inspire innovation and collaboration within the global engineering community.

Prof. Dr. Kumar Mahtani
Prof. Dr. Carlos Platero
Guest Editors

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Keywords

  • electrical machines
  • power electronics
  • motor drives
  • control
  • diagnostics
  • protection
  • fault detection
  • condition monitoring
  • predictive maintenance

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Published Papers (1 paper)

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Research

25 pages, 4273 KB  
Article
A Multi-Task Learning and GCN-Transformer-Based Forecasting Method for Day-Ahead Power of Wind-Solar Clusters
by Jianhong Jiang, Yi He, Yumo Zhang, Jian Yan, Zhiwei Lv, Zifan Liu, Haonan Dai and Zhao Zhen
Electronics 2026, 15(2), 462; https://doi.org/10.3390/electronics15020462 - 21 Jan 2026
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Abstract
With the rapid increase in renewable energy penetration and the expansion of multi-regional interconnected power systems, there is a growing need to forecast the power output of renewable energy power plant clusters within a region. Existing methods primarily utilize spatio-temporal correlations between stations [...] Read more.
With the rapid increase in renewable energy penetration and the expansion of multi-regional interconnected power systems, there is a growing need to forecast the power output of renewable energy power plant clusters within a region. Existing methods primarily utilize spatio-temporal correlations between stations to directly predict cluster output, but they still have the following shortcomings: (1) lack of analysis and utilization of the similar output characteristics between wind and solar power stations; and (2) inadequate integration of individual plant characteristics and adaptability across different prediction spatial scales. Therefore, this study proposes a method for forecasting and correcting daily power generation zones of wind–solar clusters based on output similarity clustering. First, the output similarity characteristics of wind and solar plants within the cluster are evaluated, and a similarity matrix is constructed to cluster the plants into sub-clusters. Second, a single-site power prediction model based on the BiLSTM model and multi-task learning is constructed to aggregate preliminary power prediction results from individual sites within sub-clusters. Finally, a cluster power prediction correction model based on the GCN-Transformer model is developed to refine preliminary predictions using spatio-temporal correlations between sub-clusters. Simulation results demonstrate that the proposed method, through its integrated approach combining clustering partitioning, multi-task learning, and spatio-temporal correlation correction within a comprehensive forecasting workflow, achieves improvements of 15.2323%, 19.0581%, and 0.0283% over the baseline GCN model in terms of MAE, RMSE, and R-score, respectively. This effectively enhances the accuracy of power forecasting for wind-solar power plant clusters. Full article
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