Research Article
Magnetron as an Effective Source of Electromagnetic Energy: Development and Application Prospects
Issue:
Volume 13, Issue 5, October 2025
Pages:
214-225
Received:
10 September 2025
Accepted:
4 October 2025
Published:
27 October 2025
Abstract: In this paper, we examine two interconnected problems. The first relates to the generation of electromagnetic energy and its widespread use in various fields, including radar, communications, and microwave technology in medicine, industry, science, agriculture, and so on. We analyze existing energy types, highlighting the role and significance of electromagnetic energy and its influence on multiple technologies and processes. The second problem focuses on effective electromagnetic energy sources, with conventional magnetrons being the most commonly used. Developing double-output magnetrons is a promising approach to improving the design of traditional magnetrons across a broad range of frequencies and power levels. We present the theoretical and experimental results of studies on low-voltage dual-output magnetrons, including prototypes for the X and Ku bands. These magnetrons achieve maximum average powers of approximately 18.6 W and 15.5 W, with frequency tuning ranges of about 220 MHz and 150 MHz, and frequency stability of no worse than 10-6. Computer modeling results for a W-band magnetron are also provided. Examples of using dual-output magnetrons in radar and communication systems include frequency tuning, stabilization, and modulation. The design methodology for low-voltage double-output magnetrons is also shared, particularly for high-power magnetrons, such as oven magnetrons, which have two RF energy outputs. The operational features and benefits of this innovative magnetron are discussed. It is demonstrated that employing a second RF output allows for frequency tuning up of the oven magnetron in the range approximately 460 MHz, with an anode voltage of 4.1 kV and an output power of 800 W. Potential application areas for this magnetron are also explored.
Abstract: In this paper, we examine two interconnected problems. The first relates to the generation of electromagnetic energy and its widespread use in various fields, including radar, communications, and microwave technology in medicine, industry, science, agriculture, and so on. We analyze existing energy types, highlighting the role and significance of e...
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Review Article
A Review on Artificial Intelligence Enabled Battery Energy Storage: Fault Diagnosis, Health Estimation and Predictive Optimization
Md Rayhan Tanvir*
Issue:
Volume 13, Issue 5, October 2025
Pages:
226-241
Received:
11 October 2025
Accepted:
23 October 2025
Published:
26 November 2025
DOI:
10.11648/j.jeee.20251305.12
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Abstract: Battery Energy Storage Systems (BESS) are key to improving the reliability, energy efficiency, and flexibility of the contemporary power grid. With their penetration in various applications, including electric vehicles, smart grids, and renewable integration, the safe and secure operation of distributed resources (DRs) becomes more crucial. Traditional Battery Management Systems (BMS) that are usually on rule-based or physics-based models can hardly adapt to various chemistries, operation conditions, and fault scenarios. Approaches based on Artificial Intelligence (AI), such as machine learning (ML) and deep learning (DL), have recently been proposed to provide more promising data-driven options for fault diagnosis, state of health (SoH) estimation, and predictive optimization. This work provides a cutting-edge review of AI deployment in BESSs, which is conceptually classified into three fundamental categories: fault detection and classification, health monitoring and degradation prognosis, and intelligent control and optimization. We discuss the merits and limitations of different AI models, including supervised / unsupervised / hybrid / reinforcement learning, deployment feasibility, interpretability, and data requirements. An analysis of novel techniques, including digital twin modeling, explainable AI, and secure learning frameworks, is also included. Using comparative analysis, taxonomy visualizations, and performance summaries, this review points out the current limitations, standardization issues, and future research directions that are required for industrial-scale deployment. Through capturing the cutting-edge advancements in this field, we hope this work will serve as a guiding reference for the researchers and industry participants to design robust, scalable, and dependable AI-supported battery management systems.
Abstract: Battery Energy Storage Systems (BESS) are key to improving the reliability, energy efficiency, and flexibility of the contemporary power grid. With their penetration in various applications, including electric vehicles, smart grids, and renewable integration, the safe and secure operation of distributed resources (DRs) becomes more crucial. Traditi...
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