Power, Control, and Data Processing Systems

Abstract Renewable energy, particularly solar energy, is critical for sustainable development, yet maintaining solar panel efficiency requires timely and accurate fault detection. Federated learning has emerged as a promising solution by enabling decentralized model training while preserving data privacy. However, conventional aggregation methods such as FedAvg are vulnerable to adversarial attacks, where malicious clients poison model updates and severely degrade performance. In this paper, we present an enhanced federated transfer learning framework for solar panel fault detection that leverages a pre-trained VGG-16 model for effective feature extraction and incorporates robust aggregation techniques to defend against model poisoning. Specifically, we simulate a poisoning attack scenario by introducing malicious clients that inject Gaussian noise into their updates, and we evaluate two robust aggregation methods—Krum and coordinate-wise median—against this threat. Experimental results demonstrate that while standard FedAvg yields a final test accuracy of only 19.21% with an exorbitantly high loss, the Krum and coordinate-wise median methods achieve significantly improved performance, with final test accuracies of 70.62% and 72.88% and test losses of 1.3161 and 0.7926, respectively. Notably, these results are closely aligned with the performance of federated learning without attack, which achieves 74% accuracy with a final loss of 0.82, and centralized learning, which reaches 75% accuracy with a loss of 0.85. These findings underscore the critical importance of robust aggregation in securing federated learning frameworks for solar panel fault detection, providing a scalable and privacy-preserving solution even in adversarial environments.

Abstract Today, energy management in a microgrid requires development to improve the management of produced and consumed energy while maintaining network balance on both supply and demand sides. This paper proposes an optimal method to reduce microgrid costs, incorporating a diesel generator, wind unit, solar panel, combined heat and power (CHP) unit, and battery storage. The proposed method is solved optimally using the Particle Swarm Optimization (PSO) algorithm. The main objectives of this research include reducing the costs of distributed generation resources, minimizing environmental pollution in the microgrid system, and enhancing network efficiency. To demonstrate the positive impact of the proposed method, various tables and charts related to network requirements, solar radiation angle, and wind speed are utilized. By implementing the proposed method in hybrid microgrids using MATLAB software, the effectiveness of the optimization approach is shown, achieving a 39% reduction in environmental pollution and a 23% reduction in fuel consumption in the optimal state.

Abstract Lithium batteries have emerged as one of the most advanced energy storage technologies in recent decades, playing a crucial role in transforming industries such as electric vehicles, portable electronics, and renewable energy systems. Due to their high energy density, long lifespan, and lightweight design, lithium batteries have become a preferred alternative to older technologies like nickel-cadmium and lead-acid batteries. However, the development and optimization of these batteries face challenges such as safety concerns, performance degradation under extreme temperatures, and the limited availability of critical raw materials like lithium and cobalt. This article provides a comprehensive review of lithium battery technology, including recent advancements in active material structures, electrode design, and manufacturing processes. Nanotechnology, in particular, has played a significant role in enhancing the performance, safety, and sustainability of lithium batteries. The use of nanomaterials such as nanocomposites, nanoelectrolytes, and nanostructured electrodes has led to improvements in energy density, safety, and cycle life. Furthermore, the article addresses current technological challenges and explores innovative solutions, including the use of alternative materials, advanced thermal management techniques, and nanotechnology applications in cell design. The future outlook of this technology is discussed, with an emphasis on improving sustainability, reducing costs, and enhancing safety. This review aims to provide valuable insights for researchers, engineers, and policymakers, guiding the continued development of lithium batteries toward achieving clean and sustainable energy solutions.

Abstract Software-Defined Networking (SDN) revolutionizes network architecture by decoupling the control plane from the data plane, which allows for centralized control, enhanced programmability, and remarkable network agility. This paradigm facilitates more straightforward management and dynamic policy enforcement across a variety of devices and environments. The main methodologies explored include OpenFlow-based architectures, which emphasize strict protocol-driven communication between the controller and the data plane switches, enabling direct programmability. Another approach involves API-driven control of legacy or traditional network devices, leveraging standard interfaces like SNMP, CLI, or REST APIs to integrate them into an SDN framework without full replacement. The hypervisor-based overlay virtualization networks method creates virtual networks atop physical infrastructures, enhancing flexibility and isolation, while hybrid SDN deployments combine legacy and SDN elements to balance innovation with operational continuity.

Each methodology presents unique benefits and limitations regarding scalability, security, hardware dependencies, and deployment complexity. For instance, OpenFlow architectures provide high programmability but require compatible hardware, whereas API-driven SDN extends SDN benefits to existing infrastructure at the cost of some openness. Current research challenges addressed include scalability in controller design, robust security mechanisms to prevent attacks such as denial-of-service and spoofing, and standardization to ensure interoperability among diverse vendors and platforms. This comprehensive review assists both researchers and network practitioners in choosing appropriate SDN implementation strategies compatible with specific operational requirements and future-proofing networks through this adaptive technology.

Abstract {R1-1} This article presents a modified direct single-phase boost AC–AC converter utilizing a switching cell (SC). The main objective is to eliminate voltage stress and minimize the size of magnetic components. The suggested non-isolated converter has a very straightforward design, which employs two inductors. The suggested design eliminates the need for DC link and provides boost capability with simple control. {R1-1} Furthermore, the proposed topology reduces current levels and mitigates body-diode recovery issues, which in turn decreases switching and conduction losses and effectively minimizes the losses associated with MOSFET body-diode recovery. Therefore, this converter represents a practical option for many applications, such as renewable energy systems, energy storage systems, and microgrids. To improve this system's reliability and efficiency, exact and steady power regulation is essential in these domains. The operational principles of the recommended converter in various modes are analyzed comprehensively, and based on the results of the simulation in MATLAB/Simulink. {R2-3} Finally, the suggested converters' performance accuracy is confirmed by simulation analysis at 20V input, D = 0.7, 50kHz switching frequency, 122V output, and 150W output power.

Abstract Uncertainties in renewable generation and load demand are challenges to the efficient operation of renewable energy systems. This paper aims to review energy management systems for multi-microgrid networks under uncertainty. This review primarily focuses on uncertainty modeling approaches for multi-microgrid energy management, while also providing an integrated discussion on communication architectures, hierarchical control strategies, and market-oriented coordination. Therefore, the paper serves as a hybrid survey—comprehensive in structure yet focused in its analytical depth on uncertainty and distributed optimization frameworks. It provides an overview of optimization techniques and control strategies to address issues related to uncertainty in renewable generation, load demand, and network dynamics. This paper is organized as follows: we first introduce the concept of multi-microgrid networks, discuss the relevance of efficient energy management systems in such systems, then we analyze the impact of uncertainty on the economic and environmental performance of multi-microgrid networks and present approaches to mitigate their effects. The novelty of this review is to provide an integrated conceptual framework of uncertainty modeling, distributed optimization and machine-learning-based forecasting, as well as a critical comparative synthesis of recent studies (2020–2024). Specifically, 50 recent studies are analyzed, with probabilistic and robust optimization methods representing nearly 45% of the reviewed works, and machine-learning-based forecasting models accounting for 30%. Comparative analysis reveals that hybrid ML–robust frameworks achieve the best trade-off between cost-efficiency and reliability, highlighting trends and gaps in current research.