Distributed renewable generation introduces spatially dispersed power fluctuations that must be balanced under local operating and communication constraints. Carbon dioxide energy storage systems (CO 2 -ESS) offer a viable option for distributed storage applications, yet their economic dispatch is complicated by thermo-mechanical operating constraints, heterogeneous unit characteristics, and the need for global power balance. This study develops a decentralized energy management framework for distributed CO 2 -ESS based on a constrained distributed Nesterov-accelerated primal-dual gradient tracking (C-DNGT) algorithm. The method embeds the power-balance constraint into the distributed optimization process and employs a dynamic gradient tracker to exchange local information and compensate for power mismatch without a centralized coordinator. Nesterov acceleration is introduced to improve convergence under heterogeneous storage parameters and operating limits. Theoretical analysis shows that, under standard convexity and connectivity assumptions, the CDNGT iterates converge linearly to the Karush-Kuhn-Tucker saddle point of the economic dispatch problem. Numerical studies on PJM Interconnection load data under heterogeneous CO 2 -ESS configurations indicate that C-DNGT reduces the number of communication iterations required for convergence compared with conventional distributed primal-dual and gradient-tracking methods, while preserving power-balance feasibility. The proposed C-DNGT framework enables fully decentralized, coordinator-free economic dispatch of distributed CO 2 -ESS with accelerated convergence and guaranteed power-balance feasibility, demonstrating its potential for heterogeneous distributed storage networks.
Kaibo Hu, Lifeng Yu, Wuyin Gu, Kefeng Qian, Jiahe Sun
Power transformers are critical equipment in power systems, and their operational reliability directly affects the safety and stability of grid operation. Conventional transformer operation and maintenance methods are often limited by fixed alarm rules and insufficient integration of monitoring information, making it difficult to achieve intelligent fault diagnosis and timely warning support. To address these issues, this paper proposes a digital twin-assisted framework for transformer fault diagnosis and early warning by integrating transformer digital modeling, online monitoring information, and dissolved gas analysis (DGA)-based intelligent diagnosis. A three-dimensional transformer digital model is constructed using laser point cloud reconstruction and parametric modeling to support equipment state representation and information mapping. Meanwhile, a Snake Optimization-Random Forest (SO-RF) model is developed for multi-class transformer fault diagnosis using five characteristic dissolved gas features. The proposed model is trained and tested using dissolved gas data from a power company's transformer fault case library and compared with Support Vector Machine (SVM) and Random Forest (RF) models. Experimental results show that the proposed SO-RF model achieves an overall diagnostic accuracy of 93.80%, which is higher than those of RF and SVM, with accuracies of 89.98% and 86.39%, respectively. The proposed framework provides a feasible technical route for combining data-driven transformer fault diagnosis with digital twin-assisted state visualization and warning support for intelligent transformer operation and maintenance.
Digital twin (DT) technology has gained significant relevance and application in electrical engineering due to its capabilities in simulation, real-time monitoring, and system optimization. Although DT research has demonstrated benefits across multiple engineering disciplines, a focused synthesis of research specifically within electrical engineering remains limited. Therefore, this study aims to systematically review the application of DT technologies in electrical engineering research, map the current state of knowledge, and identify future directions for integration and scalability. A systematic literature review was conducted following predefined eligibility criteria. A total of 84 publications published between 2015 and 2025 were selected for the final review. The studies were analyzed and categorized into seven major themes: power and energy systems applications, electric machines and transportation, fault diagnosis and condition monitoring, real-time control and hardware-in-the-loop implementation, modeling and simulation, IoT and computing platforms, and cybersecurity and lifecycle management. The review findings indicate a steady increase in scholarly attention toward DT technologies over the examined period. The literature showed strong emphasis on applications in power systems and IoT-supported platforms, while also demonstrating the relevance of DTs across a broad range of electrical engineering domains. The thematic analysis highlighted the diverse functionalities of DTs in improving system performance, predictive maintenance, operational efficiency, and real-time decision-making. The study demonstrates that DT technology has become an important research area within electrical engineering and continues to expand into multiple application domains. However, the review also identified the need for more robust, scalable, and standardized frameworks to support wider implementation and interoperability. Future research should focus on addressing challenges related to integration, cybersecurity, standardization, and lifecycle management to enable broader adoption of DT technologies in electrical engineering systems.
Commutation failure poses a significant operational risk on HVDC transmission systems, with its prediction facing challenges from complex underlying mechanisms and stringent real-time requirements. Data-driven techniques show promise, but practical implementation of fully data-driven solutions remains unresolved. This paper introduces a novel fully data-driven fast prediction framework for commutation failures, featuring three principal innovations: (i) A second-order determinant-based feature extraction algorithm that compresses data dimensionality while preserving critical disturbance characteristics; (ii) A cost-sensitive learning technique integrated with sample augmentation strategies to address sample bias problem; (iii) A classification performance evaluation protocol tailored for engineering applications. Experimental validation on the modified CIGRE benchmark system demonstrates 1.25 and 1% false alarm rate and miss rate respectively, with response time reduced to within 2 ms (at 2.5 kHz sampling rate). The proposed methodology significantly reduces dependency on extensive training datasets, offering a viable purely data-driven solution for real-time commutation failure pre-judgement.
Novel cable traction power supply can eliminate phase splits and improve regenerative energy utilization in metro systems. However, the cable-based traction network has a relatively complex structure, making internal energy consumption and train-network line loss evaluation insufficiently studied. To quantify train-network line losses, an equivalent solid-circuit model of the traction power supply system is established using an external cascade strategy. Combined with actual train operation load modeling, the traction transformer output power and network losses are computed and compared under both traction and regenerative braking operating conditions. The proposed modeling-and-calculation framework enables accurate evaluation of power output and line losses across operating modes, and effectively captures the electrical characteristics of the cable traction energy supply system. The developed theoretical line-loss calculation approach provides a quantitative basis for energy-consumption assessment and operational analysis of cable traction networks, supporting performance evaluation and planning of metro traction power supply systems.
