ABSTRACT Distributed energy resources (DERs) are increasingly proliferating worldwide, driven by their benefits in promoting energy sustainability, efficiency, and resiliency. Coordinated approaches are crucial for aggregating diverse DERs across large areas, yet the increasing reliance on information technology exposes power systems to cyberattacks. Integrating DERs into the power system increases exposure to cyber threats due to their distributed nature and often insufficient security measures. A layered, defence‐in‐depth strategy is crucial for protecting these resources. It should incorporate robust technical safeguards and continuous monitoring to ensure secure and reliable operation of DERs in modern power grids. Unfortunately, the increased information flow will increase the potential attack surface and introduce new vulnerabilities. In this paper, we proposed blockchain‐enabled secure IoT‐based smart grid for distributed energy resource systems using federated learning. The simulation results clearly demonstrate the robustness and effectiveness of the proposed blockchain‐enabled secure smart grid for distributed energy resource systems using federated learning in detecting and classifying cyberattacks across multiple benchmark datasets. The model achieves consistently high detection accuracy, reaching 99.12% on CICIDS2017, 98.75% on UNSW‐NB15, and 99.3% on CICDDoS2019, indicating strong generalisation capability across diverse network traffic patterns and attack scenarios. The results confirm that the proposed system achieves enhanced scalability, reduced computational complexity, faster processing, and improved real‐time responsiveness for secure smart grid and distributed energy resource management applications.
ABSTRACT Critical infrastructure systems such as the electric grid are increasingly cyber–physical; however, despite their inherent cyber–physical characteristics, the physical process system and communication/control network system are traditionally analysed in silos. As these systems become more cyber–physical, it is crucial that models and methods are available to assess the cyber–physical system (CPS) interdependencies, characteristics and event propagation for improved planning, operation and response. Thus, we propose an integrated structural and temporal CPS interdependency analysis framework that provides insight into the CPS function during normal operation as well as disturbances. This structural and temporal analysis of real‐time techniques (START) framework is uniquely designed for assessing CPSs by accounting for the challenges of analysing cyber and physical data streams together due to data availability, data type and time scale differences. By leveraging both structural (e.g., graph analysis) and temporal (e.g., data analytics) techniques, different CPS behaviours and configurations can be accounted for. This paper presents the START framework, a subset of techniques applied to exemplar transmission and microgrid systems and analysis of resulting structural and temporal CPS interdependency insights.
Unmanned aerial vehicles have been widely adopted owing to their compact size, high maneuverability and low cost. Consequently, research on unmanned aerial vehicles guidance, navigation and control systems has emerged as a prominent topic. Path planning constitutes a critical component of guidance, navigation and control systems. To overcome the limitations of existing unmanned aerial vehicles path planning algorithms, including restricted environmental perception, low planning efficiency and diminished learning efficacy-we propose a novel method that integrates the artificial potential field with deep reinforcement learning. We devise a reward strategy that incorporates artificial potential field and path fitting, and construct a hierarchical mechanism that fuses reinforcement learning with classical algorithms. This mechanism effectively optimises the search path, mitigates inefficient exploration and substantially accelerates the path planning process. Finally, experiments conducted in a simulation environment, along with comparative analyses against state-of-the-art techniques, demonstrate the superiority of the proposed method in terms of path planning efficiency.
ABSTRACT Rotor imbalance in wind turbines can lead to significant safety risks and economic losses, which makes accurate fault analysis essential for reliable operation. However, accurate and robust analysis under varying wind conditions remains challenging. To address these issues, a multistage rotor imbalance analysis framework is proposed, which includes fault detection, fault type classification, fault severity classification and blade localisation. First, a knowledge‐based expert system with a multinode decision structure is developed, where nacelle vibration is used for rotor status detection and output power is used for imbalance type classification. Second, a convolutional neural network with training interference that incorporates variable dropout and microbatch training strategies is introduced to improve noise tolerance. Moreover, an adaptive deep interpolation convolutional neural network (DI‐DCNN) is proposed for handling wind‐induced variations through a multisubnetwork weighting and fusion mechanism. Third, the harmonic components of the nacelle vibration are compensated for using information on the rotor azimuth to achieve blade localisation. Experimental results under various wind conditions demonstrate that the proposed method achieves high accuracy across all the tasks and maintains stable performance under varying turbulence intensities. Moreover, the framework does not require additional sensors and has good generalisation ability. The proposed method provides a unified and robust solution for rotor imbalance detection, classification, and localisation in wind turbines.
ABSTRACT This study examines the scientific literature on digital twin technology (DTT) in supply chain management (SCM), utilising bibliometric analysis to investigate the annual publication trend, keyword co‐occurrence, bibliographic coupling and co‐citation regarding foundational themes in SCM, as well as international collaborations. The Scopus dataset is analysed for the period from 2009 to 2024, encompassing a total of 1256 publications, which are analysed using VOSviewer software. This study reviews trends in the application of DTT within SCM, featuring tables, graphs and maps that illustrate key performance metrics for article production. The findings indicate a rising number of publications over the years, with the United States, United Kingdom, Germany and India being the most prolific nations in terms of publishing and collaboration leaders. The most influential keywords related to DTT in SCM include supply chains (SCs), additive manufacturing (AM), 3D printing, digital twin (DT) and sustainability. The authors identify four distinct thematic clusters: AM and SC innovation; shaping parts and service SCs; sustainability and global impact; and DT and Industry 4.0, which are relevant for current and future researchers in this area. The study is useful for researchers, practitioners and policymakers interested in comprehending, implementing and further studying DTT in various sectors of SCM.
