International Journal of Digital Twin Systems and Computing
The International Journal of Digital Twin Systems and Computing (IJDTSC) ISSN:3108-0790 is a high-quality, peer-reviewed journal focused on the theory, development, and application of digital twin technologies across diverse domains. It provides a leading platform for researchers, engineers, and practitioners to publish innovative work spanning the entire digital twin lifecycle from modeling and simulation to real-time synchronization, intelligent decision-making, and system evolution. IJDTSC promotes a multidisciplinary, systems-thinking approach and welcomes both foundational and applied research that advances the design, integration, and deployment of digital twin systems in fields such as manufacturing, smart cities, healthcare, energy, aerospace, and more. JOURNAL PARTICULARS Title: International Journal of Digital Twin Systems and Computing Frequency: Bi-Monthly (JAN-FEB, MAR-APR, MAY-JUNE, JULY-AUG, SEP-OCT, NOV-DEC) ISSN: 3108-0790 Publisher Name: Egnitron Scientific Press Starting Year: 2025 Subject: Engineering Subjects Language: English Publication Format: Online Email ID: editor@egnitronscientificpress.com Each issue includes a combination of original research articles, review papers, case studies, and technical notes within the journal’s scope. IJDTSC also accepts early access (online-first) articles, which are published immediately upon acceptance and final formatting, ahead of the scheduled issue. The journal is free of charge for all authors before the end of 2026.
Experimental Determination of the Voltage–Current Characteristics of a 100W PEM Fuel Cell and a Fuel-Cell-Based Framework for Residential DC Energy Management
This paper reports the experimental determination of the voltage–current (V–I) characteristics of a 100W proton-exchange-membrane (PEM) fuel cell on a trainer test bench and develops a fuel-cell-based framework for residential DC energy management built on the measured quantities. Using an electronic load, the stack voltage and current were recorded at several load currents after the cell was warmed to its operating temperature, and the stack power was recomputed from the readings. Two data issues are reported transparently rather than hidden. First, the tabulated current is dimensionally consistent with the recorded power only when read in amperes–the original ‘mA’ unit label is a transcription error, confirmed by the procedure’s explicit warm-up at approximately five amperes–so the maximum recorded power is about 75W at 150 A and 0.5 V. Second, the measured V–I curve is non-monotonic: the voltage rises from 0.3 V to a peak of 0.7 V near 15 A before declining, which is opposite to the monotonically decreasing polarization curve expected of a fuel cell and is attributed to incomplete activation or thermal conditioning at the lowest currents and to the small number of sample points. The governing electrochemical relations (Nernst potential and activation, ohmic, and concentration overpotentials are summarized, an ideal polarization curve is shown for contrast and clearly labeled as illustrative, and an energy-management architecture coupling a DC–DC stage, a battery or supercapacitor buffer, and prioritized loads is proposed. Because the fuel cell is a soft, slow source, the framework emphasizes transient buffering and operating point control, and the paper specifies the additional measurements required to obtain a clean polarization curve and a quantitative efficiency.
PEM Fuel Cell, Polarization Curve, Voltage–Current Characteristics, Electrochemical Model, Battery Buffer, DC Microgrid, Energy Management, Hydrogen.
Copyright (c) 2026 International Journal of Digital Twin Systems and Computing
Experimental Performance Assessment of a Micro Wind Energy Generator and a Charge-Controller-Based Framework for Residential DC Energy Management
This paper reports a bench-level performance assessment of a micro wind energy generator and develops a charge-controller-based framework for residential DC energy management built on the measured quantities. In the test configuration, a three-phase wind generator feeds a diode-bridge rectifier and a charge controller that supplies a DC load and a battery; measurements were taken at the charge-controller output. The recorded dataset–battery terminal voltage and current during a ‘load running with battery only’ test–is reported transparently together with the recomputed battery power, whose maximum is about 0.376W. An important and openly stated limitation is that the dataset is sparse (five points,four sharing the same 0.04 A current) and that the DC-load and turbine three-phase quantities, as well as wind speed, were not logged; consequently the charge-controller efficiency that the experiment defines cannot be evaluated from the available data, and no efficiency value is fabricated. A pronounced drop of battery voltage from 22.4 V at open circuit to roughly 8.4–9.4 V under a 0.04 A load is identified and discussed as a likely high-source-impedance or near-depleted-source effect rather than concealed. The paper formalizes the wind-power, rectifier, power-balance, and efficiency relations; proposes an energy-management architecture coupling maximum power point tracking (MPPT), battery storage, and prioritized loads; and specifies exactly which additional measurements (load and turbine voltage and current, wind speed, and time stamps) are required to make the assessment fully quantitative. The contribution is a disciplined, end-to-end, fully traceable path from a limited teaching measurement to a deployable micro-wind energy-management concept.
Micro Wind Turbine, Permanent-Magnet Generator, Three-Phase Rectifier, Charge Controller, Maximum Power Point Tracking, Battery Storage, DC Microgrid, Energy Management.
Experimental Analysis of Photovoltaic Panel Characteristics and Application to Smart Residential Energy Management
This paper reports an experimental characterization of a photovoltaic (PV) panel and proposes a framework that uses the measured characteristics for smart residential energy management. Voltage–current (V–I) data were recorded for a PV module under a controlled laboratory light source, and the power–voltage (P–V) relationship, fill factor, and operating points were derived from the raw measurements. The dataset yields an open-circuit voltage of 20.3 V, a short circuit current of 5.7 A, a maximum power of 47.95W at the maximum power point (13.7 V, 3.5 A), and a fill factor of 0.41. A single-diode model was fitted to the measured I–V data and reproduces the dominant operating region with a current root-mean-square error of approximately 0.23 A; a discrepancy between the measured short-circuit current and the current plateau is identified and discussed as a measurement artifact rather than concealed. Building on the validated characteristics, a rule-plus-optimization energy management architecture is proposed that couples maximum power point tracking (MPPT), battery storage, and priority-based load scheduling. The framework is described through energy-balance and state-of-charge formulations, and its expected benefits in self-consumption, peak shaving, and load continuity are evaluated qualitatively and through clearly labeled illustrative assumptions. Module conversion efficiency is reported conditionally because irradiance and module area were not recorded; this limitation, and the steps required to resolve it, are stated explicitly. The work links a standard teaching experiment to a deployable residential energy-management concept and identifies the additional instrumentation required for a fully quantitative evaluation.
Photovoltaic Panel, I–V Characteristics, Fill Factor, Single-Diode Model, Maximum Power Point Tracking, Battery Storage, Home Energy Management, Load Prioritization.
Quantum Machine Learning Framework for Early Detection of Cyber Attacks in Critical Infrastructure
Due to technological changes leading to increased digitization and networked architecture, critical infras- tructures including electrical power systems, transportation, healthcare and industrial control systems, among others, are more susceptible to highly sophisticated cyber attacks than before. Traditional intrusion detection techniques have been found to be inefficient in identifying attacks because of challenges associated with detecting complex patterns and anomalies in real time. This paper discusses a Quantum Machine Learning (QML) approach to early detection of cyber attacks on critical infrastructure systems. In the approach described herein, quantum feature encoding, quantum circuits, and hybrid quantum and classical optimization processes are exploited for improving detection rate and computational effectiveness. Network traffic and behavior data are represented using quantum states and processed via quantum-inspired machine learning algorithms for detecting attack patterns and cyber intrusions. Experimentation indicates high detection rate, low false positive detections and fast convergence compared to conventional machine learning techniques. Different types of cyber threats are detected including DDoS attacks, malware infections, insider attacks, and Advanced Persistent Threats (APTs).
Quantum Machine Learning (QML), Cybersecurity, Critical Infrastructure, Intrusion Detection Systems (IDS), Cyber Attack Detection, Artificial Intelligence, Smart Grid Security.
Measurement of Extremely Low-Frequency Emissions from Compact Distribution Substations in Proximity to Commercial Areas in Sibu
Distribution substations are a critical component of the electricity distribution network . They step down high transmission voltages to levels usable by consumers . The output capacity of each substation varies depending on its location and the energy needs of the population in that area. For this reason, substations are constructed and installed as per the load requirements of the area. Working substations produce electromagnetic fields (EMF). The level of these fields can differ according to the design of the substation, the configuration of its equipment and its application. This paper studies the level of extremely low frequency (ELF) electromagnetic fields produced by the tiny distribution substations in the Unicity’s business area, Sibu. Measurements were taken to determine whether ELF exposure levels are safe for the environment. The measurement process took into account temperature, humidity and presence of shielding materials, as these factors could influence the EMF values measured. The purpose of this study is to establish the intensity of ELF emissions from small power plants and to compare the intensity to limits established by the International Commission on Non-Ionizing Radiation Protection. The study’s findings are intended to provide individuals a clearer picture of how much EMF they are exposed to near tiny substations and to assist in recommendations to make them safer and more compliant with the standards.
Electromagnetic Fields (EMF), Extremely Low Frequency (ELF), Compact Distribution Substation, EMF Measurement, ICNIRP Guidelines, EMF Exposure, Substation Monitoring, EMF Compliance.
Adaptive Digital Twin Framework for Real-Time Fault Detection in Distributed Computing Systems
The rapid expansion and complexity of distributed computing systems have rendered traditional, static fault diagnosis methods inadequate, often resulting in high latency, reactive maintenance, and prolonged downtime. To address these systemic vulnerabilities, the Intelligent Adaptive Digital Twin (IADT) paradigm has emerged as a transformative solution, leveraging real-time bidirectional data synchronization and advanced machine learning to autonomously predict and detect operational anomalies. This paper presents a comprehensive review of state-of-the-art IADT frameworks tailored for real-time fault detection in distributed environments. We systematically examine the deployment of multilayered edge-cloud architectures, which optimize telemetry processing and significantly reduce latency. Furthermore, we analyze dynamic predictive modeling techniques including Long Short-Term Memory (LSTM) networks and Support Vector Machines (SVM) utilized for continuous state estimation and robust fault classification. A critical focus is placed on the evolution of adaptive monitoring policies that dynamically adjust diagnostic frequencies to conserve computational resources during normal operations. Finally, we synthesize empirical findings demonstrating the superior accuracy and resource efficiency of these data-driven frameworks and outline open research challenges, including federated data privacy, advanced AI integration, and the implementation of immersive visual analytics.
Adaptive Digital Twin, Anomaly Detection, Distributed Computing Systems, Edge-Cloud Architecture, Machine Learning, Predictive Maintenance, Real-Time Fault Detection.
Federated Learning-Based Digital Twin Framework for Decentralized Smart City Systems
The rapid proliferation of Internet of Things (IoT) technologies has severely amplified the energy consumption bottlenecks of decentralized sensor networks. This paper presents a comprehensive review of energy-efficient Digital Twin (DT) architectures designed to mitigate the computational and transmission burdens placed on resource constrained edge devices. By shifting from monolithic cloud structures to multi-layered, edge-centric deployments, DTs provide a synchronized virtual environment to intelligently offload complex tasks. We evaluate state-of-the-art AI-driven mechanisms, including Reinforcement Learning (D3QN) for adaptive task offloading and ensemble regression models (CatBoost) for proactive energy load forecasting. Furthermore, this review analyzes practical implementations in smart environments, demonstrating how non-intrusive occupancy monitoring and dynamic Building Information Modeling (BIM) can yield significant quantitative energy reductions, such as a 79% decrease in intelligent lighting costs. Ultimately, this framework establishes a pathway for transforming reactive IoT networks into proactive, energy-optimized ecosystems.
Digital Twin, Energy Efficiency, Internet of Things (IoT), Edge Computing, Predictive Modeling, Task Offloading, Smart City.
Privacy-Preserving Digital Twin Models for Smart Healthcare Applications
The integration of Digital Twin technology into Cyber-Physical Healthcare Systems signals a paradigm shift towards a new generation of personalized and predictive medicine. Through the continuous synchronization of physical biological states with dynamic virtual models using the Internet of Medical Things (IoMT) technology, it is now possible for medical professionals to transition from reactive medicine to proactive medicine. Nevertheless, the continuous transmission and aggregation of extremely sensitive physiological data pose a series of critical security challenges that include false data injection, massive data breaches, and the total compromise of data sovereignty for patients. In this paper, we present a comprehensive review of the latest privacy-preserving mechanisms for smart healthcare digital twins. We systematically examine the underlying multi-tier architecture of DTs and their underlying threat models. Moreover, we synthesize the latest results from three key technology pillars for digital twin technology that include Federated Learning (FL) for privacy-preserving machine learning, blockchain technology for establishing data ownership using Non-Fungible Tokens (NFTs), and edge computing for encrypted refreshment services. We then discuss key challenges for the technology that include consensus latency, algorithmic optimization on constrained devices, and regulatory compliance.
Digital Twin, Healthcare, Privacy-Preserving, Federated Learning, Blockchain, Internet of Medical Things (IoMT), Edge Computing, Data Sovereignty, Cyber-Physical Systems, Unscented Kalman Filter.
Cloud-Edge Integrated Digital Twin Platform for Scalable Smart City Applications
The proliferation of Internet of Things (IoT) technology and mobile crowdsensing technology in modern day smart cities has accelerated the development of smart cities, thereby resulting in the generation of unprecedented amounts of data that require real-time response and large-scale analytics. The concept of Digital Twin (DT) technology has become the foundational concept for the management of smart cities, thereby providing virtual representations of physical entities within the smart city environment using bidirectional real-time data flows. The use of centralized cloud computing technology for the management of smart cities results in unacceptable latency for performance-critical applications, whereas the use of edge computing technology alone lacks the computational power for the management of complex global scenarios. The present paper provides an exhaustive systematic review of the efficacy of Cloud-Edge Integrated Digital Twin technology for addressing the inherent latency problem associated with centralized cloud computing technology for the management of smart cities. The efficacy of the proposed three-tier taxonomy of deployment strategies, including Edge-Heavy (Localized), Hybrid Cloud-Edge (Distributed), and AI-Orchestrated Dynamic frameworks, is assessed for the management of the following four critical smart city scenarios: Intelligent Transportation Systems, Smart Grids, Endpoint Security, and Massive IoT Crowdsensing. Key performance indicators like latency reduction, bandwidth utilization, and energy efficiency are discussed. From the review, it is clear that cloud-edge integration, if further enabled by deep reinforcement learning and generative AI, can ensure that DT platforms can attain inference latency below 50 milliseconds. This is while maintaining scalability across millions of connected endpoints. Additionally, the paper identifies the common challenges facing the DT domain, like interoperability, edge security, and dynamic twin migration. It further outlines the research roadmap towards the creation of fully autonomous and sustainable smart cities.
Digital Twin, Cloud-Edge Computing, Smart Cities, Internet of Things, Vehicular Edge Computing, Deep Reinforcement Learning, Federated Learning, Urban Cyber-Physical Systems
Machine Learning-Enabled Digital Twins for Smart Electronic Systems: A Review
Digital Twin (DT) is a new change of paradigm in the electronic engineering and it offers a possibility to synchronize real-time between physical and virtual electronic systems. Digital Twins enable predictive maintenance, optimizing performance, diagnosing of faults, and managing lifecycle of electronic devices and systems through the integration of new modeling techniques, Internet of Things (IoT) connectivity, artificial intelligence (AI), cloud computing, and data analytics. This is a review paper where the author provides an in-depth discussion of Digital Twin architectures and modeling approaches, communication systems, and implementation schemes in electronic engineering such as power electronics, embedded systems, communication networks, semiconductor devices, and smart grids. Some of the enabling technologies investigated in the study include cyber-physical systems, edge computing, machine learning algorithms, and simulation platforms with high fidelity. Moreover, the data interoperability issues, computational complexity, cybersecurity, real-time synchronization and scalability are also discussed critically. An analytical overview of the current Digital Twin frameworks is also given to emphasize the performance metrics, system integration strategies and application specific adoptions. Lastly, the future research directions toward AI-controlled autonomous Digital Twin, energy-efficient hardware co-designed and next-generation intelligent electronic infrastructures are described. The objective of the review is to inform the researchers and practitioners with a systematized knowledge of the Digital Twin innovations and how they can transform the concept of intelligent electronic systems design, monitoring, and control.
Digital Twin (DT); Electronic Engineering; Cyber-Physical Systems (CPS); Internet of Things (IoT); Artificial Intelligence (AI); Machine Learning (ML); Power Electronics; Embedded Systems; Smart Grids; Predictive Maintenance; Real-Time Monitoring; Cloud and Edge Computing; System Modeling and Simulation; Fault Diagnosis; Lifecycle Management.
High Fidelity Digital Twin Modeling for Adaptive Aircraft Flight Control Systems
It is a new advanced systems of flight control which is required to adapt to dynamic conditions of operation and uncertain conditions of the system in the face of an increasing sophistication of modern aircraft and growing demands of safety, autonomy, and performance. The paper introduces a digital twin modeling high-fidelity framework of adaptive aircraft flight control systems that allows real-time synchronization of the real aircraft with the virtual one. The digital twin proposed will combine physical aerodynamic, structural, and propulsion models with data-driven learning processes to effectively model the nonlinear flight dynamics, environmental perturbations, and degradations of the components. A control architecture is created based on an adaptive control, in which the digital twin continuously integrates system parameters using real-time sensor data and enables proactive control of control laws to respond to changing flight conditions and fault cases. The framework helps to predict online performance, check the stability, and reconfigure the fault-tolerant control without disrupting the flight activities. Nominal, turbulent and fault scenarios at high-fidelity level exhibit considerable enhancement of tracking accuracy, intensity and versatility relative to the traditional fixed-parameter control strategies. The findings affirm that the presented adaptive flight control system based on a digital twin can improve the situational awareness, minimise anomaly response times, and increase flight safety and efficiency in general. The paper forms a scaled base of intelligent and autonomous aircraft systems of the next generation.
Digital Twin; Adaptive Flight Control; High-Fidelity Modeling; Aircraft Dynamics; Real-Time System Synchronization; Fault-Tolerant Control; Aerospace Cyber-Physical Systems; Intelligent Aircraft Systems.
Digital Twin Driven Fault Detection in Embedded Control Systems Using Real-Time Sensor Fusion
The growing sophistication of embedded control systems in contemporary electronic and cyber-physical systems requires a stable and smart fault detection system to guarantee safety in operations and system reliability. A digital twin-driven fault detection system in embedded control systems is presented in this paper based on real-time sensor fusion. The proposed method combines a high-fidelity digital twin that constantly reflects the dynamic behaviour of the physical system with multi-sensor data taken in real time. The digital twin allows the accurate state estimation and early fault detection of the heterogeneous sensor signals including voltage, current, temperature, and vibration by the fusion of sensor signals in different operating conditions. A hybrid approach to fault detection with a model-based and data-driven approach to anomaly detection is used to differentiate between normal operational deviations and incipient faults. Active synchronisation between the physical system and its digital counterpart means that it is possible to perform adaptive thresholding and robust fault isolation in the presence of sensor noise and environmental uncertainties. The framework runs on a built-in platform, reflecting minimal computational costs and real-time capability. The experimental data verify that the suggested technique is much more effective than the traditional single-sensor and non twin based methods of fault detection and false alarms, as well as fault response time. The introduced digital twin-based architecture is a scalable and intelligent design of health monitoring and fault diagnosis of embedded control systems, which is applicable to power electronics, industrial automation, and smart cyber-physical systems.
Digital Twin–Based Modeling and Health Management of Analog and Mixed-Signal Circuits
Modern electronic systems are based on analog and mixed-signal (AMS) circuits that interface the physical world to digital number-crunching units. The changing process variations, aged effects, environmental stress and inability of observing them when operational is increasingly becoming a challenge to guarantee their reliability and long term performance. The paper proposes a modeling and health control framework of analog and mixed-signal circuits based on digital twins, which allows providing real-time monitoring, predicting the performance of the system, and diagnosing faults proactively. The proposed digital twin combines both physics-based circuit models and data-driven learning strategies to reproduce the dynamic behavior of AMS circuits with different operating conditions accurately. Through constant alignment of simulation models with real-time measurements, the digital twin records parametric drifts, nonlinearities, and degradation by-products like bias temperature variability and electromigration. Health indicators such as gain deviation, compensating drift and bandwidth reduction, and variation in noise are reported and processed to determine a circuit state and further life expectancy. The efficacy of the suggested method in early fault detection, performance degradation monitoring, and reliability improvement is proved by a case study with analog and mixed-signal representatives blocks. Both simulation and experimental findings demonstrate that the digital twin enhances prediction accuracy and provides an opportunity to make timely maintenance-related decisions as compared to traditional methods of working with steady models. The suggested framework presents a scalable and powerful approach to smart health management of new-generation electronic systems to uphold better reliability, less down time, and long service life.
Digital Twin; Analog and Mixed-Signal Circuits; Health Monitoring; Fault Diagnosis; Predictive Maintenance; Reliability; Degradation Modeling; Real-Time Systems.
Machine Learning-Driven Digital Twin for Real-Time Optimization of Hybrid Energy Storage Systems
The growing adoption of renewable energy sources and the electric mobility has heightened the requirement of intelligent energy storage control that can act under dynamic and uncertain conditions. In this paper, we suggest a machine-learning-based algorithmic digital twin of real-time optimization of hybrid energy storage systems (HESS) consisting of batteries and supercapacitors. The created digital twin creates a high-fidelity cyber-physical model of the physical HESS by keeping system conditions in parity with real-time measurements of voltage, current, power, and state-of-charge variables. The digital twin uses machine learning models to forecast the demand of the load, availability of renewable power, and the dynamics of energy storage systems to allow it to anticipate and react to changes in energy management. The proposed framework is the best method to divide power between the battery and the supercapacitor because it separates low-frequency and high-frequency power elements and, therefore, allows decreasing the current load in the battery, which will increase the efficiency of the entire system. The machine learning predictions are combined with control constraints via a real-time optimization layer, and the resulting reference signals are optimum ones to the power electronic converters. The simulation results at different operating conditions such as the rapid loading transient and renewable power variations have shown that the proposed digital twin-based solution is considerably better than the existing rule-based energy management solutions in terms of power tracking performance, battery current ripple, and battery life. The findings prove the usefulness of the proposed framework as a scalable and smart proposal to next generation hybrid energy storage systems in renewable energy and electric mobility applications.
Digital Twin, Hybrid Energy Storage System, Machine Learning, Battery–Supercapacitor, Energy Management, Real-Time Optimization.
Artificial Intelligence-Enhanced Digital Twins: A Review of Algorithms, Applications, and Implementation Barriers
Artificial Intelligence-Enhanced Digital Twins (AI-DTs) have been a revolutionary paradigm that allows real-time monitoring, prediction, and optimization of complex physical systems. By combining advanced AI algorithms and dynamic virtual replicas, AI-DTs play a major role in improving decision-making accuracy, system adaptability, and operational efficiency in a variety of sectors, including manufacturing, healthcare, smart cities, transportation, and energy. This review discusses the key AI methods such as machine learning, deep learning, reinforcement learning and predictive analytics that enable Digital Twins to learn from data, predict the behavior of systems and respond specifically to changing conditions. Additionally, the paper identifies key application areas where AI-DTs have had significant impact and measurable impact, including predictive maintenance, process automation, personalized medicine, structural health monitoring, and intelligent infrastructure management. Despite their emerging adoption, widespread implementation of AI-DTs faces a number of critical barriers, such as data integration challenges, computational complexity, cybersecurity risks, model interpretability issues and the lack of standardized frameworks. This review synthesizes existing research to lay out a comprehensive understanding of the technological landscape, identifies key gaps that limit real-world deployment, and outlines future research directions that aim to enable more secure, scalable, and interoperable AI-driven Digital Twin eco-systems
Algorithms, Artificial Intelligence, Big Data, Cyber-Physical Systems, Digital Twin, Industry 4.0, Internet of Things, Machine Learning, Predictive Analytics, Real-Time Monitoring, Reinforcement Learning, Simulation Models.
Digital Twin Systems in Industry 4.0: A Review of Computing Techniques and Practical Applications
The industry 4.0 has used digital twin systems which strongly integrate physical properties with their clients to monitor in real-time, do predictive analytics and optimize lifecycle at design, production, and service stages. It is a review of the computing techniques that underpin digital twins, including IoT sensing and edge/cloud computing to acquire data and scale and physics-based and data-driven modeling to provide the high-fidelity representation and AI/ML to support diagnostics, prognostics, and decision making, as well as computing capabilities maturity models and reference architectures to progress capabilities. Applications Practical implementations in the area of discrete and process manufacturing, smart logistics, energy and utilities, and urban systems have shown significant improvements in shrinking downtime, increasing quality, speeding up design cycles, and cost reduction through predictive maintenance and what-if simulation. Spite of the fast maturation rate, the adoption processes are challenged with a long-running equivocation: interoperable data management with non homogenous systems, the complexity and validation of models, security and privacy concerns with cyberspace, and the absence of standards and solid KPI to compare across domains. The review outlines the emerging directions which include AI-native twins, closed-loop autonomy, scalable cloud-edge orchestration, and blockchain and BIM to promote resilient, sustainable and self-optimizing industrial operations. The results overall meaning that digital twins are a key facilitator to data-centric and adaptive manufacturing ecosystems at the core of further development of Industry 4.0.
Digital Twin, Cloud Computing, Data Governance, Edge Computing, Hybrid Modeling, Industry 4.0, Internet of Things, Interoperability, Predictive Maintenance, Physics-Based Modeling, Smart Manufacturing, Supply Chain Optimization.
Copyright (c) 2025 International Journal of Digital Twin Systems and Computing
Digital Replica and Twin Systems for Cyber-Physical Power Systems: Technology Development and Foresight
Digital Twin (DT) technology is emerging as a transformative force in the modernization of cyber-physical systems (CPSs), enabling seamless integration between physical infrastructure and its virtual counterpart. This paper presents a comprehensive review of DT concepts, historical evolution, and enabling technologies—such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and cyber-physical systems to support real-time monitoring, predictive maintenance, and system optimization. The paper begins with foundational frameworks for modelling digital replica modules and cooperative operation as twin systems. It discusses the application of DTs in improving monitoring, control, system resilience, network integration, component tuning, and managing decentralized edge-based cyber-physical systems is explored in depth. The paper draws attention towards the applicability of digital replica and twin system technology in the power grid as an evolving cyber-physical system. It also includes key challenges like data accuracy, communication latency, and system interoperability in terms of digital twin operation and management.
Digital Replica, Digital Instances, Digital Twin, Cyber-Physical System, Smart Grid, Artificial intelligence (AI), and machine learning (ML).
Adaptive Digital Twin Framework for Real-Time Asset Monitoring and Predictive Maintenance
This study suggests a novel adaptive digital twin system, a synthesis between real-time IoT telemetry, physics-ML hybrid modeling, and closed-loop control to allow systematic asset health assessment and scalable predictive maintenance. The architecture is multi-layered: data acquisition and edge filtering; a streaming analytics layer with anomaly detection and remaining useful life (RUL) estimation; an adaptive model layer with self-calibration, online learning and parameter estimates; and a decision orchestration layer giving prescriptive maintenance instructions and scheduling updates back to the physical asset. Bidirectional data flows between models and automated model management is used to reduce model-plant mismatch in nonstationary environments, i.e., wear, drift, and changing operating regimes. We make recalibration triggers of model recalibration, between physics residuals and map embeddings define health, and uncertainty-aware schedules improve timing in maintenance and spare parts logistics and production constraints. The architecture achieves heterogeneous assets and old systems through common interfaces, hence resolving interoperability gaps found in recent survey of experts. The representative industrial machinery assessment shows a better early-fault detectability, less falseness and longer maintenance times than when using the static models, as well as not compromising explain ability via physics-constrained predictors. The findings point to a road to stable, self improving digital twins that bring quantifiable uptime, cost and safety ascent in real time functioning.
Asset monitoring, predictive maintenance, adaptive digital twin, anomaly detection, interoperability, predictive maintenance, remaining useful life, Self-calibrating models, virtual sensing, uncertainty quantification.
Optimized ANN–LSTM Approach for High-Precision Day-Ahead Photovoltaic Power Forecasting
The growing integration of PV systems in the power grid requires an ideal forecasting approach of energy management. One of the new concepts that are presented in this research paper is the possibility to forecast photovoltaic power generation 1 day before with the assistance of long short-term memory (LSTM) algorithms which will provide the adequate possibility to predict the future photo generation of power, offer the opportunity to distribute resources much quicker and reach a better result in the utilization of the resources of the renewable energy. It discusses the limitations that relate to traditional forecasting models and presents that LSTM is a great answer to the problem because of its possibility to capture the long-term dependency of time series records. The section on methodology explains how the LSTM networks were implemented, the approach used to preprocess the data, the structure of the model and the training process. This paper has highlighted the role of appropriate selection of input features like historic solar exposure, temperature and past-historical power generation information in positive prediction. The trained LSTM-based forecasting model is validated and evaluated on actual photovoltaic power generation data. Measures of performance The following are applications of the measures of Mean Absolute Error (MAE) Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) mobilize, using, measures of HFES performance. Results show that the LSTM solution is superior to the traditional forecasting methods, which makes sense considering it should be used to identify the complex trends and improve the accuracy of day-ahead electric generation using photovoltaic panels. This article describes what the specific predicting means the stability of the grid, power trading and coherent renewable power and identifies the sense of the setting forth in practice of what has been proposed.
Photovoltaic power forecasting, Long Short-Term Memory (LSTM) algorithm, Day-ahead prediction, Power generation data, Prediction accuracy.
Grid-Connected PV-Wind System with Energy Storage and Electric Vehicle Integration
The addition of renewable energy sources into power systems is essential to meet growing energy demands while reducing environmental impact. This study presents an innovative approach to enhancing power generation and optimizing power quality in a grid-connected hybrid system comprising photovoltaic (PV), wind energy, energy storage, and electric vehicle (EV) integration. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Maximum Power Point Tracking (MPPT) algorithm is employed to maximize energy harvesting from the PV and wind subsystems under varying environmental conditions. The proposed system also incorporates energy storage to ensure reliability and stability by mitigating fluctuations in renewable energy output. Additionally, the bidirectional interaction with EVs enables energy balancing during peak and off-peak hours. Simulation results demonstrate the system's ability to achieve efficient power management, with reduced Total Harmonic Distortion (THD) of 3.08 % and improved voltage regulation during grid disturbances. The ANFIS-MPPT algorithm exhibits superior performance providing efficiency of 99.5%, ensuring rapid convergence to the optimal operating point. This comprehensive approach highlights the potential of hybrid renewable systems with advanced MPPT techniques and energy storage in delivering enhanced power quality and system reliability, paving the way for sustainable and efficient energy utilization in modern grids.