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.
IntroductionDigital 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.MethodsA 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.ResultsThe 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.DiscussionThe 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.
Accurate prediction of line loss rate enables more economical and efficient decision-making in the power sector. This paper presents a short-term line loss rate forecasting method for distribution networks based on an integrated RF-CNN-SVM approach. Given the high-dimensional time-series characteristics of line loss data, the random forest algorithm is first applied to evaluate and rank the importance of multiple feature variables. A combined convolutional neural network and support vector machine (CNN-SVM) model is then constructed to perform the prediction. To demonstrate the superiority of the proposed model, comparative experiments are conducted against the CNN-SVM and standalone SVM models, without feature selection via random forest. The results confirm that the RF-CNN-SVM model achieves superior predictive performance.
Existing photovoltaic (PV) output simulation methods often rely on artificial neural networks for short-term forecasting, and there has been a struggle to capture long-term patterns and stochastic fluctuations when using Markov Chain Monte Carlo techniques. To address these limitations, this paper proposes an improved headroom model-based approach that enhances traditional methods in three key aspects. First, unlike traditional headroom models that ignore temporal dependencies in output fluctuations, the approach integrates probabilistic distributions with soft sequential constraints to preserve time-dependent patterns. Second, whereas previous studies often overlooked seasonal weather variations, here PV output curves are classified into representative weather types and seasonally adaptive Markov chains are constructed to model radiation dynamics and transition probabilities. Third, to address the oversimplification of sunrise and sunset transitions, the method introduces a specialized statistical correction tailored to these critical periods. The method accurately models PV output patterns and fluctuations, demonstrating < 1% deviation in annual duration (4,121 h) and utilization (1,297 h), with a 7.80%−14.59% lower root mean square error and 10.27%−14.07% reduced mean absolute error vs. conventional methods. It efficiently generates realistic long-term sequences from limited data, enhancing the accuracy and efficiency of PV power sequence simulation.
IntroductionMobile robots increasingly support inspection and emergency response in smart-grid infrastructure but require accurate, interpretable backend diagnostics. This work is proposing a hybrid model that integrates CatBoost (for tabular features) with a deep 1D-CNN (for spatial feature extraction) and integrates Local Interpretable Model-agnostic Explanations (LIME) to provide transparent, instance-level rationales.MethodsWe evaluate on a synthetic DSGC-based stability dataset (14 features) and externally on the IEEE PES 2018 fault-clearing corpus. The hybrid concatenates CatBoost output probabilities with a three-layer CNN feature vector, followed by dense layers (ReLU and Sigmoid). Models are trained using the Adam optimizer. Performance is reported via Accuracy, Precision, Recall, F1, confusion matrices, ROC-AUC, and LIME explanations.ResultsOn the generated synthetic data, the hybrid achieved 98.23% accuracy (F1 = 97.56%), outperforming ANN, DNN and CNN baselines. External validation on IEEE PES 2018 yielded F1 = 97.6%.DiscussionCombining gradient-boosted trees with deep convolutional features improves discrimination while and it is preserving local explainability. This way it can be supporting both grid operations and stability-aware robotic mission planning. Future work will extend to multiclass/regression settings and compare XAI methods (e.g., SHAP) alongside additional tabular learners (XGBoost/LightGBM).
As the grid-connected capacity of distributed photovoltaic (PV), energy storage, electric vehicle (EV), and other devices gradually increases, new source-load equipment becomes an important demand response (DR) resource in the distribution network (DN). To fully utilize the DR's capability for EVs and other devices and reduce the system operating costs and line network loss, this article presents a DR scheduling strategy for EVs based on a time-of-use (TOU) price dynamic adjustment mechanism. First, a fuzzy C-mean (FCM) clustering algorithm is used to calculate the typical operating curves of PV and electrical load and their optimal number of classifications. The deterministic scenarios express the PV's output characteristics and the users' electricity consumption characteristics. Second, a dynamic adjustment mechanism of TOU price is proposed based on the load operation curve of the DN, and the interactive price-incentive signal for DR within the DN is formulated. Finally, a DR scheduling strategy for EVs in the DN that considers the economic cost of system operation and line network loss is proposed. CPLEX in MATLAB is employed to simulate the cases. After applying the TOU price dynamic adjustment mechanism proposed, the peak total load and peak–valley load difference decreased by 6.9% and 33.8%, respectively, compared to implementing fixed electricity prices. At the same time, the operating revenue of the distribution network increased by 13.2%, and the line network loss decreased by 12.9%. The analysis results demonstrate that the proposed EV DR scheduling strategy can realize the price guidance and orderly scheduling of EVs and reduce the operation cost and line network loss in the DN.
Integrating electric vehicles (EVs) into the electricity and carbon markets presents a promising pathway toward sustainable transportation futures. This article proposes a comprehensive framework that synergizes the operations of the electricity and carbon markets with the growing adoption of EVs. The proposed framework includes a low-carbon transmission network operation model that integrates the electricity and carbon markets, facilitating optimal energy dispatch while minimizing carbon emissions. In addition, the framework extends to distribution network operations, incorporating a double carbon taxation mechanism to address emissions at both the generation and consumption levels. A carbon emission flow model is employed to meticulously trace carbon emissions across the supply chain, enhancing transparency and accountability. The framework also introduces an EV-integrated traffic flow model that captures the interactions between transportation networks and energy demand, influencing traffic dynamics and EV charging behaviors. Furthermore, a planning and pricing model for EV charging stations is developed, incorporating carbon costs into the pricing strategy to incentivize eco-friendly practices. The multilevel solution algorithm ensures an iterative convergence of decision variables across transmission, distribution, and transportation networks, ultimately fostering an integrated eco-transport system. This work contributes to the development of sustainable transport systems by promoting efficient EV integration and supporting decarbonization efforts in both the energy and transportation sectors.
To address the degradation of grid quality and charging efficiency associated with the large-scale integration of electric vehicles (EVs), a multi-stage balanced flexible load scheduling method is proposed. This approach is designed to facilitate peak shaving and valley filling, balance intermittent energy fluctuations, and provide auxiliary services, thereby significantly altering system load characteristics, smoothing energy fluctuations, reducing operational costs, and enhancing the regulatory capabilities of power grid dispatching operations. A multi-objective optimization mathematical model is developed, focusing on key indicators that impact the scheduling process, including network loss, operational cost, and user satisfaction. A multi-stage flexible load scheduling framework is introduced within the competitive swarm optimization (CSO) algorithm, resulting in the design of an advanced CSO algorithm. This algorithm is distinguished from traditional methods by the implementation of advanced learning based on grouping after a random competitive learning phase, which enhances the efficiency of particle swarm learning while ensuring stable population convergence throughout the optimization process. Furthermore, the CSO framework is maintained to ensure effective population diversity, greatly improving the optimization performance. Simulation results indicate that the voltage fluctuation index of the proposed algorithm is 1.8% lower than that of the standard CSO algorithm, while network loss and operational costs are reduced by 2.83 and 5.81%, respectively, thereby validating the effectiveness and efficiency of the proposed approach.
Introduction With the rise of distributed energy resources, the interconnection of distribution networks and Flexible Multi-State Switch (FMSS) has become a key technology in the construction of new distribution networks. FMSS plays a significant role in enhancing the reliability and flexibility of the system. Methods This paper investigates the impact of mode-switching in FMSSs on voltage shocks, current shocks, and power fluctuations in the event of a feeder fault in a multi-port flexible interconnected distribution system. Firstly, an improved state-tracking control method is proposed to effectively mitigate these impacts. Secondly, for feeder faults connected to the fixed DC bus voltage ( U dc -Q ) control port, a reselection method for the U dc -Q control port at the main station is introduced, aiming to select the optimal port to maintain the stability of the DC bus voltage. Results Simulation experiments have validated the effectiveness of the proposed control method in reducing voltage and current shocks as well as power fluctuations. Additionally, the proposed control strategy has demonstrated an enhancement in the safe and stable operation of the system under emergency fault conditions. Discussion The control strategy presented in this paper is capable of addressing the challenges posed by feeder faults and ensuring the stability of the DC bus voltage and reliable power supply to feeder loads, thereby enhancing the overall performance and reliability of the flexible interconnected distribution system. The research findings have been verified on the MATLAB/Simulink simulation platform.
Virtual synchronous generator (VSG) control technology for photovoltaic, energy storage, wind power, and other new energy to provide flexibility in the grid interface characteristics, is conducive to improving the stability of the power system, and has been widely considered by many scholars. Firstly, an improved VSG control method is proposed through simulation and analysis, which realizes the complete decoupling of the frequency response time constant and the inertia quantity of the active power control loop and reduces the complexity of the parameter design of the VSG system. Secondly, to avoid the frequent action of the VSG system caused by small-scale frequency change perturbation, this study proposes a VSG frequency optimization control method for VSG frequency control with rated angular velocity ωset feedforward composed of multivariate factors when considering a primary frequency regulation dead zone. Thirdly, the impact of VSG parameter design on the system is investigated through the system response characteristics of power scheduling, primary frequency regulation at the grid connection, and the small-signal dynamic characterization of the improved VSG. Finally, Simulation and experimental verification yielded an active power overshoot of 7% and a maximum frequency deviation of 0.17 Hz for the improved system. The improved control method resulted in an improvement of 0.1 s in the frequency response time and a reduction of 0.15 Hz in the oscillation amplitude. The response speed of the improved control method is much better, while the oscillation amplitude is reduced to meet the grid's regulation requirements. The simulation and experimental analysis verify the feasibility of the improved VSG control method.
The paper introduces a novel method for identifying the location of harmonic-generating sources in smartgrids. The method utilizes a Dual-Tree Complex Wavelet Transform (DTCWT) of voltage and current signals measured at a specific point in the network. By applying DTCWT Transform, the signals are decomposed, and three non-active power quantities are extracted to represent the harmonic components within the system exclusively. These chosen non-active power quantities serve as indicators of the presence of harmonics in the system. Through analysis and comparison of these quantities, the method enables determining the precise location of the dominant harmonic generating source. This information is valuable for effectively addressing and mitigating harmonic issues in the network. Leveraging DTCWT and focusing on non-active power quantities provides a valuable tool for power system engineers and operators to diagnose and mitigate harmonic issues, ultimately improving power quality and system performance. This study presents a new feature extraction method to compute Non-active power quantities based on DTCWT due to its shift-invariant property.
Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.
For a more efficient monitoring and control of electrical energy, the physical components of conventional power systems are continuously integrated with information and communication technologies, converting them into smart grids. However, energy digitalization exposes power systems into a wide range of digital risks. The term cyber resilience for electrical grids expands the conventional resilience of power systems, which mainly refers to extreme weather phenomena. Since this is a relatively new term, there is a need for the establishment of a solid conceptual framework. This paper analyzes and classifies the state-of-the-art research methodologies proposed for strengthening the cyber resilience of smart grids. To this end, the proposed work categorizes the cyberattacks against smart grids, identifies the vulnerable spots of power system automation and establishes a common ground about the cyber resilience. The paper concludes with a discussion about the limitations of the proposed methods in order to extract useful suggestions for future directions.
A large proportion of new energy generation is integrated into the power grid, making it difficult for the power grid system to maintain reliable, stable, and efficient operation. To address these challenges, this article proposes a multiple microgrid hierarchical optimization structure based on energy routers as the core equipment for energy regulation within microgrids. Considering the uncertainty of renewable energy generation within microgrids, a two-layer energy management bidding strategy based on risk indicators is further proposed. In the process of trading, with the goal of maximizing a comprehensive economy, the energy trading model of the distribution network center and energy routers is divided into two sub-objectives for solving. In the first stage, based on the interests and energy supply and demand relationships of each microgrid, a risk assessment model considering wind and solar uncertainty is established to determine the risk preferences and expected returns of each microgrid. In the second stage, the original problem is decomposed into two sub-problems: the minimum cost sub-problem and the maximum transaction volume sub-problem. An asymmetric bargaining mechanism is adopted to determine the production and sales payment of the microgrid containing energy routers based on the contribution values of energy routers in each microgrid. Finally, the rationality and effectiveness of energy routers as an intelligent decision-maker in energy optimization are verified in a three-node system.
Modern power systems, characterized by complex interconnected networks and renewable energy sources, necessitate innovative approaches for protection and control. Traditional protection schemes are often failing to harness the vast data generated by modern grid systems and are increasingly found inadequate and challenging for some applications. Recognizing the need to address these issues, this paper explores data-driven solutions, focusing on the potential of machine learning (ML) in power system protection and control. It presents a comprehensive review highlighting various applications which are challenging to address from conventional methods. Despite its promise, the integration of ML into power system protection introduces unique challenges. These challenges are examined in the paper, and suggestions are provided to overcome them. Furthermore, the paper identifies potential future research directions, reflecting the progressive trends in ML and its relevance to power system protection and control. This review thereby serves as an essential resource for practitioners and researchers working at the intersection of ML and power systems.
This paper focuses on the effective use of smart inverters for Volt/Var control (VVC) on a distribution system. New smart inverters offer Var support capability but for their effective use they need to be coordinated with existing Volt/Var schemes. A new VVC scheme is proposed to facilitate such coordination. The proposed scheme decomposes the problem into two levels. The first level uses Load Tap Changer (LTC) and Voltage Regulators (VRs) and coordinates their control with smart inverters to adjust the voltage level on the circuit to keep the voltages along the circuit within the desired range. The second level determines Var support needed from smart inverters to minimize the overall power loss in the circuit. The results of the supervisory control are sent to the devices which have their local controllers. To avoid frequent dispatch, smart inverters are supervised by shifting their Volt/Var characteristics as needed. This allows for the smart inverters to operate close to their optimal control while meeting the limited communication requirements on a distribution system. A case study using the IEEE 34 bus system shows the effectiveness of this supervisory control scheme compared to traditional volt/var schemes.
The challenge of ensuring the reliable running of power systems has gotten more difficult in recent years due to the rising complexity of power system networks. The decreasing accessibility of fossil fuels has necessitated a greater dependence on renewable energy sources, such as solar systems, wind power, and hydroelectric power, by the international community. As a result, there is an increasing demand for AC microgrids to offer an effective approach for distributing power. The power system networks that consist of microgrids frequently have a significant number of failures, surpassing 80%. These failures occur because microgrids are susceptible to unexpected changes in different distributed generating sources. The variations greatly impair the operating efficiency of the microgrid and have negative consequences for the distribution system. The microgrid consists of numerous dispersed generation units and local loads. The load in a microgrid exhibits parametric uncertainty, which adds to the fluctuation observed in its performance. The formulated control strategy is Model Predictive Control, which aims to achieve robust performance even in the presence of unaccounted-for loads, dynamic loads, harmonic loads, and both balanced and unbalanced loads. The authors of this paper have developed a control approach that utilizes model predictive control (MPC) and is characterized by its robustness and optimality. MPC has the ability to predict the future behavior of a certain system. The controller successfully mitigates and reduces any disruptions that may occur within the power distribution system by taking into account its healthy characteristics. The model is implemented in the MATLAB Simulink environment, where it produces an accurate and appropriate total harmonic distortion value. The model was compared to previous efforts and significantly improved by increasing some crucial parameters by up to 90%. The value functions as a measure of the controller's performance quality and the improved efficiency of the microgrid system.
With the proliferation of alternative energy sources, power grids are increasingly dominated by grid-tied power converters. With this development comes the requirement of grid-forming, but current architectures exclude high-voltage applications through serial connectivity. Lattice power grids allow for the generation of both higher voltages and currents than their individual modules by marrying the advantages of serial and parallel connectivity, which include reduced switching and conduction losses, sensorless voltage balancing, and multiport operation. We use graph theory to model lattice power grids and formalize lattice generation processes for square, triangular, and hexagonal lattice grids. This article proposes depth-first-search based algorithms for the control and efficient operation of lattice power grids, achieving voltage and current objectives while minimizing switching losses. Furthermore, we build upon previous algorithms by harnessing multiple input/output operation. The algorithm allows for sequential operation (in which loads are added one by one), simultaneous operation (in which several loads are added at the same time), and combined sequential-simultaneous operation. These methods were applied to a variety of lattice structures, and simulations of dc analysis and pulse train generation were performed. These modeled results validate the proposed algorithms and improve versatility in the operation of lattice power grids in both grid-connected and standalone applications. The potential of applying this method in transcranial magnetic stimulation (TMS) is discussed.
