Iet Generation Transmission & Distribution最新文献

The load frequency control (LFC) scheme, as a vital application in power systems' stability, makes the power system susceptible to cyber-attacks due to its dependence on information technologies and communication networks. This paper studies the LFC performance of Kundur's 4-unit-12-bus power system under false data injection (FDI) attacks. The available defence schemes are either based on the system's model or data-driven. The effectiveness of these schemes depends on the precise mathematical modelling or the extensive historical data of the power system. Therefore, it is necessary to design a defence strategy without depending on the mathematical model and the historical data of the system. To this end, this paper proposes a model-free resilient defence strategy, comprising a model-free detection scheme and an event-triggered mechanism. The presented detection scheme accomplishes the manipulated signal estimation using the measurement and control signals and compares the difference between the estimated and observed signals with a predefined threshold value. When the difference exceeds the threshold value, the detection scheme announces that an attack has occurred on the system. After detecting an attack, the event-triggered mechanism is activated to mitigate the attack's effect on the system frequency response. Accordingly, the event-triggered mechanism blocks the falsified signal and submits the estimated signal to the LFC controller. The presented scheme is independent of the system's mathematical model and historical data and can be employed in any cyber-physical power system. The design process of this strategy is simple and independent of the size and complexity of the power system. A deep reinforcement learning algorithm is also employed to tune the adjustable parameters of the proposed method. The real-time results obtained by the OPAL-RT simulator show that the developed scheme can timely identify FDI attacks and completely mitigate the attack's effect on the system's dynamic performance.

The increasing frequency of extreme cold waves exacerbates wind power uncertainty, intensifying the trade-off between robustness and economy in high wind penetration power systems. To address the problem, this paper proposes a DRO method based on distributionally robust Bayesian inference (DRBI). An ambiguity set defined by the Wasserstein metric is first constructed utilising historical wind data. Secondly, the likelihood distribution of wind power output is predicted using an XGB-transformer model. To accurately characterise wind power output during cold waves, a posterior distribution is then constructed using the proposed DRBI framework. Next, a DRO dispatch model is constructed to ensure operational robustness while minimising total operating cost. Constraints include power balance, wind power uncertainty and system security requirements. The model is solved based on strong duality theory. Finally, the model is validated on a regional 30-bus system and a modified IEEE 118-bus system. Experimental results show that, compared to stochastic optimisation and robust optimisation models, the proposed model effectively balances robustness and economy under cold waves. Besides, accounting for wind power uncertainty, experimental results suggest maintaining wind power penetration at 10–20%. Moreover, the economic efficiency of the optimal schedule can be further improved by adjusting the sample size of cold-wave scenarios.

The ever-increasing demand for electricity necessitates constant innovation in the electric utility sector. In this regard, high surge impedance loading (HSIL) transmission lines can be a promising technology. While conventional HSIL designs rely on more subconductors located symmetrically on circular bundles with a larger radius, unconventional HSIL lines can achieve even more natural power by optimally positioning subconductors in space. This paper focuses on determining the optimal location and number of shield wires for a newly designed 500 kV unconventional HSIL line, whose surge impedance is reduced to 141.5$���\Omega �$, resulting in a 74% increase in SIL compared to a conventional configuration (996 MW). New equations for calculating line inductance and capacitance, considering transposition for both phase and bundle arrangements, are developed. The shielding design aims to maintain an SFFOR of 0.05 flashovers per 100 km-years. Numerical analysis indicates that placing the shield wire at $�x$ = 7.2 m yields SFFOR = 0.047 under high lightning activity (Td = 30), while $�x$ = 6.35 m maintains SFFOR < 0.05 under low activity (Td = 5). These results are validated through geometric circle diagrams, demonstrating effective shielding for all three phases without increasing tower height. This study presents a practical shielding method for unconventional HSIL lines that have the potential to revolutionize bulk power transmission.

The utilization of data for generation output forecasting in combined cycle power plants (CCPPs) makes room for attackers to exploit and degrade the performance of machine learning models. This work investigates the potential impacts of cyberattacks on energy generation forecasting models for CCPPs. Attacks are implemented on the four top-performing forecasting models: gradient boosting, extreme gradient boosting, Random Forest, and CatBoost, identified through a comparative analysis of 12 models, including various tree-based methods, support vector machines, deep learning models, and linear regression techniques. Scaling, denial of service, fast gradient sign method, and basic iterative method attacks are employed with diverse attack volumes and perturbations to investigate the vulnerability of these models. To counteract these vulnerabilities, a two-layer defence scheme employing ensemble adversarial training is proposed, aimed at mitigating the adverse effects of these cyberattacks. The findings underscore the significance of the proposed robust defence strategy in ensuring the reliability of forecasting models in the presence of cyberattacks.

With the continuous energy transition and digital industrial upgrades, the collection and analysis of electricity consumption data have become the foundation for supporting the intelligent power system operations and load-side energy efficiency optimisation. However, this development raises significant concerns regarding user privacy and potential data leakage. To address the issue, this paper proposes a battery-aided personalised differential privacy framework that generates physically realisable noise through battery operations, thereby overcoming grid stability limitations associated with conventional virtual noise injection methods. The proposed method enables adaptive privacy preservation through bounded noise probability functions with configurable privacy parameters and incorporates time-of-use pricing via a mean-drift mechanism to enhance the economic viability of battery dispatch strategies. A multi-objective sand cat swarm optimisation algorithm is employed to determine the optimal configuration of battery parameters. Numerical experiments demonstrate that the proposed method effectively defends against load forecasting attacks and significantly reduces electricity costs for users, offering a viable solution for balancing privacy protection and economic benefits in smart grids.

The rapid growth of electric vehicles (EVs) has made accurate forecasting of charging station loads essential for ensuring grid stability and supporting infrastructure planning. While previous studies have investigated this problem, correlation-based feature selection remains relatively underexplored, which may lead to redundant features and reduced prediction accuracy. To address this issue, this paper proposes a hybrid forecasting model, RIME-CNN-LSTM-Attention, which integrates correlation-driven feature selection with advanced deep learning. Pearson, Spearman, and Kendall's tau-b analyses are first applied to identify the most influential factors affecting charging demand. The RIME algorithm is then used to optimise the hyperparameters of the CNN-LSTM network, while the attention mechanism dynamically emphasises critical load fluctuation periods. Case studies utilising actual charging station data illustrate that the proposed model substantially surpasses benchmark methodologies, thereby improving the accuracy and resilience of electric vehicle charging load forecasting.

Electric vehicles (EVs) integration into urban distribution system is surging globally, leading to an increase in the demand for EV charging load, which brought significant hosting risk in urban power systems, such as transformer overload. This study aims to characterize and model the spatial-temporal impact of EV charging on hosting risk of urban power systems across various urban scales considering the recent development of fast charging stations. To achieve this, it proposes a novel framework for calculating the hosting risk. This provides sufficient guiding information for maintaining reliability in urban power systems. In detail, we systematically model the EV charging patterns by using high-resolution real-world EV trajectory and charging record data. K-means clustering is utilized to identify typical charging patterns in various urban scales. Then, the novel hosting risk indicators considering the fast charging station development and the EV growth based on urban EV charging pattern distribution are proposed, where random forest is used to model the critical parameters. Finally, the hosting risk indicators under different urban scale and different EV charging conditions are illustrated. The finding indicates that the hosting risk of urban distribution system is closely related to the EV charging patterns, showing significant regional differences. In Shanghai, the overall city charging pattern exhibits a double-peak structure with morning and evening peaks at 10:14 and 21:04, respectively. The maximum 24-h hosting risk indicators for different charging types vary significantly, with Type 2 charging patterns showing the highest risk. The spatial-temporal changes of which can provide significant information for future plans of EV charging stations and reliability maintenance schemes.

In active distribution networks (ADNs), power electronic devices like inverters introduce noise interference into fault monitoring signals, compromising identification accuracy. This challenge is particularly pronounced in high-impedance grounding faults, where weak fault signatures degrade conventional detection performance. This study proposes an improved residual CNN architecture, ResNet-Kolmogorov-Arnold-network (R-KAN), for accurate single line-ground fault (SLGF) identification. The method leverages the rich fault features contained in transient zero-sequence current (ZSC) and zero-sequence voltage (ZSV) waveforms following SLGFs in DG-integrated systems. The model employs superimposed ZSC-ZSV images as inputs and replaces standard ReLU activation with KAN functions, reducing linear components and computational burden. A comprehensive dataset generated through PSCAD simulations trains the R-KAN alongside conventional neural networks. Comparative evaluations demonstrate R-KAN's superior classification performance across multiple metrics. Rigorous testing, including high-resistance fault scenarios, noise interference conditions, and missing data cases confirms the model's enhanced generalization capability. Field validation using actual recorded waveforms further verifies the model's practical effectiveness in real-world SLGF identification. The proposed approach addresses critical challenges in modern ADNs by combining advanced network architecture with optimized feature extraction from transient zero-sequence components.

This paper presents a novel ferromagnetically shielded fault current limiter (FS-FCL) configuration and control strategy for power systems. The proposed system introduces a controlled DC-reactor design that effectively limits fault currents without significantly affecting the system voltage during normal operation. The main novelty of this work lies in the development of a fully ferromagnetically shielded core and the series connection of the FS-FCL with the power line, which provides an immediate response to fault conditions without delay. In this configuration, the reactor's magnetic flux density is distributed throughout the ferromagnetically shielded core, which increases the effective inductance and prevents magnetic core saturation during fault events. In addition, a control-oriented model of the FS-FCL is developed, together with an improved control algorithm based on reference voltage and current thresholds. Both qualitative analyses and quantitative simulations were performed to evaluate the system's transient and steady-state performance. Simulation results under various fault conditions confirm that the proposed controlled FS-FCL achieves faster current limitation and lower voltage distortion compared with uncontrolled configurations, demonstrating its feasibility for practical power grid applications.

The penetration of renewable energy sources (RES) in Cyprus's power system has increased significantly in recent years. However, this growth poses substantial challenges, primarily due to limitations in distribution networks arising from network congestion and voltage security issues. This paper presents a comprehensive review of existing solutions for enhancing RES hosting capacity, together with a robust methodology for evaluating their effectiveness. To address voltage-related challenges, centralised voltage control strategies at power transformers are combined with adaptive inverter settings to ensure stable operation under increasing RES penetration. Network reinforcements and medium-voltage (MV) upgrades are also considered as complementary measures. At the low-voltage (LV) level, export limitation schemes (ELS) tailored for residential prosumers are proposed, with optimal limits determined for both single- and three-phase installations at targeted RES penetration levels. The effectiveness of the proposed solutions is validated using real MV and LV networks from the Cyprus distribution system, ensuring alignment with the strategic planning framework of the distribution system operator of Cyprus. The findings provide a scientific basis for optimising RES integration, addressing both operational and strategic challenges in modern power systems.