Iet Generation Transmission & Distribution最新文献

The low resolution and blurred details of transmission line inspection images lead to suboptimal defect detection performance. To address this issue, this paper proposes a defective fittings super-resolution (DFSR) method based on stable diffusion. First, DFSR introduces a multi-fittings low-rank adaptation module to incorporate various fittings concepts into the stable diffusion model and fine-tune it, enabling it to learn different fittings concepts effectively. Then, the conditional constraints module is designed, including an edge-guided structural constraint and a histogram-based colour constraint, to optimize structural reconstruction and colour consistency during the super-resolution process, thereby improving the overall image quality and visual performance. Experimental results demonstrate that DFSR achieves high-quality super-resolution for fittings and outperforms existing methods across multiple reference and no-reference metrics. Specifically, it improves PSNR and MUSIQ by 3.46 dB and 12.29, respectively, over the baseline model. Furthermore, a localized super-resolution enhancement strategy is proposed to enhance fittings defect detection by performing super-resolution on defective regions in inspection images. Its effectiveness was validated on the YOLOv11 model, achieving a $��1.6�\%�$ improvement in $�{\text{mAP}}_{50}$, which further proves its practical applicability in real-world transmission line inspection.

This paper proposes an enhanced data-driven fault detection scheme in power systems with sparse sensors. This scheme utilizes data augmentation techniques specifically designed to improve the accuracy of deep learning-based fault detection classifiers. The enhancement is achieved by expanding limited training data through the implementation of the Auxiliary Classifier Wasserstein Generative Adversarial Network with gradient penalty (ACWGAN-gp) network. The three-phase voltage signals from sparse sensor locations in power grids are converted into grayscale images, simplifying the application of image classification for fault detection. A two-stage Convolutional Neural Network (CNN)-based fault detection scheme first determines a potential faulted subnetwork and then identifies the faulted line within it. The ACWGAN-gp-driven data augmentation generates high-quality synthetic samples that are not duplicates but varied representations, enriching the dataset in quantity and fault scenarios. The effectiveness of this approach is demonstrated through tests on the IEEE 39-bus test system.

Situation awareness is a critical foundation for the safe operation of offshore wind power. To address the uncertainty risk caused by offshore wind power penetration in the extremely complex marine environment, a novel security situation awareness method for the offshore wind power networking system is proposed on the basis of the vague-CNN-LSTM model. Firstly, a partition model of offshore wind system topology is built according to the location of offshore wind farm access nodes, which can quickly capture the elements of the system situation. Secondly, a vague set is introduced to propose an interlaced offshore wind power interval division method integrating the truth-membership degree and pseudo-membership degree functions. Thirdly, the vague set interval prediction model based on vague-CNN-LSTM for offshore wind power is established to forecast the future fluctuation range of offshore wind power from different criteria, including support, opposition and hesitation uncertainty. Fourthly, early warning indexes for the security situation of the offshore wind power networking system are proposed so that the real-time and future security risks of the entire system can be perceived from the multiple layers of node-branch-area-network. Finally, the effectiveness of the proposed method is validated using a case study of an actual offshore wind farm in China.

This paper introduces an optimized distributed secondary control framework for islanded DC microgrids that simultaneously achieves voltage regulation, economic dispatch, and voltage ripple reduction. The framework integrates consensus-based distributed optimization to compute optimal power outputs and applies particle swarm optimization (PSO) to generate ripple-minimizing reference signals. Both values are directly injected into the secondary control loop to adjust the system's operating point dynamically. This structure enables real-time coordination of multiple control objectives without mutual interference, ensuring that improvements in one objective do not adversely affect the others. Simulation results on a six bus DC microgrid under varying load conditions confirm significant improvements in voltage stability, reduction of generation costs, and effective ripple suppression. The proposed approach offers a scalable, robust solution that balances technical performance with economic and power quality considerations.

The neglect of randomness in electric vehicle (EV) users' charging behaviour and existing charging stations' impacts leads to irrational service range division and capacity allocation for newly added charging stations. To address this, a multi-stage optimal allocation method for newly added electric vehicle charging stations (EVCSs) based on comprehensive service performance is proposed. First, an improved Huff model incorporating charging station attributes, travel time value, and individual user preferences analyses probabilistic charging behaviour to determine service range and charging demand. Second, a comprehensive evaluation method using power utilisation rate, effective service rate, and capacity redundancy is established, with indicator weights determined by an Analytic Hierarchy Process-Entropy Weight (AHP-EW) method integrated into a fuzzy theory-based scoring system. Third, an allocation model integrating comprehensive service evaluation from a combined operator-user economic perspective is formulated. Finally, an Immune Clone Selection–Variable Neighbourhood Search (ICS–VNS) hybrid algorithm is designed for efficient optimisation. Case studies demonstrate that, compared to the existing planning scheme, both the single-station and two-station deployment schemes reduce peak demands at existing busiest charging stations by 16.1% and 27.5%. These improvements are achieved with controllable investment and operational costs, ensuring a balance between enhanced service performance and cost efficiency.

Over the past two decades, there have been frequent large-scale power outages worldwide caused by voltage collapse, resulting in significant economic losses. With the development of technologies such as renewable energy and HVDC (high voltage direct current) transmission, the power system is gradually moving towards a trend of high penetration of renewable energy and a high proportion of power electronic devices. As a result, the mechanism of transient voltage stability is becoming increasingly complex, posing serious challenges to transient voltage stability analysis. Therefore, it is of great importance to quickly and effectively assess and quantify transient voltage stability for the safe and stable operation of the system. This paper first explores the concept of transient voltage stability, and then provides a detailed review and analysis of transient voltage stability practical criteria in various countries and regions, summarizing a more adaptable and practical three-stage transient voltage stability criterion. Additionally, it also classifies and summarizes existing transient voltage stability assessment indices and highlights three key aspects in the future research of the indices.

To address the issue of frequency regulation in AC-islanded microgrids (MGs), this paper introduces a novel approach for adjusting primary controller gains for AC-islanded MGs. The proposed approach uses the linear matrix inequality (LMI) for polytopic linear parameter varying (P-LPV) modelling based on the H_∞ control theory and principal component analysis (PCA) algorithm. The objective is to regulate the MG frequency while considering the nonlinearity and uncertainty of the system. To achieve optimal control gains, the method considers all the system uncertainties in a P-LPV modelling of the system. The PCA algorithm reduces the scheduling parameter region, and the optimal control gains are computed by solving the relevant LMIs defined on the obtained P-LPV model based on H∞ performance and stability achievement. The primary control gains are optimised to minimise the errors between the optimal and actual control signals. Importantly, the suggested method preserves the order and structure of the primary control, making it applicable to implement on digital hardware devices. In addition, the MG is simulated in MATLAB/Simulink, and the simulation results demonstrate the authenticity, effectiveness, and efficiency of the proposed process for MG frequency regulation in the presence of uncertainties, disturbances, nonlinearity, and dynamic changes in MGs.

This paper presents a practical method for allocating inertia and damping in power systems integrated with renewable energy sources (RES). The frequency response model of the power system is first established, with distinct modeling of the dynamics of synchronous generators (SGs) and three types of RES units. A state-space model, which describes the relationship between frequency response and disturbances, is derived through the disturbance allocation process. A fitting method for frequency and power responses is proposed based on modal analysis. Analytical formulas for frequency stability indices (FSIs) and power indices are derived. Furthermore, applying matrix perturbation theory, an iterative optimization method for inertia and damping allocation of RES units is proposed and validated through simulation cases using the modified IEEE 39-bus system, including scenarios with multiple disturbance locations, which demonstrate the effectiveness and flexibility of the proposed approach.

As the integration of renewable energy into power grids, accurately determining the direction of active power flow has become a crucial aspect of grid management. This paper proposes a novel method for detecting the direction of active power flow in single-phase power systems with fast dynamic response and high precision. An algebraic relationship between the power factor, grid voltage, and current is derived through a trigonometric transformation. Then, an adaptive estimator is proposed to estimate both the power factor and the active power flow direction. A Lyapunov-based argument is introduced to demonstrate the accuracy of the estimated power factor and flow direction. Comprehensive experimental results validate the proposed method, emphasising its response time and accuracy in practical applications.

Effective pre- and post-disaster strategies are crucial for mitigating the impacts of extreme events and enhancing the resilience of networked microgrids (NMGs). However, traditional methods often fail to comprehensively and efficiently consider fault scenarios before disasters, and the inefficient challenge of addressing large-scale post-disaster recovery problems necessitate advanced computational approaches. This paper proposes a quantum-assisted resilience enhancement framework for power distribution systems with NMGs, fully accounting for potential failure risks. The main contributions include a two-stage quantum-assisted resilience enhancement framework that integrates quantum amplitude estimation (QAE) for quantifying failure risks, and generating scenarios, the quantum-encoded model establishment, and the proposed quantum surrogate absolute-value Lagrangian relaxation (Q-SAVLR) algorithm for achieving quantum-accelerated parallel optimization. Numerical tests on a modified IEEE system show that our approach reduces computation time by roughly 40%–75% relative to the classical solver, enabling faster repair resource deployment and more efficient resilience optimization for NMGs.