Iet Generation Transmission & Distribution最新文献

The coordination and interconnections of power networks are crucial for power systems dispatch with high-proportion variable renewable energy (VRE). Recent studies have separately explored the transmission-distribution system integration and the interconnections of transmission (distribution) systems via voltage source converter multi-terminal direct current (VSC-MTDC) grids. However, few researches concentrate on the combination of these two technologies, which contains admirable potential operation flexibility to absorb massive VREs. This paper proposes a dispatch model for VSC-MTDC-based integrated transmission-distribution systems based on the distributionally robust joint chance-constrained programming (DRJCCP) framework. At first, two approximation approaches of network losses according to different reactance-resistance ratios are proposed for transmission and distribution grids, respectively. Then, the extended affine strategy is embedded in the DRJCCP framework to map the relation between VRE uncertainties and dispatch decisions. Finally, based on the hierarchy, a decentralised optimisation algorithm based on analytical target cascading is utilised, which divides the whole dispatch problem into the bi-level problem of transmission and distribution grids. Numerical tests on different scale systems demonstrate that the proposed method balances decision-making realism and robustness, highlighting the strategic and operational aspects of coordinated dispatch across different hierarchies and regions.

With the large-scale integration of distributed energy sources, considering their intermittent and volatile nature, grid voltage regulation has become more challenging than ever before. Issues such as voltage fluctuations and even two-way over-limit violations have become increasingly prominent. To achieve the optimized dispatch of reactive power and the stable operation of the grid voltage, a comprehensive review of the reactive power regulation device and related issues in the ancillary service market is conducted. Firstly, the development history and future trend of the reactive power market are systematically summarized, with a comparison of different power market architectures and their impact on reactive power regulation. Subsequently, the relevant mathematical theories of reactive power pricing are analysed, followed by typical optimization models of the reactive power market. Finally, the current deficiencies and challenges of the reactive power market are concluded. It is noted that research on the reactive power market, adapted to the context of high renewable energy integration, remains scarce, and the collaborative dispatch mechanism for distributed reactive power sources requires improvement. Future research directions are provided in the end.

Resilience assessment is also relevant for highly reliable power systems, such as meshed networks. The paper deals with resilience of an urban dense-meshed low voltage network to supply feeder outages and subsequent cascading failures. Presented resilience analysis evaluates the amount of preserved load during multiple feeder outages. The simulations account for a time-span of 1 year of network operation with regard to load variations. Based on disturbances simulation results, the weakest elements of the network are identified. To increase resilience, corrective measures are proposed and incorporated into the simulations. Resilience improvements of applied measures are evaluated and discussed.

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.