Iet Generation Transmission & Distribution最新文献

{"title":"Experimental Validation and Parametrisation of a High Impedance Fault Model for Overhead Distribution Networks Vegetation Simulations: Insights From Medium Voltage Laboratory Tests on Tree Branches","authors":"Juan Carlos Huaquisaca Paye,&nbsp;João Paulo A. Vieira,&nbsp;André P. Leão,&nbsp;Jonathan M. Tabora,&nbsp;Ghendy Cardoso Junior","doi":"10.1049/gtd2.70074","DOIUrl":"https://doi.org/10.1049/gtd2.70074","url":null,"abstract":"<p>The detection and localisation of high impedance faults (HIFs) in overhead distribution networks, particularly those caused by tree contact, remain challenging due to their low fault current magnitude, nonlinear behaviour, and high waveform variability. This study addresses these challenges by experimentally validating and parameterising a two-variable-resistor series model for HIFs. Controlled tests were conducted at the 13.8 kV Extra High Voltage Laboratory (LEAT) of the Federal University of Pará (UFPA), Brazil, using three tree types to collect waveform data. The faults were modelled in ATPDraw software and validated through waveform comparisons, achieving an R² coefficient above 0.89, demonstrating excellent agreement between simulated and measured signals. Additionally, comprehensive parameterised data tables detailing resistance variations over time and voltage are provided. These findings not only reduce the need for costly experimental setups in early-stage studies but also provide a solid foundation for developing and testing advanced HIF detection and localisation algorithms, thus contributing to improved power distribution system reliability.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sub-Model Method for Ion Flow Electric Field Analysis Including Complex Buildings Adjacent to UHVDC Transmission Lines","authors":"Jianhui Wang,&nbsp;Tiebing Lu,&nbsp;Kun He,&nbsp;Li Xie,&nbsp;Bo Chen","doi":"10.1049/gtd2.70070","DOIUrl":"https://doi.org/10.1049/gtd2.70070","url":null,"abstract":"<p>Corona discharge on high-voltage direct current (HVDC) transmission lines generates space charges that affect the ion flow electric field on the buildings nearby. According to China's national standards, the electric field on building platforms must be controlled under the limit. Due to the variety of buildings near HVDC lines, extensive calculations are needed to obtain the distribution of the electric field. A sub-model method for the upstream finite element method is proposed to improve the calculation efficiency of the 3-D ion flow electric field by setting a reasonable cut-boundary condition, which is obtained by calculating the complete model without buildings. By analytic derivation, the potential and charge density errors at the cut-boundary are proved to have nearly linear influence on the electric field calculation results. Then the setting principle of the cut-boundary is determined by analysing various calculation results. The sub-model method was found to be over 14.7 times faster than the complete model after calculating 432 models, and the results are in good agreement with the complete model. Finally, the enhancing and weakening characteristics of the electric field on one-layer and two-layer building platforms near HVDC lines are obtained. The research results are helpful for the design of HVDC projects with buildings nearby.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70070","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Method for Voltage Sag Consequence State Recognition for Industrial Users","authors":"Bin Zhang,&nbsp;Xin Chen,&nbsp;Zhe-ling Zhou,&nbsp;Yu-ji Wang,&nbsp;Wei-sheng Xu,&nbsp;Xue-yan Xu","doi":"10.1049/gtd2.70068","DOIUrl":"https://doi.org/10.1049/gtd2.70068","url":null,"abstract":"<p>With the advancement of automation in industrial production, the sensitivity of user equipment and processes to voltage sags has progressively increased. Voltage sags are highly likely to cause adverse consequences for production. Voltage sag events exhibit different characteristics, resulting in varying consequence states for users. Accurately identifying these consequence states is essential for determining the user's voltage sag mitigation needs and planning the optimal mitigation strategy. However, due to the low-frequency, high-damage nature of voltage sags, events with more severe consequences are less likely to occur, resulting in insufficient sample data. Furthermore, users are unable to provide detailed sample data due to the protection of production information, making data-driven assessments of industrial process voltage sag consequences even more challenging. To address these challenges, this paper proposes a method for identifying the consequence state of voltage sags in industrial users based on a data-driven method. First, voltage sag event features and consequence category labels for industrial processes are established. An improved semi-supervised fuzzy C-means (SSFC) algorithm is introduced to classify the consequence states of industrial processes. Second, a data augmentation technique based on the least squares generative adversarial network (LSGAN) is applied to expand the dataset of voltage sag samples with the consequence category labels. Next, based on the augmented dataset, a recognition model with VTC-Attention-bidirectional long short-term memory (VA-BiLSTM) is developed to explore the latent features of voltage sag consequences in industrial processes. A recognition library for voltage sag consequence states is created, allowing industrial users to input easily obtainable voltage sag data and obtain corresponding consequence states. Finally, a case study involving a manufacturer in South China is conducted to validate the effectiveness of the proposed method.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and Characterisation of Atypical Harmonic Patterns in Big Power Quality Data","authors":"Olga Zyabkina,&nbsp;Max Domagk,&nbsp;Jan Meyer,&nbsp;Peter Schegner,&nbsp;Marco Lindner,&nbsp;Heiko Mayer,&nbsp;Christoph Butterer","doi":"10.1049/gtd2.70062","DOIUrl":"https://doi.org/10.1049/gtd2.70062","url":null,"abstract":"<p>With the proliferation of harmonic sources, network operators face significant challenges in identifying and interpreting sudden changes in harmonic emission behaviour due to the large volume of power quality data and the lack of automated analysis tools. This article introduces a novel algorithm that detects and characterises atypical harmonic emission patterns based on a comprehensive framework that includes data preparation, anomaly detection, and knowledge acquisition stages. By employing context-based features, the underlying data properties of both typical and atypical patterns are captured effectively. Sliding-window thresholds enable a flexible adaption of the algorithm to variations caused by seasonality and trends. In the knowledge acquisition stage, the significance and properties of atypical patterns are summarised using aggregated anomaly scores, significance categories, and a classification scheme. The algorithm's effectiveness is demonstrated through its application to over 5000 harmonic time series collected in the transmission system in Germany.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of Different Sliding Mode Control Approaches for Load Frequency Control in Edge of the Grid System","authors":"Pritom Aich,&nbsp;Anis Ahmed,&nbsp;Rakibuzzaman Shah,&nbsp;Md Rabiul Islam,&nbsp;Naruttam Kumar Roy,&nbsp;Ali Nasir","doi":"10.1049/gtd2.70061","DOIUrl":"https://doi.org/10.1049/gtd2.70061","url":null,"abstract":"<p>The integration of renewable energy sources (RESs) at the edge of the grid plays a contributory role in advancing the transition to a sustainable energy future. However, this transition introduces significant variability, which challenges the maintenance of frequency stability in power systems. To address the impacts of RES variability, various robust control strategies have been developed. This paper compares the performance of different advanced sliding mode control (SMC) approaches, i.e., super twisting (ST) algorithm-based second order sliding mode control (STSOSMC), second order sliding mode control (SOSMC), double integral sliding mode control (DISMC) and integral sliding mode control (ISMC) with conventional sliding mode control (SMC) for load frequency control with stochastic RESs such as biogas generators (BG) and wind-based doubly fed induction generators (DFIG). The impact of the advanced sliding mode controllers on the voltage and rotor angle stability of the system is also explored.  The efficacy of the controllers is tested under different load disturbances, considering nonlinearities such as communication delay (CD), sensor inaccuracy (SI), governor dead band (GDB), and generation rate constraint (GRC). The results demonstrate that STSOSMC achieves superior frequency regulation with minimal chattering and outperforms other controllers in terms of settling time, overshooting, and undershooting in frequency deviation. The result also demonstrates the effectiveness of STSOSMC in managing the variability and disturbances of RESs. Therefore, it could be considered the most robust control mechanism from the edge of the grid system with high penetration of RESs.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ROCOF-Constrained Solid-State Transformer Frequency Predictive Control","authors":"Carlos Fuentes,&nbsp;Hector Chavez,&nbsp;Karina Barbosa,&nbsp;Esteban Riquelme,&nbsp;Matias Diaz","doi":"10.1049/gtd2.70066","DOIUrl":"https://doi.org/10.1049/gtd2.70066","url":null,"abstract":"<p>The increasing integration of power electronic-based generation has significantly weakened power system frequency control due to reduced system inertia. In this context, the load contribution, through its frequency droop and inertia, plays a crucial role in stabilizing frequency fluctuations. However, the integration of solid-state transformers (SST) prevents this contribution from occurring, because the DC-link in the power converters decouples load dynamics from the main grid. Existing solutions attempt to mimic load contributions through synthetic demand responses; they often overlook both to meet grid code requirements and optimize control actions. This paper proposes a predictive frequency control algorithm to maximize load contribution via SSTs, constrained to grid code requirements. The proposal introduces two key innovations: from a transmission perspective, the control formulation is designed to minimize the rate of change of frequency (RoCoF) of the system frequency response; from a distribution feeder perspective, it incorporates constraints on voltage, frequency, and the RoCoF to ensure power quality and prevent the disconnection of distributed generation units (DG). Simulation results are obtained through a model of the involved dynamics, which are compared with a proportional controller. The proposed strategy achieves better performance indicators in all analysed scenarios.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143846179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power System Short-Term Voltage Stability Assessment Method Based on Spatial-Temporal Graph Attention Network","authors":"Hailiang Li,&nbsp;Zhuanglin Liang,&nbsp;Dexin Ma,&nbsp;Shiyuan Zhang,&nbsp;Weike Mo","doi":"10.1049/gtd2.70067","DOIUrl":"https://doi.org/10.1049/gtd2.70067","url":null,"abstract":"<p>Short-term voltage stability (STVS) assessment is critical for ensuring the operational security of modern industrial internet-based power systems. For data-driven STVS evaluation approaches, effectively leveraging both time-series data and topological structure of complex industrial power networks to extract critical spatial-temporal features remains a challenge. This paper introduces a novel spatial-temporal feature learning framework, termed spatial-temporal graph attention network (STGAT), which integrates graph attention network (GAT) and bidirectional gated recurrent unit (BiGRU). In the framework, channel attention mechanism (CAM) is incorporated into the GAT to enhance spatial representation, while temporal attention mechanism is applied to the BiGRU to capture essential temporal features. By considering highly representative spatial-temporal correlations of power system dynamics, the recommended STGAT model delivers a fast, accurate, and robust classification framework for STVS assessment. Extensive testing on the IEEE 39-bus system validates the feasibility and preeminence of the recommended STGAT method compared to existing models, ensuring its suitability for online STVS assessment in industrial environments.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimised Scheduling for Distribution Networks, Microgrids and Demand-Side Using Multi-Level Game Theory","authors":"Xiangwu Yan,&nbsp;Heyang Cao,&nbsp;Chen Shao,&nbsp;Zehua Wang,&nbsp;Jiaoxin Jia","doi":"10.1049/gtd2.70058","DOIUrl":"https://doi.org/10.1049/gtd2.70058","url":null,"abstract":"<p>This paper introduces a multi-level leader-follower game scheduling method, which accounts for the coordinated interests of multiple stakeholders in the integrated operation of distribution networks (DNs), microgrids (MGs), and flexible loads (FLs) systems. Firstly, to address the challenges posed by significant non-linearity and computational burden in the DN game model, optimisation efficiency is enhanced from two perspectives. An improved Bayesian optimisation algorithm is implemented to reduce the number of optimisation iterations for electricity prices, while a graph theory-based forward-backward sweep method is employed to enhance power flow calculation efficiency. Secondly, distinct optimisation strategies are applied to MGs with varying operational profiles: the internet data centre MGs implement the differential evolution algorithm, while the time-shift load MG employs the covariance matrix adaptation evolution strategy, striking a balance between computational speed and solution accuracy for the MG cluster. Moreover, the demand response load layer is modelled and solved as a linear optimisation problem. Finally, based on the IEEE-33 bus test case analysis, the proposed model fully integrates photovoltaic energy, resulting in a 24.5% improvement in the system's overall economic performance and increasing the benefits for each stakeholder.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progressive Identification of Distribution Network Topology Based on User-Side Internet of Things Device Measurement Data","authors":"Lei Zhu,&nbsp;Shutan Wu,&nbsp;Qi Wang,&nbsp;Yang Li","doi":"10.1049/gtd2.70064","DOIUrl":"https://doi.org/10.1049/gtd2.70064","url":null,"abstract":"<p>The flexibility requirements of network topologies in advanced distribution networks (ADNs) have led to increasingly complex distribution network topologies, posing a significant challenge for topology identification. The integration of a vast array of internet of things (IoT) devices at existing distribution network terminals facilitates the real-time collection and transmission of physical measurement data, thus providing new ideas for identifying distribution network topologies. This paper introduces a distribution network topology progressive identification method grounded in measurement data captured by IoT devices. First, the study employs similarity mining of IoT measurement data to identify the topological connection relationships within the low-voltage distribution network. Second, by integrating real-time measurement data with historical data, the medium-voltage-side data are consolidated, and the topology of the medium-voltage distribution network is identified by an inverse power flow model based on a fixed parameter ratio. Furthermore, to address the limitations of low recognition accuracy and non-unique recognition outcomes associated with the aforementioned methods, this study examines the regional similarity among distribution networks and proposes a hyperparameter to increase the recognition accuracy. Finally, different case studies show that the proposed method maintains a topological identification accuracy of more than 80%, and shows a wide range of applicability in different types and sizes of distribution networks.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electric Thermal Balance Control Method of Plant Level Integrated Energy Systems Based on Robust Linear Optimization With Probability Density Bias Prediction Characteristics","authors":"Jiakui Shi,&nbsp;Menghui Li,&nbsp;Shuangshuang Fan,&nbsp;Kun Yao,&nbsp;Jie Wan,&nbsp;Peng E","doi":"10.1049/gtd2.70051","DOIUrl":"https://doi.org/10.1049/gtd2.70051","url":null,"abstract":"<p>The rapid development of renewable energy has promoted the research of the integrated energy system. In particular, the joint optimal scheduling of renewable energy and traditional thermal power units is the key technology to solve the current renewable energy integration and the source network load balance. At present, the total installed capacity of cogeneration units is huge, which hinders the integration of renewable energy and the flexibility of the power grid regulation. Therefore, this paper proposes a robust optimal scheduling strategy for the plant-level integrated energy system considering electricity-heat balance. The wind power and solar energy are connected in parallel with the heating network of cogeneration units to decouple heat and power in a more economical and flexible way, while improving the integration of renewable energy. Based on the principle of Kang's robust optimization, robust optimization, a linear robust optimization probability density bias prediction optimization method for the plant-level integrated energy system considering the electric-heat balance is proposed in this paper. The dynamic nonlinear constraints in the output process of thermal and electrical loads are transformed into linear, which takes into account the conservatism of robust optimization and the economy of the objective function. The proposed optimization algorithm facilitates the integration of a high proportion of renewable energy into the power grid and is also applicable to other integrated energy systems with nonlinear constraint characteristics.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}