Iet Generation Transmission & Distribution最新文献_第8页

Electric vehicle load forecasting based on convolutional networks with attention mechanism and federated learning method 基于卷积网络的电动汽车负荷预测：关注机制和联合学习法

IF 2 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2024-06-17 DOI: 10.1049/gtd2.13192

Ruien Bian, Long Wang, Yadong Liu, Zhou Dai

{"title":"Electric vehicle load forecasting based on convolutional networks with attention mechanism and federated learning method","authors":"Ruien Bian, Long Wang, Yadong Liu, Zhou Dai","doi":"10.1049/gtd2.13192","DOIUrl":"https://doi.org/10.1049/gtd2.13192","url":null,"abstract":"Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple factors. At present, the load forecasting model for EVs mainly uses collected sample data to build a data-driven model. But these algorithms need to collect all the data together to train the model, ignoring the privacy of each data collection source. In a competitive market environment, each device service provider is not willing to share the sample data they store. Aiming at this problem, this paper proposes an EV load diagnosis algorithm considering data privacy. Firstly, a convolutional neural network with dual attention mechanism is constructed as the basic time series forecasting model. The association rule algorithm is used to select weather data with strong associations as the inputs of the model. Each service provider uses local data to perform deep learning network. All models are then trained using a federated learning framework. During the entire training process, historical data is stored locally, and only model parameter information is shared and interacted; thus data privacy is protected. Finally, the validity of the algorithm in this paper is verified by using real collected EV load data.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536873","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}

引用次数: 0

A black-box method for the stability analysis of multi-inverter-fed power systems using Bode diagrams 利用 Bode 图进行多逆变器馈电电力系统稳定性分析的黑箱方法

IF 2 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2024-06-17 DOI: 10.1049/gtd2.13182

Yonggang Li, Binyuan Wu, Jianwen Li, Yue Wang

{"title":"A black-box method for the stability analysis of multi-inverter-fed power systems using Bode diagrams","authors":"Yonggang Li, Binyuan Wu, Jianwen Li, Yue Wang","doi":"10.1049/gtd2.13182","DOIUrl":"https://doi.org/10.1049/gtd2.13182","url":null,"abstract":"A multi-inverter-fed power system is susceptible to small-signal instability owing to weak grid influence. This study proposes a black-box method for small-signal stability analysis of a multi-inverter-fed power system based on Bode diagrams. Not only did it consider the technical confidentiality on the engineering site, but it also took into account the presence of right-half-plane (RHP) pole in the aggregated impedance, achieving Nyquist circle acquisition through Bode diagrams. The stability analysis process is thus more accurate, intuitive, and convenient. First, black-box impedance fitting for a single grid-connected inverter and impedance aggregation for a multi-inverter-fed power system are discussed. Second, the principle of the proposed method is elaborated. Using the Bode diagram, the number of RHP poles of the aggregated impedance and actual number of circles for Nyquist curves are identified. Third, a multi-inverter-fed power system is constructed, and the effectiveness of the proposed method is verified using case analysis and hardware-in-the-loop real-time experiments based on RT-LAB. Finally, the advantages of the proposed method are revealed by comparing it to the IF method.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13182","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536872","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}

引用次数: 0

Estimating the region of attraction of wind integrated power systems based on improved expanding interior algorithm 基于改进的扩展内部算法估算风力综合电力系统的吸引区域

IF 2 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2024-06-16 DOI: 10.1049/gtd2.13201

Yang Liu, Huanjin Yao, Zengjie Chen, Xiangyu Pei, Yuexi Yang, Qinghua Wu

{"title":"Estimating the region of attraction of wind integrated power systems based on improved expanding interior algorithm","authors":"Yang Liu, Huanjin Yao, Zengjie Chen, Xiangyu Pei, Yuexi Yang, Qinghua Wu","doi":"10.1049/gtd2.13201","DOIUrl":"https://doi.org/10.1049/gtd2.13201","url":null,"abstract":"This paper proposes an improved expanding interior algorithm (EIA) to estimate the region of attraction (ROA) of power systems with wind power generation based on sum of squares (SOS) programming. An ordinary differential equation (ODE) model is derived for the doubly-fed induction generator-based wind turbine (DFIGWT), which is named as an enhanced synchronous-generator-mimicking (ESGM) model. The ESGM model bridges the gap between the requirement of an ODE model in ROA estimation and the conventional differential-algebraic equation (DAE) model of the DFIGWT system. The ESGM model is able to accurately reflect the low frequency dynamics of the DFIGWT. Moreover, an improved EIA is designed to estimate the ROA based on SOS programming, which has higher efficiency than the existing ROA estimation algorithms based on SOS programming. It is able to adaptively search for the Lyapunov function and obtain an optimal estimation of the ROA in an iterative process. The accuracy and efficiency of this algorithm are verified in three test systems composed of DFIGWTs and synchronous generators (SGs). The morphological changes in the ROA of the test systems caused by the penetration of DFIGWT are examined.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13201","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141488547","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}

引用次数: 0

Optimal configuration of hybrid hydrogen-to-power system for power systems with high wind energy penetration 风能渗透率高的电力系统中氢-电混合系统的优化配置

IF 2 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2024-06-13 DOI: 10.1049/gtd2.13177

Hongzhen Wang, Boyu Qin, Tao Ding, Fan Li

{"title":"Optimal configuration of hybrid hydrogen-to-power system for power systems with high wind energy penetration","authors":"Hongzhen Wang, Boyu Qin, Tao Ding, Fan Li","doi":"10.1049/gtd2.13177","DOIUrl":"10.1049/gtd2.13177","url":null,"abstract":"Hydrogen energy storage plays an important role in improving the operation efficiency and reliability of power systems with high wind energy penetration. Hydrogen to power (HtP) system is the key link of hydrogen applications. However, the single HtP equipment is limited in power output range and efficiency. Hybrid HtP system is an important scheme to realize the performance complementary. A wider power output range can enrich the application scenarios of hydrogen energy storage and provide flexible and reliable energy scheduling in larger and more complex energy systems. In this study, first, a power system including traditional units, wind power generation and hybrid HtP system is established. Second, a bi-layer hybrid HtP system optimal configuration model considering planning and operation is constructed. Then, a bi-layer optimal configuration method based on the improved PSO algorithm is proposed. Finally, the optimization model and its solution method are applied to IEEE RTS-96. The discussions based on the optimization results show that the hybrid HtP system has significant contributions in optimizing the output cost of traditional units, improving the utilization of wind energy, and reducing load shedding losses.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13177","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141348792","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}

引用次数: 0

A review of asset management using artificial intelligence-based machine learning models: Applications for the electric power and energy system 使用基于人工智能的机器学习模型进行资产管理的综述：电力和能源系统的应用

IF 2 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2024-06-12 DOI: 10.1049/gtd2.13183

Gopal Lal Rajora, Miguel A. Sanz-Bobi, Lina Bertling Tjernberg, José Eduardo Urrea Cabus

{"title":"A review of asset management using artificial intelligence-based machine learning models: Applications for the electric power and energy system","authors":"Gopal Lal Rajora, Miguel A. Sanz-Bobi, Lina Bertling Tjernberg, José Eduardo Urrea Cabus","doi":"10.1049/gtd2.13183","DOIUrl":"10.1049/gtd2.13183","url":null,"abstract":"Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large-scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141354352","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}

引用次数: 0

Three-phase state estimation in transmission networks with unbalanced loading utilising SCADA measurements 利用 SCADA 测量对具有不平衡负载的输电网络进行三相状态估计

IF 2 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2024-06-11 DOI: 10.1049/gtd2.13187

Mohammad Sadeq Mofidnakhaei, Ahmad Salehi Dobakhshari

{"title":"Three-phase state estimation in transmission networks with unbalanced loading utilising SCADA measurements","authors":"Mohammad Sadeq Mofidnakhaei, Ahmad Salehi Dobakhshari","doi":"10.1049/gtd2.13187","DOIUrl":"10.1049/gtd2.13187","url":null,"abstract":"State estimation stands as one of the most crucial applications in energy management systems of control centres, ensuring the secure operation of the power system. The real-time availability of accurate system variables, including voltages, currents, and power flows, presents an excellent opportunity to consider the actual network characteristics, including imbalances. This paper presents a linear and non-iterative method to solve the three-phase state estimation problem in transmission systems with unbalanced loading. The method utilises supervisory control and data acquisition measurements obtained from remote terminal units. The formulation is based on complex variables derived from traditional supervisory control and data acquisition measurements, with assumptions made regarding the availability of certain three-phase bus voltages, as well as a number of three-phase currents and active and reactive power flows. The proposed algorithm employs the classical weighted least squares technique for solving the three-phase state estimation problem. Due to its straightforward and linear nature, the method ensures no convergence issues. To validate its effectiveness, the proposed method is validated on a three-bus network and the IEEE 39-bus network simulated using the three-phase model within the DIgSILENT Power Factory environment.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360327","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}

引用次数: 0

Dynamic restoration electricity price optimization method to enhance the resilience of distribution networks with multiple-microgrids 提高多微电网配电网恢复能力的动态恢复电价优化方法

IF 2 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2024-06-11 DOI: 10.1049/gtd2.13199

Hongkun Wang, Yujie Gao, Hong Zhang, Dongmei Yan, Hongwei Li

{"title":"Dynamic restoration electricity price optimization method to enhance the resilience of distribution networks with multiple-microgrids","authors":"Hongkun Wang, Yujie Gao, Hong Zhang, Dongmei Yan, Hongwei Li","doi":"10.1049/gtd2.13199","DOIUrl":"10.1049/gtd2.13199","url":null,"abstract":"Resilience is one of the main features of smart distribution networks, and a microgrid (MG) access to the distribution network provides an effective way to improve resilience. MG and distribution network belong to different interests, so it is necessary that MGs and flexible resources are actively guided through price leverage. In this way, MGs take part in the post-disaster restoration and enhance its resilience. Firstly, this paper proposes a dynamic restoration electricity price response mechanism after extreme disasters and constructs a power response model for loads and electric vehicles within the MGs. Secondly, the optimal scheduling model of the distribution network with multiple-microgrids (MMG) is proposed to improve the restoration rate of critical loads (RRCL). Single microgrid achieves the largest microgrid revenue and restoration contribution, and MMG uses the power headroom index to optimize the dynamic restoration electricity price to achieve the smallest power purchase cost of distribution network. Finally, the optimal scheduling method for resilience enhancement of distribution networks with MMG considering dynamic restoration electricity price response mechanism is validated by dual microgrid access to an IEEE 33-node distribution system. The simulation results show that the proposed optimization method effectively improves the RRCL of distribution network.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358423","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}

引用次数: 0

A customised artificial neural network for power distribution system fault detection 用于配电系统故障检测的定制人工神经网络

IF 2.5 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2024-06-10 DOI: 10.1049/gtd2.13186

Arnav Bhagwat, Soham Dutta, Vinay Kumar Jadoun, Arigela Satya Veerendra, Sourav Kumar Sahu

{"title":"A customised artificial neural network for power distribution system fault detection","authors":"Arnav Bhagwat, Soham Dutta, Vinay Kumar Jadoun, Arigela Satya Veerendra, Sourav Kumar Sahu","doi":"10.1049/gtd2.13186","DOIUrl":"https://doi.org/10.1049/gtd2.13186","url":null,"abstract":"Machine learned fault detection approaches are being increasingly used for fault detection in distribution grid. However, the performance of the models can be improved by customizing the models. In this regard, a customised artificial neural network (CANN) for fault detection in a distribution grid is proposed in this paper. The proposed work develops a CANN that combines the “up-pyramid” and “down-pyramid” model of ANN into a “custom-pyramid” model. As a result, the same model can be used both for determining the types of fault as well as its location. The data needed to train the model has been taken from a reconfigured IEEE-33 bus distribution system developed in Typhoon HIL real-time simulator. Spectral-kurtosis is utilized for extraction of features of the faulted transient signals which are used as input data to develop the CANN. The result showcases that the reduction of input features reduces computational complexity without compromising its accuracy. The proposed model classifies fault location with an accuracy of 95.43%. The proposed method also identifies fault type with an accuracy of 96.08%. Several test cases have been developed to test the method. The method proved to be able to perform in most of the cases.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141329362","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}

引用次数: 0

Frequency control of power systems under uncertain disturbances based on input-output finite-time stability 基于输入输出有限时间稳定性的不确定扰动下的电力系统频率控制

IF 2 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2024-06-10 DOI: 10.1049/gtd2.13180

Lixuan Zhu, Yiping Yu, Ping Ju

引用次数: 0

Robust LFC design using adaptive neuro-fuzzy inference-aided optimal fractional-order PIDA control for perturbed power systems with solar and wind power sources 利用自适应神经模糊推理辅助优化分数阶 PIDA 控制，为具有太阳能和风能资源的扰动电力系统设计鲁棒 LFC

IF 2 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2024-06-10 DOI: 10.1049/gtd2.13185

Tushar Kanti Roy, Samson S. Yu, Md. Apel Mahmud, Hieu Trinh

{"title":"Robust LFC design using adaptive neuro-fuzzy inference-aided optimal fractional-order PIDA control for perturbed power systems with solar and wind power sources","authors":"Tushar Kanti Roy, Samson S. Yu, Md. Apel Mahmud, Hieu Trinh","doi":"10.1049/gtd2.13185","DOIUrl":"10.1049/gtd2.13185","url":null,"abstract":"Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional-order proportional-integral-derivative-acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro-fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi-area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS-FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie-line power exchange. Simulations on a New England IEEE 10-generator 39-bus power system demonstrate the approach's effectiveness under various disturbances, including random load-generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)-FOPIDA, WOA-PIDA, and WOA-ANFIS-PIDA, and recent control approaches highlight the superior performance of the WOA-ANFIS-FOPIDA method in enhancing power system stability.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141362025","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}

引用次数: 0