Energy Informatics最新文献

{"title":"The development of an intelligent comprehensive detection instrument for circuit breakers in power systems and its key technologies","authors":"Weimin Guan,&nbsp;Han Hu,&nbsp;Chao Sun,&nbsp;Jie Ji","doi":"10.1186/s42162-025-00497-6","DOIUrl":"10.1186/s42162-025-00497-6","url":null,"abstract":"<div><p>To improve the accuracy and reliability of circuit breaker detection in power systems, this study proposes an intelligent detection instrument. The instrument addresses key issues found in traditional methods, such as limited real-time performance, inadequate data integration capabilities, and poor environmental adaptability. The instrument integrates multimodal data fusion technology to comprehensively analyze electrical parameters, mechanical characteristics, and environmental factors, enabling full awareness of the circuit breaker’s status. Additionally, this study optimizes the fault diagnosis algorithm, enhancing detection stability and robustness. By improving the model architecture, the computational burden is reduced, making the system more suitable for real-time monitoring and resource-constrained environments. Experimental results demonstrate that the intelligent detection instrument outperforms existing methods in terms of accuracy, detection efficiency, and anti-interference capabilities. It can more effectively identify the operational status of circuit breakers while maintaining high detection performance under complex operating conditions. Compared to traditional methods, the proposed solution shows significant advantages in reducing false alarms, optimizing detection speed, and improving environmental adaptability. Therefore, the study provides efficient and stable technical support for intelligent circuit breaker detection in power systems, laying a solid foundation for the development of smart grids.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00497-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on load frequency control system attack detection method based on multi-model fusion","authors":"Feng Zheng,&nbsp;Weixun Li,&nbsp;Huifeng Li,&nbsp;Libo Yang,&nbsp;Zengjie Sun","doi":"10.1186/s42162-025-00533-5","DOIUrl":"10.1186/s42162-025-00533-5","url":null,"abstract":"<div><p>Load frequency control (LFC) in power systems faces increasingly complex cyber-physical attack threats, while existing detection methods have limited capability to identify intelligent attacks. This paper constructs an LFC system model considering dynamic response characteristics and establishes a reinforcement learning-based method for generating multiple attack strategies, covering typical scenarios such as false data injection (FDI) and load switching attacks. A multi-model fusion attack detection framework is proposed, integrating (Long Short-Term Memory) LSTM supervised learning and autoencoder unsupervised learning algorithms, with an adaptive weight adjustment mechanism that dynamically optimizes detection strategies. Experimental results demonstrate that the fusion mechanism achieves 99.4% comprehensive identification accuracy across four system states, outperforming single supervised models (98%) and single unsupervised models (76.4%). Detection accuracy exceeds 99% for three different frequency characteristic attacks, with an average detection delay of only 0.12 seconds. The fusion mechanism effectively reduces false positive and false negative rates (FNRs), showing significant advantages in identifying and defending against unknown attacks, providing a new approach for LFC system security protection.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00533-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Management system and optimal control for three-dimensional visualization and maintenance of thermal power plant","authors":"Zhiqiang Feng,&nbsp;Qiuxiang Liang,&nbsp;Mingyi Wei,&nbsp;Lei Li,&nbsp;Youzhu Bu,&nbsp;Yanqing Xin","doi":"10.1186/s42162-025-00491-y","DOIUrl":"10.1186/s42162-025-00491-y","url":null,"abstract":"&lt;div&gt;&lt;p&gt;With the evolution of energy pattern and the advancement of science and technology, the operation and maintenance management of thermal power plants has encountered bottlenecks. The traditional model is difficult to meet the current demand. The purpose of this study is to build an advanced three-dimensional (3D) visualization and maintenance system suitable for thermal power plants, and to optimize it with the technology of convolutional neural network (CNN). Firstly, literature research is carried out, and the achievements and existing shortcomings in related fields are deeply excavated. Then, this study systematically analyzes the operation and maintenance ecology of thermal power plants, focuses on equipment operation data trajectory and process flow context, and accurately anchors key pain points. Based on this, a basic 3D visualization and maintenance system is constructed. Its data acquisition and processing module is customized for thermal power generation conditions. It can accurately capture multiple data from core equipment such as boilers and steam turbines and integrate them efficiently. According to the actual situation and equipment details of the power plant, the 3D modeling module designs a highly realistic digital model. The visual interface module is user-experience-oriented, presenting an intuitive and convenient interactive window. It is convenient for operation and maintenance personnel to monitor and make efficient decisions in real time. Then, CNN technology is introduced to deeply analyze the data content and find out the operation and maintenance value. The experimental data shows the effectiveness, and the basic system performs well in the dimensions of accuracy, completeness and accuracy, with the numerical value exceeding 85%, which is more prominent than the traditional system. After optimization by CNN technology, the response time of the system is increased by 5%. The calculation cost is reduced by 15%, and the data throughput is increased by 13%. However, there is still room for improvement in the system. For example, the stability of data acquisition in complex electromagnetic and high-temperature environment needs to be strengthened. The calculation accuracy of the model for extreme working conditions and microscopic changes of equipment needs to be improved. The dimension of personalized customization of visual interface needs to meet the demands of multiple users. The system scalability needs to meet the requirements of technical iteration and equipment update, and the technical application process needs to be simplified for promotion. This study injects innovative vitality into the operation and maintenance management of thermal power plants, and significantly improves the quality and efficiency of operation and maintenance. Looking forward to the future, it is still necessary to test and analyze in many aspects and optimize in many dimensions to drive the operation and maintenance management of therma","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00491-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Charging pile fault prediction method combining whale optimization algorithm and long short-term memory network","authors":"Yansheng Huang,&nbsp;Atthapol Ngaopitakkul,&nbsp;Suntiti Yoomak","doi":"10.1186/s42162-025-00530-8","DOIUrl":"10.1186/s42162-025-00530-8","url":null,"abstract":"<div><p>As the world’s energy structure is gradually changing, the automotive industry is shifting its focus to new energy vehicles in an effort to improve the performance and service life of the charging pile. To solve the problem that traditional models tend to fall into locally optimal solutions (i.e., the model optimization process stays in the non-optimal regional minimum) in complex parameter space, the study innovatively proposes a hybrid prediction model that combines the whale optimization algorithm with the gated recurrent unit-long short-term memory neural network. By introducing the whale optimization mechanism to globally optimize the key parameters of the neural network, the method improved the model’s ability to model complex time series data. Moreover, the method also effectively avoided the problem of traditional methods falling into local optimal solutions, thus improving the training efficiency and generalization ability while maintaining the model accuracy. It took only 21 s to complete the training of 600 samples, and the prediction accuracy was as high as 91%. In the four classes of fault classification experiments, the proposed model performs well in classification accuracy in all classes, showing strong multi-class fault recognition capability. Therefore, the fault prediction model developed in this study can accurately and effectively identify and predict charging pile faults, and shows high performance. This not only provides a strong theoretical foundation for the application of deep learning in charging pile fault prediction, but is also of great significance in terms of reducing operation and maintenance costs, supporting energy structure transformation, and promoting green development.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00530-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement error evaluation method for voltage transformers in distribution networks based on self-attention and graph convolutional networks","authors":"Xiujuan Zeng,&nbsp;Tong Liu,&nbsp;Huiqin Xie,&nbsp;Dajiang Wang,&nbsp;Jihong Xiao","doi":"10.1186/s42162-025-00525-5","DOIUrl":"10.1186/s42162-025-00525-5","url":null,"abstract":"<div><p>Accurately evaluating the error of voltage transformers in distribution networks is crucial for the safe operation of power systems and the fairness of electricity trade. This paper uses the connection relationship between distribution transformers and voltage transformers to predict the secondary voltage of voltage transformers through the secondary voltage of transformers, constructing a voltage transfer characteristic model between the two to achieve accurate evaluation of voltage transformer errors. To address the challenge of extracting complex nonlinear features from multivariate electrical data, a combined model of a self-attention mechanism and a graph convolutional network (GCN) is proposed. The self-attention mechanism captures global dependencies among power parameters, while the GCN effectively constructs the multivariate data structures in distribution networks. By integrating both approaches, the model can fully extract the intrinsic features of the data as well as the hidden dependency information between data points. Additionally, to prevent gradient vanishing as the combined model’s structure deepens, a multi-head residual structure is introduced to enhance the self-attention mechanism. Experimental results show that compared to a single model, the proposed combined model reduces the mean squared error by 82.35% and increases the coefficient of determination R² by 9.07%, demonstrating significant accuracy advantages in voltage transformer error evaluation.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00525-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Day-ahead photovoltaic power forecasting with multi-source temporal-feature convolutional networks","authors":"Ziming Ouyang,&nbsp;Zhaohui Li,&nbsp;Xiangdong Chen","doi":"10.1186/s42162-025-00531-7","DOIUrl":"10.1186/s42162-025-00531-7","url":null,"abstract":"<div><p>Photovoltaic (PV) power forecasting technology enhances the absorption capacity of renewable energy. However, the PV power generation process is highly sensitive to fluctuations in weather conditions, making accurate forecasting challenging. In this paper, we propose a composite data augmentation method and a model that can effectively utilize the augmented data. The PV power generation process has a fluctuating nature over time, so an augmented sample set with temporal correlation was created. This was achieved by reconstructing meteorological features and screening measurements similar to historical meteorological conditions. To improve the feature extraction capability for multi-source heterogeneous data and the temporal modeling capability for fine-grained periods, a multi-source temporal-feature convolutional networks (MSTFCN) model is proposed. MSTFCN employs parallel convolution to capture local temporal patterns and improves global feature representation via a channel attention mechanism. Based on this, redundant information is suppressed by a cascading channel compression approach, and a temporal segmentation strategy is applied to model fine-grained temporal features. We conducted experiments on two publicly available datasets, and the results demonstrate that the proposed data augmentation method effectively improves the forecasting performance of the deep learning model. Moreover, MSTFCN achieves higher forecasting accuracy and exhibits stronger environmental adaptability than the compared models.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00531-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power grid energy storage system planning method based on optimized butterfly algorithm","authors":"Xiang Yin,&nbsp;Xiaojun Zhang,&nbsp;Fuhai Cui","doi":"10.1186/s42162-025-00528-2","DOIUrl":"10.1186/s42162-025-00528-2","url":null,"abstract":"<div><p>In response to the power supply security of power grid system caused by a large number of clean energy connected to the distribution network, based on the grid side energy storage investors, the butterfly optimization algorithm is improved by combining the dynamic switching probability coordination algorithm and the dynamic Gaussian mutation strategy. A Distributed Energy Storage System (DESS) planning for power grid is constructed. The results showed that the research model had high stability and convergence accuracy, which was superior to comparison algorithms. When two DESS power stations were connected to nodes 4 and 32, with rated powers of 1.63 MW and 1.78 MW, and rated capacities of 5.71 MWh and 7.33 MWh, the annual benefits of capacity decision, location decision, and system were 783,000 RMB, 394,400 RMB, and 388,600 RMB, respectively. This showed that the research method could help operators obtain the maximum equal life return and meet their investment expectations. Before connecting to DESS, the overall voltage deviation of each typical state decreased by 5.28 p.u., 5.79 p.u., 2.84 p.u., and 2.37 p.u., and the overall active power loss of the daily power grid decreased by 1.41 MW, 1.83 MW, 1.79 MW, and 1.68 MW, respectively, indicating significant optimization effects. The research results indicate that the proposed solution can improve the overall stability and economy of the power grid, with strong applicability. This is of great significance for leveraging the supportive role of energy storage in safe operation and promoting the large-scale application of energy storage systems.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00528-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A case study on the calculation of carbon emissions over the entire life cycle of commercial office buildings based on measured data","authors":"Meijiu Zhao,&nbsp;Xiaochun Zhao","doi":"10.1186/s42162-025-00527-3","DOIUrl":"10.1186/s42162-025-00527-3","url":null,"abstract":"<div><p>Against the backdrop of the “dual carbon” strategy, building carbon emissions have been incorporated into the construction review process. Currently, the calculation and accounting of carbon emissions throughout the entire building lifecycle have become a hot and challenging issue in the field of building carbon emissions. Commercial office buildings, due to their large scale, high energy consumption, and significant carbon emission base, have become a key area for energy conservation and carbon reduction in public buildings. According to the building lifecycle carbon emission assessment system, the lifecycle of commercial office buildings can be divided into four stages: production of building materials, construction, operation and maintenance, and dismantling and recycling. This study takes an existing commercial office building in Beijing, China, as a case study, and based on data from energy audit reports, calculates the carbon emissions of each lifecycle stage using national standards and relevant software, and discusses the factors affecting building carbon emissions. At the same time, a comparative analysis of the differences between Chinese and Western national standards is conducted. Ultimately, strategies to reduce building carbon emissions are proposed. This study is of reference value for the precise calculation of carbon emissions throughout the lifecycle of commercial office buildings.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00527-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing energy flow in advanced microgrids: a prediction-independent two-stage hybrid system approach","authors":"Mohamad Javad Mohamadi,&nbsp;Mohammad Tolou Askari,&nbsp;Mahmoud Samiei Moghaddam,&nbsp;Vahid Ghods","doi":"10.1186/s42162-025-00523-7","DOIUrl":"10.1186/s42162-025-00523-7","url":null,"abstract":"<div><p>This paper presents a two-stage optimization framework for long-term energy management in microgrids, aiming to efficiently integrate various energy sources, storage systems, and consumption elements while addressing uncertainties in load demand and renewable generation. The framework consists of an offline optimization stage and an online optimization stage, each with distinct roles to balance long-term planning and real-time adaptability. In the offline stage, a robust two-stage mixed-integer linear programming (MILP) model is used to set annual targets for the state of charge (SoC) of energy storage systems. This stage applies a min-max-min approach to optimize for worst-case scenarios, establishing a cost-effective and reliable baseline plan that reduces dependency on conventional power sources and minimizes load deficits. The online stage, on the other hand, employs a new online convex optimization model that dynamically adjusts energy storage and dispatch decisions based on real-time data, allowing the microgrid to respond flexibly to fluctuations in demand and renewable generation. Simulation results using the Elia and North China datasets demonstrate the effectiveness of this two-stage approach. Offline optimization achieved up to 25% cost savings and reduced unmet demand by up to 99%, providing a stable foundation for efficient energy management. The online optimization stage further improved system responsiveness, minimizing reliance on backup generators and enhancing load reliability. This combined framework offers a comprehensive solution for optimizing microgrid performance, balancing predictive planning with real-time adaptability in complex, variable energy environments.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00523-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143938169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid energy storage device based on multi-port transformer and direct current bus connection","authors":"Xiu Zheng,&nbsp;Haixu Chen,&nbsp;Jiyang Zhang,&nbsp;Xiaohe Zhao,&nbsp;Dantong Wang","doi":"10.1186/s42162-025-00520-w","DOIUrl":"10.1186/s42162-025-00520-w","url":null,"abstract":"<div><p>In the context of energy management during digital transformation, traditional energy storage devices face challenges in multi-source coordination and efficient management. The key issue for system optimization is how to stabilize the management of multiple energy storage units. To address this, the study innovatively proposes a Hybrid Energy Storage System integrating a Multi-Port Transformer and Direct Current Bus. By constructing multi-port control factors, the system achieves coordinated optimization of the energy storage units, through dynamic adjustment of multi-port control factors and energy conversion matrices, the system can flexibly allocate power output from various energy storage units according to load demands, ensuring stable system operation. Experimental results in a microgrid system show that the integrated control system has a response time of 2.3 ms under 80% load, significantly outperforming the Proportional Integral Control (8.7 ms) and during the energy storage unit switching process, the voltage fluctuation rate is only 0.8% with a switching time of just 1.8 ms, and system stability reaching 98.5%. Under high-load conditions, the energy conversion efficiency is 96.8%, and the power distribution error is only 1.2%. Compared to traditional energy storage devices, the initial investment cost of this device is reduced by 7.4%, and the annual maintenance cost is reduced by 21.7%. These results indicate that the improved hybrid energy storage device not only possesses excellent energy management capabilities but also significantly reduces operational costs and environmental impact. The study provides an efficient technical solution for managing complex energy systems, which is of great significance for promoting smart grid construction and achieving green, low-carbon goals.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00520-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}