Electric Power Systems Research最新文献

{"title":"An adaptive online monitoring system using NFSOGI-PLL for three-phase voltage unbalance in grids","authors":"HangYu Guo ,&nbsp;Jiafeng Ding ,&nbsp;Ruyingjing Zhang ,&nbsp;Guangwei Yang","doi":"10.1016/j.epsr.2024.111210","DOIUrl":"10.1016/j.epsr.2024.111210","url":null,"abstract":"<div><div>The extensive utilization of unbalanced and nonlinear loads has significantly impacted power quality in modern grids. Among various metrics, the three-phase voltage unbalance factor (TPVUF) stands out as a crucial indicator. To mitigate the challenges posed by harmonics and DC components that interfere with accurate TPVUF measurements, this paper introduces the Notch Filter-based Second-Order Generalized Integrator Phase-Locked Loop (NFSOGI-PLL) for the first time and designs an adaptive online monitoring system for TPVUF based on NFSOGI-PLL. To validate its performance, the hardware design and rapid software development of the NFSOGI-PLL-based TPVUF online monitoring system are thoroughly implemented, and the system is tested under various conditions. The results indicate that the proposed system not only provides real-time performance and high accuracy, complying with the rigorous standards set by GB/T 15543-2008 and IEC 61000-4-27, but also demonstrates remarkable stability under diverse influences. These findings underscore the exceptional filtering and tracking abilities of the NFSOGI-PLL in power systems, as well as the accuracy and anti-interference capabilities of the NFSOGI-based TPVUF online monitoring system, which holds great potential for widespread application in power system control and research.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"239 ","pages":"Article 111210"},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Switch monitoring algorithm for 220 kV terminal substation startup process based on multi time scale graph model","authors":"Yin Wu ,&nbsp;Jie Lin ,&nbsp;Jintao Liu ,&nbsp;De Li ,&nbsp;Zhou Li ,&nbsp;Yufu Lu","doi":"10.1016/j.epsr.2024.111181","DOIUrl":"10.1016/j.epsr.2024.111181","url":null,"abstract":"<div><div>The switch monitoring in the startup process of 220 kV terminal substation is a dynamic and changeable process, which has obvious characteristics of diversified scales in time, so as to solve the problem of switch monitoring under multivariable and multi-scale interference. The switch monitoring framework for the startup process of 220KV terminal substation is built. The process layer uses the graphic configuration software and combines the internal and external models of the substation to build the objective function for the generation of the substation graphic model. The particle swarm optimization algorithm is used to solve it to generate the optimal substation graphic model. Through the online monitoring device, the signals such as sensors, normally open and normally closed contacts are collected in real time, and the status of the switchgear is obtained through processing, the reduced half trapezoidal cloud model multivariable multi-scale sample entropy similarity tolerance criterion is used for softening treatment, and the multivariable multi-scale cloud sample entropy is determined to achieve the extraction of multiple time scale switch fault feature vectors, which are used as the input of the SE-DSCNN fault diagnosis model. Combined with the substation diagram, the switch fault identification during the startup process of the substation is realized, and the fault switch position is located. The experimental results show that the algorithm can accurately generate the substation model, which includes all the equipment in the substation, accurately describes the connection relationship and operation status of the equipment, and has high accuracy, integrity and aesthetics; The algorithm can effectively extract and analyze the MMCES entropy characteristics of different types of switch faults; The algorithm can realize the switch monitoring during the startup of substation C, and determine the fault switch and fault location.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"239 ","pages":"Article 111181"},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the performance of solar-powered EV charging stations using the TOSSI-based CTF technique","authors":"Manasi Pattnaik ,&nbsp;Manoj Badoni ,&nbsp;Rajeev Kumar ,&nbsp;Pavan Khetrapal ,&nbsp;Pratibha Kumari","doi":"10.1016/j.epsr.2024.111206","DOIUrl":"10.1016/j.epsr.2024.111206","url":null,"abstract":"<div><div>In this paper, the design and analysis of a novel solar-powered EV-charging system employing a third-order sinusoidal signal integrator (TOSSI) based-CTF (character of triangular function) is proposed. The TOSSI-based CTF is used to extract fundamental active components by eliminating harmonic distortions from the load currents. This control structure has the unique capability of active current separation, employing simple mathematical operations. Moreover, the response is further refined with the help of optimized gain parameters. The designed system is capable of handling various power quality issues, including harmonic mitigation, current balancing, and power factor improvement. The EV battery is primarily charged by solar-PV power employing a bidirectional DC-DC converter. Alternatively, the EV battery may be charged by the grid supply during the unavailability of sunlight by taking the power quality issues into due consideration. The suggested control topology is used to enhance the dynamic operation of solar-powered EV charging stations experiencing solar power intermittency and variation of load. Using MATLAB, the efficacy of the proposed control topology is tested for different operating scenarios. The suggested control topology is also verified and validated using prototype hardware developed in the laboratory, where the suggested controller has proven its utility over and above existing state-of-the-art controllers in the domain.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"239 ","pages":"Article 111206"},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Impact of DC Bipole Configuration on the Lightning Performance of a HVDC Transmission Line in terms of Backflashover","authors":"Amanda A.C. Moraes,&nbsp;Fernando H. Silveira,&nbsp;Silvério Visacro","doi":"10.1016/j.epsr.2024.111174","DOIUrl":"10.1016/j.epsr.2024.111174","url":null,"abstract":"<div><div>This work presents a discussion on the influence of both the polarity and position of the phase conductors (DC poles) of a double circuit 500 kV HVDC transmission line (TL) on lightning overvoltages developed across their line insulator strings and the corresponding lightning performance in terms of backflashover, considering computational simulations with the Hybrid Electromagnetic Model (HEM) and the Leader Progression Model (LPM). Several polarity arrangements of the DC poles were considered and their influence on the probability of backflashover occurrence due to negative downward lightning was assessed for tower-footing grounding impedances varying from 10 to 100 Ω. The study indicates the worst lightning performances for configurations B, C1 and D2, with critical current and backflashover probability varying from 96 to 49 kA and from 4% to 26%, respectively. These results show the importance of both the position and polarity of the phase conductors and tower-footing grounding impedance to define the HVDC TL performance, indicating the TL configurations with the lower phases with positive polarity as the one with worst lightning performance in terms of backflashover.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"239 ","pages":"Article 111174"},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A graph and diffusion theory-based approach for localization and recovery of false data injection attacks in power systems","authors":"Yixuan He,&nbsp;Jingyu Wang,&nbsp;Chen Yang,&nbsp;Dongyuan Shi","doi":"10.1016/j.epsr.2024.111184","DOIUrl":"10.1016/j.epsr.2024.111184","url":null,"abstract":"<div><div>False Data Injection Attacks (FDIAs) pose a serious threat to power systems by interfering with state estimation and jeopardizing their safety and reliability. Detecting and recovering from FDIAs is thus critical for maintaining power system integrity. The increasing integration of renewable energy sources and the extensive use of power electronic devices introduce significant randomness in both power generation and loads, leading to significant power fluctuations and dynamic changes in power flows. These variations challenge the accuracy of existing FDIA detection and recovery methods. To address these challenges, an innovative data recovery framework is proposed, comprising two key stages: the FDIA localization stage and the FDIA data recovery stage. In the first stage, a Line Message Passing Neural Network (LMPNN) based FDIA localization model is employed to precisely identify the attacked data and generate a mask input for the recovery stage. In the data recovery stage, an FDIA data recovery model, named Denoising Diffusion Graph Models (DDGM), is designed to recover data with minimal error while conforming to the physical laws of the grid. Both models utilize node graph and line graph representations to depict measurements on buses and branches. By leveraging an optimized graph neural network, and inviting a loop-structured framework that combines a denoising diffusion model with a graph neural network these models effectively extract data features and inherent dynamic properties, enabling superior localization of FDIAs both in node and edge spaces and ensuring accurate recovery of compromised data even in the presence of high uncertainty and significant power fluctuations. By incorporating physical laws through a customized loss function embedding Kirchhoff’s circuit laws into the training process of DDGM, the model ensures the recovered data to be physically consistent with power system dynamics. Experimental validations on IEEE 39-bus and 118-bus test systems, under conditions of high fluctuations in generation and loads, demonstrate that the proposed models outperform existing methods, achieving significant improvements in accuracy and robustness.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"239 ","pages":"Article 111184"},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance evaluation of a low-voltage SVC utilizing IoT streamed data for distribution systems","authors":"Roman N. Krasnoperov ,&nbsp;Dmitry I. Panfilov ,&nbsp;Michael G. Astachev ,&nbsp;Ahmed M. Elkholy","doi":"10.1016/j.epsr.2024.111187","DOIUrl":"10.1016/j.epsr.2024.111187","url":null,"abstract":"<div><div>This paper presents the design, implementation, and performance evaluation of a novel 50 kvar Static Var Compensator (SVC) integrated with an Internet of Things (IoT) controller for a low-voltage power distribution system in Moscow. This integration enhances real-time monitoring and control capabilities over traditional SVC systems. The study focuses on advanced control systems for reactive power compensation, voltage stabilization, and overall system efficiency. Data collected over two months demonstrate the SVC’s effectiveness in maintaining a stable power factor close to unity and reducing reactive power demand. The device successfully kept voltage levels within acceptable limits across all phases, reducing fluctuations and ensuring balanced voltage levels. Key findings include enhanced reactive power compensation, voltage stabilization (minimum voltage not less than 218 V), improved system efficiency (reactive power demand from the source near zero most of the time), and significant improvements in power quality and grid stability. The case study at Kosinskaya Street, Moscow, confirms the device’s role in improving power quality and grid stability. These results support the broader deployment of IoT-integrated SVC technology in low-voltage power distribution systems.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"239 ","pages":"Article 111187"},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping and assessment of harmonic voltage levels for railway traction supply stations in Sweden","authors":"Rafael S. Salles ,&nbsp;Rebecca Asplund ,&nbsp;Sarah K. Rönnberg","doi":"10.1016/j.epsr.2024.111195","DOIUrl":"10.1016/j.epsr.2024.111195","url":null,"abstract":"<div><div>Assessing harmonic distortion measurements in the electric railway power systems (ERPS) requires evaluating the time-varying behavior, interactions, and performance in different time scales. This paper aims to map and assess harmonic voltage levels in 13 traction converter stations for the Swedish railway power supply system, with findings that have direct practical implications. For that, measurements from the public and railway grid sides for 69 weeks are analyzed. Statistical values are explored for the harmonic voltage spectra and total harmonic distortion (THD) variation. The public grid side measurements are investigated using 95th percentile weekly values, and performance is evaluated by comparing the recommended planning levels of IEC 61,000–3–6. The intraweek variation complements the information about the time-varying behavior of the THD. The 95th percentile, minimum daily values, and intraday variation are explored to understand the time-based behavior since there are no reference limits from standards for comparison, looking to the railway grid side. Extended analysis is placed on the railway grid side to highlight some aspects of measurement time-aggregation based on 10-min values, and time-series trend analysis is used to confirm traffic planning impact. Discussion and findings regarding railway operation, the technology deployed at the traction converter station, time-varying behavior, traffic planning impact, measurement time-aggregation, and spectra patterns were presented.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"239 ","pages":"Article 111195"},"PeriodicalIF":3.3,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"A black-start strategy for active distribution networks considering source-load bilateral uncertainty and multi-type resources✰","authors":"Fuxing Yao,&nbsp;Shihong Miao,&nbsp;Tingtao Wang,&nbsp;Jiaxu Wang,&nbsp;Baisheng Wang,&nbsp;Haoyu Tan","doi":"10.1016/j.epsr.2024.111161","DOIUrl":"10.1016/j.epsr.2024.111161","url":null,"abstract":"<div><div>The integration of multi-type resources provides new ideas for the black-start of active distribution networks (ADNs). However, the inability to deal with uncertainty will lead to problems such as frequency/voltage crossing limits, scheduling difficulties, and even restoration failures. To this end, an ADN black-start strategy considering source-load bilateral uncertainty and multi-type resources is proposed. The forecast error uncertainties of renewable energy sources (RESs) and loads are characterized in intervals based on Copula theory, which are then introduced into the black-start model of ADNs and solved by the column-and-constraint generation algorithm. Case studies based on the improved IEEE 33-node system indicate that the proposed strategy can effectively cope with source-load bilateral uncertainty and realize robust restoration of ADNs. The system also achieves a 99.97 % RESs consumption ratio and a 67.36 % power utilization ratio of energy storage devices. Compared with existing methods, our strategy can give a more economical and faster restoration scheme while considering safety, which can be deployed in dispatch centers to help operators make full use of existing resources to achieve black-start safely and stably after outages. However, the computational time will increase if it is migrated to grids with large topologies, which needs to be further investigated.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"238 ","pages":"Article 111161"},"PeriodicalIF":3.3,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based remaining useful life prediction for transmission systems: Integrating operating conditions with TimeGAN and CNN-LSTM networks","authors":"Yeon-Sub Sim ,&nbsp;Chun-Kwon Lee ,&nbsp;Jae-Sang Hwang ,&nbsp;Gu-Young Kwon ,&nbsp;Seung Jin Chang","doi":"10.1016/j.epsr.2024.111151","DOIUrl":"10.1016/j.epsr.2024.111151","url":null,"abstract":"<div><div>The remaining useful life (RUL) prediction is key for ensuring the stability of transmission power systems. However, there is no sufficient actual life-cycle, and no mature physics-of-failure model of the power transmission facilities, which make it difficult to predict RUL. In this paper, we propose an AI-based transmission line RUL prediction method which incorporates the measured operating conditions of each line. The proposed method sets the basic linear asset unit as one cable segment and joint boxes on both sides. A feature extraction and piecewise-based RUL model was designed using asset data from 1,458 actual transmission lines accumulated by measuring unit over a period of 44 years. Consequently, the RULs which depend on load operating conditions of target assets can be successfully predicted using CNN-LSTM. In addition, a data augmentation algorithm based on time-series generative adversarial networks was developed to address the issue of imbalanced failure data and further improve the accuracy of RUL prediction. The performance of the proposed RUL estimation method is further verified using real-world data. The proposed method shows an improvement in fault-healthy classification accuracy by 35.72%, 21.43%, and 7.14% compared to existing feature extraction methods, including deep neural networks (DNN), convolutional neural networks (CNN), and autoencoder (AE), respectively. Additionally, when compared to representative deep learning models for RUL estimation, it achieves the best performance with RMSE and Score of 0.074 and 0.066, respectively. Moreover, the proposed method is capable of accurately estimating RUL even for equipment in the early failure period, where the actual operating time is short.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"238 ","pages":"Article 111151"},"PeriodicalIF":3.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer fault diagnosis method based on the three-stage lightweight residual neural network","authors":"Hang Liu,&nbsp;Ben Niu,&nbsp;Zhijian Liu,&nbsp;Ming Li,&nbsp;Zhiyu Shi","doi":"10.1016/j.epsr.2024.111142","DOIUrl":"10.1016/j.epsr.2024.111142","url":null,"abstract":"<div><div>The fault diagnosis method for dissolved gas in transformer oil based on deep learning has the problems of complex structure, over-parameterization, and high resolution in practical application. This paper presents a three-stage lightweight residual neural network method for transformer fault diagnosis. In the first stage, based on the 50-layer residual networks, the residual block is enhanced using the inverted bottleneck idea, and the Swish activation function and a simple, parameter-free attention module are incorporated to optimize the model structure and performance. In the second stage, an adaptive channel pruning method is proposed, selectively eliminating redundant filters and channels based on the fault data complexity during the training process, thereby realizing network lightweight. In the third stage, a quantization-aware method is introduced, which converts all 32-bit floating point parameters in the network to 8-bit integers, reduces the bit width of each parameter, and accomplishes a reduction in parameter size. The experimental results for the transformer oil dissolved gas fault dataset indicate that the three-stage lightweight model, sizing at 2.20 MB—only 1.51 % of the original—achieves a fault diagnosis accuracy of 97.64 %, 1.49 % higher than the original, achieving a well-balanced between accuracy and complexity.</div></div>","PeriodicalId":50547,"journal":{"name":"Electric Power Systems Research","volume":"238 ","pages":"Article 111142"},"PeriodicalIF":3.3,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}