Relevant bibliographies by topics / Line Outage Identification

Academic literature on the topic 'Line Outage Identification'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Line Outage Identification"

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Chen, Yu Christine, Taposh Banerjee, Alejandro D. Dominguez-Garcia, and Venugopal V. Veeravalli. "Quickest Line Outage Detection and Identification." IEEE Transactions on Power Systems 31, no. 1 (January 2016): 749–58. http://dx.doi.org/10.1109/tpwrs.2015.2394246.

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Arabali, A., M. Majidi, M. S. Fadali, and M. Etezadi-Amoli. "Line outage identification-based state estimation in a power system with multiple line outages." Electric Power Systems Research 133 (April 2016): 79–86. http://dx.doi.org/10.1016/j.epsr.2015.12.013.

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Kundu, Shubhrajyoti, Sumit Banerjee, Siddhartha Sankar Thakur, and Mehebub Alam. "Line outage identification using comparison of bus power mismatch considering PMU outage." International Journal of Nanoparticles 12, no. 3 (2020): 238. http://dx.doi.org/10.1504/ijnp.2020.10031799.

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Alam, Mehebub, Shubhrajyoti Kundu, Siddhartha Sankar Thakur, and Sumit Banerjee. "Line outage identification using comparison of bus power mismatch considering PMU outage." International Journal of Nanoparticles 12, no. 3 (2020): 238. http://dx.doi.org/10.1504/ijnp.2020.109547.

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Ding, Xue Yang, and Xin Dong Liu. "A Blackout Model Based on Optimal Risk Index and High-Risk Line Identification." Advanced Materials Research 383-390 (November 2011): 2390–97. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2390.

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This paper summarizes the defects of both OPA model and its improved models and then proposes a new model in order to simulate real power system roundly. There is an inner loop based on DC power flow optimization to minimize the change in generation or load shed and an outer loop based on the risk theory. In the outer loop, whether to improve the line capacity and how to improve are determined by risk index of line improvement (RILI) and line improvement degree function, respectively. A new prevention strategy against cascading failure is proposed by identifying the high-risk lines and determining optimal RILI which minimize line improvement cost and load loss. In the end, simulation based on 39-bus New England System reveals that new model is closer to real power system than the original one by comparing outage probability and load loss between the two models, and risk-based assessment interval and line improvement delay play important roles in prevention against power system outages, and improvement based on risk assessment can reduce outage probability to varying degrees, which illustrates the practical significance of optimal RILI.
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Amin Mohammed, Dara H. "Voltage Stability Analysis for Kurdistan Region Power System Using Fast Voltage Stability Index." Tikrit Journal of Engineering Sciences 21, no. 2 (September 18, 2013): 75–79. http://dx.doi.org/10.25130/tjes.21.2.10.

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In any stressed power system, Identification of the weakest bus and transmission lines is veryimportant for planning and operation to prevent voltage collapse which leads to total blackout ofthe whole system. The aim of the paper is to use a technique to determine the weakest buses ofthe power system. Increasing load in power system could lead to the event of voltage collapsewhich implies the contingency in the system. Increasing load and line outage contingencies areranked so that the line which highly affects the system could be identified. This paper proposesto utilize an established index called FVSI (Fast Voltage Stability Index) to identify the sensitivelines and critical line outages in the system. Test system of this paper is the KRPS-30-Bus-132kV. The contingency ranking process can be conducted by computing the line stability indexof each line for a particular Increasing load and for line outage contingency. The contingencywhich is ranked the highest implies that it contributed to the system instability.
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He, Jia, and Maggie X. Cheng. "Machine learning methods for power line outage identification." Electricity Journal 34, no. 1 (January 2021): 106885. http://dx.doi.org/10.1016/j.tej.2020.106885.

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Emami, Roozbeh, and Ali Abur. "External system line outage identification using phasor measurement units." IEEE Transactions on Power Systems 28, no. 2 (May 2013): 1035–40. http://dx.doi.org/10.1109/tpwrs.2012.2220865.

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Kim, Taedong, and Stephen J. Wright. "PMU Placement for Line Outage Identification via Multinomial Logistic Regression." IEEE Transactions on Smart Grid 9, no. 1 (January 2018): 122–31. http://dx.doi.org/10.1109/tsg.2016.2546339.

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Vijay, R. "Transmission Line Outage Detection and Identification by Communal Spider Optimization Algorithm." CVR Journal of Science & Technology 14, no. 01 (June 1, 2018): 38–42. http://dx.doi.org/10.32377/cvrjst1408.

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Dissertations / Theses on the topic "Line Outage Identification"

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Li, Wen-Tai, and 李文泰. "Study on Location Identification Method of Power Line Outages Using PMU Measurements for Power System." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/d7u556.

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Abstract:
博士
國立中山大學
通訊工程研究所
106
Detecting and identifying power line outage events and locations quickly and accurately, which are providing the potential of preventing a large-scale blackout due to line outages as well as correct power network status for other monitoring and control tasks, e.g., state estimation and optimal power flow, are of paramount importance in the operation and maintenance of electric power systems.Recently, the widespread installation of phasor measurement units (PMUs) on transmission grids of most major power systems has attracted considerable attention for their potential to improve the reliability of power transmission.With the obtainment of wide-scale, real-time PMU data, the use of phasor angle measurements provided by PMUs in identifying locations of power line outages is regarded as a promising method, and thus a surge of interest in exploring applications with utilizing PMUs in line outages identification has been evident. The aim of this paper is to develop novel identification methods of power line outages with PMU measurements. Owing to the problem of power line outage identification has traditionally been formulated as a combinatorial optimization problem, the optimal solution of which can be found through an exhaustive search. However, the size of the search space grows exponentially with the number of outages and may thus pose a potential problem for the practical implementation of an exhaustive search, especially when multiple power line outages are considered in a power system. Hence, we propose a novel global stochastic optimization technique based on cross-entropy optimization to correctly and promptly identify multiple line outages. Then, we consider the cases of phasor angle measurements with bad data due to that communication errors or system malfunctions may introduce errors to the measurements and thus yield bad data. Most of the existing methods on line outage identification fail to consider such error. Hence, this paper further investigates the impact of identifying line outage using PMU measurements with bad data, and then develops a framework for identifying multiple power line outages based on the PMUs'' measurements in the presence of bad data. In particular, we design an algorithm to identify locations of line outage and recover the faulty measurements simultaneously. To validate the effectiveness and efficiency of the proposed approaches, case studies are carried on IEEE standard test systems.
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Book chapters on the topic "Line Outage Identification"

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Alam, Mehebub, Shubhrajyoti Kundu, Siddhartha Sankar Thakur, Anil Kumar, and Sumit Banerje. "A New AC Model for Transmission Line Outage Identification." In Advances in Systems, Control and Automations, 223–36. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8685-9_21.

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He, Jia, Maggie X. Cheng, Yixin Fang, and Mariesa L. Crow. "A Machine Learning Approach for Line Outage Identification in Power Systems." In Machine Learning, Optimization, and Data Science, 482–93. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13709-0_41.

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Tiwari, Sarveshmani, Hemant Palivela, and Pramod Kumar. "Classification and Identification of Partial Outage in Transmission Lines Using Deep Learning." In Lecture Notes in Electrical Engineering, 155–67. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8248-3_13.

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Guo, Jingyuan, Tao Yang, Hui Feng, and Bo Hu. "Line outage identification based on partial measurement of Phasor Measurement Unit (PMU)." In Signal and Information Processing, Networking and Computers, 175–84. CRC Press, 2016. http://dx.doi.org/10.1201/b21308-24.

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Conference papers on the topic "Line Outage Identification"

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Liao, Yizheng, Yang Weng, Chin-Woo Tan, and Ram Rajagopal. "Urban distribution grid line outage identification." In 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2016. http://dx.doi.org/10.1109/pmaps.2016.7764218.

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Jha, Vibhakar, Biswaranjan Mishra, and Shubham Kumar Vishwakarma. "Power Line Outage Identification Using PMU." In 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC). IEEE, 2022. http://dx.doi.org/10.1109/isssc56467.2022.10051400.

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He, Jia, and Maggie Cheng. "Graph Convolutional Neural Networks for Power Line Outage Identification." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9413093.

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Zare, Javad, and Farrokh Aminifar. "Synchrophasor-assisted line outage identification: A simple and iterative algorithm." In 2015 23rd Iranian Conference on Electrical Engineering (ICEE). IEEE, 2015. http://dx.doi.org/10.1109/iraniancee.2015.7146467.

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Rahim, M. N. A., I. Musirin, Z. A. Hamid, and N. A. M. Kamari. "BCO based technique for line outage identification in congestion management." In 2012 IEEE International Power Engineering and Optimization Conference. IEEE, 2012. http://dx.doi.org/10.1109/peoco.2012.6230896.

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Wang, Boyu, Chao Gan, Jing Yang, Chinmay Hedge, and Jingxian Wu. "Graph-based multiple-line outage identification in power transmission systems." In 2017 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2017. http://dx.doi.org/10.1109/pesgm.2017.8274462.

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Soleymani, Mohammad, and Amir Safdarian. "Unsupervised Learning for Distribution Grid Line Outage and Electricity Theft Identification." In 2019 Smart Grid Conference (SGC). IEEE, 2019. http://dx.doi.org/10.1109/sgc49328.2019.9056579.

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Dai, Zhen, and Joseph Euzebe Tate. "Line Outage Identification Based on AC Power Flow and Synchronized Measurements." In 2020 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2020. http://dx.doi.org/10.1109/pesgm41954.2020.9281410.

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Alam, Mehebub, Shubhrajyoti Kundu, Siddhartha Sankar Thakur, Anil Kumar, and Sumit Banerjee. "An Efficient Algorithm for Line Outage Identification in Smart Power Grid." In 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE). IEEE, 2020. http://dx.doi.org/10.1109/pesgre45664.2020.9070662.

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Jiang, Xichen, Yu Christine Chen, Venugopal V. Veeravalli, and Alejandro D. Dominguez-Garcia. "Quickest line outage detection and identification: Measurement placement and system partitioning." In 2017 North American Power Symposium (NAPS). IEEE, 2017. http://dx.doi.org/10.1109/naps.2017.8107394.

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