Lightning Performance Evaluation of Transmission Line Based on Data-Driven Lightning Identification, Tracking, and Analysis

Bao, Jie; Wang, Xin; Zheng, Yongjun; Zhang, Feng; Huang, Xuyong; Sun, Peng

doi:10.1109/temc.2020.2988902

Cited by 18 publications

(8 citation statements)

References 36 publications

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“…The third kind of algorithm is mainly based on the method of sentence and semantics. It distinguishes words by analyzing the content of the full text, judges the segmentation mouth, and then segments the text [11] .…”

Section: Chinese Word Segmentationmentioning

confidence: 99%

Text analysis of power customer complaint work order based on data mining

Huang,

Zhu,

Wang

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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In order to explore the problem of text analysis of power customer complaint work order, a text analysis of power customer complaint work order based on data mining is proposed. This method explores the research on the text analysis of power customer complaints work order through the key technical problems and solutions of information recommendation represented by data mining. The research shows that the combined curve is closer to the actual situation, and all prediction indicators have improved, indicating that the combination effect is good. At present, the data platform is still in the process of improvement. Therefore, enterprises cannot be complacent and stay at the current analysis stage. They should keep pace with the times, strive to improve the big data platform, and further improve their data mining and analysis capabilities to adapt to the increasingly fierce market competition.

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Section: Chinese Word Segmentationmentioning

confidence: 99%

Text analysis of power customer complaint work order based on data mining

Huang,

Zhu,

Wang

et al. 2023

International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023)

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“…L IGHTNING strikes impose a high risk of power system outages [1], [2], [3]. Substations, critical part of a power system, shall operate with high reliability and experience a practically zero outage rate [4].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Direct-Stroke Shielding Performance of Substations Through Stochastic Modeling of Lightning Attachment

Ioannidis,

Tsovilis

2024

IEEE Trans. Electromagn. Compat.

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This article introduces a methodology for assessing the direct-stroke shielding performance of high-voltage substations. The proposed methodology considers both negative and positive lightning polarity and can be employed by any lightning attachment model that follows a probabilistic approach. A stochastic lightning attachment model is used, which considers the lightning discharges probabilistic progression. Stochastic model results on lightning discharge propagation, lightning attachment, and induced electric fields are in satisfactory agreement with field observations; comparisons are also made and discussed with respect to other well-established lightning attachment models. An application of the proposed methodology is performed to estimate the lightning incidence rate and shielding failure flashover rate of a 69 kV substation; the effect of lightning activity at the substation location is elucidated.

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“…The temporal and spatial parameters of lightning clusters in the identification process mainly come from the parameters of radar data. The volume scan of radar is generally 5 min (Dixon & Wiener, 1993; Wang et al., 2017), 6 min (Rigo et al., 2010; Wang et al., 2017) or 10 min (Kyznarová & Novák, 2009), so lightning density maps often use the same temporal resolution as radar data (Bao et al., 2021; Wang et al., 2017). Meyer (Meyer et al., 2013a) chose 2.5 min as time interval for lightning data, which is half of the volume scanning time.…”

Section: Introductionmentioning

confidence: 99%

“…To evaluate the lightning performance of transmission line better, Wang et al. presented the Lightning Identification, Tracking, and Analysis algorithm based on the Density‐Based Spatial Clustering of Applications with Noise to identify and track lightning clusters, and to classify different risk levels (Bao et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

“…On the basis of statistical analysis of lightning data, monitoring the track of lightning activity can provide warning for power systems to protect important facilities. To evaluate the lightning performance of transmission line better, Wang et al presented the Lightning Identification, Tracking, and Analysis algorithm based on the Density-Based Spatial Clustering of Applications with Noise to identify and track lightning clusters, and to classify different risk levels (Bao et al, 2021).…”

mentioning

confidence: 99%

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