Special field measurement results of an onshore wind farm connected to power grid of Taiwan Power System subject to Typhoon Matmo

Wang, Li; Ke, Shun-Chin; Hong, Ting-Wei; Yu, Chien-Hsiang; Yeh, Chuan-Chieh; Liu, Weisheng; Kuan, Bing-Lin; Lu, Xiu-Yu; Prokhorov, Anton V.

doi:10.1109/icps.2017.7945115

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Typhoon "Jebi" landed in Japan in 2018, and approximately 2.18 billion power consumers suffered from power failures. It was almost without exception that in the above typhoon disasters the power distribution network (PDN) was the source of a large amount of power system loss [3], which brought high operational risk and economic losses [4]. According to historical data [5], the power outages in typhoon disasters are mostly caused by the collapses of concrete poles of the PDN, especially in underdeveloped areas [6,7].…”

Section: Introductionmentioning

confidence: 99%

A risk management framework for power distribution networks undergoing a typhoon disaster

Hou

et al. 2021

IET Generation Trans & Dist

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With the increasing distribution network accidents caused by typhoon disasters, risk management in typhoon scenarios is necessary. A risk management framework for power distribution networks (PDNs) undergoing a typhoon disaster is proposed. First, a risk prediction model of the PDN under typhoon conditions based on the Batts model is developed to predict the loss of load risk (LLR) index and economic loss risk (ELR) index of the PDN before the typhoon lands. Then, a vulnerability index of overhead lines, considering the failure frequency of specific overhead lines and their load importance, is introduced for use in a weak link identification model to identify weak links in the PDN. Finally, a two‐time‐step risk relief model based on typhoon forecast information and the identified weak links is developed to allocate PDN relief materials to reduce the LLR and ELR during a typhoon disaster. An IEEE RBTS BUS6 system is employed as a test system to verify the risk management strategy. The vulnerability index proposed by the strategy considers both the importance of load and line failure. The risk management strategy adopts a two‐time‐step model, which reduces the impact of meteorological forecast errors and enables more efficient use of materials. The results show that the proposed framework can take more effective use of relief materials to reduce the loss of PDNs compared with the traditional strategy.

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Section: Introductionmentioning

confidence: 99%

A risk management framework for power distribution networks undergoing a typhoon disaster

Hou

et al. 2021

IET Generation Trans & Dist

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Special Field Measurement Results of an Onshore Wind Farm Connected to Power Grid of Taiwan Power System Subject to Typhoon Matmo

Wang

Hong

et al. 2019

IEEE Trans. on Ind. Applicat.

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Prediction Algorithm of Wind Waterlogging Disaster in Distribution Network Based on Multi-Source Data Fusion

Li¹,

Lu²,

et al. 2022

Mathematical Problems in Engineering

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It is very important for power grid development research and related technical improvement to obtain the disaster situation of fine-scale distribution network, such as the transportation condition evaluation of distribution network and the wind waterlogging disaster prediction of distribution network. Among them, the wind waterlogging disaster prediction of distribution network is the main one, and the prediction of its disaster degree often determines whether the distribution network can be prevented before and rescued after the disaster. Therefore, in view of the above problems, combined with the actual transmission situation of the distribution network, after collecting the measured disaster data of the distribution network in relevant areas, combined with the multi-source data fusion technology and neural network modeling technology, this paper analyzes the disaster degree indicators of different distribution networks and constructs the relevant fuzzy matrix through the fuzzy theory to evaluate the disaster degree, which is verified by the measured data. This distribution network disaster loss prediction model can effectively implement the disaster loss prediction of distribution network and compare its prediction results with the other two different common models. The comparison results show that the prediction accuracy of the multi-source data fusion prediction model constructed in this paper is more than 0.95 compared with the other two models, while the prediction accuracy of the other two models is not more than 0.9, which proves that the model constructed in this paper has smaller errors. It has the advantages of higher accuracy and faster convergence speed.

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Special field measurement results of an onshore wind farm connected to power grid of Taiwan Power System subject to Typhoon Matmo

Cited by 3 publications

References 13 publications

A risk management framework for power distribution networks undergoing a typhoon disaster

A risk management framework for power distribution networks undergoing a typhoon disaster

Special Field Measurement Results of an Onshore Wind Farm Connected to Power Grid of Taiwan Power System Subject to Typhoon Matmo

Prediction Algorithm of Wind Waterlogging Disaster in Distribution Network Based on Multi-Source Data Fusion

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