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Weighted cloud prediction model for distribution network reliability

Authors: Qingyu Wang; Liwen Qin; Chen Shaonan; Biyun Chen; Zhao Bo;

Weighted cloud prediction model for distribution network reliability

Abstract

Aiming at the difficulty of determining the exact topology of distribution network during long-term reliability evaluation, a weighted cloud prediction model is proposed to predict the reliability of distribution network in long-term planning. Firstly, the digital features of cloud model of the influencing factors are obtained from the statistical historical data by an improved backward cloud generator. Secondly, based on the above digital features, the average weighted cloud prediction model are set up where the weight of each influencing factor is calculated by analytic hierarchy process according to the sensitivity of the influencing factors on reliability. Finally, the effectiveness and feasibility of the proposed method is demonstrated by comparison with the traditional cloud prediction method and the least square support vector machine method from the indices of maximum, minimum and average error.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average