Fault Location in Active Distribution Networks Using Multiple Measurement-Based Bayesian Learning
ieee pes asia pacific power and energy engineering conference(2020)
摘要
Efficient and accurate fault location techniques are beneficial to pinpoint fault position and reduce power outages. Faced with this issue, this paper proposes a novel fault location technique for active distribution networks that utilizes multiple measurement-based Bayesian learning. Specifically, fault location problem in this paper is firstly transformed into solving a block-sparse signal recovery model. In order to enhance robustness in noisy conditions and achieve satisfactory recovery performance, this paper extends the recovery model to a multiple measurement-based model and adopts a block-sparse Bayesian learning (BSBL) algorithm for block-sparse signal recovery. The proposed method requires only a limited number of distribution-level synchronized measurements to be placed, instead of yielding a full observable network. The effectiveness of the proposed method under different noise levels is verified by using IEEE 33-node active distribution system.
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关键词
fault location,active distribution networks,multiple measurement-based model,block-sparse Bayesian learning (BSBL)
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