Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models
Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models
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DOI：
10.1109/tsg.2021.3128752
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发表时间：
2022-03
影响因子：
9.6
通讯作者：
Yuxuan Yuan;K. Dehghanpour;Zhaoyu Wang;Fankun Bu
中科院分区：
文献类型：
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作者：
Yuxuan Yuan;K. Dehghanpour;Zhaoyu Wang;Fankun Bu
有效的停电地点对于提高配电系统的恢复能力至关重要。然而，准确的停电定位需要结合从不同数据来源收到的大量证据，包括智能电表(SM)的最后喘息信号、客户故障电话、社交媒体消息、天气数据、植被信息和网络的物理参数。由于配电网中数据的高维性，这是一项计算复杂的任务。本文提出了一种基于贝叶斯网络的多源数据融合方法来定位部分可观测配电系统中的停电事件。该方法的一个新颖之处在于，它使用概率图方法来考虑多源证据和配电系统的复杂结构。该方法可以从根本上降低高维空间中断电位置推理的计算复杂度。所提出的BN的图形结构是基于网络的拓扑结构和随机变量之间的因果关系而建立的，例如分支机构/客户的状态和证据。利用该图形模型，通过利用吉布斯抽样(GS)方法来推断所有分支机构的断电概率，从而获得准确的停电位置。与通常使用的精确推理方法具有指数复杂度的BN相比，GS能够及时量化目标条件概率分布。给出了几个实际配送系统的算例，验证了所提方法的有效性。
Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. A case study of several real-world distribution systems is presented to validate the proposed method.