Topology identification method for residential areas in low-voltage distribution networks based on unsupervised learning and graph theory

Li, Haifeng; Liang, Wenzhao; Liang, Yuansheng; Zhikeng, Li; Wang, Gang

doi:10.1016/j.epsr.2022.108969

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…The voltage of each node is related to the impedance parameters of the line and the customer's power consumption. The closer the node is to the transformer, the higher the Complex algorithms and relatively low accuracy [17,18] Voltage series High accuracy High data quantity requirements for learning, low interpretability [20] Active power Effective in incomplete measurement stations Dependence on large power fluctuations of customers [21,22] Energy/power Multi-level topology identification Complete measurement of network [23,24] Voltage and active power More distinguishing features and high accuracy Less effective for stations with a low degree of threephase unbalance voltage is, and the farther away the voltage is lower. Moreover, the active power of any upstream branch node P t busi in the line satisfies power balance:…”

Section: Power Flow Characterisationmentioning

confidence: 99%

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Improved topology identification for distribution network with relatively balanced power supplies

Luan,

Xu,

Liu

et al. 2024

IET Energy Syst Integration

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Having correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data‐driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer‐customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K‐Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer‐customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.

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Section: Power Flow Characterisationmentioning

confidence: 99%

“…[16] to identify the latent nodes in network. References [17,18] use machine learning approach to extract features by reducing the dimension of the voltage time series, and achieve station distinction and phase identification using feature clustering methods. The harmonic voltage correlation is proposed in Ref.…”

Section: Introductionmentioning

confidence: 99%

Improved topology identification for distribution network with relatively balanced power supplies

Luan,

Xu,

Liu

et al. 2024

IET Energy Syst Integration

View full text Add to dashboard Cite

show abstract

“…The implementation of graph theory also spread out to wireless power transfer (WPT) system, where graph sets method is proposed for simplified modeling with comprehensive analysis of multicoil WPT systems [4,64]. Topology identification in [27], shows some promising benefits bringing unsupervised learning with graph theory for applications in low-voltage distribution systems.…”

Section: B Recent Developmentsmentioning

confidence: 99%

“…Serving as a common language among various disciplines, graph theory can be leveraged as a powerful tool to enable systematic modelling and analysis [3][4][5][6][7], design new converters [8][9][10][11][12], control their operations [13,14], estimate and identify potential issues [15][16][17], optimize the systems [18][19][20], or even facilitate the understanding of the interconnections and interactions between components in power-electronics-based systems [21][22][23]. Especially in recent years, innovative research keeps emerging, covering component-level, converter-level and system-level of power electronics [24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Leveraging the power of graph, these research works not only feature systematic understanding but also open more possibilities, e.g., integrated with automation and AI.…”

Section: Introductionmentioning

confidence: 99%

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

Li,

Xue,

Zargari

et al. 2023

IEEE Access

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Graph theory within power electronics, developed over a 50-year span, is continually evolving, necessitating ongoing research endeavors. Facing with the never-been-seen explosion of graphstructured data, the state-of-the-art deep learning technique-Graph Neural Networks (GNNs), becomes the leading trend in machine learning within just recent five years and demonstrated surprisingly broad and prominent benefits covering from new drug discovery to better IC design. However, its promising applications in Power Electronics are still rarely discussed and its full potential remains unexplored. Addressing this gap, this review paper is the first to outline GNNs' general workflow in power electronics, laying the groundwork and examining current GNN methodologies within the field. To bridge the gap in the sparse GNN literature within this domain, we also provide extended discussions on leveraging insights from GNN-aided circuit design to enrich power electronics research. Our work includes in-depth GNNbased case studies that demonstrate promising applications from converters to system-level power electronics, showcasing GNNs' unique benefits and untapped possibilities (e.g., accurate component design, voltage predictions on IEEE-13 bus and 118 bus systems). Additionally, we provide a comprehensive survey of GNNs' latest and successful applications, emphasizing their impact on energy-centric sectors, such as transportation electrification, smart grids. Considering the interdisciplinary nature of power electronics in modern energy systems, our review highlights the potential of GNNs emerge as a promising tool to decode the intricate behavior and dynamics of power electronics systems, and we hope such synergies between advanced AI methodologies like GNNs with the ever-evolving graph theory can lead to more powerful tools, novel methodologies, and advancements in the power electronics community.

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“…The automated inspection of low-voltage metering boxes is important in their production, transportation, installation, operation and maintenance [3][4]. The type identification and structural size inspection are of significance for the safety and the long-term use of the low-voltage metering boxes [5][6].…”

Section: Introductionmentioning

confidence: 99%

Automated Type Identification and Size Measurement for Low-Voltage Metering Box Based on RGB-Depth Image

Liu¹,

Jin²,

Yan³

et al. 2023

IJACSA

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The low-voltage metering box is a critical piece of equipment in the power supply system. The automated inspection of metering boxes is important in their production, transportation, installation, operation and maintenance. In this work, an automated type identification and size measurement method for low-voltage metering boxes based on RGB-D images is proposed. The critical components, including the door shell and window, connection terminal block, and metering compartment in the cabinet, are segmented first using the Mask-RCNN network. Then the proposed Sub-Region Closer-Neighbor algorithm is used to estimate the number of connection terminal blocks. Combined with the number of metering compartments, the type of metering box is classified. To refine the borders of the metering box components, an edge correction algorithm based on the Depth Difference (Dep-D) Constraint is presented. Finally, the automated size measurement is implemented based on the proposed Equal-Region Averaging algorithm. The experimental results show that the accuracies of the automated type identification and size measurement of the low-voltage metering box reach more than 92%.

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Topology identification method for residential areas in low-voltage distribution networks based on unsupervised learning and graph theory

Cited by 12 publications

References 23 publications

Improved topology identification for distribution network with relatively balanced power supplies

Improved topology identification for distribution network with relatively balanced power supplies

From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power Electronics

Automated Type Identification and Size Measurement for Low-Voltage Metering Box Based on RGB-Depth Image

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