Real-time autonomous dynamic reconfiguration based on deep learning algorithm for distribution network

Ji, Xingquan; Yin, Ziyang; Xu, Bo; Liu, Qi

doi:10.1016/j.epsr.2021.107132

Cited by 30 publications

(13 citation statements)

References 35 publications

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“…where S max i represents the rated capacity of branch i. (4) Network topology constraints The reconstructed network should meet [8]:…”

Section: Three-phase Symmetric Dynamic Dnr Modelmentioning

confidence: 99%

“…To validate that the heuristic rules in the proposed H-IFWA algorithm can effectively reduce the solution spaces, we assume that z = 3, and p S can be obtained, and this, compared with the results based on the fundamental loop matrix, is displayed in Table 4. {9, 10, 11, 12, 13, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0} {12, 13, 14, 34} 3 {2, 3,4,5,6,7,8,9,10,11,18,19,20,21, 0, 0, 0, 0, 0, 0} {9, 10, 11, 35} 4 {6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 25, 26, 27, 28, 29, 30, 31, 32} {17, 31, 32, 36} 5 {3, 4, 5, 22, 23, 24, 25, 26, 27, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0} {24, 27, 28, 37}…”

Section: Solution Spacesmentioning

confidence: 99%

“…DNR is usually divided into two types [4]: DNR in the normal operation status and DNR after an accident [5]. DNR in the normal operation status further consists of two types: static reconfiguration [6,7] and dynamic reconfiguration [8][9][10]. The former optimizes the section switches and tie switches of the distribution network based on the premise that the load does not change [11].…”

Section: Introductionmentioning

confidence: 99%

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Three-Phase Symmetric Distribution Network Fast Dynamic Reconfiguration Based on Timing-Constrained Hierarchical Clustering Algorithm

Zhang

Yin

et al. 2021

Symmetry

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This paper develops a novel dynamic three-phase symmetric distribution network reconfiguration (DNR) approach based on hierarchical clustering with timing constraints, which can divide the time period according to the time-varying symmetric load demand and symmetric distributed generations (DGs) output condition for a given time interval. The significance of the proposed technique is that by approximating the cluster center as the load status and DGs output status of the corresponding period, in this way, the intractable dynamic reconfiguration problem can be recast as multiple single-stage static three-phase symmetric DNR problems, which can effectively reduce the complexity of the three-phase symmetric dynamic reconfiguration. Furthermore, an improved fireworks algorithm considering heuristic rules (H-IFWA) is proposed and investigated to efficiently manage each single-stage static three-phase symmetric DNR problem. In order to avoid trapping into a local optimum or to facilitate the computational performance, the power moment method and the coding method based on heuristic rules are employed to reduce the solution space. The effectiveness of the proposed H-IFWA is validated on the IEEE 33, 119-bus system and a practical-scale Taiwan power company (TPC) 84-bus test system with DGs.

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“…where S max i represents the rated capacity of branch i. (4) Network topology constraints The reconstructed network should meet [8]:…”

Section: Three-phase Symmetric Dynamic Dnr Modelmentioning

confidence: 99%

Section: Solution Spacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Three-Phase Symmetric Distribution Network Fast Dynamic Reconfiguration Based on Timing-Constrained Hierarchical Clustering Algorithm

Zhang

Yin

et al. 2021

Symmetry

Self Cite

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“…We aim to reconfigure the network topology after a fault occurs in order to optimize various performance criteria. Dating back to the 1970s [46], distribution network reconfiguration is a widely studied problem in power systems engineering and remains an active research topic [48,66,68,63,67,21,58,32,17]. The goal of distribution network reconfiguration is to optimize one or more performance criteria (typically related to service reliability and network losses) while maintaining an operable distribution network configuration.…”

Section: Introductionmentioning

confidence: 99%

“…The goal of distribution network reconfiguration is to optimize one or more performance criteria (typically related to service reliability and network losses) while maintaining an operable distribution network configuration. Many types of algorithms have been applied to this problem, including, e.g., traditional optimization methods such as linear programming [58] and mixed-integer programming [8,20], heuristic decision strategies [33,68], machine learning methods [67,21,68,32], approximation algorithms [25], and metaheuristic techniques [67,63,17]. See [48] for a recent survey of the network reconfiguration literature.…”

Section: Introductionmentioning

confidence: 99%

Fair and Reliable Reconnections for Temporary Disruptions in Electric Distribution Networks using Submodularity

Hettle¹,

Gupta²,

Molzahn³

2021

Preprint

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We analyze a distributed approach for automatically reconfiguring distribution systems into an operational radial network after a fault occurs by creating an ordering in which switches automatically close upon detection of a downstream fault. The switches' reconnection ordering significantly impacts the expected time to reconnect under normal disruptions and thus affects reliability metrics such as SAIDI and CAIDI, which are the basis for regulator-imposed financial incentives for performance.We model the problem of finding a switch reconnection ordering that minimizes SAIDI and the expected reconnection time as Minimum Reconnection Time (MRT), which we show is a special case of the well-known minimum linear ordering problem [29] from the submodular optimization literature, and in particular the Min Sum Set Cover problem (MSSC) [19]. We prove that MRT is also NP-hard.We generalize the kernel-based rounding approaches of Bansal et al. [5] for Min Sum Vertex Cover to give tight approximation guarantees for MSSC on c-uniform hypergraphs for all c. For all instances of MSSC, our methods have a strictly better approximation ratio guarantee than the best possible methods for general MSSC.Finally, we consider optimizing multiple metrics simultaneously using local search methods that also reconfigure the system's base tree to ensure fairness in service disruptions and reconnection times and reduce energy loss. We computationally validate our approach on the NREL SMART-DS Greensboro synthetic urban-suburban network. We evaluate the performance of our reconfiguration methods and show significant reductions compared to single-metric-based optimizations.

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CNN data-driven active distribution network: Integration research of topology reconstruction and optimal scheduling in multi-source uncertain environment

Lyu,

Ni,

Bai

et al. 2024

Energy

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Real-time autonomous dynamic reconfiguration based on deep learning algorithm for distribution network

Cited by 30 publications

References 35 publications

Three-Phase Symmetric Distribution Network Fast Dynamic Reconfiguration Based on Timing-Constrained Hierarchical Clustering Algorithm

Three-Phase Symmetric Distribution Network Fast Dynamic Reconfiguration Based on Timing-Constrained Hierarchical Clustering Algorithm

Fair and Reliable Reconnections for Temporary Disruptions in Electric Distribution Networks using Submodularity

CNN data-driven active distribution network: Integration research of topology reconstruction and optimal scheduling in multi-source uncertain environment

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