Topology detection in power distribution system using kernel‐node‐map deep networks

Xiao, Mengmeng; Wang, Shaorong; Ullah, Zia; Li, Yan; Arghandeh, Reza

doi:10.1049/iet-gtd.2020.0048

Cited by 8 publications

(6 citation statements)

References 39 publications

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“…The network topology i the closed or open state (indicated as 1 or 0) of switches S1, S2, S3, S4, and S the represent single-phase load of phases A, B, and C, respectively. The driven topology detection can be conducted based on the method proposed of this paper, see [29]. For example, if the compensation capacity for A AAEA Unit2 is not enough, then AAEA Unit3 is employed by closing swit…”

Section: Coordination Control Methods Of Imbalanced Mitigation Unitsmentioning

confidence: 99%

“…L a , L b , and L c the represent single-phase load of phases A, B, and C, respectively. The D-PMU data-driven topology detection can be conducted based on the method proposed by the author of this paper, see [29]. For example, if the compensation capacity for AAEA Unit1 or AAEA Unit2 is not enough, then AAEA Unit3 is employed by closing switch S 4 or S 5 .…”

Section: Test Casesmentioning

confidence: 99%

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D-PMU and 5G-Network-Based Coordination Control Method for Three-Phase Imbalance Mitigation Units in the LVDN

2021

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Three-phase imbalance is a long-term issue existing in low-voltage distribution networks (LVDNs), which consequently has an inverse impact on the safe and optimal operation of LVDNs. Recently, the increasing integration of single-phase distributed generations (DGs) and flexible loads has increased the probability of imbalance occurrence in LVDNs. To overcome the above challenges, this paper proposes a novel methodology based on the concept of "Active Asymmetry Energy-Absorbing (AAEA)" utilizing loads with a back-to-back converter, denoted as “AAEA Unit” in this paper. AAEA Units are deployed and coordinated to actively absorb asymmetry power among three phases for imbalance mitigation in LVDNs based on the high-precision, high-accuracy, and real-time distribution-level phasor measurement unit (D-PMU) data acquisition system and the 5th generation mobile networks (5G) communication channels. Furthermore, the control scheme of the proposed method includes three control units. Specifically, the positive-sequence control unit is designed to maintain the voltage of the DC-capacitor of the back-to-back converter. Likewise, the negative-sequence and zero-sequence control units are expected to mitigate the imbalanced current components. A simple imbalanced LVDN is modeled and tested in Simulink/Matlab (MathWorks, US). The obtained results demonstrate the effectiveness of the proposed methodology.

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Section: Coordination Control Methods Of Imbalanced Mitigation Unitsmentioning

confidence: 99%

Section: Test Casesmentioning

confidence: 99%

D-PMU and 5G-Network-Based Coordination Control Method for Three-Phase Imbalance Mitigation Units in the LVDN

2021

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“…Because of the limited number of distribution‐network branch switches in practice, the number of changed structure for a particular distribution network is finite. The distribution‐network topology identification problem can be transformed into a machine‐learning multi‐classification problem by mining the relationship between the measurement data and the distribution‐network topology using methods such as deep learning [26].…”

Section: Topology Identification Model Based On Acnnmentioning

confidence: 99%

Hybrid data and model‐driven joint identification of distribution‐network topology and parameters

Yang

Jiang

Liu

et al. 2022

IET Generation Trans & Dist

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Because of frequent changes in the topologies of distribution networks, the aging of lines and insufficient monitoring capacity compared to the transmission grid, the topology and line parameters are difficult to determine. Here, a hybrid data and model‐driven method is proposed for identifying the topologies and line parameters of distribution networks in the absence of voltage‐angle measurements. First, a topology identification model based on an attention mechanism and convolutional neural networks is constructed as an upper‐layer model, which requires a small number of voltage measurement snapshots of key nodes to identify the current topology. Second, a model‐driven two‐stage line‐parameter identification model is constructed as the lower‐layer model. Case studies based on the IEEE33 and PG&E69 node distribution systems are performed to validate the proposed method. The results confirm that the proposed method is effective for a stable and accurate identification of the topology and line parameters of distribution networks within a certain error range. In addition, the proposed method exhibits a reasonable generalization capability, and all models are equally applicable to both radial and ring networks.

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“…Fortunately, the advent of advanced monitoring technologies, such as D-PMU and AMI, provides new possibilities for refined parameter estimation in distribution networks. Indeed, the D-PMUs have been employed for event detection [5], state estimation [6], topology detection [7], and islanding detection [8] in the distribution networks. Likewise, the extensive deployment of smart meters in the distribution networks offers intelligent monitoring and control for various applications, such as observability enhancement [9], energy management [10], line outage identification [11], topology and parameter estimation [12,13], and distribution system state estimation [14,15].…”

Section: Motivation and Incitementmentioning

confidence: 99%

Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

Xiao

Xie

Fang

et al. 2021

IET Generation Trans & Dist

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This paper proposes a novel distribution line parameter estimation method, driven by the probabilistic data fusion of the distributed phasor measurement unit (D-PMU) and the advanced measurement infrastructure. The synchronized and high-precision D-PMU is utilized to tackle the challenge risen by the a-synchronization of smart meters. Correspondingly, a time-alignment algorithm is proposed to obtain the time-synchronous error (TSE) dataset for the up-stream smart meter. The non-parametric estimation method is performed then to evaluate the probabilistic density curve of TSE. Furthermore, TSE data of down-stream smart meters are generated by implementing the acceptance-rejection process based on the obtained probabilistic density curve. Leveraging the generated TSE dataset, a new time-shifted D-PMU curve is probabilistically aligned or fused with the down-stream advanced measurement infrastructure curves. According to the complete voltage drop model, the line parameter estimation of resistance and reactance is formulated as a quadratic programming problem and solved by Optimal Toolbox in MATLAB by conducting multi-run Monte-Carlo simulations under various scenarios. Simulation results demonstrate the effectiveness and robustness of the proposed methodology.

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Topology detection in power distribution system using kernel‐node‐map deep networks

Cited by 8 publications

References 39 publications

D-PMU and 5G-Network-Based Coordination Control Method for Three-Phase Imbalance Mitigation Units in the LVDN

D-PMU and 5G-Network-Based Coordination Control Method for Three-Phase Imbalance Mitigation Units in the LVDN

Hybrid data and model‐driven joint identification of distribution‐network topology and parameters

Distribution line parameter estimation driven by probabilistic data fusion of D‐PMU and AMI

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