Power flow matching-based topology identification of medium-voltage distribution networks via AMI measurements

Liang, Dong; Zeng, Lin; Chiang, Hsiao-Dong; Wang, Shouxiang

doi:10.1016/j.ijepes.2021.106938

Cited by 20 publications

(6 citation statements)

References 27 publications

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“…No noise for the bus frequency measurement is supposed, since the WLS rotor speed estimator used in this work is quite sensitive to these 3-5 h 10 −1 -10 0 100 [19] 30 min ≈ 10 −2 ≈ 99.9 [20] Not reported 10 −1 -10 0 98-100 [21] 10-90 h ≈ 10 −4 99.6-99.9 [23] Not applicable ≈ 10 −2 100 [24] Not applicable 10 0 -10 1 62.5-100 [25] Not applicable 10 0 -10 1 100 [26] Few minutes ≈ 10 −2 ≈ 99.4 [27] Not applicable ≈ 10 1 90-100 This work 1-30 min ≈ 10 −5 ≈ 99.4…”

Section: Simulation Resultsmentioning

confidence: 99%

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A Fast Data-Driven Topology Identification Method for Dynamic State Estimation Applications

2022

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Section: Simulation Resultsmentioning

confidence: 99%

“…Furthermore, reported results exhibit CPU times on the order of tens of milliseconds, whereas the method proposed in this work is significantly faster. In [27], a two-stage topology identification algorithm is proposed. In the first stage, a mixed-integer programming model is used to carry out a preliminary TI.…”

Section: Introductionmentioning

confidence: 99%

A Fast Data-Driven Topology Identification Method for Dynamic State Estimation Applications

2022

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“…However, the method diverges if the initial condition is far away from the actual solution. Liang et al [18] and Liao et al [19] investigated power flow matching-based methods for topology detection using the SM measurements, which are able to identify the topology of medium voltage networks accurately. To deal with data quality issues and filter out the measurement noises effectively, Zhao et al [20] investigated the deep belief network surrogate model for distribution network topology detection.…”

Section: A Related Workmentioning

confidence: 99%

Topology and Parameter Identification of Distribution Network Using Smart Meter and µPMU Measurements

Srinivas

2022

IEEE Trans. Instrum. Meas.

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Incomplete and inaccurate information of network topology and line parameters affects state monitoring, analysis, and control of active distribution networks. To solve this issue, this article proposes a method for identifying distribution network topology and line parameters using the measurements obtained from smart meters (SMs) and microphasor measurement units (μPMUs) installed at various locations in a distribution network. A data-driven approach was developed, which uses a probabilistic method (unscented Kalman filter (UKF) based) and a deterministic method (Newton Raphson (NR) based) iteratively for accurate identification of network topology and parameters. The impact of the measurement noise with SMs and μPMUs is analyzed, and the acceptable noise levels are quantified. The impact of the identification algorithm on the network state estimation is examined. Moreover, optimal installation locations of the μPMU equipment are identified based on the estimation accuracy of the algorithm. The method is validated on benchmarked IEEE 33-bus and IEEE 123-bus test systems, while the impact of the renewable power injections at the different network nodes is studied as well. The qualitative and quantitative analysis is performed over the state-of-the-art methods, to highlight the effectiveness of the proposed methodology.

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“…Furthermore, reported results exhibit CPU times on the order of tens of milliseconds, whereas the method proposed in this work is significantly faster. In [63], a two-stage topology identification algorithm is proposed. In the first stage, a mixed-integer programming model is used to carry out a preliminary TI.…”

Section: Introductionmentioning

confidence: 99%

Inertia Estimation of a Power System Area Based on Iterative Equation Error System Identification

Gotti,

Bizzarri,

Brambilla

et al. 2024

IEEE Trans. Power Syst.

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por el privilegio de cursar mis estudios doctorales con una financiación, a pesar de la incertidumbre de los últimos tiempos. En este sentido, quiero expresar un agradecimiento particular al profesor Santiago Arnaltes por estar involucrado en el proceso de búsqueda de la beca.Quisiera también expresar mi gratitud a mis directores de tesis por haberme permitido ingresar al programa y darme la oportunidad de realizar esta experiencia enriquecedora. Los consejos recibidos en estos años sobre la redacción y la estructura de los artículos han sido muy valiosos.Considero necesario reconocer a los profesores Juan Carlos Burgos y Mónica Chinchilla el interés, la ayuda y los consejos recibidos durante la preparación de las prácticas de sus asignaturas.Un ringraziamento doveroso va al gruppo del Politecnico di Milano che mi ha ospitato durante i 3 mesi del periodo di ricerca all'"estero". In particolare, vorrei esprimere la mia gratitudine a Federico Bizzarri e ad Angelo Brambilla per i preziosi consigli elargiti durante le riunioni e le videochiamate; grazie per mostrarmi dove fissare l'asticella. Sulla falsariga di una frase che scrissi circa nove anni fa in una circostanza simile a questa, ritengo di dover ringraziare anche il mio paese, l'Italia, che, seppur non priva di difetti, mi ha dato l'opportunità di ricevere una formazione di primo livello. Indipendentemente da quello che il futuro ha in serbo per me, le sarò sempre grato.Finalmente, quiero dar las gracias a Lourdes, por su apoyo incondicional, por ayudarme a tomar conciencia de mí mismo, por poner la vida en perspectiva y, simplemente, por ser ella.

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Power flow matching-based topology identification of medium-voltage distribution networks via AMI measurements

Cited by 20 publications

References 27 publications

A Fast Data-Driven Topology Identification Method for Dynamic State Estimation Applications

A Fast Data-Driven Topology Identification Method for Dynamic State Estimation Applications

Topology and Parameter Identification of Distribution Network Using Smart Meter and µPMU Measurements

Inertia Estimation of a Power System Area Based on Iterative Equation Error System Identification

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