Learning With End-Users in Distribution Grids: Topology and Parameter Estimation

Park, Sejun; Deka, Deepjyoti; Backhaus, Scott; Chertkov, Michael

doi:10.1109/tcns.2020.2979882

Cited by 35 publications

(12 citation statements)

References 23 publications

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“…Similarly, in [19], a joint systems, mainly because of expensive PMU installations. In [20], an accurate topology and parameter estimation method was developed using time-stamped terminal voltages and complex power samples. The technique performs accurately without any prior notice of the grid nodes and permissible lines.…”

Section: Literature Reviewmentioning

confidence: 99%

Three‐phase feeder parameter estimation using radial basis function neural networks and multi‐run optimisation method with bad data preparation

Yang

Rui

Guo

2021

IET Generation Trans & Dist

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The difference between the actual feeder parameters and feeder parameter data stored in a database or offered by manufacturers is significant owing to the ambient environment, temperature, and skin effect. Here, a parameter estimation method is proposed for unbalanced three‐phase distribution feeders based on the bus voltages and branch power flows measured from two terminals of the feeder. In the proposed method, a high‐precision phasor measurement unit is not required to estimate the magnitude and phase angle of the phasor quantity using a common time source for synchronisation. A radial basis function neural network with multi‐run optimisation (RBFNN‐MRO) is proposed to map the complex nonlinear relations between the distribution feeder parameters and electrical quantities. The feasibility and performance of the proposed RBFNN‐MRO method were verified using the four IEEE test systems. The comparison between the proposed RBFNN‐MRO method and the multi‐run method based on the quasi‐Newton method is implemented via the maximum absolute percentage error (MAPE) curves. The results reveal that the proposed RBFNN‐MRO method has excellent potential for improving the accuracy of feeder parameter estimation, even for bad data preparation.

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Section: Literature Reviewmentioning

confidence: 99%

Three‐phase feeder parameter estimation using radial basis function neural networks and multi‐run optimisation method with bad data preparation

Yang

Rui

Guo

2021

IET Generation Trans & Dist

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“…By this way, the reformulation in (14) can not only make use of pseudomeasurements, but also avoids the bias induced by errors in outage estimation. 4) An Additional Island-Component Detection Procedure: Although the above reformulating in (14a) can avoid the biased estimation results in the non-outage area (i.e., the energized region), it cannot avoid the biased results in the outage region where no meter is placed.…”

Section: A Bayesian Formulation Of Thementioning

confidence: 99%

“…This justifies setting such a bound. This is important in Bayesian inference since for a nonlinear optimization problem such as the one formulated in (14), setting a bound gives the algorithm a certain possibility to jump out from a local optimum to better search for a global optimum. Otherwise, the estimation accuracy will be inevitably reduced to some degree.…”

Section: A Demonstration On the Ieee 123-bus Test Systemmentioning

confidence: 99%

“…More specifically, Deka et al [13] propose to utilize a spanning-tree-based graphical model to jointly estimate the topology and the power injections. Similarly, the topology and the line parameter joint estimation are explored by Park et al [14] using graph theory, and by Yu et al [15], [16] using a data-driven approach, etc. Besides, a topology and outage joint estimation scheme is recently addressed by Gandluru et al [9].…”

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confidence: 99%

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An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System

Valinejad

Korkali

et al. 2021

Preprint

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The reliable operation of a power distribution system relies on a good prior knowledge of its topology and its system state. Although crucial, due to the lack of direct monitoring devices on the switch statuses, the topology information is often unavailable or outdated for the distribution system operators for real-time applications. Apart from the limited observability of the power distribution system, other challenges are the nonlinearity of the model, the complicated, unbalanced structure of the distribution system, and the scale of the system. To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system. Specifically, by using the very limited number of measurements available that are associated with the forecast load data, we efficiently recover the full Bayesian posterior distributions of the system topology under both normal and outage operation conditions. This is performed through an adaptive importance sampling procedure that greatly alleviates the computational burden of the traditional Monte-Carlo (MC)sampling-based approach while maintaining a good estimation accuracy. The simulations conducted on the IEEE 123-bus test system and an unbalanced 1282-bus system reveal the excellent performances of the proposed method.

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“…Other researches address the joint estimation of topology and the line admittance, by formulating the problem as the maximum likelihood estimation [5]- [9]. The above works often make some assumptions to simplify the problem, including uncorrelated nodal power/current injections [3], [4], [10], [11], radial network topologies [2], [8], [10]- [13], sufficient phasor measurements [5], [6], [8], [9], or accurate voltage measurements [7]. Those assumptions may hold in some cases, but hinder the practical implementation under more general distribution system cases.…”

Section: Introductionmentioning

confidence: 99%

Topology and Admittance Estimation: Precision Limits and Algorithms

Liu¹,

Zhang²,

Hou³

et al. 2021

Preprint

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Distribution grid topology and admittance information are essential for system planning, operation, and protection. In many distribution grids, missing or inaccurate topology and admittance data call for efficient estimation methods. However, measurement data may be insufficient or contaminated with large noise, which will introduce fundamental limits to the estimation accuracy. This work explores the theoretical precision limits of the topology and admittance estimation (TAE) problem, with different measurement devices, noise levels, and the number of measurements. On this basis, we propose a conservative progressive self-adaptive (CPS) algorithm to estimate the topology and admittance. Results on IEEE 33 and 141-bus systems validate that the proposed CPS method can approach the theoretical precision limits under various measurement settings.

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Learning With End-Users in Distribution Grids: Topology and Parameter Estimation

Cited by 35 publications

References 23 publications

Three‐phase feeder parameter estimation using radial basis function neural networks and multi‐run optimisation method with bad data preparation

Three‐phase feeder parameter estimation using radial basis function neural networks and multi‐run optimisation method with bad data preparation

An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System

Topology and Admittance Estimation: Precision Limits and Algorithms

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