Analytic Considerations and Design Basis for the IEEE Distribution Test Feeders

Schneider, Kevin P.; Mather, Barry; Pal, Bikash C.; Ten, Chee‐Wooi; Shirek, Greg; Zhu, Hao; Fuller, Jason C.; Pereira, J.L.R.; Ochoa, Luis F.; Araujo, Leandro Ramos de; Dugan, R.C.; Matthias, Sauer; Paudyal, Sumit; McDermott, Thomas E.; Kersting, W.H.

doi:10.1109/tpwrs.2017.2760011

Cited by 497 publications

(203 citation statements)

References 44 publications

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“…In this section, the proposed graph-pruned NN is utilized to estimate the state of the benchmark IEEE-37 distribution feeder. This network was recommended by the Test Feeder Working Group of the Distribution System Analysis Subcommittee of the IEEE PES for evaluating the performance of the state estimation algorithms [23]. The feeder has several deltaconnected loads and is known to be highly unbalanced.…”

Section: Resultsmentioning

confidence: 99%

Physics-Aware Neural Networks for Distribution System State Estimation

Zamzam

Sidiropoulos

2020

IEEE Trans. Power Syst.

136

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The distribution system state estimation problem seeks to determine the network state from available measurements. Widely used Gauss-Newton approaches are very sensitive to the initialization and often not suitable for real-time estimation. Learning approaches are very promising for real-time estimation, as they shift the computational burden to an offline training stage. Prior machine learning approaches to power system state estimation have been electrical model-agnostic, in that they did not exploit the topology and physical laws governing the power grid to design the architecture of the learning model. In this paper, we propose a novel learning model that utilizes the structure of the power grid. The proposed neural network architecture reduces the number of coefficients needed to parameterize the mapping from the measurements to the network state by exploiting the separability of the estimation problem. This prevents overfitting and reduces the complexity of the training stage. We also propose a greedy algorithm for phasor measuring units placement that aims at minimizing the complexity of the neural network required for realizing the state estimation mapping. Simulation results show superior performance of the proposed method over the Gauss-Newton approach.

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Section: Resultsmentioning

confidence: 99%

Physics-Aware Neural Networks for Distribution System State Estimation

Zamzam

Sidiropoulos

2020

IEEE Trans. Power Syst.

136

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“…IV. NUMERICAL SIMULATIONS Numerical tests have been performed using the IEEE 37bus and 123-bus test feeders (see e.g., [27] for a description of the feeders) on a standard laptop with Intel R Core TM i7-7500 CPU @2.70Hz. The 37-bus test feeder consists of 32 nodes (phases) that are connected to non-zero loads, all delta-connected.…”

Section: Algorithm 1 Fopc For Ddsementioning

confidence: 99%

Dynamic Distribution State Estimation Using Synchrophasor Data

Song

Dall’Anese

Simonetto

et al. 2020

IEEE Trans. Smart Grid

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The increasing deployment of distribution-level phasor measurement units (PMUs) calls for dynamic distribution state estimation (DDSE) approaches that tap into high-rate measurements to maintain a comprehensive view of the distributionsystem state in real time. Accordingly, this paper explores the development of a fast algorithmic framework by casting the DDSE task within the time-varying optimization realm. The timevarying formulation involves a time-varying robustified leastsquares approach, and it naturally models optimal trajectories for the estimated states under streaming of measurements. The formulation is based on a linear surrogate of the AC power-flow equations, and it includes an element of robustness with respect to measurement outliers. The paper then leverages a first-order prediction-correction method to achieve simple online updates that can provably track the state variables from heterogeneous measurements. This online algorithm is computationally efficient as it relies on the Hessian of the cost function without computing matrix-inverse. Convergence and bounds on the estimation errors of proposed algorithm can be analytically established.

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“…For the cost function, p PV τ,i is the maximum available real power of the photovoltaic panel connected to inverter i at time τ ; and c p,i and c q,i are positive constants for each i. For the constraints, p L τ ∈ R m and q L τ ∈ R m represent the time-varying real and reactive loads, V j : R n × R n × R m × R m → R is the implicit function derived from the power flow equations that represents the mapping from power injections to the (normalized) voltage magnitude at bus j in a modified IEEE 37-node test feeder [46,37], V min and V max are the bounds on the (normalized) voltage magnitudes, and X τ,i is given by…”

Section: Numerical Examplementioning

confidence: 99%

A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization

Tang

Dall’Anese

Bernstein

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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This paper considers a nonconvex optimization problem that evolves over time, and addresses the synthesis and analysis of regularized primal-dual gradient methods to track a Karush-Kuhn-Tucker (KKT) trajectory. The proposed regularized primal-dual gradient methods are implemented in a running fashion, in the sense that the underlying optimization problem changes during the iterations of the algorithms. For a problem with twice continuously differentiable cost and constraints, and under a generalization of the Mangasarian-Fromovitz constraint qualification, sufficient conditions are derived for the running algorithm to track a KKT trajectory. Further, asymptotic bounds for the tracking error (as a function of the time-variability of a KKT trajectory) are obtained. A continuous-time version of the algorithm, framed as a system of differential inclusions, is also considered and analytical convergence results are derived. For the continuous-time setting, a set of sufficient conditions for the KKT trajectories not to bifurcate or merge is proposed. Illustrative numerical results inspired by a real-world application are provided.

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Analytic Considerations and Design Basis for the IEEE Distribution Test Feeders

Cited by 497 publications

References 44 publications

Physics-Aware Neural Networks for Distribution System State Estimation

Physics-Aware Neural Networks for Distribution System State Estimation

Dynamic Distribution State Estimation Using Synchrophasor Data

A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization

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