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arXiv · 2311.06973

Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation

Behrouz Azimian·Shiva Moshtagh·Anamitra Pal·Shanshan Ma
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Abstract

Recently, we demonstrated success of a time-synchronized state estimator using deep neural networks (DNNs) for real-time unobservable distribution systems. In this letter, we provide analytical bounds on the performance of that state estimator as a function of perturbations in the input measurements. It has already been shown that evaluating performance based on only the test dataset might not effectively indicate a trained DNN's ability to handle input perturbations. As such, we analytically verify robustness and trustworthiness of DNNs to input perturbations by treating them as mixed-integer linear programming (MILP) problems. The ability of batch normalization in addressing the scalability limitations of the MILP formulation is also highlighted. The framework is validated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system, both of which are incompletely observed by micro-phasor measurement units.

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Behrouz Azimian, Shiva Moshtagh, Anamitra Pal, Shanshan Ma. 2023-11-12. Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation. https://doi.org/10.35833/mpce.2023.000432

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