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

• URL: http://arxiv.org/abs/2311.06973v4
• Date: Thu, 22 Feb 2024 16:33:10 GMT
• Title: Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation
• Authors: Behrouz Azimian, Shiva Moshtagh, Anamitra Pal, Shanshan Ma
• Abstract summary: Recently, we demonstrated success of a time-synchronized state estimator using deep neural networks (DNNs) In this letter, we provide analytical bounds on the performance of that state estimator as a function of perturbations in the input measurements.
• Score: 0.18726646412385334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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