首页|Analytical Verification of Performance of Deep Neural Network Based Time-synchronized Distribution System State Estimation
Analytical Verification of Performance of Deep Neural Network Based Time-synchronized Distribution System State Estimation
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Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution systems.In this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input mea-surements.It has already been shown that evaluating perfor-mance based only on the test dataset might not effectively indi-cate the ability of a trained DNN to handle input perturbations.As such,we analytically verify the robustness and trustworthi-ness 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 val-idated 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.
Deep neural network(DNN)distribution sys-tem state estimation(DSSE)mixed-integer linear programming(MILP)robustnesstrustworthiness
Behrouz Azimian、Shiva Moshtagh、Anamitra Pal、Shanshan Ma
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School of Electrical,Computer and Energy Engineering,Arizona State Universi-ty,Tempe,AZ 85281,USA
School of Electrical,Computer and Energy Engineering,Arizona State University,Tempe,AZ 85281,USA
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