Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914688925696000 |
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| author | Azimian, Behrouz Moshtagh, Shiva Pal, Anamitra Ma, Shanshan |
| author_facet | Azimian, Behrouz Moshtagh, Shiva Pal, Anamitra Ma, Shanshan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_06973 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation Azimian, Behrouz Moshtagh, Shiva Pal, Anamitra Ma, Shanshan Machine Learning Systems and Control 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. |
| title | Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2311.06973 |