{"_id":"tEJF2WFmoAvSBceay","bibbaseid":"azimian-moshtagh-pal-ma-analyticalverificationofperformanceofdeepneuralnetworkbasedtimesynchronizeddistributionsystemstateestimation-2024","author_short":["Azimian, B.","Moshtagh, S.","Pal, A.","Ma, S."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Azimian"],"firstnames":["Behrouz"],"suffixes":[]},{"propositions":[],"lastnames":["Moshtagh"],"firstnames":["Shiva"],"suffixes":[]},{"propositions":[],"lastnames":["Pal"],"firstnames":["Anamitra"],"suffixes":[]},{"propositions":[],"lastnames":["Ma"],"firstnames":["Shanshan"],"suffixes":[]}],"journal":"Journal of Modern Power Systems and Clean Energy","title":"Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation","year":"2024","volume":"12","number":"4","pages":"1126-1134","keywords":"Perturbation methods;Robustness;Artificial neural networks;Power systems;Phasor measurement units;Neurons;Training;Deep neural network (DNN);distribution system state estimation (DSSE);mixed-integer linear programming (MILP);robustness;trustworthiness","doi":"10.35833/MPCE.2023.000432","bibtex":"@ARTICLE{10345460,\r\n author={Azimian, Behrouz and Moshtagh, Shiva and Pal, Anamitra and Ma, Shanshan},\r\n journal={Journal of Modern Power Systems and Clean Energy}, \r\n title={Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation}, \r\n year={2024},\r\n volume={12},\r\n number={4},\r\n pages={1126-1134},\r\n keywords={Perturbation methods;Robustness;Artificial neural networks;Power systems;Phasor measurement units;Neurons;Training;Deep neural network (DNN);distribution system state estimation (DSSE);mixed-integer linear programming (MILP);robustness;trustworthiness},\r\n doi={10.35833/MPCE.2023.000432}}\r\n\r\n\r\n","author_short":["Azimian, B.","Moshtagh, S.","Pal, A.","Ma, S."],"key":"10345460","id":"10345460","bibbaseid":"azimian-moshtagh-pal-ma-analyticalverificationofperformanceofdeepneuralnetworkbasedtimesynchronizeddistributionsystemstateestimation-2024","role":"author","urls":{},"keyword":["Perturbation methods;Robustness;Artificial neural networks;Power systems;Phasor measurement units;Neurons;Training;Deep neural network (DNN);distribution system state estimation (DSSE);mixed-integer linear programming (MILP);robustness;trustworthiness"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://raw.githubusercontent.com/Anamitra-Pal-Lab/pal_website_bib/main/Pal_Web.bib","dataSources":["zWwhYvSfNTW4Rg2Rm"],"keywords":["perturbation methods;robustness;artificial neural networks;power systems;phasor measurement units;neurons;training;deep neural network (dnn);distribution system state estimation (dsse);mixed-integer linear programming (milp);robustness;trustworthiness"],"search_terms":["analytical","verification","performance","deep","neural","network","based","time","synchronized","distribution","system","state","estimation","azimian","moshtagh","pal","ma"],"title":"Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation","year":2024}