{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97343"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97343","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Improvements to power system dynamic load model parameter estimation","abstract":"Transient stability analysis is becoming increasingly important for power systems engineers and researchers. Accurate dynamic models are required, but aggregate load models are an area of weakness. Measurement-based system identification methods based on least-squares minimization have difficulty uniquely identifying the model parameters because the models exhibit parameter insensitivity and interdependency: vastly different model parameters can produce the same output waveform for a given disturbance. One could argue that the parameters of a model are unimportant, as long as the simulation output waveforms are correct. While this is true for the training set — the disturbance(s) we used to determine the parameters — we show that, when measurement noise exists, the model fails when we try to use it to predict the result of other disturbances. We present three methods for reducing the effect of parameter unidentifiability. First, we try increasing the size of the training data to include multiple disturbances, but this does not have a significant impact. Second, we present an algorithm based on a maximum a-posteriori (MAP) estimator, which can take advantage of prior knowledge of the parameters of the grid. The MAP estimator is both more accurate and more robust than least squares. Third, we make use of complex power measurements in addition to voltage. Complex power was found to be much more robust to noise, but many more monitoring devices would need to be deployed to provide the necessary measurements. We also consider the practical computational aspects of large-scale parameter estimation. We propose a geographical region of influence method to define zones where lower resolution models could be substituted to reduce the computational burden. We then investigate alternative metrics for defining the difference between two time series, because the Euclidean distance was shown to be inadequate.","abstract_html":"Transient stability analysis is becoming increasingly important for power systems engineers and researchers. Accurate dynamic models are required, but aggregate load models are an area of weakness. Measurement-based system identification methods based on least-squares minimization have difficulty uniquely identifying the model parameters because the models exhibit parameter insensitivity and interdependency: vastly different model parameters can produce the same output waveform for a given disturbance. One could argue that the parameters of a model are unimportant, as long as the simulation output waveforms are correct. While this is true for the training set — the disturbance(s) we used to determine the parameters — we show that, when measurement noise exists, the model fails when we try to use it to predict the result of other disturbances. We present three methods for reducing the effect of parameter unidentifiability. First, we try increasing the size of the training data to include multiple disturbances, but this does not have a significant impact. Second, we present an algorithm based on a maximum a-posteriori (MAP) estimator, which can take advantage of prior knowledge of the parameters of the grid. The MAP estimator is both more accurate and more robust than least squares. Third, we make use of complex power measurements in addition to voltage. Complex power was found to be much more robust to noise, but many more monitoring devices would need to be deployed to provide the necessary measurements. We also consider the practical computational aspects of large-scale parameter estimation. We propose a geographical region of influence method to define zones where lower resolution models could be substituted to reduce the computational burden. We then investigate alternative metrics for defining the difference between two time series, because the Euclidean distance was shown to be inadequate.","abstract_has_math":false,"creators":["Guo, Siming"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Overbye, Thomas","Chen, Deming","Sauer, Peter","Zhu, Hao"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:14:57Z","date_published":"2017-08-10T19:14:57Z","updated_at":"2026-07-22T22:24:32Z","subjects":["Dynamics","Fault","Induction motor","Load model","Noise","Parameter estimation","Phasor measurement unit","Power system","Sensitivity","Similarity measure","System identification","Transient stability"],"languages":["en"],"rights":["Copyright 2017 Siming Guo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97343","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Overbye, Thomas","Chen, Deming","Sauer, Peter","Zhu, Hao"]},{"key":"dc:creator","label":"Author","values":["Guo, Siming"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:14:57Z","2017-04-12","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Dynamics","Fault","Induction motor","Load model","Noise","Parameter estimation","Phasor measurement unit","Power system","Sensitivity","Similarity measure","System identification","Transient stability"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Siming Guo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97343"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Transient stability analysis is becoming increasingly important for power systems engineers and researchers. Accurate dynamic models are required, but aggregate load models are an area of weakness. Measurement-based system identification methods based on least-squares minimization have difficulty uniquely identifying the model parameters because the models exhibit parameter insensitivity and interdependency: vastly different model parameters can produce the same output waveform for a given disturbance. One could argue that the parameters of a model are unimportant, as long as the simulation output waveforms are correct. While this is true for the training set — the disturbance(s) we used to determine the parameters — we show that, when measurement noise exists, the model fails when we try to use it to predict the result of other disturbances. We present three methods for reducing the effect of parameter unidentifiability. First, we try increasing the size of the training data to include multiple disturbances, but this does not have a significant impact. Second, we present an algorithm based on a maximum a-posteriori (MAP) estimator, which can take advantage of prior knowledge of the parameters of the grid. The MAP estimator is both more accurate and more robust than least squares. Third, we make use of complex power measurements in addition to voltage. Complex power was found to be much more robust to noise, but many more monitoring devices would need to be deployed to provide the necessary measurements. We also consider the practical computational aspects of large-scale parameter estimation. We propose a geographical region of influence method to define zones where lower resolution models could be substituted to reduce the computational burden. We then investigate alternative metrics for defining the difference between two time series, because the Euclidean distance was shown to be inadequate.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Siming Guo, accepted the attached license on 2017-04-12 at 15:26.","The student, Siming Guo, submitted this Dissertation for approval on 2017-04-12 at 16:35.","This Dissertation was approved for publication on 2017-04-12 at 16:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10728 on 2017-08-10 at 13:39:34","Made available in DSpace on 2017-08-10T19:14:57Z (GMT). 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Measurement-based system identification methods based on least-squares minimization have difficulty uniquely identifying the model parameters because the models exhibit parameter insensitivity and interdependency: vastly different model parameters can produce the same output waveform for a given disturbance. One could argue that the parameters of a model are unimportant, as long as the simulation output waveforms are correct. While this is true for the training set — the disturbance(s) we used to determine the parameters — we show that, when measurement noise exists, the model fails when we try to use it to predict the result of other disturbances. We present three methods for reducing the effect of parameter unidentifiability. First, we try increasing the size of the training data to include multiple disturbances, but this does not have a significant impact. Second, we present an algorithm based on a maximum a-posteriori (MAP) estimator, which can take advantage of prior knowledge of the parameters of the grid. The MAP estimator is both more accurate and more robust than least squares. Third, we make use of complex power measurements in addition to voltage. Complex power was found to be much more robust to noise, but many more monitoring devices would need to be deployed to provide the necessary measurements. We also consider the practical computational aspects of large-scale parameter estimation. We propose a geographical region of influence method to define zones where lower resolution models could be substituted to reduce the computational burden. We then investigate alternative metrics for defining the difference between two time series, because the Euclidean distance was shown to be inadequate.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Siming Guo, accepted the attached license on 2017-04-12 at 15:26.","The student, Siming Guo, submitted this Dissertation for approval on 2017-04-12 at 16:35.","This Dissertation was approved for publication on 2017-04-12 at 16:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10728 on 2017-08-10 at 13:39:34","Made available in DSpace on 2017-08-10T19:14:57Z (GMT). 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