[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125890-en":3,"doc-seo-125890-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125890,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","ARTIFICIAL SENSOR FOR ELECTRIC POWER SYSTEM USING MACHINE LEARNING - Master’s thesis","The demand for sustainable, reliable, and efficient electric power systems continues to increase as new generation methods are introduced and grids expand to meet consumer needs. Accurate monitoring depends on reliable sensor and data collection, yet sensor malfunctions and dropped communication packets can create missing or erroneous measurements. This study develops a machine-learning method to predict missing or incorrect Phasor Measurement Unit (PMU) data, even during unexpected abnormal oscillations. A trained neural network predicts missing PMU entries across six abnormal oscillation event types with under 5% error, enabling power system observers to maintain accurate state estimation under data losses.","Calhoun: The NPS Institutional Archive  \nDSpace Repository  \n\n| 2024-06\u003Cbr>ARTIFICIAL SENSOR FOR ELECTRIC POWER SYSTEM USING MACHINE LEARNING\u003Cbr>Fisher, Nathan L.\u003Cbr>Monterey, CA; Naval Postgraduate School |\n| --- |\n| [https://hdl.handle.net/10945/73113](https://hdl.handle.net/10945/73113) |\n\nNPS Scholarship Theses  \nThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.  \nDownloaded from NPS Archive: Calhoun  \nNAVAL POSTGRADUATE  \nSCHOOL MONTEREY, CALIFORNIA  \nTHESIS  \nARTIFICIAL SENSOR FOR ELECTRIC POWER SYSTEM USING MACHINE LEARNING  \nby  \nNathan L. Fisher  \nJune 2024  \nThesis Advisor: Wei Kang  \nSecond Readers: Ralucca Gera  \nThor Martinsen  \nDistribution Statement A. Approved for public release: Distribution is unlimited.  \nTHIS PAGE INTENTIONALLY LEFT BLANK  \n\n| REPORT DOCUMENTATION PAGE |  |  |  |  | Form Approved OMB No. 0704-0188 |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC, 20503. |  |  |  |  |  |  |  |\n| 1. AGENCY USE ONLY (Leave blank) |  | 2. REPORT DATE\u003Cbr>June 2024 |  | 3. REPORT TYPE AND DATES COVERED\u003Cbr>Master’s thesis |  |  |  |\n| 4. TITLE AND SUBTITLE\u003Cbr>ARTIFICIAL SENSOR FOR ELECTRIC POWER SYSTEM USING MACHINE LEARNING |  |  |  |  |  | 5. FUNDING NUMBERS |  |\n| 6. AUTHOR(S) Nathan L. Fisher |  |  |  |  |  |  |  |\n| 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)\u003Cbr>Naval Postgraduate School\u003Cbr>Monterey, CA 93943-5000 |  |  |  |  |  | 8. PERFORMING\u003Cbr>ORGANIZATION REPORT NUMBER |  |\n| 9. SPONSORING / MONITORING AGENCY NAME(S) AND\u003Cbr>ADDRESS(ES)\u003Cbr>N/A |  |  |  |  |  | 10. SPONSORING / MONITORING AGENCY REPORT NUMBER |  |\n| 11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. |  |  |  |  |  |  |  |\n| 12a. DISTRIBUTION / AVAILABILITY STATEMENT\u003Cbr>Distribution Statement A. Approved for public\u003Cbr>release: Distribution is unlimited. |  |  |  |  |  | 12b. DISTRIBUTION CODE\u003Cbr>A |  |\n| 13. ABSTRACT (maximum 200 words)\u003Cbr>The demand for sustainable, reliable, and efficient electric power systems continues to increase as new methods of power generation are introduced and power systems expand to meet consumer needs. Reliable sensor and data collection is critical to accurately monitor and assess the state of an electric power system. The purpose of this study is to develop a method for predicting missing or erroneous data entries for a Phasor Measurement Unit (PMU) in the event of a malfunctioning sensor or dropped communication packet, even during unexpected periods of abnormal oscillations. To do this, we propose using machine learning to build a neural network to accurately and efficiently predict missing or erroneous observations from real world PMU data. The trained neural network is capable of predicting missing PMU data entries for six different abnormal oscillation events, with less than 5% error. Power system observers can utilize this method to maintain an accurate system state estimation, even while undergoing sensor malfunctions or dropping communication packets. |  |  |  |  |  |  |  |\n| 14. SUBJECT TERMS\u003Cbr>PMU, neural network, machine learning |  |  |  | ","cbCaicX5TTpxtW9v","https://ap.wps.com/l/cbCaicX5TTpxtW9v","pdf",4805771,7,1,65,"English","en",105,"# Abstract\n## Problem and motivation\n## Proposed machine-learning approach\n## Model capability and performance\n## Applications to state estimation","[{\"question\":\"What problem does the thesis address in electric power system monitoring?\",\"answer\":\"It addresses how to handle missing or erroneous PMU data caused by malfunctioning sensors or dropped communication packets.\"},{\"question\":\"How does the proposed method work?\",\"answer\":\"The study proposes training a neural network using machine learning to predict missing or incorrect PMU observations from real-world PMU data.\"},{\"question\":\"How accurate is the neural network in predicting missing PMU entries?\",\"answer\":\"For six abnormal oscillation event types, the model predicts missing PMU data entries with less than 5% error.\"}]","ARTIFICIAL SENSOR FOR ELECTRIC POWER SYSTEM USING MACHINE LEARNING - Master’s thesis | PDF",1785901856,164,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"artificial-sensor-for-electric-power-system-using-machine-learning-masters-thesis","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/artificial-sensor-for-electric-power-system-using-machine-learning-masters-thesis/125890/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does the thesis address in electric power system monitoring?","Question",{"text":77,"@type":78},"It addresses how to handle missing or erroneous PMU data caused by malfunctioning sensors or dropped communication packets.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the proposed method work?",{"text":82,"@type":78},"The study proposes training a neural network using machine learning to predict missing or incorrect PMU observations from real-world PMU data.",{"name":84,"@type":75,"acceptedAnswer":85},"How accurate is the neural network in predicting missing PMU entries?",{"text":86,"@type":78},"For six abnormal oscillation event types, the model predicts missing PMU data entries with less than 5% error.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]