DATA-DRIVEN AUTOMATED AND PROACTIVE ASSET MANAGEMENT TO ENHANCE RELIABILITY AND RESILIENCE OF RURAL DISTRIBUTION GRIDS THE GOAL OF THIS PROJECT IS TO DEVELOP A DATA-DRIVEN DT LOADING MONITORING APPROACH BY DEVELOPING A PHYSICS-INFORMED MACHINE LEARNING MODEL TO RECTIFY TRANSFORMER-CUSTOMER (T-C) CONNECTIVITY RECORDS. THEN, THE DT LOADING MONITORING RESULTS WILL BE USED TO ACHIEVE ONLINE DT HEALTH ASSESSMENT AND REMAINING LIFE ESTIMATION USING BOTH LIVE-STREAM AND HISTORICAL AMI DATA.

Iowa State University Of Science And Technology

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