Data-Driven Anomaly Detection for Rural Solar-PV SystemsJanuary 1, 2021
Mee Panyar Engineering/Global, 2020–21
Liaison(s): Natasha Allen ’16, Sierra Fan, Trevor Apple ’13
Advisor(s): Angie Lee
Students(s): Hunter Whaples (TL-S), Martha Gao (TL-F) , G Missaka, Marz Barnes, Anuragini Arora (S), Ingrid Tsang (S)
Mee Panyar is a start-up social enterprise in Myanmar that is pioneering community-based management models for operating and maintaining rural solar mini-grids. Modern mini-grid installations include a suite of sensors that may be monitored in real-time. However, rural technicians rarely oversee such monitoring, increasing the chance of system failure. The HMC Clinic Team has designed a suite of statistical and machine learning models that will be used to identify anomalous behavior and flag components for maintenance operations.