Skip to main content

Intelligent Prediction of States in Multi-port Autonomous Reconfigurable Solar power plant (MARS)...

by Suman Debnath, Shruti R Kulkarni, Catherine D Schuman
Publication Type
Conference Paper
Book Title
2021 IEEE Energy Conversion Congress and Exposition (ECCE)
Publication Date
Page Numbers
1339 to 1346
Conference Name
IEEE ECCE 2021: IEEE Energy Conversion Congress and Exposition
Conference Location
Virtual, Canada
Conference Sponsor
Conference Date

In power electronics, prediction of states may be used for identification of faults, determination of aging of components, identification of bad data measurements, among others. Prediction of states in power electronics have broadly been based on: (a) physics-based models, (b) data-driven models, and (c) hybrid models. In this paper, data-driven approaches are presented for intelligent prediction of states in multi-port autonomous reconfigurable solar power plant (MARS) and compared. The data-set needed to train the data-driven models based on artificial intelligence (AI) algorithms has been identified and the trained models are evaluated under different extrapolated normal and abnormal operating conditions. The AI algorithms include nonlinear auto-regressive exogenous model (NARX), spiking neural networks (SNN), and decision tree. The models are compared and contrasted. The best model (NARX) is evaluated under different normal and abnormal operating conditions that have indicated accurate prediction.