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Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at Scale by Fan, Yangxin; Wieser, Raymond; Bruckman, Laura; French, Roger; Wu, Yinghui is a scholarly article available to read on EtoBox.

What is Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at Scale about?

We propose a novel Spatio-Temporal Graph Neural Network empowered trend analysis approach (ST-GTrend) to perform fleet-level performance degradation analysis for Photovoltaic (PV) power networks. PV power stations have become an integral component to the global sustainable energy production landscape. Accurately estimating the performance of PV systems is critical to their feasibility as a power generation technology and as a financial asset. One of the most challenging problems in assessing the Levelized Cost of Energy (LCOE) of a PV system is to understand and estimate the long-term Performance Loss Rate (PLR) for large fleets of PV inverters. ST-GTrend integrates spatio-temporal coherence and graph attention to separate PLR as a long-term "aging" trend from multiple fluctuation terms in the PV input data. To cope with diverse degradation patterns in timeseries, ST-GTrend adopts a paralleled graph autoencoder array to extract aging and fluctuation terms simultaneously. ST-GTrend imposes flatness and smoothness regularization to ensure the disentanglement between aging and fluctuation. To scale the analysis to large PV systems, we also introduce Para-GTrend, a parallel algorithm t

Author

Fan, Yangxin; Wieser, Raymond; Bruckman, Laura; French, Roger; Wu, Yinghui

Published

2024

Language

EN