Assessment of Accelerated Stress Testing Data using Tensor Decomposition Methods

DuraMAT 2.0 Spark Project

Central Data Resource

Category: Predictive Simulation

Recipient National Renewable Energy Laboratory (PI: Glaws, Andrew)

Status Awarded

Abstract The photovoltaic (PV) industry is simultaneously targeting long warranties (>50 years) and new materials/designs for high-energy-yield modules, requiring an advanced methodology to forecast long-term durability of products with un-proven materials combinations. This works seeks to develop a pathway towards faster learning cycles in accelerated stress testing by combining multi-modal data from sequential stress tests with data-driven machine learning (ML) tools. Extended, sequential, and combined stress testing methods are gaining popularity for assessing durability of PV modules/materials beyond the early-stage mortalities identified in the standard tests in IEC 61215. Importantly, multiple degradation mechanisms can proceed simultaneously, and their separate contributions to the overall power loss should ideally be quantified. We traditionally use different measurement types to qualitatively assess the possible simultaneous degradation mechanisms. In this project, we seek to develop ML tools for decompositional analysis of PV image sequences to identify constituent spatial structures that may have separate impacts on current-voltage (IV) metrics. These new tools will separately display the evolution of distinct spatial patterns in PV image series at different rates upon degradation, enabling us to extract separate degradation rates and assess their impacts on power loss.