Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2475484

Experimental Setup and Learning-Based AI Model for Developing Accurate PV Inverter Models

Bennai, Salma·Prabakar, Kumaraguru (ORCID:0000000229620743)·Velaga, Yaswanth Nag (ORCID:0000000305555825)·Ganguly, Subhankar (ORCID:0000000333070614)·Vaidhynathan, Deepthi (ORCID:0000000179908887)·Reynolds, Matthew·Frantz, Jonathan·Balasubramaniam, Karthikeyan
View original source ↗Export metadata ↓

Also available from

Abstract

The integration of power electronics-based interfaces presents challenges due to the absence of detailed models and the high computational complexity. Generic models used in system studies lack accuracy in capturing converter dynamics. This paper proposes a data-driven approach developed from experimental setup data. This approach enhances accuracy in photovoltaic inverter modeling. We used two types of PV inverters in the experiment. The recorded experimental data undergo processing through a machine learning model. Results from the model trained through machine learning is also presented.

Explore related subjects

artificial intelligenceexperimental setupinverter black box modelinverter under testinvertersmachine learningMATHEMATICS AND COMPUTING,SOLAR ENERGY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bennai, Salma, Prabakar, Kumaraguru (ORCID:0000000229620743), Velaga, Yaswanth Nag (ORCID:0000000305555825), Ganguly, Subhankar (ORCID:0000000333070614), Vaidhynathan, Deepthi (ORCID:0000000179908887), Reynolds, Matthew, Frantz, Jonathan, Balasubramaniam, Karthikeyan. 2024-09-18. Experimental Setup and Learning-Based AI Model for Developing Accurate PV Inverter Models. https://doi.org/10.1109/kpec61529.2024.10676225

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Control Room of the Future Testbed Workshop – After-Action Report

The U.S. Department of Energy’s Office of Electricity is supporting a one-year, multi-laboratory effort to define the needs and requirements for a Control Room of the Future testbed, or CROFT. The effort responds to increasing grid complexity driven by large new loads, dynamic generation resources, and the growing adoption of advanced technologies and tools, including artificial intelligence (AI) and machine learning (ML). To support safe, secure, and effective grid modernization, CROFT will focus on how emerging technologies and tools can be rigorously evaluated in realistic operational settings, with attention to human-machine interaction, cognitive load, and workforce readiness. The project team includes Argonne National Laboratory, Idaho National Laboratory, National Laboratory of the Rockies, and Pacific Northwest National Laboratory. As part of the scoping effort, the team conducted two industry-focused workshops: one at DTECH on February 5, 2026, informed by prior industry interviews, and a second on May 4, 2026, adjacent to IEEE T&D. These engagements brought together utilities, vendors, consultants, national laboratories, academia, and government stakeholders to identify and prioritize use cases, barriers, validation needs, data-sharing constraints, and near- and longer-term requirements. This feedback will directly inform CROFT’s architecture and research focus areas, ensuring the testbed is grounded in real-world operational needs and designed to evaluate emerging technologies and tools in realistic control-room environments.

artificial intelligence↗
DOE OSTI2026

A foundation model for non-destructive defect identification from vibrational spectra

Defects are ubiquitous in solids and strongly influence materials’ functional properties. However, non-destructive characterization and quantification of defects, especially when multiple types coexist, remain a long-standing challenge. Here, we introduce DefectNet, a foundation machine learning model that predicts the chemical identity and concentration of substitutional point defects with multiple coexisting elements directly from vibrational spectra, specifically phonon density-of-states (PDoS). Trained on over 16,000 simulated spectra from 2,000 semiconductors, DefectNet employs a tailored attention mechanism to identify up to six distinct defect elements at concentrations ranging from 0.2% to 25%. The model generalizes well to unseen crystals across 56 elements and can be fine-tuned on experimental data. Validation using inelastic scattering measurements of SiGe alloys and MgB 2 superconductor demonstrates its accuracy and transferability. Furthermore, our work establishes vibrational spectroscopy as a viable, non-destructive probe for bulk point defect quantification, and highlights the promise of foundation models in data-driven defect engineering.

artificial intelligence↗
DOE OSTI2026

Feedback, physics, and forecasts: The emerging paradigm of machine learning-driven battery research

Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies.

artificial intelligence↗