June 19, 2025
Conference Paper

A Convolution Neural Network for Voltage Event Classification at a Photovoltaic Inverter

Abstract

This paper presents a convolutional neural network (CNN) developed to identify voltage events in photovoltaic (PV) inverters. The CNN is trained on synthetic data generated using the IEEE 13-bus distribution feeder model and evaluated on field measured data collected from Energy Northwest’s Horn Rapids Solar, Storage, and Training (HRSST) facility. The study focuses on two common voltage events: faults and voltage sags. The CNN is configured to analyze voltage and current waveforms from three-phase PV systems, demonstrating excellent accuracy during training. Field data from the HRSST facility is employed to assess its real-world performance, where the CNN achieves perfect identification of faults and voltage sags in a sample of nine events. This work highlights the potential of the proposed method to enhance PV protection schemes, providing a robust foundation for improved voltage event detection and grid reliability.

Published: June 19, 2025

Citation

Cornachione M.A., M. Ramesh, K. Kwon, B.A. Ross, T. Mcdermott, and J. Follum. 2025. A Convolution Neural Network for Voltage Event Classification at a Photovoltaic Inverter. In IEEE Green Technologies Conference (GreenTech 2025), March 26-28, 2025, Wichita, KS, 61-65. Piscataway, New Jersey:IEEE. PNNL-SA-207235. doi:10.1109/GreenTech62170.2025.10977718

Research topics