Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics

• Main Authors: Karar Mahmoud, Mohamed Abdel-Nasser, Heba Kashef, Domenec Puig, Matti Lehtonen
• Format: Article
• Language: English
• Published: Universidad Internacional de La Rioja (UNIR) 2021-03-01
• Series: International Journal of Interactive Multimedia and Artificial Intelligence
• Subjects: machine learning; neural network; energy; large-scale unbalanced distribution system; photovoltaics
• Online Access: https://www.ijimai.org/journal/bibcite/reference/2803
• ISSN: 1989-1660; 1989-1660

In the recent years, the penetration of photovoltaics (PV) has obviously been increased in unbalanced power distribution systems. Driven by this trend, comprehensive simulation tools are required to accurately analyze large-scale distribution systems with a fast-computational speed. In this paper, we propose an efficient method for performing time-series simulations for unbalanced power distribution systems with PV. Unlike the existing iterative methods, the proposed method is based on machine learning. Specifically, we propose a fast, reliable and accurate method for determining energy losses in distribution systems with PV. The proposed method is applied to a large-scale unbalanced distribution system (the IEEE 906 Bus European LV Test Feeder) with PV grid-connected units. The method is validated using OpenDSS software. The results demonstrate the high accuracy and computational performance of the proposed method.