TY - JOUR
KW - Machine Learning
KW - Neural Network
KW - Energy
KW - Large-Scale Unbalanced Distribution System
KW - Photovoltaics
AU - Karar Mahmoud
AU - Mohamed Abdel-Nasser
AU - Heba Kashef
AU - Domenec Puig
AU - Matti Lehtonen
AB - 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.
IS - Regular Issue
M1 - 4
N2 - 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.
PY - 2020
SP - 157
EP - 163
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Machine Learning Based Method for Estimating Energy Losses in Large-Scale Unbalanced Distribution Systems with Photovoltaics
UR - https://www.ijimai.org/journal/sites/default/files/2020-11/ijimai_6_4_17.pdf
VL - 6
SN - 1989-1660
ER -