Analysis and Prediction of PV Impacts on Medium Voltage Australian Distribution Networks

Eity Sarker, Shama Naz Islam, Ameen Gargoom, Md Apel Mahmud, Aman Maung Than Oo, Jalil Yaghoobi, David Dart

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper studies the impact of solar generation on the distribution network voltage and analyses the impact using a number of voltage indices. These voltage indices take into account any deviation or rise from nominal voltage, voltage ramps, and voltage variances. In addition, a machine learning (ML) based model to predict the voltage indices as a function of solar generation has been implemented. A real-life case study considering 18 medium voltage distribution feeders in Australia including residential, industrial, hospital and laboratory feeders with three phase voltage data from phasor measurement units and postcode level solar generation data has been presented. Statistical analysis of the voltage indices shows that most of the feeders experience voltage rise during maximum solar generation periods. The analysis also considers the impact of a generation trip event and finds that the voltage indices violate significantly before a power outage. It can be observed that the ML models can predict the voltage indices more accurately when these indices are discretized.

Original languageEnglish
Title of host publication2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference, APPEEC 2022
ISBN (Electronic)9781665467384
ISBN (Print)9781665467391
Publication statusPublished - 20 Nov 2022
Externally publishedYes
Event14th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2022 - Melbourne, Australia
Duration: 20 Nov 202223 Nov 2022

Publication series

NameAsia-Pacific Power and Energy Engineering Conference, APPEEC
ISSN (Print)2157-4839
ISSN (Electronic)2157-4847


Conference14th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2022

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