Estimating the Distributed Generation Unit Sizing and Its Effects on the Distribution System by Using Machine Learning Methods

Pürlü M., Türkay B.

ELEKTRONIKA IR ELEKTROTECHNIKA, vol.27, no.4, pp.24-32, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 27 Issue: 4
  • Publication Date: 2021
  • Doi Number: 10.5755/j02.eie.28864
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Central & Eastern European Academic Source (CEEAS), Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Page Numbers: pp.24-32
  • Keywords: Distributed generation, Distribution system planning, DG sitting, Dispersed generation, Forecasting, Machine learning, Weka, DISTRIBUTION NETWORKS, LINEAR-REGRESSION, LOAD, MODEL, ALLOCATION, OPTIMIZATION, PLACEMENT, LOCATION, SIZE, DGS
  • Istanbul Technical University Affiliated: Yes


Many approaches about the planning and operation of power systems, such as network reconfiguration and distributed generation (DG), have been proposed to overcome the challenges caused by the increase in electricity consumption. Besides the positive effects on the grid, contributions on environmental pollution and other advantages, the rapid developments in renewable energy technologies have made the DG resources an important issue, however, improper DG allocation may result in network damages. A lot of studies have been practised with analytical and heuristic methods based on load flow for optimal DG integration to the network. This novel method based on estimation is proposed to determine the size of DG and its effects on the network to get rid of the coercive and time-consuming load flow techniques. Machine learning algorithms, such as Linear Regression, Artificial Neural Network, Support Vector Regression, K-Nearest Neighbor, and Decision Tree, have been used for the estimations and have been applied to well-known test systems, such as IEEE 12-bus, 33-bus, and 69-bus distribution systems. The accuracy of the proposed estimation methods has been verified with R-squared and mean absolute percentage error. Results show that the proposed DG allocation method is effective, applicable, and flexible.