سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / International Conference on Artificial Intelligence; City, Industry and Health

GIS-Based Data Analysis and Optimization: Enhancing Electricity Distribution Operations

نویسندگان :

Bahar Assarzadegan¹ Ali Yousefi²

1- Department of English, Na.C., Islamic Azad University, Najafabad, Iran 2- Department of Management- Operations Research, University of Isfahan, Isfahan, Iran

کلمات کلیدی :

GIS،SAIDI،Artificial intelligence،Mathematical optimization

چکیده :

In this article, with the approach of analyzing and optimizing spatial data and applying artificial intelligence in planning and decision-making in the field of improving the functions and performance indicators of electricity distribution networks, the proposed clustering model is combined with the meta-heuristic artificial bee algorithm. In order to achieve the optimal deployment points of various field teams in electricity distribution companies, it was presented. The proposed model was tested and analyzed on the two-year blackout data related to one of the large cities covered by the Isfahan Province Electricity Distribution Company. Time distribution of outages is very close to uniform distribution and geographical distribution of blackouts has a normal distribution compared to the density of subscribers. Based on this, the spatial data of blackouts recorded during the last two years in the studied city were analyzed and clustered as the data of the proposed algorithm. The number of clusters is equal to the number of operation teams in the city, which was considered equal to three in this research. In the next step, the average location coordinates of each of the clusters resulting from the clustering of blackout points were calculated, which after reviewing the geographic conditions in the geographic information system (GIS), indicates the optimal point for deploying the operation team related to that cluster. The findings of the research prove that if the optimal points are observed, SAIDI will decrease by an average of 23% compared to the same period of the last two years.