Modeling synthetic power distribution network and datasets with industrial validation

• Published in: Journal of industrial information integration 2023-02, Vol.31, p.100407, Article 100407
• Main Authors: Ali, M., Prakash, K., Macana, C., Raza, M.Q., Bashir, A.K., Pota, H.
• Format: Article
• Language: English
• Subjects: Data analytics; Industrial validation; Power distribution networks; Real-time data processing; Synthetic networks; Validation case study
• ISSN: 2452-414X; 2452-414X
• DOI: 10.1016/j.jii.2022.100407

Creating synthetic networks and datasets for power distribution network is challenging due to continuous expansion of networks, integration of new low carbon technologies and large penetration of renewable energy resources in network. In this paper, a practical approach for generating synthetic distribution networks and datasets using public databases and data synthesis algorithms is proposed. A synthetic power distribution network is developed by leveraging the open-data from local government databases, OpenStreetMaps and mapping engines such as Google Street View. New data synthesis algorithms are proposed to obtain the missing network datasets. The proposed algorithms include a topology for designing power lines, a method for computing the lengths of power lines, a hub-line algorithm for determining the number of consumers connected to a single transformer, a virtual layer approach based on FromNode and ToNode for establishing electrical connectivity, and a technique for ingesting raw data into industrial data platforms. The practical feasibility of the proposed solutions is demonstrated by an illustrative case study of the Colac region in Australia. Synthetic network and datasets are created for the distribution feeder, and then evaluated in industry servers. The results are compared using a three-step validation procedure: comparing the synthetic and actual network datasets using geo-based visualizations, by including feedback from industry experts familiar with the analysis, and by testing the generic applicability of the proposed techniques to other regions. The paper compares network elements that include 4714 power lines, 48 distribution transformers, 4155 energy consumers, 609 electrical nodes, and 1 substation. The comparison results demonstrate the efficacy of developed networks and datasets as they show resemblance to real network and datasets while providing the geographical validation of distribution network models.