Phase topology identification in low-voltage distribution networks: A Bayesian approach. (January 2023)

• Record Type: Journal Article
• Authors: García, Sebastián; Mora-Merchán, Javier M.; Larios, Diego F.; Personal, Enrique; Parejo, Antonio; León, Carlos
• Is Part Of: International journal of electrical power & energy systems. Volume 144(2023)
• Publication Date: 2023-01
• Subjects: Phase identification -- Distribution networks -- Data analytics -- Smart meters -- Distribution system operators; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2022.108525
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A novel data-driven approach to discover phase topology of low-voltage customers is proposed. An up-to-date certainty about phase connection of each customer can be given thanks to Bayesian inference. It obtains accuracies higher than 97 % in almost any case regardless of the Smart Meter penetration. Prior knowledge of the grid improves the performance even if it contains some errors. Abstract: Knowledge of customer phase connection in low-voltage distribution networks is important for Distribution System Operators (DSOs). This paper presents a novel data-driven phase identification method based on Bayesian inference, which uses load consumption profiles as inputs. This method uses a non-linear function to establish the probability of a customer being connected to a given phase, based on variations in the customer's consumption and those in the phase feeders. Owing to the Bayesian inference, the proposed method can provide up-to-date certainty about the phase connection of each customer. To improve the detection of those customers that are more difficult to identify, after obtaining the up-to-date certainty for all users, the consumption of those who have an up-to-date certainty above a certain percentile compared with the rest of the substation (those that are more likely to be correctly classified) is subtracted from the phase in which they are classified. The performance of the proposed method was evaluated using a real (non-synthetic) low-voltage distribution network. Favourable results (with accuracies higher than 97 %) were obtained in almost all cases, regardless of the percentage of Smart Meter penetration and the size of the substation. A comparison with other state-of-the-art methods showed that the proposed method outperforms (or equals) them. The proposed method does not necessarily require previously labelled data; however, it can handle them even if they contain errors. Having previous information (partial or complete) increases the performance of phase identification, making it possible to correct erroneous previous labelling.