Adaptive protection combined with machine learning for microgrids. Issue 6 (8th March 2019)

• Record Type: Journal Article
• Authors: Lin, Hengwei; Sun, Kai; Tan, Zheng‐Hua; Liu, Chengxi; Guerrero, Josep M.; Vasquez, Juan C.
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 6(2019)
• Page Start: 770
• Page End: 779
• Publication Date: 2019-03-08
• Subjects: support vector machines -- distributed power generation -- power engineering computing -- neural nets -- smart power grids -- data mining -- learning (artificial intelligence); protective settings -- microgrid model -- machine learning -- rule‐based adaptive protection scheme -- machine‐learning methodology -- extensive distribution automation -- uncertain elements -- Pearson correlation coefficients -- data mining -- hybrid artificial neural network -- support vector machine -- state recognition -- growing massive data streams -- adaptive reconfigurations; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6230
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : This paper presents a rule‐based adaptive protection scheme using machine‐learning methodology for microgrids in extensive distribution automation (DA). The uncertain elements in a microgrid are first analysed quantitatively by Pearson correlation coefficients from data mining. Then, a so‐called hybrid artificial neural network and support vector machine (ANN‐SVM) model is proposed for state recognition in microgrids, which utilises the growing massive data streams in smart grids. Based on the state recognition in the algorithm, adaptive reconfigurations can be implemented with enhanced decision‐making to modify the protective settings and the network topology to ensure the reliability of the intelligent operation. The effectiveness of the proposed methods is demonstrated on a microgrid model in Aalborg, Denmark and an IEEE 9 bus model, respectively.