A two-level neural network approach for flicker source location. (June 2021)

• Record Type: Journal Article
• Authors: Samet, Haidar; Khosravi, Mahdi; Ghanbari, Teymoor; Tajdinian, Mohsen
• Is Part Of: Computers & electrical engineering. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Autoregressive Moving Average (ARMA) -- Flicker source identify -- Power quality -- Hilbert transform -- ANN; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107157
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Identification of flicker sources is necessary to find who is responsible for the measured flicker and improve power quality. This paper puts forward a new method for identifying flicker sources with minimum measurement units. Contrary to the previous works where flicker sources are considered a single-frequency signal, the autoregressive moving average (ARMA) is used to model active and reactive power variations. First, the envelope of the network voltage at the considered busbars is derived by the Hilbert transform. Then, appropriate flicker indices are extracted from the power spectral density (PSD) of the voltage envelope. A novel two-level structure of a set of ANNs is proposed, which needs a low number of voltage measurement units to locate the flicker sources. Using the captured data from different simulations of various scenarios, the Artificial Neural Networks (ANNs) are trained to categorize flicker sources.