Pattern Recognition Methods for Detecting Voltage Sag Disturbances and Electromagnetic Interference in Smart Grids

• Autorzy: Yalcin T. , Ozdemir M.

Identyfikatory

DOI

10.22149/teee.v1i3.50

• Konferencja: International Conference on Environment and Electrical Engineering EEEIC (16 ; 06-08.06.2016 ; Florence, Italy)
• Języki publikacji: EN

Abstrakty

EN

Identification of system disturbances, detection of them guarantees smart grids power quality (PQ) system reliability and provides long lasting life of the power system. The key goal of this study is to find the best accuracy of identification algorithm for non-stationary, non-linear power quality disturbances such as voltage sag, electromagnetic interference in smart grids. PQube, power quality and energy monitor, was used to acquire these distortions. Ensemble Empirical Mode Decomposition is used for electromagnetic interference reduction with first intrinsic mode function. Hilbert Huang Transform is used for generating instantaneous amplitude and instantaneous frequency feature of real time voltage sag power signal. Outputs of Hilbert Huang Transform is intrinsic mode functions (IMFs), instantaneous frequency (IF), and instantaneous amplitude (IA). Characteristic features are obtained from first IMFs, IF, and IA. The six features—, the mean, standard deviation,skewness, kurtosis of both IF and IA are then calculated. These features are normalized along with the inputs classifiers. The proposed power system monitoring system is able to detect power system voltage sag disturbances and capable of recognize electromagnetic interference component. In this study based on experimental studies, Hilbert Huang Transform based pattern recognition technique was used to investigate power signal to diagnose voltage sag and in power grid. Support Vector Machines and C4.5 Decision Tree were operated and their achievements were matched for precision and CPU timing. According to the analysis, decision tree algorithm without dimensionality reduction produces the best solution.

Słowa kluczowe

EN

C4.5 decision trees; electromagnetic interference; feature extraction; hilbert huang transform; power quality disturbance; smart grids; support vector machine

• Wydawca: EEEIC International Publishing
• Czasopismo: Transactions on Environment and Electrical Engineering
• Rocznik: 2016
• Tom: Vol. 1, No. 3
• Strony: 86--93
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Yalcin T.

• Ondokuz Mayis University, Department of Electrical & Electronic Engineering, 55139, Samsun, Turkey

autor

Ozdemir M.

• Ondokuz Mayis University, Department of Electrical & Electronic Engineering, 55139, Samsun, Turkey

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).