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A Novel Method for Multiple Power Quality Disturbance Classification using Dynamic Mode Decomposition

Publication Type : Conference Paper

Publisher : IEEE

Source : In 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (pp. 182–186). IEEE.

Url :

Keywords : classification, Decision trees, Dynamic mode decomposition, eigenvalues and eigenfunctions, Feature extraction, Machine learning, Matrix decomposition, Power quality, power quality disturbances, Support vector machines, vegetation

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication

Year : 2019

Abstract : The quality supply of power plays a major role in power systems. Ensuring the quality supply of power has become a prominent issue in modern days due to the introduction of microgrids (MG) with distributed generation systems (DGS) and renewable energy sources (RES) such as solar, wind etc. A novel method based on dynamic mode decomposition (DMD) features are used for multiple power quality disturbance classification. The algorithms's intelligence to extract elemental dynamic patterns over time of the power quality data is used for accurate classification. The different features such as eigenvalues, eigen-vectors and dynamic mode frequencies extracted through DMD are classified using multi-class classifiers such as random forest, support vector machines and decision tree. The advantage of the proposed method is evaluated under different noise and noiseless power quality events and variations. The promising results obtained using the proposed method highlight the potential usage of DMD based features for time-series identification of power quality disturbances (PQD) in power systems.

Cite this Research Publication : ⦁ Nair, A. R., Soman, K., & Mohan, Neethu. (2019). A novel method for multiple power quality disturbance classification using dynamic mode decomposition. In 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (pp. 182–186). IEEE.

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