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

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.