TKEO-DESA-Based decision tree for power quality events detection and classification

• A novel signal-processing technique based on the Teager-Kaiser energy operator (TKEO) and the discrete energy separation algorithm (DESA) for feature extraction is proposed. • A novel TKEO-DESA-based decision tree for PQ events detection and classification in power grids is proposed. • The second-order discrete-time TKEO to track the signal energy is used in the detection stage. • The DESA algorithm to extract the features of frequency and amplitude is used to classify the PQ event. This paper proposes a novel strategy to detect and classify power quality disturbances. The strategy comprises the Teager-Kaiser energy operator (TKEO) and the discrete energy separation algorithm (DESA). The TKEO is used to track the signal energy and capture the energy changes, whereas the DESA algorithm is utilized to estimate the frequency and amplitude of the signal. These features are employed to build a ruled decision tree to detect and classify 15 PQ disturbances. PQ events with a complex nature are generated given the IEEE-1159 standard by PQ-SyDa toolbox. To confirm the effectiveness and performance of the proposed strategy, it is evaluated using synthetic signals, time-domain simulations performed in Matlab/Simulink, and real sag events where an accuracy prediction of around 97% is reached.