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This paper presents a modified approach based on symmetrical components in time domain for the detection and classification of various power-quality (PQ) disturbances. Single-phase PQ disturbance signal along with the two other ideal phases generated using phase-locked loop (PLL) is processed to find out the symmetrical components of all phases in time domain. Triggering points for PQ disturbances have been detected from the negative-sequence component of disturbance phase and have been easily classified from the signatures of the waveforms using the summation of the positive- and negative-sequence components. PQ disturbances, such as voltage sag, swell, interruption, harmonics, flicker, transients, notches, and spike present in a distribution system, have been investigated and are easily localized and segmented with this technique. Being a time-domain technique, it can be adopted for the online monitoring and assessment of the PQ disturbances. Simulated and real-time results are presented for a number of PQ disturbances to show the effectiveness of the proposed method for the PQ disturbances’ detection and classification. [ABSTRACT FROM PUBLISHER]

This paper deals with a modified technique for the recognition of single stage and multiple power quality (PQ) disturbances. An algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed in this paper. The analysis and classification of single stage PQ disturbances consisting of both events and variations such as sag, swell, interruption, harmonics, transients, notch, spike, and flicker are presented. Moreover, the proposed algorithm is also applied on multiple PQ disturbances such as harmonics with sag, swell, flicker, and interruption. A database of these PQ disturbances based on IEEE-1159 standard is generated in MATLAB for simulation studies. The proposed algorithm extracts significant features of various PQ disturbances using S-transform, which are used as input to this hybrid classifier for the classification of PQ disturbances. Satisfactory results of effective recognition and classification of PQ disturbances are obtained with the proposed algorithm. Finally, the proposed method is also implemented on real-time PQ events acquired in a laboratory to confirm the validity of this algorithm in practical conditions. [ABSTRACT FROM PUBLISHER]