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This paper presents a technique for recognizing the single stage and multiple PQ (Power Quality) events using an algorithm based on ST (Stockwell's-Transform) and ANN (Artificial Neural Network) based classifier and a rule based decision tree. The ST which combines elements of WT (Wavelet Transform) and STFT (Short-Time Fourier Transform) is used for the analysis of various single stage and multiple power quality events. Single stage PQ events such as sag, swell, interruption, harmonics, transients, notch, spike, flicker and multiple power quality events which include the harmonic disturbances with sag, swell, flicker and interruption are analyzed using the proposed algorithm. A data base of these events is generated in MATLAB as per IEEE-1159 standard. Significant features of various PQ events are extracted using the S-transform and are used as an input to this hybrid classifier. The results are presented for the effective recognition of the PQ events with the proposed algorithm.

In this paper, different voltage injection schemes for dynamic voltage restorers (DVRs) are analysed with particular focus on the methods used to minimize the rating of the voltage source converter (VSC) used in DVR. The control and operation of a DVR is demonstrated with reduced rating VSC. The reference load voltage is estimated using the unit vectors and the synchronous reference frame (SRF) theory is used for the control of DVR. The compensations of sag, swell and harmonics in supply voltage using the reduced rating DVR are demonstrated using MATLAB with its Simulink power system blockset (PSB) toolboxes.