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This paper presents a method based on S-transform and artificial neural network for detection and classification of power quality disturbances. The input features of the neural network are extracted using S-transform. The features obtained from the S-transform are distinct, understandable and immune to noise. These features after normalization are given to a feed forward neural network trained by the back propagation algorithm. The data required to develop the network are generated by simulating various faults in a test system. The proposed method requires less number of features and less memory space without losing its original property. The simulation results show that the proposed method is effective and can classify the power quality signals even under noisy environment.

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This paper presents an improved Zero-Voltage-Transition Technique (ZVT-Technique) in a single-phase active power factor correction circuit based on a dc-dc boost converter topology and operated in a continuous-inductor-current mode with fixed-switching frequency control. An additional circuit for reducing the turn-off switching loss of the auxiliary switching circuit was applied. Experimental work was carried out with a circuit operated at 220 V rms input voltage, 400 V dc output voltage, 500 W output power and 40 kHz switching frequency. The test results showed that the efficiency was improved from 95 to 97%t with the proposed circuitry, while the power factor was constant.