• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.915-918
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• 2000

In this paper, we propose an efficient feature vector extraction and decision fusion methods for the automatic classification of power system disturbances. Here, FFT and WPT(wavelet packet transform) are und to extract an appropriate feature for classifying power quality disturbances with variable properties. In particular, the WPT can be utilized to develop an adaptable feature extraction algorithm using best basis selection. Furthermore. the extracted feature vectors are applied as input to the decision fusion system which combines the decisions of several classifiers having complementary performances, leading to improvement of the classification performance. Finally, the applicability of the proposed approach is demonstrated using some simulations results obtained by analyzing power quality disturbances data generated by using Matlab.

• Journal of IKEEE
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• v.25 no.4
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• pp.710-715
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• 2021

Power line signal can show disturbances due to various causes. Typical anomalies are temporary sag/swell of the amplitude, flat topped signal, and harmonic distortions. The disturbances need to be detected and treated properly for the quality of the power signal. In this study, the power disturbances are detected using the inflection points (IP). The inflection points are defined as points where local maxima/minima or the slope changes occur. The power line signal has a fixed IP pattern since it is basically sinusoidal, and it may have additional inflection points if there is any disturbance. The disturbance is detected by comparing the IP patterns between the normal signal and distorted signal. In addition, by defining a cost function, the time instant where the disturbance happens can be decided. The computer simulation shows that the proposed method is useful for the detection of various disturbances. The simple sag or swell signal only shows the amplitude changes at the detected inflection points. However, the flat top signal and harmonically distorted signal produce additional inflection points and large values in the cost function. These results can be exploited for the further processing of disturbance classification.

• Proceedings of the KIPE Conference
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• 2005.07a
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• pp.487-489
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• 2005

This paper presents a wavelet-based neural network technology for the detection and classification of the short durations type of power quality disturbances. Transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and In an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of TMS320C6711 DSP based with 16 channel 20Mhz sampling rate A/D(Analog to Digital) converter and some case studies are described.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.69-71
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• 2005

This paper presents a wavelet-based neural network technology for the detection and classification of the short durations type of power quality disturbances. Transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and in an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of TMS320C6711 DSP based with 16 channel 20Mhz sampling rate A/D(Analog to Digital) converter and some case studies are described.