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Pituk Bunnoon
Rajamangala University of Technology Srivijaya
Computer Science & IT Electrical & Electronics Engineering

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Fault Detection Approaches to Power System: State-of-the-Art Article Reviews for Searching a New Approach in the Future Pituk Bunnoon
International Journal of Electrical and Computer Engineering (IJECE) Vol 3, No 4: August 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (150.962 KB)

Abstract

This paper proposes the state-of-the-art of fault detection approach a power system. Severalarticles presented it in each implementation and method from the last to present (2013). Theadvantage of the approach would be developed to the new detection in the future. Manyinterested topics used for detection of fault in the power system. In this research can beclassified into two types interesting in fault detection. This review of many paper will beused to develop the research or find the new method for an appropriate fault detection in thepower system.DOI:http://dx.doi.org/10.11591/ijece.v3i4.3195
Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods Pituk Bunnoon
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 1: February 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (309.721 KB) | DOI: 10.11591/ijece.v6i1.pp12-20

Abstract

One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.
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