scholarly journals Advantage of wavelet technique to highlight the observed geomagnetic perturbations linked to the Chilean tsunami (2010)

2014 ◽  
Vol 119 (4) ◽  
pp. 3077-3093 ◽  
Author(s):  
V. Klausner ◽  
Odim Mendes ◽  
Margarete O. Domingues ◽  
Andres R. R. Papa ◽  
Robert H. Tyler ◽  
...  
Keyword(s):  
Chilean Tsunami ◽  
Geomagnetic Perturbations ◽  
Wavelet Technique

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

scholarly journals Developing Tsunami fragility curves using remote sensing and survey data of the 2010 Chilean Tsunami in Dichato

2012 ◽  
Vol 12 (8) ◽  
pp. 2689-2697 ◽  
Author(s):  
E. Mas ◽  
S. Koshimura ◽  
A. Suppasri ◽  
M. Matsuoka ◽  
M. Matsuyama ◽  
...  
Keyword(s):  
Pacific Coast ◽  
Fragility Curves ◽  
Practical Method ◽  
South American ◽  
Survey Team ◽  
Inundation Depth ◽  
Structural Fragility ◽  
Tsunami Fragility ◽  
Chilean Tsunami ◽  
The Town

Abstract. On 27 February 2010, a megathrust earthquake of Mw = 8.8 generated a destructive tsunami in Chile. It struck not only Chilean coast but propagated all the way to Japan. After the event occurred, the post-tsunami survey team was assembled, funded by the Japan Science and Technology Agency (JST), to survey the area severely affected by the tsunami. The tsunami damaged and destroyed numerous houses, especially in the town of Dichato. In order to estimate the structural fragility against tsunami hazard in this area, tsunami fragility curves were developed. Surveyed data of inundation depth and visual inspection of satellite images of Dichato were used to classify the damage to housing. A practical method suitable when there are limitations on available data for numerical simulation or damage evaluation from surveys is presented here. This study is the first application of tsunami fragility curves on the South American Pacific coast and it might be of practical use for communities with similar characteristics along the west Pacific coast. The proposed curve suggests that structures in Dichato will be severely damaged – with a 68% probability – already at 2 m tsunami inundation depth.

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