Relation between Charge Amounts of Lightning Discharges Derived from ELF Waveform Data and Severe Weather

Chiharu Shimizu; Mitsuteru Sato; Yasuji Hongo; Fuminori Tsuchiya; Yukihiro Takahashi

doi:10.1541/ieejfms.136.252

Relation between Charge Amounts of Lightning Discharges Derived from ELF Waveform Data and Severe Weather

IEEJ Transactions on Fundamentals and Materials ◽

10.1541/ieejfms.136.252 ◽

2016 ◽

Vol 136 (5) ◽

pp. 252-258 ◽

Cited By ~ 2

Author(s):

Chiharu Shimizu ◽

Mitsuteru Sato ◽

Yasuji Hongo ◽

Fuminori Tsuchiya ◽

Yukihiro Takahashi

Keyword(s):

Severe Weather ◽

Waveform Data ◽

Lightning Discharges

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Emergency management decision-making during severe weather

PsycEXTRA Dataset ◽

10.1037/e577572012-037 ◽

2006 ◽

Author(s):

Leigh A. Baumgart ◽

Ellen J. Bass ◽

Brenda Philips ◽

Kevin Kloesel

Keyword(s):

Decision Making ◽

Emergency Management ◽

Severe Weather ◽

Management Decision ◽

Management Decision Making

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Decision making in severe weather uncertainty

PsycEXTRA Dataset ◽

10.1037/e621652012-031 ◽

2011 ◽

Author(s):

Jared E. LeClerc ◽

Susan Joslyn

Keyword(s):

Decision Making ◽

Severe Weather ◽

Weather Uncertainty

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Morphology of upward lightning discharges (the TLEs) from Indian Subcontinent

2019 URSI Asia-Pacific Radio Science Conference (AP-RASC) ◽

10.23919/ursiap-rasc.2019.8738468 ◽

2019 ◽

Author(s):

Rajesh Singh

Keyword(s):

Indian Subcontinent ◽

Lightning Discharges

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

Scientific Research Journal ◽

10.24191/srj.v2i2.9330 ◽

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.

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