LIGHTNING STRIKE MAPPING FOR PENINSULAR MALAYSIA … · LIGHTNING STRIKE MAPPING FOR PENINSULAR ......

Journal of Theoretical and Applied Information Technology 31st December 2011. Vol. 34 No.2

© 2005 - 2011 JATIT & LLS. All rights reserved.

ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195

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LIGHTNING STRIKE MAPPING FOR PENINSULAR MALAYSIA USING ARTIFICIAL INTELLIGENCE

TECHNIQUES

1M.K.HASSAN, 2R. Z. ABDUL RAHMAN, 3A..CHE .SOH., 4M.Z.A..AB KADIR 1,2,3,4Department of Electrical and Electronics, Faculty of Engineering,

University Putra Malaysia, 43400, Serdang, Selangor Darul Ehsan, Malaysia

E-mail: [email protected], [email protected], [email protected], [email protected]

ABSTRACT

This research focuses on artificial intelligence (AI) techniques on mapping the lightning strike area in Peninsular Malaysia. Three AI techniques such as fuzzy logic, neural network and neuro-fuzzy techniques are selected to be explored in classifying the characteristics of lightning strike which are based on; level of strike (high, medium, low) and category of lightning (positive cloud-to-ground, negative cloud-to-ground, flash). Nine predefined areas in Peninsular Malaysia were chosen as a case study. The analysis was carried out according to twelve months lightning data strikes which had been made available by Global Lightning Network (GLN). All three AI techniques have successfully demonstrated the ability to mapping and classify lightning strikes. Each technique has shown very good percentage of accuracy in term of determining the area and characterizing the lightning strikes. The finding of this research can be made use in risk management analysis, lightning protection analysis, township planning projects and the like. Keywords: Lightning Strike, Classification, Fuzzy Logic, Neural Network, Neuro-Fuzzy 1. INRODUCTION Lightning strike comes about every day in the world. The lightning strike towards the surface on earth has been estimated at 100 times every second. Thus, almost every governments suffer major loses because of this phenomenon every year. It also would cause horrific injury and fatality to humans and animals. The lightning may affect almost every organ system as the current passes through the human body taking the shortest pathways between the contact points. There are 25.9% of lightning strike occurrences for victims who have sheltered under trees or shades, whereas 37% at open space area. Head and neck injury are two common areas which have an effect on the lightning strike victims with 77.78% and 74% respectively. Only 29.63% of the cases presented with ear bleeding [1]. United State National Lightning Safety Institution reported that Malaysia has highest lightning activities in the world whilst the average-thunder day level for Malaysia’s capital Kuala Lumpur within 180 - 260 days per annum

[2, 3]. The isokeraunic level is approximately 200 thunderstorm days a year. The lightning ground flash density is about 15-20 strike per km2 per year. Lightning has an extremely high current, high voltage and transient electric discharge. It is transient discharge of static electricity that serves to re-establish electrostatic equilibrium within a storm environment [1]. Malaysia lies near the equator and therefore it is categorized as prone to high lightning and thunderstorm activities [2]. Observations performed by the Malaysian Meteorological Services indicate that thunders occur 200 days a year in Malaysia. Thunderstorms have been suspected to have caused between 50% and 60 % of the transient tripping in the transmission and distribution networks for Tenaga Nasional Berhad (TNB), Malaysia’s electric power provider. The main reason could be short of precise and consistent

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Table 3: Fuzzy rules for the region classification

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(NE) 4 A2 B1 West (W) 5 A2 B2 Central (C) 6 A2 B3 East (E) 7 A3 B1 South West

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East(SE)

ii) Neural Network The implementation of neural network for classification problems is dependable on their structure and functions. By considering a set of classes, the objective in classification is an assignment of a random sample to one of this class with minimum probability error. Each sample is described by a set of parameter which then forms a vector, usually referred as the feature vector. The development of such classification system can be achieved as a result of neural network training so that it produces the output which corresponds to one of these classes. However, the training sample must have similar form as its input that is belong to the same class. The ability of neural network to correctly classify the test sample is subjected to it generalization ability. Back propagation method has been used to train the data from Global Lightning Network (GLN) database. The network consists of an input layer, one hidden layer and an output layer. The input layer consists of 2 input neurons which represent the longitude and latitude as it input, hidden layer with 16 hidden neurons while the output layer represent the 9 output regions that need to be classified their distributions. Each neuron in the hidden layer and output layer has a bias which is connected via weight matrix to the previous layer. The number of hidden layer depends on the performance index of the system. Trial and error approached has been implemented to get an accurate number of hidden layers for the system. It begins with a modest number of hidden neurons and gradually increasing the number if the network fails to reduce the error. A much used approximation for the number of hidden neurons for a three layered network is;

N =1/2(j + k) +P,

(2)

where N is the number of neuron, j and k are the number of input neurons and P is the number of patterns in the training set. As the pattern is set to 1 the number of hidden layer begins with 6 and amplifies to 16.

Optimum weight was calculated using Widrow-Hoff Rule. The algorithm of the Widrow-Hoff Rule shows that the network weights are moved along the negative of the gradient of the performance function. The derivative of the

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Figure 8: Percentage of lightning’s level current for 12 month

Figure 9: Graph number of lightning incidence corresponds to the types of lightning

From the results, 90% of the lightning incidence occurred are negative lightning. Meanwhile, there are no flash occurred. This is because flash occurred between the clouds and do not strike the ground as shown in Figure 9. The number of lightning incidence occurred in each month are analyzed corresponds to their region. Table 5 show the sampled of classified data for lightning strike for each month. From the analysis results, October 2009 has the highest number of lightning incident with 94628 strikes followed by April 2010 with 87459 strikes. The least number of lightning occurred in February 2009 with 7056 strikes. In October 2009, the lightning strike the most in Region Central (C)

with 15919 strikes, followed by Region South (S) with 15764 strikes. The state which included in these regions are Wilayah Persekutuan Kuala Lumpur, Melaka, Selangor, Johor, Negeri Sembilan, Perak and Pahang. A high level of lightning current which occurred in locations such as Kuala Lumpur and Selangor have the possibilities to cause a flash flood. This is due to the locations which is situated in the center of city.

Table 5: Samples of classified data

Latitude Longitude Curre

nt (A)

Level of

Current

Types of

Lightning

Region

3.9039866

99.8766398

-2450

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3.4932748

100.8141976

-3280

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4.448587

99.4433357

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0

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100.9970393

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99.449027

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0

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6.7442604

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-1380

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3.4340879

100.744215

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3.2193951

100.8315325

-2370

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1.8678969

102.1172289

-4870

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3.99088 99.9881279

-3370

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2.1323426

101.7601432

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2.1273015

101.7917015

-1620

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3.2061696

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210

The obtained results are then mapped into the Malaysian map using Google Earth as shown in Figure 10. This figure shows the locations where the lightning strike and Figure 11 shows the mapping of lightning characteristics for one month data. The light blue, purple and red icons indicate low, medium and high level of current respectively. By clicking the icon, a dialog box as shown in figure below appeared. It tells user the lightning current value, level of current and also the region its corresponds to. Based on the mapping, most of the lightning occurred at the west coast of Peninsular Malaysia. Most of the states in the west coast of Peninsular Malaysia are catogarized as a developed states. Thus, a high population density and plenty of industrial location attract the lightning strike.

Figure 10: Mapping of the lightning strike

locations

Figure 11: Mapping of the lightning

characteristics

The developed classifier program are able to classify the lightning parameters according to the desired characteristics. Based on the statistical analysis, 90 % percent of lightning incidence are negative lightning. Meanwhile, the majority of the levels of lightning current are Low level. Areas that are situated in the west coast of Peninsular Malaysia have a higher number of lightning incidence compared to the east coast. There are some limitations found in mapping the lightning characteristics using Google Earth. 3.2 Neural Network Analysis The data is classified into nine regions as follows;

• Southern Regions: S1-latitude =10 to 2.50 & longitude =

990 to 100.50 S2-latitude =10 to 2.50 & longitude =

100.50 to 103.50 S3-latitude =10 to 2.50 & longitude =

103.50 to 1050 • Center Regions:

C1-latitude =2.50 to 5.50 & longitude = 990 to 100.50

C2-latitude =2.50 to 5.50 & longitude = 100.50 to 103.50

C3-latitude =2.50 to 5.50 & longitude = 103.50 to 1050

• Northern Regions: N1-latitude =5.50 to 70 & longitude =

990 to 100.50 N2-latitude =5.50 to 70 & longitude =

100.50 to 103.50 N3-latitude =5.50 to 70 & longitude =

103.50 to 1050 The system stops at 250 epochs with MSE of 0.0462 as shown in Figure 12.

Figure 12: The Mean Square Error change

with the change in epoch value

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212

(ii) Data Analysis

From the analysis, lightning were mainly strikes at the Central region with an average of 5172 strikes during September 2009 to October 2010. The Southern region was identified the second largest lightning strikes area with an average of 4096.5 strikes per year. Meanwhile, the lowest area was identified at Northern East region with average of 435.7 strikes per year. However, the lightning strikes were very minimal during November to March 2010.

The level of lightning strike has shown that Peninsular Malaysia has received a relatively low strike with average of 17380.8 strikes per month. May 2010 was considered a good month because it has a well balance of lightning strike level distribution. The Low strike recorded at 38.86%, Medium at 34.51% and High at 26.63%. The medium and high Strike was very minimal during November 2009 until February 2010. However, the high Strikes were extended until April 2010. 4. CONCLUSION Fuzzy logic has been successfully applied in order to characterize the location into eight regions to identify the location where the lightning strikes. Moreover, Basic ‘IF rule’ has been successfully implemented to characterize the other two characteristics which are type of lightning and level of lightning current. The fuzzy logic and ‘IF rule’ are successfully implemented and mapped into Malaysian map using Google Earth. The paper successfully designs the proposed back propagation neural networks that combine with properties of IF THEN Rules that performs as the classifier. They are trained and tested to classify lightning characteristics. It also included the design of a software tool suitable for the training and testing NN for dataset. The results have shown considerable degree of confidence of 97% of accuracy in classification by referring to the graph of MSE. It was evident that as the number of epoch increased, the MSE will reduces with certain parameter been initialize first. The proposed neuro-fuzzy system has achieved 73.5% accuracy. The proposed neuro-fuzzy is able to classify the data. Thus further study to increase the accuracy could be done to enlighten prospective of lightning classification with artificial intelligence method.

ACKNOWLEDGEMENT

I would like to express sincere gratitude to WSI Corp and Centre of Excellence for Lightning Protection (CELP) UPM Collaborate to provide Global Lightning Data especially for Peninsular Malaysia. An appreciation also goes to CELP researchers in Faculty of Engineering, Universiti Putra Malaysia and research grant from UPM under RUGS (91864) for their support throughout the project.

REFERENCES [1]. K. Srinivasan and J.Gu. Lightning as

Atmospheric Electricity, IEEE CCECE/CCGEI, Otawa, May 2006.

[2]. L.M. Ong, and H. Ahmad. Lightning Air Terminals Performance Under Conditions Without Ionization And With Ionization,” Institute Of High Voltage And High Current, 2003. UTM Skudai, Malaysia.

[3]. National Lightning Safety, USA, 2010 [4]. Bjarne Hansen, Dr. M. E. El-Hawary, State

Of The Art of Neural Networks In Meteorology. Technical University Of Nova Scotia, March 10, 1997.

[5]. N. Abdullah, M. P. Yahaya and Dr. N. S. Hudi. Implementation and Use of Lightning Detection Network in Malaysia, 2nd IEEE International Conference on Power and Energy (PECon 08), Johor Baharu, Malaysia.2008.

[6]. Negative and positive lightning, http://www.weatherimagery.com/blog/positive-negative-lightning. 2010

[7]. A.R Hileman, "Insulation Coordination for Power Systems.1999.Marcel Dekker, Inc.

[8]. Jang J.S, S. S. Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. New Jersey: Prentice Hall, Upper Saddler River. 1997.

[9]. Al-Alawi, R. A Hybrid n-tupleneuro-fuzzy Classifier for handwritten Numerals Recognition. 0-7803-8359-1/04/2004 IEEE , 2379-2383.




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[10]. Victor, I.-F.Face Recognition Using a Fuzzy-Gaussian Neural Network. Proceedings of the First IEEE International Conference on Cognitive Informatics (ICCI’02). 2002

[11]. A.Faghfouri, W.Kinser. Multifractal Characterization and Fuzzy Classification of Lightning Strike Maps. IEEE, 658-663., 2002.

[12]. Hongxing Li, C. P.-P. Fuzzy Neural Intelligent Systems: Mathematical Foundations and the Applications in Engineering. Florida: CRC Press LLC. 2001.

[13]. Vishwanath Hedge, Udaya Kumar. Classification of Strokes and Identification of Stroke location from the Measured Tower Base Current of LPS to Indian Satellite Launch Pad-I. The 8th International Power Engineering Conference (IPEC 2007) , 1161-1166.

[14]. Kamiyama, N. Tuning of Learning Rate and Momentum Rate on Backpropagation. Singapore ICCS/ISITA (IEEE),528-532. 1992.




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AUTHOR PROFILES:

M.K Hassan was born in Melaka, Malaysia. He received the B.Eng (Hons) degree in Electrical and Electronic from University of Portsmouth, UK in 1998, M.Eng Electrical Engineering from Universiti

Teknologi Malaysia in 2001 and Ph.D degree from Universiti Putra Malaysia in 2011. Currently he is a senior lecturer at the Department of Electrical and Electronic Engineering, Universiti Putra Malaysia. His area of interest includes; control system, automotive control, electric vehicle, AI applications and renewal energy.

A. Che Soh, received her B.Eng. degree in Electronic/Computer and M.Sc. degree in Electrical and Electronics Engineering from the Universiti Putra Malaysia (UPM), Malaysia

in 1998 and 2002, respectively. In year 2011, she received a PhD Degree at Universiti Teknologi Malaysia (UTM), Malaysia. Currently, she is a Senior Lecturer at the Universiti Putra Malaysia (UPM), Malaysia. Her research interests include artificial intelligence, process control, control systems, robotics, and automation.

R.Z. Abdul Rahman received her B.E. degree in Electrical and Electronics Engineering from the Liverpool John Moores University, United Kingdom in 1998, and her M.E. degree

in Electrical Engineering from Universiti Teknologi Malaysia, Malaysia in 2001. Presently, she is a lecturer at Universiti Putra Malaysia, Malaysia. She is currently pursuing her PhD degree in fault detection and diagnosis at Universiti Teknologi, Malaysia. Her research interests include artificial intelligence, process control, control systems, fault detection, and diagnosis.

M. Z. A. Ab Kadir graduated with B.Eng degree in Electrical and Electronic from University Putra Malaysia in 2000 and obtained his PhD from the University of Manchester, United Kingdom in 2006 in

High Voltage Engineering. Currently, he is an Associate Professor in the Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Putra Malaysia. To date he has authored and co-authored over 80 technical papers comprising of national and international conferences proceedings and citation indexed journals. His research interests include high voltage engineering, insulation coordination, lightning protection, EMC/EMI, keraunamedicine and power system transients.

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