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TIME DIFFERENCE OF ARRIVAL-BASED THREE-STATION LIGHTNING
LOCATING SYSTEM IN MALAYSIA
BEHNAM SALIMI
UNIVERSITI TEKNOLOGI MALAYSIA
TIME DIFFERENCE OF ARRIVAL-BASED THREE-STATION LIGHTNING
LOCATING SYSTEM IN MALAYSIA
BEHNAM SALIMI
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Doctor of Philosophy (Electrical Engineering)
Faculty of Electrical Engineering
Universiti Teknologi of Malaysia
NOVEMBER 2015
iii
“To my beloved parents and wife, for their encouragement and support”
iv
ACKNOWLEDGEMENT
First of all, gratefulness of thanks to our creator, “ALLAH” for his continuous
blessing, which make this work neither the first nor the last.
Special thanks go to Prof. Dr. Zulkurnain Bin Abdul-Malek for giving this
opportunity to work under his supervision and for sharing his great knowledge and
experience with me.
I would like to convey my deepest gratitude to all people who gave the author
heartfelt corporation and shared their knowledge and for giving some of their valuable
time.
Finally, I would like to send my deep appreciations to my family who brought
me up with love.
v
ABSTRACT
Lightning is an electrical discharge that happens during thunderstorms. The
discharge type can be either within cloud (IC), cloud-to-cloud, or cloud-to-ground
(CG). The characterisation of a discharge activity based on the analysis of the
measured electric field is widely studied in various geographical conditions. The
technique of locating a CG flash is also widely researched. However, little is known
on the discharge characteristics in equatorial and tropical regions, such as those
associated with preliminary breakdown pulses (PBPs) and return strokes (RSs).
Similarly, improvements can still be made on Lightning Locating Systems (LLSs),
especially those using the time of arrival (TOA) technique. In particular, the operation
of a three-station LLS to correctly locate a CG flash is very much desired to be proven.
This work aims to obtain and analyse the lightning discharge characteristics in
Malaysia. The work also proposes a new TOA based technique to correctly locate a
CG flash using only three measuring stations. Measurements had been made in
southern Malaysia using a purposely designed lightning detection system comprising
a broadband antenna. A three-station TOA based LLS had also been implemented. The
new TOA based technique was developed using the three-station LLS modelling in
Matlab and artificial neural network (ANN). A discrete wavelet transform based
technique was successfully developed to classify the discharge type. Self-Organizing
Maps and Levenberg–Marquardt algorithms can identify the correct strike position
with 2.5% error. The trained ANN engine was used to determine flash locations in a
400 km2 region. The three-station LLS gives superior results in terms of detection
accuracy and efficiency when compared with those measured by Malaysian
Meteorological Department.
vi
ABSTRAK
Kilat adalah discas elektrik yang berlaku semasa ribut petir. Discas berlaku di
dalam awan (IC), awan ke awan, atau awan ke bumi (CG). Pencirian aktiviti discas
berdasarkan analisis medan elektrik yang diukur telah dikaji dengan meluas dalam
pelbagai keadaan geografi. Teknik penentuan lokasi kilat CG juga dikaji dengan
meluas. Walau bagaimanapun, sedikit yang diketahui berkaitan pencirian discas di
kawasan Khatulistiwa dan tropika, seperti yang berkaitan dengan denyutan pecahtebat
awal (PBPs) dan panahan balik (RSs). Penambahbaikan juga masih boleh dibuat ke
atas Sistem Lokasi Kilat (LLSs), terutamanya yang menggunakan teknik masa
ketibaan (TOA). Khususnya, operasi LLS tiga-stesen untuk penentuan lokasi kilat CG
secara tepat amat dikehendaki untuk dibuktikan. Kajian ini bertujuan untuk mendapat
dan menganalisis ciri discas kilat di Malaysia. Kajian ini juga mencadangkan satu
teknik baru berasaskan TOA untuk menentukan lokasi kilat CG secara dengan hanya
menggunakan tiga stesen pengukur. Pengukuran telah dibuat di selatan Malaysia
menggunakan sistem pengesanan kilat yang telah direka menggunakan antena jalur
lebar. LLS tiga-stesen berasaskan TOA telah dilaksanakan. Teknik baru berasaskan
TOA telah dibangunkan menggunakan pemodelan LLS tiga-stesen menggunakan
Matlab dan rangkaian neural buatan (ANN). Satu teknik berasaskan jelmaan wavelet
diskret untuk mengklasifikasikan jenis discas telah berjaya dibangunkan. Algoritma-
algoritma Self-Organizing Maps and Levenberg–Marquardt boleh mengenal pasti
kedudukan panahan dengan ralat 2.5%. Enjin ANN terlatih telah digunakan untuk
menentukan lokasi kilat di sesebuah kawasan seluas 400 km2. LLS tiga-stesen
memberikan keputusan yang lebih baik dari segi ketepatan dan kecekapan pengesanan
berbanding data dari Jabatan Meteorologi Malaysia.
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
AKNOWLEDGMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xii
LIST OF FIGURES xiv
LIST OF ABBREVIATIONS xix
LIST OF SYMBOLS xx
LIST OF APPENDIX xxi
1 INTRODUCTION 1
1.1 Research Background 1
1.2 Problem Statement 2
1.3 Research Objectives 4
1.4 Research Scope 4
1.5 Contributions of Research 5
1.6 Thesis Outline 7
2 LITERATURE REVIEW 9
2.1 Introduction 9
2.2 Lightning Phenomenon 9
2.3 Mechanism of Lightning 12
2.4 Lightning Types 13
viii
2.4.1 Cloud to Ground Discharge 13
2.4.1.1 Preliminary Breakdown
Process
14
2.4.1.2 Stepped Leader 16
2.4.1.3 Attachment Process 17
2.4.1.4 Return Stroke 17
2.4.2 Cloud Discharge 18
2.4.3 Compact Intracloud Discharge (CID) 19
2.5 Lightning Detection Principle 20
2.6 Method of Detection 25
2.6.1 Time of Arrival (TOA) 25
2.6.1.1 TDOA Location Error 27
2.6.2 Magnetic Direction Finding (MDF) 28
2.6.2.1 MDF Location Error 29
2.6.3 Interferometry 31
2.6.4 Combined Direction Finding and
Time of Arrival (MDF+TOA)
32
2.6.5 Time to Thunder Method 32
2.6.6 Evaluating LLS performance 33
2.7 Application of Lightning Location System
(LLS) Data
34
2.8 Overview of Global Positioning System
(GPS)
35
2.8.1 Triangulation 36
2.8.2 Measuring Accurate Time of Travel
and Correcting Error
38
2.8.3 GPS and Lightning Detection System 39
2.9 Artificial Neural Network 40
2.9.1 Feed-Forward Neural Network 41
2.9.2 The Levenberg–Marquardt Algorithm 42
2.9.3 The Self-Organizing Map Algorithm 44
2.9.4 ANN and Lightning Research Area 45
2.10 Wavelet Analysis and Theory 46
2.10.1 Multiresolution Signal
Decomposition
47
ix
2.10.2 Application of Wavelet Analysis in
Lightning Research
48
2.12 Summary 49
3 METHODOLOGY 51
3.1 Introduction 51
3.2 Lightning Detection System (LDS) 53
3.2.1 Experimental Setup 53
3.2.1.1 Antenna Design 53
3.2.1.2 Buffer Circuit Design 62
3.2.1.3 RC-Filter 62
3.2.1.4 The voltage buffer amplifier 66
3.2.1.5 Simulation and Testing of
Circuit Board
69
3.2.1.6 Digital Data Logger 74
3.2.1.7 GPS Antenna 75
3.3 Characterization of Lightning Electric field 77
3.3.1 Layout of Measurement 77
3.3.2 Analysis Method of Lightning
Electric Field
78
3.4 Lightning Location System 80
3.4.1 Hardware Description of Lightning
Location System
80
3.4.1.1 Central and Measuring Station 81
3.4.1.2 Geographical Location of
Measuring Station
81
3.4.2 Software Description of Lightning
Location System
82
3.4.2.1 Software Development in
Measuring Station
83
3.4.2.2 Lightning Flash Classification
a Wavelet Approach
83
3.4.3 Software Development in Central
Processor
90
3.4.3.1 Time of Arrival Method 90
x
3.4.3.2 New Approach Identifying
Correct Position of Lightning
Strike Using Levenberg–
Marquardt Algorithm
96
3.4.3.3 New Approach on
Identifying Correct Position
of Lightning Strike Using
Combination of Levenberg–
Marquardt and Self-
Organising Map Algorithm
103
3.5 Validation Method of Lightning Detection
System
104
3.6 Summary 106
4 RESULT AND DISCUSSION 108
4.1 Introduction 108
4.2 Characterization of Lightning Electric Field 108
4.2.1 Statistical Analysis of BIL Model 109
4.2.2 Preliminary Breakdown/Return
Stroke Ratio
115
4.2.3 Isolated Breakdown 118
4.3 Lightning Location System 120
4.3.1 Single Station of Field Measurement 122
4.3.1.1 Selection of Appropriate
Mother Wavelet
122
4.3.1.2 Differentiation of CG and IC
Lightning Flash by Using
Wavelet Energy Coefficients
126
4.3.2 Signals Detection Using Three
Measuring Stations
128
4.3.2.1 New Approach on Identifying
Correct Position of Lightning
Strike Using Levenberg–
Marquardt Algorithm
132
4.3.2.2 New Approach on Identifying
Correct Position of Lightning
Strike Using a Combination of
Levenberg–Marquardt and
Self-Organizing Map
Algorithm
137
xi
4.3.2.3 Quarter Recognition of
Lightning Ground Flash Using
SOM Algorithm
141
4.3.3 Visualization and mapping of
Lightning Ground Flash
147
4.3.4 Validation of Results 151
4.4 Summary 162
5 CONCLUSIONS AND RECOMMENDATIONS 163
5.1 Conclusion 163
5.2 Future work 166
REFERENCES 167
Appendices A – G 183-206
xii
LIST OF TABLES
TABLE NO. TITLE PAGE
3.1 Specification of THS4631 and MSK0033 buffer amplifier. 69
3.2 Description of time stamp come out from GPS. 77
3.3 Three level of danger. 96
4.1 The meteorological conditions of three separate
thunderstorms.
110
4.2 Number of strokes per flash (multiplicity) and percentage of
single-stroke flashes.
114
4.3 Summarized data of BP/RS ratio and BP/RS separation of
lightning discharges recorded in Malaysia.
118
4.4 Statistics of (a) initial breakdown pulses leading to return
stroke in cloud-to-ground flashes (b) breakdown pulses not
leading to any subsequent activity.
120
4.5 Characteristics of three signals detected by measuring
stations.
130
4.6 The results parameters of ANN engine based on the
Levenberg–Marquardt algorithm for four different quarters
separately.
140
4.7 Six parameters extracted from points P1 and P2. 145
4.8 Weather conditions of measurement days. 149
4.9 Number of signals detected and its parameters in 6
measuring days.
150
4.10 Time, coordinates, and peak current of UTM (LLS) and
MMD data.
153
4.11 Comparison of three different measurement systems (MMD,
TNBR and UTM (LLS)).
160
xiv
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Thundercloud showing height and temperature in different
geographical condition [20].
11
2.2 Types of cloud-to-ground lightning flashes: comprising (a)
downward negative lightning, (b) downward positive
lightning, (c) upward negative lightning, and (d) upward
positive lightning (Adapted from [27]).
14
2.3 Preliminary breakdown pulse train activity. 15
2.4 Lightning process and relative timing. 18
2.5 VLF and VHF propagation of lightning. 23
2.6 Time of arrival (TOA) location technique. The difference
of time of arrival for each pair of sensors is confined on a
hyperbola. The intersection of the hyperbolas gives the
source location. Example of an ambiguous location for a
three-sensor hyperbolic intersection. A fourth sensor is
needed to pinpoint the exact location.
26
2.7 Optimal location algorithm for direction finding [71]. 29
2.8 Two-dimensional intersection of two satellite signals. 37
2.9 Two-dimensional intersection of three satellite signals. 37
2.10 Effect of the receiver clock offset on TOA measurements. 38
2.11 Schematic of a Feed-Forward Neural Network. 42
2.12 Discrete Wavelet Transform (DWT) using subband coding.
48
3.1 General overview of utilization for lightning
characterization and lightning locating system (LLS).
52
3.2 Schematic description of the lightning detection system
(LDS).
53
xv
3.3 The signal wavelength for various frequencies in
comparison to a fixed size antenna.
54
3.4 Single plate above ground level in an electric field
environment.
58
3.5 a) Single plate antenna with associated electric element b)
Equivalent circuit to measure the voltage throughout the
single plate antenna.
59
3.6 a) Physical arrangement for the electric field measuring
system, b) Equivalent circuit to measure the voltage
throughout the parallel-plate antenna, including the
capacitance of the coaxial cable.
60
3.7 Schematic of (a) high pass filter (b) low pass filter. 62
3.8 Simulated high pass filter frequency response. 64
3.9 Simulated low pass filter frequency response. 64
3.10 Antenna with RC-filter. 65
3.11 Simulated frequency response for antenna with RC-filter. 65
3.12 Voltage buffer amplifier. 66
3.13 THS4631 Operational Amplifier. 68
3.14 MSK0033 Operational Amplifier. 68
3.15 Equivalent circuit of antenna, RC-filter, buffer amplifier,
oscilloscope and cables.
70
3.16 Output impedance of the THS4631 operational amplifier. 70
3.17 Simulated frequency response of a parallel plate antenna
with associated circuit.
71
3.18 Equivalent of buffer electronic circuit. 72
3.19 The final circuit in Altium PCB editor software (THS4632).
73
3.20 The final circuit in Altium PCB editor software
(MSK0033).
73
3.21 Manufactured buffer circuit using THS buffer (front view).
74
3.22 Manufactured buffer circuit using MSK0033 buffer (front
view).
74
3.23 GPS antenna and GPS PCI card utilized in this project. 75
3.24 Configuration of DIL switch and 9 pin connector. 76
xvi
3.25 Location of lightning electric field characterization
measuring station.
78
3.26 Complete procedure of lightning characterization system. 79
3.27 The geometry of the installed measuring station. 82
3.28 Wavelet decomposition levels. 84
3.29 Sample of cloud activity. 86
3.30 Decomposition of first level of detail and approximation
from signal shown in figure 3.29
86
3.31 Sample of CG lightning flash. 87
3.32 Decomposition of fist level of detail and approximation
from signal shown in figure 3.31.
88
3.33 The general overview of the lightning flash classification
procedure using DWT method.
89
3.34 The configuration of measuring station and lightning strike
coordinate.
92
3.35 Front panel of central processor software. 94
3.36 Typical flowchart of central processing data acquisition
system.
95
3.37 Lightning event generated in different coordinates started
from (-10000,-10000) to (10000, 10000) with the 100
meters interval between each coordinates.
97
3.38 All pattern of false coordinate a) Zoom out view b) close up
pattern of false coordinate form figure 7(a) with the scale
of 4500 meter c) close up view from figure 7 (a) with the
scale of 700 meters d) close up pattern of false coordinate
from figure 7(a) with scale of 220 meters.
98
3.39 Method of extracting six parameters from true coordinate
and false coordinate.
99
3.40 Architecture of feed-forward artificial neural network. 100
3.41 ANN performance based mean square error at 400 iteration.
101
3.42 Regression plot of ANN engine. 103
3.43 Steps of using ANN engine to find true point of lightning
radiation source.
104
xvii
4.1 (a) Cloud-to-ground lightning flashes showing a
pronounced preliminary breakdown pulse train,
intermediate, and step leader preceding the flash recorded
on 10/12/2012 at 07:36:04.135 with 500 ms time frame. (b)
Close up from Figure 4.1 (a) with a time frame of 27 ms.
110
4.2 (a) Electric field record of negative cloud-to-ground
lightning flash showing a pronounced preliminary
breakdown pulse train and step leader that can be fitted as
‘B, L’ description recorded on 10/12/2012 at 03:36:04.001
with 500 ms of time frame. (b) Close up from Figure 4.2 (a)
with a time frame of 30 ms.
111
4.3 Histogram of lightning signals pattern. 113
4.4 Histogram of the total duration of PBP trains (from 30
samples of cloud to ground lightning).
113
4.5 Histogram of the total time duration of the intermediate
process (from 30 samples of cloud to ground lightning).
113
4.6 Histograms of number of strokes per flash. 115
4.7 (a) Lightning signal signature recorded with preliminary
breakdown pulse amplitude larger than the first return
stroke in May 2013. (b) Close up of (a) with a time frame
of 34 ms.
116
4.8 (a) Lightning signal signature recorded with preliminary
breakdown pulse amplitude lower than the first return
stroke in May 2013. (b) Close up from Fig. 4.8 (a) with a
time frame of 14 ms.
116
4.9 Frequency distributions of BP/RS ratio for the lightning
discharges recorded in Malaysia.
117
4.10 Frequency distribution of BP/RS separation of lightning
discharges recorded in Malaysia.
117
4.11 (a) Isolated Breakdown activity, which do not lead to any
subsequent activity detected in May 2013. (b) Close up of
(a) with a time frame of 6 ms.
119
4.12 Two samples of lightning flashes (a) Cloud Flash, (b) Cloud
to Ground flash.
123
4.13 Energy of different level of wavelet coefficient for cloud flash
(shown in figure 4.12 (a)).
124
4.14 Energy of different level of wavelet coefficient for CG flash
(shown in figure 4.12 (b)).
125
xviii
4.15 Energy of each wavelet coefficient for 15 selected CG
lightning flashes.
127
4.16 Energy of each wavelet coefficient for 15 selected cloud
lightning flashes.
127
4.17 Flowchart of detection and classification of lightning flash
types according to Haar based DWT.
128
4.18 (a) Electric field record of cloud-to-ground lightning flash
with 500 ms of time frame detected by B11, IVAT and van
stations respectively.
129
4.19 Close up view of figure 4.12 with 3 ms of time frame
detected by B11, IVAT and VAN stations respectively.
130
4.20 Two possible coordinates of lightning ground flash detected
by three measuring stations on 19 August 2014,
15:50:21:165.
131
4.21 Two final possible coordinates of lightning signals detected
by three measuring stations on 19 August 2014,
15:50:21:165, plotted on Google Earth map (Matlab
software).
132
4.22 ANN performance based mean square error at 303
iterations performed at all quarters.
134
4.23 Levenberg–Marquardt algorithm output for the all quarters.
Output ≈ 0.88×Target + 40, R=0.93031.
135
4.24 Acceptable value of mean square error versus distance of
true coordinate and false coordinate.
136
4.25 Simulations of lightning separated by four different
quarters.
137
4.26 Patterns of false coordinates in different quarter (a) quarter
one (b) quarter two (c) quarter three (d) quarter four.
138
4.27 ANN performance based mean square error at 561
iterations performed at first quarter.
139
4.28 Levenberg–Marquardt algorithm output for the first
quarter. Output ≈ 1×Target + 0.033, R=0.99993.
140
4.29 Structure of ANN self-organizing map algorithm based on
the Cross - Entropy method.
142
4.30 The evaluation of trained network based on Cross-Entropy
method. Best Validation Performance is 0.63194 at epoch
385.
143
4.31 Receiver Operating Characteristics curve. 144
xix
4.32 Confusion matrices of ANN engine based output class and
target class.
145
4.33 Identifying of true coordinate and false coordinate with
comparison of data provided by MMD.
146
4.34 2D plot of lightning ground flash measured on 11th August
2014 at 3 pm to 4:30 pm (X and Y axis are longitude and
latitude respectively).
147
4.35 Estimated current intensity of the lightning ground flash
detected on 11th August 2014 at 3pm to 4:30 pm.
148
4.36 Heat map of 556 lightning ground flashes captured in six
different measuring days.
151
4.37 Lightning ground flashes detected on 11th August 2014
(red dot by MMD and Blue sign by UTM (LLS).
154
4.38 Close-up view from figure 4.37 in latitude 1.5385-1.5415°
and longitude 103.6175-103.621° range.
155
4.39 The time differences between UTM (LLS) and MMD data. 156
4.40 Distance between UTM (LLS) and MMD data. 157
4.41 Estimated current intensity by UTM (LLS) versus MMD. 158
4.42 MMD reported peak current versus UTM (LLS) reported
peak current lightning.
159
4.43 A comparison of lightning strikes recorded by LD-250 and
UTM (LLS).
161
xix
LIST OF ABBREVIATIONS
AI - Artificial Intelligence
ANN - Artificial Neural Network
CG - Cloud-to-Ground
CID - Compact intracloud discharges
DWT - Discrete Wavelet Transform
FFNNs - Feed-Forward Neural Networks
GPS - Global positioning system
IC - Inter Cloud
IMPACT - Improved Accuracy through Combined Technology
LDN - Lightning Detection Network
LLS - Lightning Location System
LMA - Lightning Mapping Array
MDF - Magnetic Direction Finding
MMD - Malaysian Meteorological Department
MSD - Multiresolusion Signal Decomposition
NBP - Narrow bipolar pulses
PBP - Preliminary Breakdown Pulse
SAFIR - Systeme d'alerte Foundre par Interferometrie
SOM - Self-organizing maps
TDOA - Time difference of arrival
TNBR - Tenaga Nasional Berhad Research
TOA - Time of Arrival
UTM - Universiti Teknologi Malaysia
UTM (LLS) - Lightning location system developed in UTM
VHF - Very High Frequency
VLF - Very Low Frequency
WWLLN - World Wide Lightning Location Network
xix
LIST OF SYMBOLS
°C - Degree Celsius
Ω - Ohm
E - Electric field intensity
ε - Relative permittivity
εo - Dielectric permittivity
I - Current
C - Capacitor
eJ - Instantaneous error
Q - Charge
d - Distance
f - Frequency
V - Voltage
fc - Cut-off frequency
Vs - Supply voltage
t - Time
Lat - Latitude
Lon - Longitude
J - Number of decomposition levels
N - Number of samples
ak - Spectrum coefficients
m - Slope
xxi
LIST OF APPENDICES
APPENDIX TITLE PAGE
A In this appendix the Universal Transverse Mercator
Coordinate (UTM) system is proposed.
183
B In this appendix the developed code in Matlab for
evaluating TOA Method is presented.
186
C Calibration of Parallel-Plate Antenna 194
D Characteristics of Coaxial Cable 200
E Characteristics of Parallel-Plate Antenna 202
F List of publications. 203
G List of awards. 206
CHAPTER 1
INTRODUCTION
1.1 Research Background
Lightning can cause harm to both human lives and properties. A readily
available system for detection and warning of an incoming lightning storm is therefore
very useful. Public individuals scarcely own or install the lightning detection systems.
It is a major disadvantage if the public is unable to receive the warning message at the
very earliest time when a lightning strike is approaching. It may expose the people
near the lighting strike location to danger, especially for those undergoing an outdoor
activity. Therefore, a lightning detector which is able to detect the lightning strike
distance will be the useful for the public citizens in protecting themselves from
lightning strikes. In order to map the precise location of a lightning strike, both distance
and direction are important. This data is useful for insurance companies and for
protection purposes.
Previous work on the lightning locating system (LLS) includes the studies
using various detection techniques such as time difference of arrival (TDOA) [1],
magnetic direction finding (MDF) [2, 3], and interferometry [4]. However, these
techniques have their own advantages and disadvantages. Among the problem
encountered in these techniques are 1. Low accuracy (All); 2. High cost (All); 3. Not
exactly real time (TDOA, DF); and 4. Time synchronization problem (TDOA). One
method of increasing the accuracy is by using VHF sensors for small coverage area.
2
Previous work [5] show that accuracy can be increased by more than 90% in the small
scale area.
Even though there is a lot of research done on the LLS, still many
improvements can be made regarding the time synchronization, time difference
measurements, detection accuracy, and detection efficiency. This work aims to provide
a localized LLS based on the TDOA method with the utilization of high resolution
time synchronization and time difference measurements.
Discrimination between a different types of lightning flashes such as cloud
activity and cloud-to-ground (CG) flashes has long been recognized as a challenge in
lightning research. Some well-known models such as BIL and BL are used to identify
the CG flashes. Several parameters such as the rise time and gradient of pulses can
also be used to differentiate cloud activities and CG lightning flashes. In short, a
precise analysis of the signals is required to differentiate the various type of discharges.
This analysis is usually carried out manually [6].
A reliable lightning location system can be considered as invaluable. It helps
to save lives and reduce the risk of losing huge capital when arranging operations such
as space shuttle launches and airport services. The technology also helps in effective
decision making, for example, the deployment of emergency crews for quick recovery
of essential electrical or electronic systems. It also serves as a warning system for
future or probable lightning occurrence so that people could plan their outdoor
activities accordingly and ultimately saves lives.
1.2 Problem Statement
Lately, there have been a lot of interest on lightning related research. In
particular, studies have been done to better characterize lightning phenomenon
including those within the cloud prior to the cloud-to-ground flash. Among the highly
3
researched topics are related to Preliminary Breakdown Pulses (PBPs), and processes
in Cloud-to-Ground (CG) flashes including the return strokes (RS). Since lightning is
much related to statistical data, lack of such data hinders the research progress.
Comparative studies on lightning discharge processes in different meteorological
conditions can contribute to better understanding of the lightning breakdown process.
However, there are only a few measurements done in equatorial regions. The
characterization of lightning discharges in equatorial regions is therefore needed to be
studied in detail.
The identification of a lightning discharge type based on the electric field
waveform analysis can be manually achieved using human eye according to some
well-known parameters such as the gradient of fluctuation and the temporal analysis.
Even though an automated identification of the lightning discharge type is difficult due
to the complexity of the electric field waveform, this capability should be available in
a modern LLS. Nevertheless, most of currently installed LLSs throughout the world
are unable to provide an automated method to differentiate between different types of
lightning discharges such as within cloud, cloud-to-cloud, and cloud-to-ground. The
development of a suitable algorithm to successfully differentiate various types of
lightning discharges is highly desirable.
Lightning detection studies had been previously carried out by many
researchers [7-10]. These include direction finding (DF), time difference of arrival
(TDOA) techniques, a combination of these two, and interferometry methods. All
techniques need a number of sensors within a network to get reliable data on the
location of a lightning flash. The TDOA method uses the small differences in the
arrival times of the radio wave at different stations to determine the optimum distance
to the flash from a network of synchronized stations. However, these techniques have
their own advantages and disadvantages. Some of the usual disadvantages faced by
these techniques are high cost, low detection accuracy, and low detection efficiency
due to their relatively large coverage area [11, 12]. Improvements can still be made on
lightning locating systems (LLSs), especially those using the time of arrival (TDOA)
technique. It is well known that the lightning location system using TDOA method
needs at least four measuring stations. The solving of the TDOA algorithm using a
4
lesser number of measuring stations will definitely results in two or more solutions for
a given strike location. The concept of using only three stations in an LLS and yet it
can correctly locate a CG flash is very much desired to be proven.
1.3 Research Objectives
The objectives of this study are listed as follows:
(i) To develop, design, and construct a prototype of an on-line lightning locating
and monitoring system consisting of three receiver sensors, a central device,
and processing programs.
(ii) To prepare statistical information on BIL model of CG lightning discharge in
Malaysia based on the measured electric field waveforms.
(iii) To develop a suitable algorithm to successfully differentiate cloud activity and
cloud to ground of lightning discharges.
(iv) To correctly locate a cloud-to-ground flash in a three-station TDOA-based LLS
with the help of Matlab modelling and artificial neural network training and
assess the performance of the developed three-station TDOA-based LLS.
1.4 Research Scope
Several scopes are listed to ensure the research is conducted within its intended
boundary. These are:
5
(i) Only the vertical component of the lightning electric field was measured. This
is considered acceptable since the analysis of the vertical component of the
electric field is sufficient to represent the lightning discharge behaviour.
(ii) Identifying the correct position of the lightning ground flash will be done in
the neural network ToolBox of Matlab. Therefore, developing and constructing
a new neural network algorithm is not part of this research study.
(iii) Only one configuration of the three-measuring station LLS was installed and
tested. The measurements are limited to the ones made in 400 km2 area around
the campus of Universiti Teknologi Malaysia, Johor Bahru, in August 2014.
Comparisons with other LLS data are also limited to this specific coverage area
only.
(iv) The lightning discharge characterisations are based on 290 flashes measured
using one measuring station located at the Observatory, Universiti Teknologi
Malaysia. The measurements were made within the period between December
2012 and May 2013. The measured data is deemed sufficient to represent the
discharge behaviour in an equatorial or tropical region.
1.5 Contributions of Research
The contributions of this thesis are outlined as below:
(i) Comparative studies on lightning discharge processes in different
meteorological conditions can contribute to better understanding of the
lightning breakdown process. However, there are only a few measurements
done in equatorial regions. One of the contribution of this study is to prepare
statistical information of lightning electric fields measured in Malaysia. The
6 measurement setup was successfully designed, constructed, and installed at the
UTM’s Observatory, and measurements were made within several months in
2012 and 2013. Analyses on 290 captured lightning electric field waveforms
led to first, the identification of main parts within the waveform such as the
Cloud activity, Isolated, and Cloud-to-Ground (CG) discharge, and second, the
key characteristics of each part, for example, the Preliminary Breakdown
Pulses (PBPs). There are some similarities and differences observed between
this study and other studies. Possible causes of any difference between this
study and other studies, may be due to the different geographical region,
latitude, and meteorological conditions.
(ii) An on-line prototype lightning location system which consists of three
measuring station parallel plate antenna sensors, has been designed,
constructed, installed, and tested. By applying the Time of Arrival (TDOA)
method, the lightning ground flash coordinate is determined using the Matlab
and LabView software. The software is to capture and analyze the recorded
signal and map the coordinates of lightning ground flashes. Linear simulation
and mathematical analysis of the TDOA method is necessary in order to
evaluate error of system. A total of 556 ground flashes were successfully
detected and mapped across the south region of Malaysia during six different
measuring days from August 2014. The current intensity value of all captured
lightning ground flash is also estimated.
(iii) It is well known that the lightning location system using TDOA method needs
at least four measuring stations to avoid the ambiguous location of lightning
ground flash. In this work, it was shown that even using three measuring
stations, a correct location of the strike can still be achieved. A new approach
to identify the correct position of a lightning strike using a combination of
artificial neural network algorithms, namely Levenberg–Marquardt and Self-
Organizing Map, is successfully developed and tested. It was demonstrated that
the ANN engine can correctly identify the true coordinate of lightning ground
flash with an acceptable error of 2.5%. The developed codes and weight
7 matrices for all trained networks were successfully obtained and can be used
as required.
(iv) An automated discrimination between a cloud activity signal and a cloud to
ground signal has long been recognized as a challenge in the lightning research
area. A Multi-resolution Signal Decomposition (MSD) technique which
utilises the Discrete Wavelet Transform (DWT) was successfully identified
and developed to classify different types of lightning electric field. A
comparison between 50 cloud flashes and 50 CG flashes illustrates that there
is no overlap in the first level of energy for all flash samples. An energy value
of 6.27 was chosen as the boundary level to distinguish between a cloud
activity and a CG flash.
(v) In evaluating a lightning location system, both the detection accuracy and
detection efficiency are needed to assess the system performance. Lightning
data had been gathered for a certain period of time using the three-station
lightning locating system developed in this study. Comparisons with the data
provided by Tenaga Nasional Berhad Reseach (TNBR) and Malaysian
Meteorological Department (MMD) show that the three-station lightning
locating system performs better in terms of detection efficiency (number of
cloud-ground flashes only) and detection accuracy (coordinate location). The
comparison statistics clearly show that more than 70 percent of data were well
matched to the MMD data. The Average of time difference between MMD and
UTM (LLS) was found to be 22.58568 second. The arithmetic mean location
error was about 296.481111 m.
1.6 Thesis Outline
Chapter 1 provides the topic background, reasons to carry out this thesis, goals
to meet to accomplish this work, objectives, scope, and achievements of the research.
8 Chapter 2 covers a comprehensive review in the lightning research area. Those
are the mechanism of lightning, lightning types and steps, method of lightning
detection, application of GPS in lightning location system, application of artificial
intelligent in the lightning location system and wavelet transform technique. Most of
the published works deliberated on the accuracy and efficiency of the lightning
detection system. However, it is found that each method may have one or several
shortages to provide a high accuracy and best efficiency.
Chapter 3 presents the hardware installation and analysis methods. In this
chapter the construction of the circuit board and the hardware implementation is
described. To overcome on the limitation of the TDOA method by three measuring
stations Levenberg–Marquardt and the Self-Organizing Map algorithms in the
artificial neural network is conducted. Classification of lightning electric field types is
considered using multi-resolution signal decomposition and a new algorithm to
identify cloud to ground signals is proposed.
Chapter 4 discusses on the results, which are obtained from the practical
measurement performed in 3 different periods during 2012 to 2014. Characteristic of
lightning electric fields before for the duration prior to, during, and after the return
stroke in tropical region is presented and characteristic of the lightning in tropical
region will be compared with other geographical region and this knowledge of
lightning will greatly advance our knowledge on lightning with respect to the spatial
and temporal occurrence of lightning all over the world. In the next part of this chapter
lightning ground flash happened on six different days in August 2014 is localized, and
the result of this study is compared with well-known currently installed lightning data
provider.
Chapter 5 presents the conclusions and discussions of this study. Future work
and recommendations are also highlighted in this chapter.
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