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Renewable and Sustainable Energy Reviews 48 (2015) 710–725
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Renewable and Sustainable Energy Reviews
journal homepage: www.elsevier.com/locate/rser
Lightning monitoring system for sustainable energy supply: A review
Muhammad Abu Bakar Sidik a,c,n, Hamizah Binti Shahroom a, Zainal Salam b,e,
Zokafle Buntat a, Zainuddin Nawawi c, Hussein Ahmad d, Muhammad ’Irfan Jambak c,
Yanuar Zulardiansyah Arief a
a
Institute of High Voltage and High Current (IVAT), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru 81310, Johor, Malaysia
Centre of Electrical Energy Systems (CEES), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru 81310, Johor, Malaysia
c
Department of Electrical Engineering, Faculty of Engineering, Universitas Sriwijaya, Ogan Ilir 30662, South Sumatera, Indonesia
d
Department of Electrical Power Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn, 101 Parit Raja,
Batu Pahat, Johor, Malaysia
e
Institute of Future Energy, Universiti Teknologi Malaysia, UTM Johor Bahru 81310, Johor, Malaysia
b
art ic l e i nf o
a b s t r a c t
Article history:
Received 18 February 2014
Received in revised form
8 January 2015
Accepted 9 April 2015
A lightning monitoring system is used to observe, collect and analyse lightning activities so that a
preventive measure to protect power equipment from severe damage can be planned. An effective
lightning monitoring system is crucial to ensure the reliability and sustainability of the electrical energy
supply. Despite numerous published papers on this topic, there seem to be an absence of comprehensive
review papers that evaluate monitoring technologies and their performances. Owing to the literature
gap, this paper is written to summarise the working principles of the relevant sensors and the various
methods of data transmission, storage and analysis, as well as the various ways of predicting the
occurrence of lightning strikes. This knowledge will contribute to the development of a new online
lightning detection system that will be more efficient, without reducing the effectiveness and sensitivity
of the system, by utilizing the available technology for data transmission and analysis.
& 2015 Elsevier Ltd. All rights reserved.
Keywords:
Sustainable energy
Lightning monitoring
Lightning sensor
Contents
1.
2.
3.
4.
5.
6.
n
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711
Overview of lightning monitoring system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712
Atmospheric electric field (AEF) and lightning sensor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712
3.1.
Electric field mill (EFM). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712
3.2.
Integrated electro-optic sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713
3.3.
Micromachined electrostatic field sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715
3.4.
Flat plate antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715
3.5.
Passive optical lightning sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716
3.6.
Photon and infrasound detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716
Lightning location technique. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716
4.1.
Single station lightning location technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716
4.2.
Multi-station lightning location technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716
4.2.1.
Magnetic direction finder (MDF) method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717
4.2.2.
Time of arrival (TOA) method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717
4.2.3.
Improved accuracy using combined technology (IMPACT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717
4.2.4.
Interferometry technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717
Methods of transmission and storage data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718
5.1.
Methods of data transmission. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718
5.2.
Methods of data storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720
Methods of observation and analysis data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721
Corresponding author. Tel.: +60 7 5535717.
E-mail addresses: [email protected], [email protected] (M.A.B. Sidik).
http://dx.doi.org/10.1016/j.rser.2015.04.045
1364-0321/& 2015 Elsevier Ltd. All rights reserved.
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
711
6.1.
Methods of data observation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.
Methods of data analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7. Discussions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cloud to cloud
discharge
Intra cloud
discharge
Signal conditioning
(High Frequency)
Electric field
sensors
Signal conditioning
(Low Frequency)
Signal
Processing
Lightning
sensors
Sensors
Cloud to ground
discharge
Data
Transmission
Reliability is a crucial aspect of modern electrical power supply
—regardless whether the system is conventional or distributed
generation. A sustainable supply of energy requires that the
transmission and distribution of electrical power are guaranteed.
Particularly for a distributed generation system, it is inevitable that
the available energy from renewable sources (e.g. wind turbines
and photovoltaic arrays) will be maximized in order to recover the
high capital cost. In addition, with the expected proliferation of
smart grid systems, the protection of sensitive equipment, such as
telecommunication and control facilities (in smart distributed
generation), must be given the priority.
Ideally, it is desirable that these power supplies be as robust as
possible; however, a zero level of vulnerability is practically
unattainable [1,2]. In many cases, system failures or disruptions
due to natural disasters, such as severe lightning and thunderstorm events, can interrupt the energy supply to consumers. More
recently, the more frequent occurrence of extreme weather conditions due to global warming and the El Niño effect [3,4] have
also been identified as factors that exaggerate the problem.
For a lightning discharge, the resulting large current and/or
voltage fluctuations can temporarily disrupt or permanently
damage parts or components of the power supply chain [5–7].
The most common scenario is a direct or indirect strike on
exposed transmission cables, with the consequence of switchgears
and relays tripping. In a more severe situation, a direct strike on a
substation, for example, can cause damage to the components of a
distribution system (transformers, relays and switches)—resulting
in a long recovery time. In a renewable energy system, the most
vulnerable part is the power electronics equipment (dc–ac, dc–dc).
A lightning strike (direct or indirect) can result in a total collapse
of generation capability. Very likely, the electronics equipment
will have to be replaced. As far as a smart grid is concerned,
there is a need to adequately protect the telecommunication and
control facilities. They are the backbone of the distributed generation, and the loss of communication significantly reduces the
effectiveness of the system. From the above discussions, it is clear
that lightning discharge must be considered one of the main
variables in estimating risk factors [8–10]. It has to be evaluated
meticulously in order to adjust the cash flows of power supply
plants [11].
Lightning is a transient discharge of static electricity that serves
to re-establish electrostatic equilibrium within a stormy environment [12]. It is commonly characterised by an extremely high
current, high voltage and short-lived electrical discharge. To
reduce the risk of being damaged or affected by lightning, various
types of monitoring systems have been developed to detect and
alert individuals of its eminent occurrence. It is important to note
that lightning phenomena cannot be prevented, but they can be
intercepted or diverted to a path that results in less damage [13].
Besides being used as an early warning system, monitored data
can be used for the long-term meteorological evaluation, which in
turn, can improve the understanding of worldwide climate
change. As the interest in this issue has grown, substantial effort
has been devoted to designing a reliable and cost-effective
lightning monitoring system. With the proliferation of various
commercial products using different concepts, the need to understand the principles behind their operations becomes more crucial.
This is particularly vital when selecting the appropriate product
for a particular meteorological condition or geographical location.
Unfortunately, its importance has always been overlooked, even
though the cost of a monitoring system is only a small fraction of
the total investment of a power plant.
Despite the numerous published research studies carried out
in this area, there appears to be an absence of a single, comprehensive review paper that evaluates the relevant lightning monitoring technologies and their performances. Owing to the
literature gap, this review paper is written with the following
goals in mind: (1) to summarise the functions and working
principles of the various sensors used in the system, (2) to
elaborate on the methods of predicting the occurrence of lightning
strikes and their locations and (3) to describe the periphery
instrumentation required for data transmission, storage and analysis. At the end of the paper, a brief comparison of the commercially available lightning monitoring systems is also provided. To
complement the analysis, an exhaustive list of references is
provided for those interested in further probing the issues raised
in this paper.
Data storage
and Data
analysis
1. Introduction
721
722
722
723
723
723
Fig. 1. General block diagram of lightning monitoring system.
712
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
Rotor
Sensor Plate
Sensing Plate
Rotor
Stator
Charged sensor plate
(Shutter open electric field)
Uncharged sensor plate
(Shutter closed electric field
blocked)
Fig. 2. Electric field mill, top configuration.
2. Overview of lightning monitoring system
There are two categories of lightning monitoring, namely
earth- and space-based systems. In this review, only the former
is considered. Generally, lightning monitoring and detection systems have the same objective, that is, to predict the location and
intensity of lightning occurrences. The main components are the
sensors. In a typical system, the sensor configuration varies. It may
consist of (1) several atmospheric electric field (AEF) sensors along
with lightning sensors or (2) lightning sensors alone. The advantage of the former configuration is that it can track thundercloud
development processes and movements. Therefore, it provides an
opportunity to predict the thunderstorm’s location before it
appears. Using the latter, such predictions cannot be achieved.
Other components of a sensing system include a signal conditioning unit to process the output signal from the sensor.
Generally, it consists of a signal amplifier and filter components.
It may also involve a microcontroller to manage the input and
output signals from the signal conditioning unit. Then, the conditioned signal will be transmitted in the form of an analogue or
digital signal, depending on the data transmission protocol
adopted. Finally, the data are collected and stored in a computer
system. They will be analysed using certain mathematical prediction tools, and the results will be used for further actions. Fig. 1
shows the general block diagram of a typical lightning monitoring
system.
3. Atmospheric electric field (AEF) and lightning sensor
An AEF sensor is a device that is used to sense the presence of an
electric field in the atmosphere. It monitors the magnitude of the
atmospheric electric field between the clouds and the ground. The
characteristics of lightning can be discovered by measuring the
atmospheric electric field (AEF), which is produced by the motion of
an electric charge within the clouds. The changes in the magnitude
and polarity of the AEF can be observed during thundercloud
formation and lightning discharge. The ability to measure the AEF
enables the following: (1) identifying the thunderstorm’s duration;
(2) differentiating between the various storm stage characteristics;
(3) estimating the values of the lightning parameters, such as
transferred charge characteristics; and (4) predicting the location of
lightning occurrences [14]. Furthermore, by knowing the trends in AEF
variation, one can predict the future weather conditions. For example,
in normal fair weather, the AEF (close to the ground) is 100–200 V/m,
while during a storm, this value can increase by as much as four
orders of magnitude. Furthermore, lightning occurrence can be
detected by measuring fast variations characterized by short AEF
pulses [15].
The electric field mill is the most popular device, but other
types, such as electro-optic integrated sensors, flat plate antennas,
micro-machined electrostatic sensors, passive optical lightning
sensors and photon and infrasound detection systems, are also
used [15–26]. For an accurate measurement, the sensor must be
installed far from buildings, trees and other tall objects. The
lightning sensor has a similar general objective to the AEF sensor,
that is, to monitor lightning occurrences. The difference, however,
is that the lightning sensors detect this by means of an electromagnetic field, which is the combination of the electric and
magnetic fields. The AEF sensor provides critical lightning information, which provides an advanced warning before lightning
actually strikes.
3.1. Electric field mill (EFM)
One of the most widely-used AEF sensors is the electric field
mill (EFM). It functions by detecting the charge induced on the
sensor electrode. In principle, the EFM measures the electric field
by alternately exposing and shielding the conductor in the atmosphere, as shown in Fig. 2. The fixed induction conductor is known
as a stator, while the rotated conductor that alternately shields the
stator is known as the rotor. Therefore, the periodic changes in the
electric field signals can be acquired accordingly. When the sensor
plate is shielded by the rotor, the sensor plate is charged with an
electric field [15,16,18,19]. The number of vanes in the stator and
rotor can be varied, depending on the design of the EFM.
The charges induced from the electrostatic field can be
expressed as follows: [27]
Z
q¼
D dA ¼ εo EA½C
ð1Þ
The output voltage of EFM, Vo, is given by
Vo ¼
εo EA
½V
2C
ð2Þ
where D is electric flux density, εo is the permittivity of the
medium of the measuring field, E is the electric field, A is the area
and C is the sensor variable capacitance. The electrostatic electric
field can be calculated as shown in Eq. (3) [17].
Xn
Q i :H i
E¼
ð3Þ
3=2 ½V=m
i¼1
2πεo : H 2i þL2i
where Qi is the volume of the charge region, Li is the horizontal
distance between the observation point and each charge region
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
713
Table 1
Summary of types and features of electric field mills.
Author
Type of EFM
Features
Hui [16]
Three-dimensional EFM
A double electrode difference structure was applied in both the axial (z) stator and the radial (x, y) stators
The axial stator has two interlaced induction electrodes, which have the same size and shape as a rotor, while the radial
stator has two pairs of planar electrodes with the same shape and size that are perpendicular to one another
Can reduce the measuring error effectively and improve the sensitivity, precision and reliability
Is simplified in that the cost and energy consumption are reduced by excluding the earthing brush and photoelectricity
synchrodetector
Optimized version of the field
mill sensor
The instrument body of the EFM is mostly made of aluminium, but some critical parts are made from stainless steel: the
Fort [21]
Boltek EFM-100
Data logging capabilities and GPS receiver
Fort [21]
Vaisala EFM 550
Data logging capabilities and GPS receiver
Zhu [17]
AMEO340
Detects lightning location in the range 0–20 km and has a measuring range from 100 kV/m toþ 100 kV/m
Bennet
[22–23]
JCI 131
No earthing of rotating chopper
Has large gaps and long insulation tracking paths in the sensor unit to operate in a wet environment
Piper [24]
Campbell scientific CS110
Uses a reciprocating shutter that is electrically connected to the ground potential
Aranguren
[25]
Field mill network of Bogota
Can cover 196 km2 area by using five electric field mills, with which it can measure the vertical electric field network
Each mill can detect electric fields between 20 kV/m and 20 kV/m at a rate of 500 samples per seconds
Fort [15]
Renno [26] Miniature sensor of cylindrical
field mill
shutter, disk sensor and body cap
Waterproof and easy to assemble
Consists of a rotating shutter, sensors, shield, front end, motor control electronics and acquisition processing data storage
Developed an isolated electric field mill that can remove the error caused by wind-borne charge transfer
The electric field ranges between 50 V/m and 1 106 V/m
Waveguide 1
Y coupling
E
a
V
φo
Input Pi
Output Po
b
electrode
Waveguide 2
LiNbO3Crystal
Fig. 3. Structure of integrated electro-optic sensor head using a Mach–Zehnder interferometer.
and Hi is the total distance to each charge region. The measured
electric field will become larger and larger as the charges of the
cloud increase and approach the electric field mill.
The atmospheric electric field is approximately perpendicular
to plane surface [20,28]. Even though the electric field mill is far
from perfect, the technology has improved over time. Table 1
shows the summary of the types of EFMs and their features.
3.2. Integrated electro-optic sensor
AEF measurement using an integrated electro-optic sensor
can reach close to 1000 kV/m [29]. This sensor exhibits several
advantages: (1) It has a non-lensing system, which is necessary
in a high-voltage environment; (2) it is easy to assemble and (3)
it does not require earthquake-proofing, because the system
is fully integrated before being placed in the high-voltage
environment.
The structure of an integrated electro-optic sensor head utilises
a Mach–Zehnder interferometer, as shown in Fig. 3. The input light
is divided into two equal parts by a left Y-coupling and is then
transmitted through two horizontal waveguides, namely Waveguides 1 and 2. The light that travels through Waveguide 1 is
modulated by an electric field. Then, the waveguides interfere with
each other at the right Y-coupling. The output power of the sensor
is an optical signal that is proportional to the applied signal. There
is a path difference of one quarter of the wavelength between
these two waveguides, which forms an optical bias of π/2. This is
desirable because it allows the sensor to be driven without a DC
bias voltage and allows the output to vary linearly with the input
voltage.
The popularity of integrated optical electrical field sensor
continued to grow until 2011; three variations have been developed [30–32]. These sensors have been designed and fabricated
with various features, as shown in Fig. 4: (a) with a vertical dipole
with two electrodes, (b) with a horizontal dipole with two
electrodes and (c) with a single-shield electrode. These sensors
are fast and strong enough to measure AEFs up to 89.0 kV/m,
12.5 kV/m and 268 kV/m, respectively [32]. In addition, they are a
very compact, immune to external EMI noise, and have a wide
broadband response (DC to GHz).
714
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
The fundamental principle of electro-optic modulation is based
on the phase shift of the light wave during propagation, i.e.,
the intrinsic phase difference between the two optical paths is π/2.
Po ¼
n πrLГ
V
ð4Þ
φ¼
λd
where n is the intrinsic refractive index of the crystal. The
refractive index of some electro-optic crystals, such as lithium
niobate (LiNbO3), will vary when exposed to the electric field. The
variable r is the electro-optic coefficient corresponding to the
polarization of the electric field, L is the length of the electrode, λ is
the wavelength, V is the voltage across the gap d and Г ( o1) is the
overlap factor of the electric field with the light wave in the
crystal.
The modulator output of the optical power can be expressed as
follows:
3
Po ¼
Pi
½ 1 þ cos ðΔφo þ φÞ
2
ð5Þ
where Po and Pi are the output and input laser power, respectively,
and Δφo is the intrinsic phase difference between the two
waveguides. The output of the optical power can be verified when
Pi
ð1 sin φÞ
2
ð6Þ
If φ{1, Eq. (4) can be expressed as
Po ¼
Pi
ð1 φÞ
2
ð7Þ
Finally, the half wave voltages, Vπ, can be identified as
Vπ ¼
λd
Гn3 rL
ð8Þ
The design of the integrated electro-optic sensor has been
improved further by Ben Niu [33]. The improvement focused on
the phase difference between the two waveguides. The integrated
electro-optic sensor with LiNbO3 crystal substrates is used to
measure high-power impulse electric fields. It can measure an
Sensing
Electrode (+)
Combdriven
structure
Waveguide
Grounded
Shutter
electrode
Folded Beam
Vertical dipole with two-electrode design
Si Subtrate
Waveguide
Sensing
Electrode (-)
electrode
Fig. 6. Schematic structure of a micromachined electrostatic field sensor.
E
Horizontal dipole with two-electrode design
Shutter
Waveguide
Sensing
Electrode (+)
Sensing
Electrode (-)
electrode
Single-shield electrode design
Si Subtrate
Fig. 7. Operating principle of a micromachined electrostatic field sensor.
Fig. 4. Variations of integrated electro-optic sensors.
Waveguide 1
Y coupling
a
Output P1
φo
Input Pi
b
Output P2
electrode
Waveguide 2
LiNbO3Crystal
Fig. 5. The structure of the integrated electro-optic sensor.
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
715
Fig. 8. Flat plate antenna with RC circuit [31,32].
electric field of more than 1100 kV/m and is very effective for the
measurement of lightning impulse electric fields. Fig. 5 shows the
structure of the integrated electro-optic sensor proposed by
Ben Niu.
The output power of the each arm can be expressed as follows:
Po ¼
Pi
ð1 7 φÞ
2
Fig. 9. Parallel flat plate antenna [32].
ð9Þ
3.3. Micromachined electrostatic field sensor
The micromachined electrostatic field sensor (EFS) can be used
to measure AEF [34]. This sensor works at the resonant frequency
for maximum sensitivity. The sensor architecture has three blocks,
a comb-driven electrode, vibration shutter and sensing electrode,
as shown in Fig. 6. The operating principle of the electrostatic field
sensor is shown in Fig. 7. The shutter oscillates back and forth,
thus covering the side walls of either the positive or negative
sensing electrodes. This sensor is constructed via the silicon-oninsulator (SOI) process, in which the sensing electrode and
grounded shutter are positioned in the same plane. The combdriven electrodes in this sensor are designed to verify that the
grounded shutter works at its resonant frequency automatically.
The micromachined electrostatic field sensor features include low
cost, small size and low power consumption. Furthermore, the
advantages of this electric field sensor are high quality factor (Q),
accuracy, sensitivity and reliability.
The differential output current of the electrodes of the electric
field sensor can be expressed as follows:
iout ¼ εo E
dA
dt
ð10Þ
where εo is the permittivity of free space, A is the effective area of
the sensing electrodes and E is the electric field.
3.4. Flat plate antenna
A flat plate antenna is used to detect the AEF and thus
determine the lightning strike distance [35]. The flat plate antenna
is capable of determining the location of a lightning strike within a
short-range radius of 10 km by measuring electric field intensity.
The plate is considered over ground level and the wavelength of
the electric field is larger than the flat plate size [35,36]. Fig. 8
shows the flat plate antenna with the measuring circuit.
Laser
Meander
Antenna
Optical
Fibre
Fig. 10. The passive optical lightning sensor.
The changing of the electric field will induce voltage on the flat
plate antenna. The induced charge is expressed as follows:
Z
Z
D:ds ¼
pdv
ð11Þ
s
v
D ¼ Eεo εr
ð12Þ
D:S ¼ Q
ð13Þ
By substituting Eq. (12) into Eq. (13), the normal electric field
can be expressed as follows:
En ¼
Q
εo εr S
ð14Þ
Hence, the voltage between the flat plate antenna and the
ground is shown below
Z d
Z d
Q
Q :d
ð15Þ
En dx ¼ ð 1Þdx ¼
Vg ¼ ε
ε
ε
S
o
r
o
o εr S
0
0
The equation can be simplified by substituting Eq. (13) into
Eq. (14).
V g ¼ En :d
ð16Þ
716
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
where D is the electric flux density, S is the area of the plate, En is
the electric field normal, Q is the induced charge, εo is the free
space permittivity, εr is the relative permittivity and d is the height
with respect to the ground.
From Eq. (16), the voltage between the parallel flat plate
antenna and the ground is directly proportional to the electric
field normal to the plate and its height with respect to the ground.
The height d of the antenna is modified to obtain the effective
height deff. The effective height is classified as the height between
the measuring circuit and the antenna. The antenna has an
effective height of approximately 0.25 m and a physical height of
1.5 m [36]. The measuring circuit is attached to the flat plate
antenna, the RC circuit of which is shown in Fig. 8. Due to the
effect of the RC circuit, Vm becomes less than Vg. R can be neglected
because R is assumed to be very large compared to C.
Cg
Cg
V m ¼ En d
¼ Vg
Cg þ C þ Cc
Cg þ C þ Cc
z
Flat plate
φ
y
ð17Þ
Fig. 11. (a) Flat plate antenna, (b) cross loop antenna.
Eq. (17) shows that the measured voltage Vm is the voltage Vg
times the capacitive divider, composed of the capacitance C, the
antenna capacitance with respect to the ground Cg and the
capacitance of the coaxial cable Cc. The standard value of Cg is
59 pF, while Cc is 100 pF for a 1 m coaxial cable [36]. The value of
Cc may change according to the length of the coaxial cable from
the lower antenna to the input of the measuring circuit. Fig. 9
shows the structure of the parallel flat plate antenna; the diameter
of the plate is 0.5 m, and the gap distance between the two plates
is 0.03 m.
3.5. Passive optical lightning sensor
Rosolem [37] proposed using a passive optical lightning sensor
to detect lightning’s electromagnetic pulses. This sensor was
mainly designed to indirectly monitor lightning strikes on overhead power lines, substations or power generation plants. Dissimilar to others, this sensing technique does not require solar panels,
batteries or electronic control circuits. It is a viable solution that
can allow the power utility to provide fast power recovery and the
precise preventive maintenance of transmission lines. This passive
optical lightning sensor is fabricated using a dipole meander
antenna [38], a low-cost Fabry–Perot semiconductor laser operating at 1310 nm and a single-mode optical fibre to carry the
detected signal to a remote optical receiver, as shown in Fig. 10.
The process of lightning detection can be described as follows:
An electromagnetic field is created by the intense currents of
lightning discharged over a wide frequency. Then, the electromagnetic fields propagate away from the lightning channel in the
form of radio waves, and their attenuation depends on the
frequency of the radiation. A transitory voltage will be induced
in the antenna when the radio waves reach the sensor. This voltage
generates an electrical current that instantly modulates a semiconductor laser attached directly to the antenna. The optical
power emitted by the laser is proportional to the intensity of the
lightning-induced electromagnetic field. Therefore, it can be
transmitted to other locations by using optical fibre as the laser
output.
3.6. Photon and infrasound detection
A combination of photon and infrasound detection is another
approach to lightning detection. Photon detection has a high
sensitivity and a fast response speed [39]. It involves photomultiplier tubes (PMT), a voltage biasing circuit, and a signal modulating circuit. The characteristics of the PMT allow it to be used with
faint light signals, as well as fast-pulse light signal. Therefore, the
PMT can be used to detect lightning.
Meanwhile, infrasound detection focuses on the audible sound
and infrasound of thunder. Infrasound detection involves an
infrasound sensor, a transformer circuit and a signal pretreatment
circuit. In order to distinguish the thunder’s sound with other
natural phenomena, such as wind, typhoons, earthquakes and
volcanic eruptions, an infrasound acquisition and analysis system
is required.
When the lightning signal occurs, PMT first attain it. Then, the
infrasound sensor receives the signal. The channels in the PMT
calculate the lightning flash azimuth and the time between the
light and sound. The time between the light and infrasound
arriving at the system, measured via the GPS-clock, is used to
compute the lightning flash distance. Therefore, the exact longitude and latitude to the point of reference can be computed using
this data.
4. Lightning location technique
The lightning location technique can be divided into two
categories: single-station and multi-station lightning location
techniques.
4.1. Single station lightning location technique
The single-station lightning location (SSLL) technique requires
a single observation station to locate lightning. The implementation of this technique involves estimating the lightning distance
that could be obtained by using several methods: (1) an
amplitude-based lightning signal, (2) wave line theory, (3) electric
field (EF) and magnetic field (MF) phase difference, (4) delay time
difference, (5) group delay, (6) Schumann Resonance and (7) the
wave impedance method. The wave impedance method is the
latest SSLL method introduced by Mingli [40]. However, the singlestation lightning technique is not so popular, as the accuracy of the
SSLL still requires further improvement compared with a multistation lightning location system.
4.2. Multi-station lightning location technique
In order to monitor the lightning, the most common electromagnetic radio-frequency-locating methods are magnetic field
direction (MDF), time of arrival (TOA), IMPACT and interferometry.
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
These methods are reviewed in Sections 4.2.1, 4.2.2, 4.2.3 and
4.2.4, respectively. These methods provide the best real-time reallocation detection of individual lightning strikes.
4.2.1. Magnetic direction finder (MDF) method
Magnetic direction finder (MDF) is a method used to determine the location, movement and intensity of lightning. The observational data can be displayed textually or/and graphically. The
MDF method can be implemented by using a vertical and orthogonal magnetic loop/cross loop antenna and a flat plate antenna
[41]. Fig. 11 shows the types of MDF antennas [35,42].
There are two types of cross loop MDF (CL-MDF) antennas
utilised in lightning detection: narrow-band (tuned) CL-MDF and
gated wideband CL-MDF [43]. Narrow-band CL-MDF operates in a
narrow frequency band, with the centre frequency commonly in
the range of 5–10 kHz. At this frequency, the lightning signal
energy is quite high, and the attenuation in the earth ionosphere
waveguide is quite low. However, there are several disadvantages
of narrow-band CL-MDF when lightning distance is less than
200 km. The inherent azimuthal error (polarization error 101) will
occur. This error results from the detection of magnetic field
components from the non-vertical (horizontal) channel. The
magnetic field lines of circles in a plane are perpendicular to the
non-vertical channel section. Also, the fact that the magnetic fields
are improperly oriented for the direction finding of the ground
strike – which occurs due to ionospheric reflections and sky waves
– causes error in narrow-band CL-MDF.
A gated wideband CL-MDF has been introduced to overcome
the problem of large polarization errors in narrow-band CL-MDF.
The north–south and east–west components of the initial peak of
the return strike magnetic field were sampled to achieve gated
wideband CL-MDF. Gated wideband CL-MDF has an operating
bandwidth from a few kilohertz to 500 kHz.
Both narrow and gated wideband CL-MDF are susceptible to
site error [44]. Site error can be defined as a systematic function of
direction, but generally, it is time-invariant. The presence of
unwanted magnetic fields – non-flat terrain and nearby conducting objects, such as underground and overhead power lines and
structures – is the source of the errors. To avoid this problem, the
area surrounding the CL-MDF must be flat, and no conducting
objects can be close to the area.
4.2.2. Time of arrival (TOA) method
The time-of-arrival (TOA) method is able to determine the
location of lightning accurately by using at least two stations [45].
The distance is obtained by calculating the arrival time of the
lightning’s electromagnetic signal at the stations because the
velocity of signals in space is a constant [46–48]. The time-ofarrival differences among remote stations will be calculated, and
this will result in several time-difference hyperbolas. The intersection of the hyperbolas will be assumed to be the location of the
lightning strike [49].
The TOA method uses three types of lightning location, which
are as follows: (1) very short baseline (ten to hundreds of meters),
(2) short baseline (tens of kilometres) and (3) long baseline
(hundreds to thousands of kilometres). Very short baseline and
short baseline commonly operate at VHF 30–300 MHz, whereas
long baseline usually operates at VLF 3–300 kHz. Generally, the
VHF is related to the air breakdown process [50,51], while the VLF
signal results from current flow in the lightning channels [52].
For the very short baseline, the time difference between the
arrivals of an individual lightning strike’s VHF pulse at the
receivers is shorter than the time between the pulses; it is in the
microsecond to hundred microsecond range. Short baseline generally purports to offer electromagnetic images of lightning
717
channels and to study the spatial and temporal development of
the discharge [43]. Long baseline is commonly used to classify the
ground strike point and the location of the flash. The long baseline
system is known as the Lightning Position and Tracking System
(LPATS). LPATS has been established since the 1980s, and it can
operate at VLF/LF. The LPATS sensor has a higher sensitivity in
terms of detecting lightning strikes as compared to IMPACT. This
sensor is expensive due to the high operating cost [49].
Another method is a wideband sensor, which employs the TOA
method. The Earth Networks Weather Bug Total Lightning Network (WTLN) is a lightning detection network that uses a wideband sensor to detect both cloud-to-ground (CG) and inter-cloud
(IC) flash signals [53]. A real-time lightning cell tracking system
and the subsequent dangerous alert system can be provided by
using WTLN total lightning data
4.2.3. Improved accuracy using combined technology (IMPACT)
Improved accuracy using combine technology (IMPACT) is a
method that has been developed to combine the MDF and TOA
methods’ techniques. In this approach, the direction finding and
absolute arrival time provide azimuth information and range
information, respectively [47,54]. Hence, IMPACT allows for an
optimized estimate of location because it has redundant information regarding timing and angle. The combination of MDF and TOA
provides some advantages compared to independent MDF or TOA.
For example, a discharge that occurs along the baseline between
two IMPACT sensors will be more accurately located by the
intersection of two direction vectors.
The Global Lightning Detection network, GLD360, was implemented using the magnetic direction finder (MDF) and long baseline timeof-arrival (TOA) methods, combined with a proprietary lightning
recognition algorithm in the VLF band. This GLD360 detects 80% of
all large negative strokes (420 kA in magnitude) according to the
National Lightning Detection Network (NLDN) [55]. The application of
MDF and TOA methods are summarized in Table 2.
4.2.4. Interferometry technique
The interferometry technique is a technique used to locate the
VHF pulses associated with lightning discharges. In the interferometry technique, no identification of individual pulses is needed,
because the interferometer measures the phase difference
between narrow-band signals corresponding to the noise-like
bursts that occur during lightning. Then, these signals are received
by two or more closely spaced sensors [43].
The principle of interferometry consists of determining the direction of the source of radiation using a combination of phase measurements in a relatively small bandwidth. The simplest interferometric
system consists of two antennas located a few meters away from one
another. Generally, the antenna is made up of an array composed of
VHF dipoles that is connected with the interferometric receiver in
order to determine the phase differences over a large dynamic range.
The phase difference is commonly acquired by mixing the signals from
several antennas, and the accuracy is achieved by integrating them
over the desired time resolution. Finally, the phase differences are
processed in order to obtain an azimuth from two-dimensional (2D)
networks or azimuth and elevation from three-dimensional (3D)
networks of the source. The information from several sensors is
received by the central processor, which will determine the location
of each individual event via triangulation [56–58].
The System de Surveillance et d’Alerte Foudre Par Interferometrie Radioelectrique (SAFIR) network is based on the VHF interferometry technique, which is able to observe lightning in 3D. In
Table 3, a summary of research using the interferometry technique
is presented.
718
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
Table 2
Summary of lightning location using the MDF and TOA methods.
Author
Receiver
Ibrahim
[41]
Orthogonal magnetic loop
Magnetic direction finder (MDF)
antenna and flat plate antenna
Rakov [43]
VHF (30–300 MHz) (Very
short baseline and short
baseline)
Time of arrival (TOA)
Electric field whip antenna
Time of arrival (TOA)
Rakov [43]
Hussein
[49]
Method
Features
Requires at least two stations
Stations are separated by distances of up to a few hundred km
The intersecting point provides the source location
The time difference between the arrivals of an individual lightning VHF pulse at
the receivers is short compared to the time between pulses, which is a
microsecond to a hundred microseconds
Long baseline, also known as the Lightning Position and Tracking System (LPATS)
The stations are 200–400 km apart in order to determine the location by
measuring the differences between the signal arrival times at the stations
A microprocessor-based system is used to store the location data, which is
displayed on a video terminal
The output data of this sensor are serial number, polarity, latitude and longitude
Amir [56]
Chonglin
[53,57]
Broadband circular flat
antenna
Time of arrival (TOA)
Wideband sensor
Time of arrival (TOA)
of striking points, error between actual time differences reported, the identity of
the three receivers used in the solution and lightning current amplitude
The efficiency is in the range of 40–55% within 400 km of the detection station
Three broadband circulating flat antennas having diameters of 30 cm each are
used to locate the position
The Earth Networks WeatherBug Total Lightning Network (WTLN) is a lightning
detection network that uses a wideband sensor
Has a detection frequency in the range of 1 Hz to 12 MHz
The WTLN consists of an antenna, a global positioning system (GPS) receiver, a
GPA-based timing circuit, a digital signal processor (DSP) and on-board storage
and Internet communication equipment
The WTLN sensor works when the signal is detected and the waveform is
recorded. It then sends the waveforms to the central lightning detection server
via the Internet
The waveform of arrival time and signal amplitude can be used to determine the
peak current of the strike and the exact location of the flash, including the
latitude, longitude and altitude
The WTLN has good IC detection efficiency, which is up to 95%
Cummins
[54,58]
IMPACT sensor
TOA and MDF
In the range of 0.4 Hz to 400 kHz, lightning can be detected by an IMPACT sensor
Provides azimuth and range information
Estimated parameters: longitude, latitude and discharge time
Ibrahim
[59]
Small active antenna
TOA and MDF
Lojou [55]
Orthogonally oriented
magnetic loop antenna
TOA and MDF, combined with a
lightning waveform recognition
algorithm
Known as the Global Lightning Detection network, or the GLD360
A worldwide detection of flashes with significant detection efficiency and
Known as the Pekan Lightning Detection System (PLDS)
Consists of two orthogonal magnetic loops
Does not contain polarization error
Simple, reliable and easy to use
Able to detect and analyse real-time lightning data, which can be displayed both
textually and graphically
location accuracy of the CG flashes, along with the polarity of the measured
discharges and an estimate of peak current
Able to accurately locate most of the lightning discharged around the world
A long-range global network that uses multiple terrestrial sensors to calculate
lightning discharge locations (latitude and longitude) and times
In a 24-h period, 588,200 events were detected
Provides accurate, early detection and tracking of severe weather
It has no data gaps, as satellite and radar systems do. Also, there is no need for
maintenance or expenditures
The data received from the GLD360 can be derived in a week, which is quite
quickly, thus providing better end-user service
In terms of accuracy, the GLD360 outperforms all other long-range lightning
detection networks, including satellites
It correctly identified the polarity in 145,901 events, which represented 94%
accuracy
5. Methods of transmission and storage data
5.1. Methods of data transmission
Data transmission is an important process in a lightning monitoring system. Several methods of data transmission collected from
approximately 22 published works are reviewed in Section 5.1.
Furthermore, because the received data must be stored securely,
several methods of data storage are reviewed in Section 5.2.
Data transmission is the physical transfer of data from one point to
another point through a particular channel [59]. Some of the channels
used to transmit data include coaxial cable [35,60–62], fibre-optic
cable [63–66] and wireless communication [67–70]. Coaxial cable has
a good bandwidth, low error rate and relatively low resistance. It can
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
719
Table 3
Summary of research using the interferometry technique.
Author
Network
Qing [63]
Baran [64]
SAFIR 3000
Features
Able to observe lightning in 3D
Located at over 1000 m in altitude
The interferometric array uses the differential phase measurement of lightning’s electromagnetic waves for the long-range
detection of lightning activity
The SAFIR LF discrimination sensor is a wideband antenna that is capable of identifying the cloud-ground characteristics of
lightning
Has the capability to provide lightning warnings via a moving trace of thunderstorm cells and also to detect the threedimensional distribution of lightning discharges
CG and IC lightning can be located, and this information is then displayed in three dimensions at a high temporal and spatial
resolution
Provides lightning warnings based on a moving trace of thunderstorm cells almost 30 min in advance
Ahmad [49]
SAFIR
Has the highest location accuracy and detection efficiency and also provides the maximum warning time
The detection efficiency is 95%, and the location accuracy is in the range of 1.0 to 2.0 km
The typical range of interferometry is about 50–150 km, and an interferometer is composed at least three sensors
Lojou [55]
Total lightning sensor
TLS200
Uses five dipole arrays to determine the direction of the lightning sources based on phase differences of the signal at each
dipole
Can be used for accurate 2D mapping of cloud flashes, as well as efficient CG flash location
Provides an increase in the detection range of the VHF total lightning sensors of more than 30%, as well as more stable
operations
Coaxial Cable 40 m
Magnetics Loop
Antenna
LabVIEW 2011
Coaxial Cable 40 m
Wire Antenna
Fig. 12. Coaxial cable connections in a single lightning detection station.
support multiple channels and a wide range of services, i.e., voice,
data, video and multimedia. The transmission speeds achievable via
coaxial cable are as high as 10 Gbps. Coaxial cable is strong, but there
are limits to its length; it will stop working if these limits are
exceeded. Usually, coaxial cable is capable of reaching hundreds of
meters in length. This type of cable is also suitable for transferring
high-speed signals without high losses. However, coaxial cable has
problems with deployment, such as bus topology [71]. The bus
topology will be susceptible to congestion, noise and security risks.
The coaxial cable may be a path via which the lightning current can
infiltrate equipment or devices and cause damage if adequate maintenance is not performed on the lightning protection system. Fig. 12
shows the example of the coaxial cable connection in a single
lightning detection station [41].
Fibre-optic cable uses thin strands of glass in its architecture.
The pulse of light travelling along the core of the fibre allows it the
highest available data transmission speed. Usually, the transmission speed of fibre-optic cable is between 100 and 2000 Mbps.
Fibre-optic cable offers high bandwidth and long-distance terminals to transmit data, as well as resistance to lightning disturbances. However, the cable can only stretch over a certain number
of kilometers before the signal becomes impossible to detect. Also,
fibre-optic cable is expensive to install and fragile.
Wireless Local Area Networking (WLAN) technology is another
option for data transmission. The coverage area of WLAN is close to
20 km2 [62]. The advantages of using WLAN in data transmission are
its flexibility (it can transmit data within the radio coverage area) and
the fact that its nodes can communicate without further restriction.
Ad hoc wireless networks allow for communication without planning,
which makes them more convenient than wired networks [72]. WLAN
technology is robust in that it is disaster-resilient [73]. In theory, the
speed of standard WLAN is rated according to its maximum bandwidth, where 802.11b offers up to 11 Mbps, 802.11a and 802.11g offer
up to 54 Mbps and 802.11n offers up to 300 Mbs. However, the
performance of WLAN is not as good as these theoretical values.
In reality, it is only capable of achieving 5.5 Mbps, 20 Mbps and
100 Mbps, respectively, according to the standard [74]. Nevertheless,
WLAN also has disadvantages, such as lower quality of service (QoS),
lower bandwidth due to limitations in transmission (1/10 Mbit/s) and
high error rates due to interference from other sources [75]. Cordless
phones, Bluetooth devices and motion detectors all employ radio
frequencies that can interfere with WLAN [76]. This interference can
720
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
Battery 9 Vdc
Oscilloscope
Voltage
Regulator 5
Vdc
I
I
E
∫
Vout
Monostable
Multivibrator
PIC 16F877A
Microcontroller
Indicator and SCI
circuit
PC
tor
uc
nd
co
Fig. 13. A lightning flash counter with an SMS data sender.
Table 4
The comparison of transmission used for collecting data of lightning.
Transmission type
Coaxial cable
Quality and significant difference
Cost benefit
Good bandwidth, low error rate and relatively low resistance
EMC/EMI susceptible without proper protection system
Suitable for transferring high-speed signals without high losses
Moderate installation cost
Fibre optic
High bandwidth and long-distance terminals to transmit data
EMC/EMI resistance
Moderate speed
Frigile
High installation cost
WLAN
Flexible—it can transmit data within the radio coverage area
Disaster-resilient
EMC/EMI susceptible
Moderate speed
Low installation cost
GSM–GPRS
EMC/EMI susceptible yes
Low speed
reduce the quality of WLAN service, and it also can weaken WLAN
security.
Data can also be transmitted by using a remote file transfer
protocol (FTP) server via a Global System for Mobile–General
Packet Radio Service (GSM–GPRS) modems [77,78]. A GSM–GPRS
modem can exist in the form of an external unit or a PC card. The
external unit could be connected through a serial cable, a USB
cable, Bluetooth or infrared. The GPRS has much higher data
transmission speed in that large amounts of data can be transferred to and from the mobile device over the Internet. One
advantage of GPRS is that it has a great backup option. The
portability factor has diminished with the emergence of much
faster data cards. GPRS can also be used to transmit SMS. The data
can also be transmitted via modular phone SMS [79,80]. The SMS
transmission speed that can be achieved is about 30 SMS messages
per minute. A GPRS modem is required to send and receive SMS
via GPRS. In this way, the exact date of the lightning event,
polarity, current crest amplitude, rise time and decay time can
be recorded and sent to users via SMS.
Hussein Ahmad presented an SMS system as a method of data
transmission [81]. The time, date and the number of lightning
strikes are required so that a lightning counter can send this
information to a specified mobile phone via SMS. The AT command
plays an important role in sending out SMS signals in that it is
important to focus on the auto SMS function. There are a few
procedures that must be followed before writing the AT command.
The mobile phone must perform a test in order to determine the
types of SMS modes that the mobile phone can support before it
Zero installation cost
Monthly subscription payment
can perform the task. There are two types of SMS modes, which
are PDU mode and text mode. The message contents in PDU mode
must be converted to Hex code before being sent. Meanwhile, for
text mode, an SMS can be written normally in alphabetical format.
Fig. 13 shows a lightning flash counter with an SMS data sender.
The AEF measurement information can also be transmitted
using wireless data transmission by using the Global System
Mobile Communication (GSM) network [77]. The coverage of
GSM, which can reach across distances of hundreds or thousands
of kilometres, is able to transmit the data through an Internet
provider to a PC. This method offers low cost and the ability to
transmit data using worldwide coverage. In addition, it will reduce
the manpower needed by eliminating the routine inspections that
were previously necessary to collect the data manually, and the
computing system does not need to be installed on-site. In Table 4,
types of transmission related to the quality and cost benefit is
presented.
5.2. Methods of data storage
It is important to safely store the data collected before analysis
is carried out. The acquired data can be stored in a secure digital
card, also known as an SD card. This can protect important data
from being accidentally overwritten. These SDs card are provided
with a mechanical switch that acts as a write protector. One
advantage of using an SD card in data storage is its non-volatile
memory, meaning that it keeps data stable on the card. The data
on this card are not threatened by the loss of power and do not
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
721
Cloud
+q i
+q1
+qo
+q2
+q3
t/2
-q1
-qo
-q2
-q3
-q i
Fig. 14. Dynamic cloud model. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
need to be periodically refreshed. Furthermore, such cards are free
from mechanical difficulties and damages. An SD card does not
produced any noise while at work, allows immediate access and is
also easy to keep track of. Moreover, it has a relatively large
amount of storage space compared to old backup devices. Unfortunately, the SD card also has the disadvantages of being easy to
break, lose, misplace or smash. In addition, SD cards may be
affected by various forms of electronic corruption, such as viruses,
making the entire card unreadable or broken.
In addition, the digital signal oscilloscope (DSO) is used to
capture and store the electric field signal [35]. The DSO provides
advanced trigger, storage, display and measurement features. The
data also can be stored by using a MySql database, which is a
database management system. The data sent from the GSM
module will be sent to the MySql database and displayed as a
Hypertext preprocessor (PHP) page [77,82]. Using this method
results in better data storage because the data are saved in an
HTML page, which is viewable via a web browser. Thus, it is easier
for users to read the data collected.
6. Methods of observation and analysis data
The data obtained from lightning monitoring systems can be
analysed further. The data are not only used for detecting lightning
but can also be used for observing and analysing climate changes
in the atmosphere. Discovering useful information within the
observational data allows us to suggest conclusions and supports
decision making. Several methods of lightning data analysis and
observation have been compiled in Sections 6.1 and 6.2. The
informative data from the sensors can be used in various ways
via these methods.
6.1. Methods of data observation
Several types of methods are used to observe the data. An isokeraunic-level (IKL) map is used to estimate the values from the
mapped thunderstorm data yearly because it is difficult to measure the ground flash density directly [83]. The IKL map is related
to the number of flashes per unit area per year. Thus, it is possible
to determine the frequency of lightning in an observed region
quite easily. Even so, the accuracy of this approach is low. Because
the data collection depends only on the sound of thunder being
heard by observers in weather observation stations, only thunderstorms occurring near the stations are registered. When the
lightning location is far enough from the station, the sound of
thunder is very quiet. Consequently, some data may be lost for that
location. In addition, the IKL map shows the average figures for
thunderstorm days gathered over many years. The IKL map only
shows the variation in lightning activities in a given area per year.
In order to present developing storms, a dynamic cloud model
is introduced [84]. In this model, the colours blue and red are
defined as the positive charge and negative charge, respectively.
The arrow pointers represent their random positions, and their
magnitudes correspond to the sizes of the circles. Some important
parameters are required to obtain the dynamic cloud model, i.e.,
(1) the positive and negative charges (7 Q), (2) the distance
between the positive and negative charges and (3) the cloud
height. These are used to calculate the electric field for a particular
time interval. Fig. 14 shows the dynamic cloud model, in which the
positions and magnitudes of the positive and negative charges
change with time as the thundercloud propagates.
In the Beijing and Heibei areas, the meteorological data, as well
as the locations of lightning discharges and the number of flashes,
were collected by using the SAFIR 3000 network. [85]. The
recorded lightning discharge locations were related to the storm
distribution along a southwest-northeast frontal disturbance at
which the two independent thunderstorm cells moved separately
to the north and south.
Furthermore, a prototype of a single-station lightning locating
system (S-LLS) has been developed that consists of three configurations. There are lightning signal sensing (LSS), lightning data
acquisition (LDA) and lightning data display (LDD) modules [86].
The LSS module is responsible for electromagnetic signal capture.
This module consists of a flat-plate antenna, two crossed-loop
antennas, a cloud-to-ground/cloud-to-cloud (CG/CC) logic circuit
and a lightning simulator for testing. Then, the results from three
analog signals and CG/CC logic are transmitted to the LDA module
for processing. The LDA module is a micro-computer-based data
processor with embedded A/D converters. This module processes
the signals from the LLS based on a proposed lightning-locating
theory. The CG/CC logic circuit in the LLS module provides
information regarding the lightning impulses. Meanwhile, the
LDA provides the time, location, strength and flash type for the
lightning events to an on-board memory and then transmits these
data to the LDD module via the Internet. The LDD module is a
software pack installed on a PC. The LDD module provides realtime lightning monitoring and off-line data playback functions, as
well as the visualization, statistical analysis and permanent
storage of lightning events.
High-speed video cameras are used to record the lightning
flash propagation. The temporal upward and downward leaders’
progress can be captured by using a high-speed video camera. One
kind of high-speed video camera can capture 5000 f/s at the
highest resolution of 1024 800 [79,87].
Instead of using a high-speed video camera, a high-speed
charge-coupled device (CCD) video camera could be deployed to
capture the development process and physical form of lightning
discharges [88]. Given the high frame rate and large resolution, the
lightning can be observed clearly. The recording of a high-speed
CCD video camera system was used to synchronize the picture
output with electromagnetic field waveform data. The synchronization accuracy between the picture output from the high-speed
CCD video camera and the electromagnetic field waveform data
can be nearly 100 ns.
A low-speed video camera, such as one that captures 50 f/s,
could be also utilised in observing lightning, but its function may
be limited to detecting significant upward lightning strikes on the
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tall telecommunication antenna and locating low-current lightning strikes [79].
A recent research was carried out by Yang et al. [89] worked on
to integrate Geographic Information System (GIS), databases,
Remote Sensing (RS), visual reality, 3D visualisation, spatial
information grid and computer networking to develop a Digital
Lightning Prototype System (DLPS) from the perspective of the
digital earth.
6.2. Methods of data analysis
Data analysis is important in obtaining conclusions from
research. Therefore, several methods have been used to analyse
data regarding lightning events. LabVIEW 8.5 software was used to
collect, calculate and analyse lightning data by developing a virtual
instrument. Using a statistic function provided in LabVIEW, the
time maximum for each signal from the receiver – a VHF antenna
(2 MHz cut-off frequency) and a microphone – was determined.
The arrival time difference (time delay) between the electric field
and acoustic signals was calculated. Meanwhile, the noise in the
captured signals was eliminated digitally by using a high pass filter
(HPF) in LabVIEW [60,90].
Another virtual instrument developed using LabVIEW was used
to monitor lightning activity within a 25 km radius from Pekan
[35,91]. The incoming lightning strikes’ signals were analysed by
using various statistical functions and techniques. Afterwards, the
lightning strike locations were estimated by calculating the ratio of
EMF to EF. The Pekan Lightning Detection System (PLDS) system
analyses the raw lightning data and transforms it into real-time
lightning data [92]. The change of colour to either yellow or red in
the border of the window is used to provide an alert. A yellow alert
will be triggered when the strike is within a 50 km radius, while a
red alert will be triggered when the strike is within a 10 km radius
of the sensor location. A special ‘storm’ is depicted to indicate the
detected flashes. When one of these storms is shown, there is a
good chance that lightning will occur in that area. The threat
assessment window is a description of storm activity over the
previous 5–20 min. This window shows information about the
average position of all lightning flashes detected in the previous 5–
20 min. To calculate the placement of the coloured area in the
threat assessment window, the average range of all strikes in a
similar direction is used. Moreover, the strike rate graph shows the
rate of change in lightning activity at a glance. The window rate is
also analysed, and this analysis is shown in the form of six counter
displays. The six counters are the total rate counter, the strong
strike rate counter, the noise rate counter, the strike rate percentage change counter and the strike rate counter.
In addition to LabVIEW, Microsoft Visual Basic 6.0 was used to
develop a graphical user interface (GUI) that was intended to
record and count every time lightning struck a specific structure.
The GUI was connected with a database that was created using
Microsoft Access. The database consisted of two columns. The first
column is used to store the frequency of lightning current
discharge through a conductor, while the second column is used
to record the date and time of each discharge [81].
Another method of analysing the data used a numerical
simulation. The numerical simulation was developed to investigate
the relationship between AEF and cloud height. The partial
differential equation (PDE) toolbox in Matlab was applied during
this analysis to simulate the static electric field distribution in a
courtyard [20].
Furthermore, the data collected from lightning events can be
analysed using artificial intelligence techniques (AI), which consist
of fuzzy logic analysis, neural network analysis and neuro-fuzzy
analysis [93]. The fuzzy logic method can be used to analyse the
data with a multiple-input multiple-output (MIMO) system.
Because the neural network develops learning, the input-output
relationship can be used to determine the threat level in real time.
By using a neural network, the exact location of positive and
negative charges based on the AEF can be predicted. The neural
network consists of an input layer, a hidden layer and an output
layer [39,94].
Neuro-fuzzy analysis is derived from two reliable techniques:
fuzzy logic and neural networks. The output is usually presented
in polynomial form with input variables x and y. There are two
types of neuro-fuzzy modes: Sugeno and Mamdani. Neuro-fuzzy
analysis can be performed using several methods, such as mean
percentage error, root mean square error and mean squared error
[95].
The area of a lightning strike can be found based on latitude
and longitude. Analysis using the fuzzy logic method created a
graph of lightning level and also a graph of the number of
lightning incidences corresponding to the type of lightning. From
the results obtained, the lightning was mapped onto the Malaysian
map using Google Earth. The characteristics of the lightning were
represented by blue, purple and red icons to indicate low, medium
and high levels of current, respectively. Nevertheless, there are
some limitations when mapping lightning characteristics by using
Google Earth; the map merely displays the lightning current value,
the level of current and the region represented.
7. Discussions
It is difficult to determine the best sensors/methods for several
reasons, such as the following: (1) in the literature, system sizes,
working principles and sensor structures vary; (2) the efficiency of
sensors/methods vary; (3) their limitations vary; (4) there are
differences in location installation and (5) the hardware used in
the sensors/methods vary. Based on these difficulties, a summary
of the working principle of every sensor/method is provided. The
electric field mill (EFM) is based on the electromagnetic induction
principle, which uses a rotor and stator to expose and shield the
sensor plate. The working principle of this sensor is approximately
the same as that of a micromachined electrostatic field sensor,
which oscillates the shutter back and forth to measure the electric
field. The integrated electro-optic sensor uses light to drive the
sensor, while photon detection also uses a fast-pulse light signal to
obtain the lightning signal. The passive optical lightning sensor’s
working principle is based on detecting the radio frequency
emitted by lightning.
In lightning location networks, the magnetic direction finder
(MDF) measures the voltage signal on the loop; two antennas are
used in this method. The direction of the flash can be determined
by comparing the voltage signals from both antennas. The time-ofarrival (TOA) method also uses a minimum of two antennas; the
intersecting hyperbolas at the two stations are used to locate the
lightning flash. The TOA method uses the time difference between
sensings of the arrivals of the electric pulse emitted by lightning.
Ideally, the TOA method is more sensitive than MDF. On the other
hand, the interferometry method also can be performed using a
minimum of two antennas. It can locate the VHF pulse in order to
determine the direction of the source using a combination of
phase measurements.
In order to transmit the data, an effective method of transmission is crucial. The connection between the sensor/antenna and
the data acquisition system requires a cable, WLAN technology or
GSM–GPRS. In terms of length and coverage area, fibre-optic cable
is capable of operating over longer distances than coaxial cable.
Given the short distance between the sensor/antenna and the data
acquisition system used to monitor the signal onsite, the researchers preferred to use coaxial cable. The coverage area for WLAN can
M.A.B. Sidik et al. / Renewable and Sustainable Energy Reviews 48 (2015) 710–725
reach 20 km2, while GPRS-GSM can transmit information for
hundreds or thousands of kilometres via an Internet provider.
Fibre-optic cable has the highest transmission speed, around 100
to 2000 Mbps. Coaxial cable is only capable of reaching transmission speeds of 10 Gbps. WLAN can reach a high transmission
speed, but it is not as good as fibre-optic cable or coaxial cable,
because many factors limit WLAN speed, such as radio interference, physical obstructions of the line-of-sight between devices,
the distance between devices and multiple devices communicating over the network simultaneously. GPRS has a slower speed
than the other methods, having an average rate of 40 to 50 kps. In
terms of the installation process and cost, fibre-optic cable is
expensive and fragile. Coaxial cable is affordable, and its installation is easier than that of fibre-optic cable. The wireless method is
preferable because of the ease of connection. GSM–GPRS usually
has associated charges based on the volume of data sent.
Generally, data storage can be performed on a device or online.
The SD card and digital signal oscilloscope (DSO) are devices used
to store data. MySQL is the online form of storage in which data
are stored in a database. The SD card offers many types of memory
capacity, i.e., 4 GB, 32 GB, etc. Unfortunately, it can be exposed to
viruses and can be easily to lost or broken because it is small in
size. The DSO cannot store data permanently, because the device
has storage limitations. If the memory limit is exceeded, the oldest
data will be deleted. MySQL is a safer way to store the data. It is
typically easier to obtain the data online than to find the device on
which the data are stored. Currently, the development of worldwide multimedia has made it easier to acquire information online.
Observational data are important in monitoring behaviour,
events or physical characteristics. The iso-keraunic-level (IKL)
dynamic cloud model, SAFIR 300, and single-station lightninglocating system (S-LLS) are methods of collecting thunderstorm
data in order to estimate the location of strikes, lightning impulse
information and meteorological data. High-speed video cameras,
low-speed video cameras and high-speed charge-coupled device
video cameras are able to record the physical characteristics of
lightning.
Generally, the data analysis is used in discovering useful
information in order to support decision making. Some of the
methods are simple, while others are complex. For LabVIEW, the
analysis is related to graphical programming because it uses a
visual programming language. The program contains a block
diagram, a front panel and a connector panel, which contain the
graphical source code. It is very interactive compared to writing in
a traditional language. On the other hand, Microsoft Visual Basic
and Matlab have been used to develop a graphical user interface
(GUI). A GUI can be created interactively or programmatically [96].
Nevertheless, C/Cþ þ is the most common programming language
used to develop software applications. The artificial intelligence
method of neural networks is complicated because it includes a
multi-layer network that must be trained in order to obtain the
prediction results. On the other hand, fuzzy logic is much simpler
than neural networks, but its performance is lower. In order to
improve the performance of fuzzy logic, it can be combined with
neural networks. The combination of both methods increases the
complexities involved.
8. Conclusion
This paper reviews and summarizes lightning monitoring
systems according to their technologies and data acquisition
systems based on various academic journals. This paper provides
the working principal and structure of each sensor/method in
every lightning monitoring system that has been constructed.
Furthermore, it reviews the methods of data transmission, data
723
storage, data observation and data analysis. It is difficult to discuss
the details of every sensor/method because every sensor/method
has its own benchmarking. Therefore, this paper includes only a
general discussion of the structure and working principle of each
method/sensor, as well as the advantages and disadvantages of the
methods of transmitting and storing data, observational methods
and algorithms of the analysis methods. It is hoped that the
information in this paper can be a source of information for future
research.
Acknowledgement
The authors gratefully acknowledge the support extended by
Ministry of Higher Education (MOHE), and the Universiti Teknologi
Malaysia (UTM) for providing the seed money as for financial
support during the course of this investigation on the Research
University Grant (GUP), no. Q.J130000.2523.03H59.
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