Telechargé par hamza bousseta

09 chapter 1

Energy is an essential component for all social activities, which is
required for the manufacture of every goods and provision of all services. The
cost of energy is increasing now-a-days due to the fast depleting of fossil
fuels. Hence, a demand for low cost energy becomes an increasingly
important concern. Due to the demand for low cost energy and the growing
concern about the global warming and the harmful environmental eơects due
to the fossil fuels, such as greenhouse effect, air pollution etc., focus has been
turned towards the alternative energy sources that are cleaner, renewable, and
produce little environmental impact. Among the energy sources coming under
the purview of the alternative energy sources, the direct conversion of
photonic energy into electrical energy through photovoltaic cells shall be
regarded as a natural source of energy that is more vital, due to its abundance,
cleanliness, freely and distributed everywhere over the earth’s surface.
One of the important advantages of the photovoltaic cells is its lack
of carbon-di-oxide emission. It has been reported by de Brito et al (2013) that
by the end of 2030, the annual reduction rate of CO2 emission due to the use
of the photovoltaic cells may be around 2GTons/year, which is equivalent to
India’s total emissions in the year 2004 or the emissions of 300 coal plants.
Moreover it is reported by Tey & Mekhilef (2014), that the electricity
generated by the photovoltaic modules is an unstable energy source because
of its strong dependence on factors such as the solar irradiation level and the
atmospheric temperature. The currently accepted limiting conversion
efficiency as reported by Goetzberger et al (2003) for single junction solar
cells is about 31%. But the highest confirmed efficiencies of a Photovoltaic
(PV) cell and a module are reported as approximately 25% and 23%
respectively by Green et al (2012) for the crystalline silicon type. Petreus et al
(2011) have confirmed that the efficiency is also further influenced by the
load connected to the PV system. Hence in order to effectively utilize the
photovoltaic cells, techniques should be used to extract maximum possible
energy from them, at any atmospheric conditions. Therefore, invariably a
Maximum Power Point Tracking (MPPT) controller is needed in order to
ensure that the PV system is working at a higher efficiency.
Extraction of power from the PV modules becomes popular and
vital in the recent days as it provides a cheaper source of energy in the
situations wherein the energy from the other conventional sources becomes
costlier. At the same time the energy from the PV sources is environment
friendly too. However, harnessing the power from the PV modules is an
arduous task as the characteristics exhibited by the PV modules are non-linear
in nature. Also the solar irradiation and the atmospheric parameters such as
temperature, wind velocity etc. alter the PV modules characteristics, creating
a situation such that the extraction of the optimal energy from them turn to be
a gruelling task.
As a consequence, extensive researches have been undergoing since
long back to find methods and means to extract maximum possible energy
from the PV modules. Some of the methods, meant for optimal power
extraction, are simple, but inaccurate and less efficient, whereas some other
techniques though having better efficiency and accuracy are complex in
nature and may not be cost effective. Hence an intensive exploration and
research have been done with the consideration of developing methods which
should be cost effective, high efficient, accurate and simple in form.
The Electrical Energy coming from the PV modules called as PV
energy should be appropriately harnessed in order to improve the efficiency
of extraction. Because when the PV modules are connected directly to the
loads, the efficiency of power extraction from them is poor. Hence in order to
improve the efficiency of extraction, the power output from the PV modules
should be harnessed properly and hence the PV modules which are connected
to the loads are controlled either through a DC-DC converter or through an
inverter depends upon the nature of the load. When the PV modules are
connected through the DC-DC converter or standalone inverter, a single task
is imposed on the converter or the inverter, which is to optimize the power
output from the PV modules, whereas when the PV modules are connected
through the grid connected inverter, then the inverter has two tasks such as to
inject sinusoidal current into the grid and to optimize the power output from
the PV modules (Tsang et al 2013).
The main intension of interposing a power electronic circuit,
between the PV source and the load is to harness them in a well-coordinated
manner at all atmospheric conditions such that maximum possible power can
be extracted from the PV source. Such power electronic circuits, called as the
maximum power point trackers (Eltawil & Zhao 2013), when used to harness
the PV energy, in a properly sized PV system, increases the power output
from the PV sources by a margin of 15% to 30%. The aim of this thesis work
is to develop new methods to harness the PV energy efficiently from the PV
The technique of Maximum Power Point Tracking (MPPT) for
photovoltaic modules is one among the techniques which was researched in a
greater extent with different heuristic approaches. Ishaque & Salam (2013a)
and Lorente et al (2014) have reported that the MPPT methods vary in
complexity, types of sensors required, convergence speed, cost, range of
eơectiveness, implementation hardware, popularity, and in other respects.
Hence the MPPT techniques for PV modules proposed so far shall be
categorized in different ways according to the perception of the researchers.
Salas et al (2006) have categorized the different techniques of
MPPT in many ways. Of them the most appealing way of categorizing the
MPPT techniques is by two different methods as “indirect or quasi seeking”
and “direct or true seeking”. Also Ishaque & Salam (2013a) have categorized
nicely all the MPPT techniques in a broad way as conventional techniques
and soft computing techniques. In addition Ishaque & Salam (2013a) have
clearly stressed the importance of another category called the hybrid MPPT
due to the availability of the tremendous computing powers in the modern
microprocessors. Further Salam et al (2013) have even provided in a heuristic
way, the various categories of the soft computing methods used in the MPPT
techniques for the PV modules.
Whereas Masoum et al (2007) have categorized the MPPT
techniques proposed so far into four main branches as Load matching,
Computational, Perturb and Observation (P&O) and Intelligent techniques. It
is also reported by Weddell et al (2012) that the most of the methods proposed
to track the MPP are suitable for outdoor applications because those methods
consume large quiescent current and hence consumes more quiescent power
inherently. Hence Weddell et al (2012) have proposed a new method enabling
successful maximum power point tracking at indoors. This has further leads to
another kind of classification as indoor and outdoor MPPT techniques.
Recently de Brito et al (2013) have given a picture about the
evaluation study of the various existing MPPT techniques. It is true as stated
by Salam et al (2013) that providing a fair benchmark is very difficult because
different researchers have used dissimilar PV systems having diverse rating,
technology, size etc and even at different atmospheric conditions. In this
scenario the research made by de Brito et al (2013) provides a reasonable
standard to various researchers for comparing the efficiency of the MPPT
technique devised by them with the popular MPPT techniques. Hence the
literature survey pertaining to this research work is classified in terms of
different MPPT techniques which are very popular and presented in the
following sub-headings:
Fractional Open Circuit Voltage (Vfrac) Technique
Castaner & Silvestre (2002), delineates that the I-V behaviour of a
PV module can be appropriately modelled analytically and can be represented
by a characteristic equation as given in Equation (1.1)
½ § V IN s Rse ·
§ V IN s Rse · °
I ph I OS ®exp ¨
¸ 1¾ ¨
° © N s Rsh ¹
© n N sVT ¹ ¿
where ‘I’ is the PV module’s terminal current, ‘Iph’ is the photo-generated
current, ‘Ios’ is the reverse saturation current, ‘V’ is the PV module’s terminal
voltage, ‘VT’ is the volt equivalent to temperature, ‘Ns’ is the number of PV
cells connected in series, ‘Rse’ is the series resistance of the PV cell, ‘Rsh’ is
the shunt resistance of the PV cell and ‘n’ is the diode ideality factor.
Generally, according to Gao et al (2013), for a typical silicon based
PV module the magnitude of ‘Rsh’ is large enough such that almost for all
practical applications the third term in Equation (1.1) can be neglected, and
can be well approximated to an expression given in Equation (1.2).
§ V IN s Rse · °
I # I ph I OS ®exp ¨
¸ 1¾
© n N sVT ¹ °
Based on the same facts described above, the P-V relationship of the
PV module can also be approximated to an expression as given in
Equation (1.3),
§ V IN s Rse · ½
P VI ph VI OS ®exp ¨
¸ 1¾
s T
¹ °
Under open circuit conditions, the Equation (1.2), describing the I-V
behaviour of the PV module can be rearranged to give a good approximation
for the open circuit voltage ‘Voc’ of the PV module, as given in
Equation (1.4),
§ I ph
Voc # n N sVT ln ¨
© I os
Taking the first derivative of the Equation (1.3) with respect to the
PV voltage ‘V ’ and equating the result to zero at the maximum power point
(MPP), the condition for obtaining maximum power from the PV module can
be derived, which is given in Equation (1.5).
I ph
I os
§ V I N R ·§
1 exp ¨ mpp mpp s se ¸¨1 mpp ¸
s T
s T ¹
where ‘Vmpp’ is the PV module’s voltage at MPP and ‘Impp’ is the PV module’s
current at MPP.
Substituting the condition, Equation (1.5), for extracting maximum
power from the PV module in equation (1.4), the relationship between the
voltage at MPP ‘Vmpp’ and the open circuit voltage ‘Voc’ can be obtained as,
§ V
nN sVT ln ¨ mpp 1¸ Vmpp I mpp N s Rse
© s T
It is obvious from the expression in Equation (1.6), that the optimal
voltage of the PV module is influenced by its open circuit voltage.
The expression given in Equation (1.6) shows a complex
relationship between the open circuit voltage of the PV module and its voltage
at MPP. It has been found by Masoum et al (2002) and Eltawil & Zhao
(2013), that there exhibits a near linear relationship between the ‘Voc’ and
‘Vmpp’ under varying temperature and irradiation levels. This near linear
relationship was given in Equation (1.7),
Vmpp k uVoc
where ‘k’ is a proportionality constant whose value is always less than 1, and
according to Esram & Chapman (2007) and Hohm & Ropp (2003), the value
of ‘k’ varies between 0.73 and 0.8.
Further Hohm & Ropp (2003) has stated that the ratio ‘k’ is not
constant, but in fact depends on the temperature and irradiance and varies by
as much as 8% (absolute) over the entire range of conditions.
The principle of operation of the MPPT based on the fractional open
circuit voltage technique is illustrated in Figure 1.1. In this technique,
periodically with a typical time interval ranging up to 30 sec (Enslin et al
1997), the PV module is disconnected from the circuit and the open circuit
voltage is measured. Thereafter the optimal voltage is computed using the
relation given in Equation (1.7), and then the PV module is forced to operate
at the computed optimal voltage. In fact the power delivered by this technique
is deviated from the true MPP by a small margin. This is due to the fact that
the factor ‘k’ varies due to change in temperature and irradiation (Esram &
Chapman 2007, Salas et al 2006, Yang et al 2012 and Hohm & Ropp 2003)
and accounts for a power loss (Weddell et al 2012 and Mei et al 2011). Also
in addition to the power loss due to the factor ‘k’, Mei et al (2011) has
reported that some more power is lost due to the periodical disconnection of
the PV module to measure the open circuit voltage.
Figure 1.1
Principle of operation of fractional open circuit voltage
MPPT technique
Difficulty in the Vfrac technique in tracking MPP
In order to continuously track the optimal power point using the
fractional open circuit voltage technique, the PV panel is disconnected
periodically to obtain the open circuit voltage, which involves some power
loss during the period of disconnection (Weddell et al 2012, Enrique et al
2010, Moradi et al 2011, López-Lapeña et al 2010 and Esram & Chapman
2007). This periodicity influences the tracking efficacy and the system
efficiency. When the periodicity is less the tracking efficacy is good but the
system efficiency becomes poorer as the PV panel is frequently disconnected
from the load.
When the periodicity is higher the tracking efficacy becomes poorer
because during that period if the irradiation level gets changed, between the
instants of sampling the open circuit voltage, the PV module’s operating point
also gets changed and the operation is diverged away from the optimal
operating point. Hence the PV module is operated at a lower efficiency until
the next sampling of the open circuit voltage is obtained at the end of the
current period. Therefore selecting the periodicity becomes a difficult task
and a compromise has to be done between the tracking efficacy and the
system efficiency. Typical sampling interval of the PV module’s open circuit
voltage and the update of the new MPP in the fractional open circuit voltage
MPP technique is in the range from 15mSec to 30 seconds (Enslin et al 1997,
Salas et al 2006 and Tafticht et al, 2008).
Also Esram & Chapman (2007) have clearly expressed that the
value of ‘k’ is no longer valid if the PV module is partially shaded caused by
the nearby vegetation’s or human made constructions which further causes
multiple local maxima and to tackle such situations Bekker & Beukes (2004)
have proposed a method to sweep the PV module voltage to update ‘k’. This
obviously adds complexity to the implementation and incurs more power loss
because during the periodical PV module’s voltage sweep, the power
extracted will not be at the optimum.
Advantages of Vfrac technique
Though the relationship given in Equation (1.7) is an approximate
one, Esram & Chapman (2007) and Enrique et al (2010) have stated that this
method is very easy and cheap to implement as it does not necessarily require
any high speed digital computing devices. Also it is discoursed on the
advantages of the open circuit voltage technique as what reported by Yu GJ et
al (2004) that even though ‘Vfrac’ technique neglects the effect of the
insolation and temperature of the PV module, it is more eơective at low
insolation levels than some other popular MPPT techniques such as the P&O
method and the Incremental Conductance method.
Fractional Short Circuit Current (Ifrac) Technique
It is shown experimentally by Matsuo & Kurokawa (1984) and
Noguchi et al (2002) that the PV module’s current at maximum power point
was proportional to its short circuited current under various irradiation
conditions. This relationship was given as
I MPP (G)= k×I SC (G)
where ‘k’ is a proportionality constant and ‘G’ represents the level of
However it has been found that the value of ‘k’ is not a constant
rather its value is varying with respect to temperature as well as with the level
of irradiation. It has been reported by Masoum et al (2002) and Hohm &
Ropp (2003) that the range of ‘k’ for fractional short circuit technique is
between 0.64 and 0.85. Generally, Subudhi & Pradhan (2013) suggest that the
value of ‘k’ can be calculated by analyzing the PV system at wide range of
solar radiations and temperatures.
As the fractional variable ‘k’ for the governing Equation (1.8) of the
Ifrac technique depends on the atmospheric parameters in an unpredictable
manner, this method also does not track the true MPP all the times. Moreover
the fractional short circuit current technique requires update of MPP
frequently; this in turn requires frequent disconnection of PV module from the
load and hence results in poor efficiency as a consequence of disruption of
power flow from the PV module to the load. Notwithstanding the reported
difficulties mentioned above the method of implementation of this technique
is quite simple and requires less cost. One of the disadvantages of this
technique in comparison with the fractional open circuit voltage technique is
that, as reported by Masoum et al (2002), the hardware requirement for
fractional short circuit method is more complicated on account of the
measurement of PV modules short circuit current.
Also it is dissertated by Xiao et al (2007a) that as the magnitude of
photovoltaic current changes dramatically with the level of irradiation, the
transient response of the MPPT tracker implementing the fractional short
circuit current technique may occasionally cause the photovoltaic current to
get saturated at the short circuit current. Due to this saturation, the power
output from the PV module drops significantly as the PV voltage drops on
account of the non-linear characteristic of the PV source. This should be
avoided otherwise the stability of the system will be under trouble.
Perturb and Observe (P&O) Technique
For obtaining maximum possible power from the PV module, the
operating point should be positioned at an unique point in the I-V
characteristic enabling the PV module to deliver maximum power at
particular atmospheric conditions. The operating point of the PV module shall
be moved to the unique point corresponding to the maximum power by
adjusting one of the operating variables either the PV voltage or the PV
current. Many researchers like Wasynczuk (1983), Koutroulis (2001), have
taken the PV voltage as the adjusting parameter to reach the MPP for the
prevailing atmospheric conditions by monitoring the change in the power
delivered. Such a technique of tracking the maximum power point by
adjusting one parameter (PV voltage) and monitoring the desired parameter
(PV power) is called Perturb and Observe technique. The P&O technique is
considered to be an important control method in PV systems as it has
inherently a simple feedback control structure.
The P&O method has the capability to converge to the maximum
power point, when the incident solar radiation (insolation) does not vary with
time rapidly. However when the insolation varies at a rapid rate randomly, the
P&O method, as reported by Wasynczuk (1983) and Femia et al (2005), gets
confused during those time intervals and fails to track adequately the MPP.
The reason for such failure as what reported by Hussein et al (1995) is due to
its inability to discern whether the change in the PV module’s power is owing
to the perturbation in PV voltage or the change in the atmospheric conditions.
This inability is caused because in the P&O technique the change in the PV
power is considered to be as a result of perturbation in the PV module’s
voltage only. Even though the P&O technique has the capability to converge
to the MPP, it seldom or never settles down at the MPP.
Two versions of the hardware implementations of the P&O
techniques are available such as analog and digital versions. The analog
version of the P&O technique as suggested by Wasynczuk (1983), consists of
an integrator to integrate the instantaneous module’s power over a fixed
interval of time, a track store which stores the past values of the module
power, an accumulator which stores the control setting etc. As all the above
said analog circuits are subjected to the concept of drift due to the degradation
or ageing process, the analog version of P&O implementation may possibly
diverge from the real MPP.
In the case of digital version of the P&O technique the concept of
drift shall be almost disregarded but as reported by Mamarelis et al (2014),
due to the quantization effect and numerical approximation, the performance
of the P&O algorithm is affected. Though the digital version has some
limitations due to digital data manipulations, it is still preferred over the
analog version of the P&O technique because it can be implemented in cheap
digital devices assuring high robustness and good MPPT efficiency.
It is also reported by Liu et al (2008), Femia et al (2005) and
Latham et al (2013) that at steady state, the oscillations in the operating point
of the PV module are always present in the P&O technique especially, as
pointed out by Yu GJ et al (2004), in the cases of constant or slowly varying
atmospheric conditions. These oscillations cause a small amount of power
loss (Tey & Mekhilef 2014) thereby reducing the extraction efficiency. The
cause for such oscillations at steady state is that the size of perturbation can
never be so small and can have only discrete values; hence the operating point
will cross the MPP while tracking when the operating point approaches
towards MPP from either side of it and oscillates thereafter.
The Perturb and Observe technique with fixed step size normally
called as classical P&O or basic P&O algorithm is illustrated in Figure 1.2. In
this technique, at every time step the PV module’s terminal voltage is
perturbed and the PV power is monitored. If the PV power is increased in the
current time step, then the direction of perturbation in the subsequent time
step will be unchanged otherwise the direction of perturbation should be
reversed. In this way finally the MPP will be reached and crossed. Thereafter
the direction of perturbation will be continuously gets reversed on account of
the control algorithm explained before, for every two time steps, and hence
the operating point oscillates around the MPP between point A and B as
shown in Figure 1.2.
Several attempts have been proposed to enhance the performance of
the P&O algorithm in order to reduce the number of oscillations around the
MPP at steady state, but it is reported by Femia et al (2005) that the efforts
unfortunately have made the dynamic response of the algorithm sluggish
when the atmospheric conditions are changing and lowers the algorithm’s
efficiency during the hazy days. Though as what commented by Tsang et al
(2013) that the step size for the search in P&O technique aơects the rate of
convergence of the MPP tracking, Petrone et al (2011) have ratified that the
P&O method may fail to produce good performance under rapidly changing
atmospheric conditions.
Figure 1.2 Principle of operation of P&O technique
Femia et al (2005) have proposed an optimization technique for the
P&O method, wherein, in order to limit the drawbacks found in classical P&O
technique, the MPPT parameters of the P&O technique were customized to
the dynamic behaviour of the specific converter adopted.
Incremental Conductance (INC) Technique
In order to avoid the drawbacks present in the P&O technique of
MPPT, a new technique having excellent dynamic behaviour was proposed by
Hussein et al (1995). In this technique the PV module’s terminal voltage is
continuously adjusted according to its value relative to the maximum power
point voltage. The idea of incremental conductance technique shall be
appreciated from the P-V characteristic of a PV module illustrated in
Figure 1.3. As can be seen from the Figure 1.3, the derivative of the power
delivered by the PV module with respect to its terminal voltage gets vanished
at the maximum power point because the tangent at the MPP of the P-V
characteristic is horizontal. It is also appreciated from the Figure 1.3 that at
the left of the MPP the rate of change of PV power is positive while at the
right side of the MPP the rate of change of PV power is negative.
These contentions can further be expressed in terms of PV module’s
voltage and current which is quite explicable by the expression in
Equation (1.9).
where ‘I’ is the PV module’s terminal current, ‘V’ is the PV module’s
terminal voltage, and ‘P’ is the PV module’s output power.
As the quantity
0 at MPP, Equation (1.9) can be written as
In practice, the condition stipulated in the incremental conductance
0 or Equation (1.10) seldom occurs. This is due to, as stated
by Hussein et al (1995), the approximations made in computing dI and dV .
Hence a small marginal error, as reported by Hussein et al (1995), Wu et al
(2003) and Esram & Chapman (2007), is incorporated in the control algorithm
for effectively implementing the incremental conductance technique.
Figure 1.3 Concept under lying incremental conductance technique
Esram & Chapman (2007) have stated critically, that the speed of
tracking is determined by the increment size. With a larger size of increment
the tracking of MPP is rapid, but unfortunately introduces oscillations at the
MPP and then behaves similar to the P&O technique. Therefore a trade-off
should be made in choosing the size of voltage increment between the
dynamic performance and the steady state oscillations.
To solve these problems, a modi¿ed INC MPPT with variable step
size was proposed by Liu et al (2008). In this modified INC technique the step
size is automatically tuned according to the inherent PV module’s
characteristics. The control algorithm increases the step size when the
operating point is far away from the MPP and as the operating point has
approached closer to the MPP, the incremental size will be reduced to a small
value to minimize the oscillations thereby increasing the efficiency.
The expression which governs the control of variable step size
adopted by Liu et al (2008) to improve the dynamics and mitigate the
oscillations at MPP is given in Equation (1.11).
G (n) G (n 1) r O.
Where ‘į’ is the duty cycle, ‘n’ is the iteration variable, ‘ 'G ’ is the step
change in the duty cycle in the previous period and ‘Ȝ’ is the scaling factor
which is tuned at the design time to adjust the step size.
Generally it is reported by many researchers that the performance of
the incremental conductance MPPT technique is better than the P&O
technique. Elgendy et al (2013) with an in depth thorough research had
reported that the better performance of incremental conductance technique
over P&O technique is not due to lesser confusion of INC technique during
rapidly changing weather conditions as what many researchers believe, rather
it is more likely due to the better noise rejection and less confusion due to
system dynamics.
MPPT Based on Artificial Intelligence Techniques
Recently with the availability of tremendous computing power,
MPPT based on soft computing techniques becomes attractive. Ishaque &
Salam (2013a) have reiterated through their detailed research that soft
computing techniques provide opportunities for more robust and Àexible
MPPT schemes which are well suited to cater power during partial shading
MPPT with artificial neural network (ANN) techniques
When ANN is used to track the MPP of the PV module, typically
the input variables can be the parameters such as irradiance, temperature,
wind speed or any combination of these parameters and depending upon the
control variables the output variable can be either the PV voltage or the PV
current or the duty cycle. The ANN structure shall consists of usually three
layers called as input, hidden and output layers (Esram & Chapman 2007).
It was reported by Kulaksiz & Akkaya (2012) that compared to
conventional MPPT methods, ANN based methods can track the maximum
power point quickly and accurately in response to varying atmospheric
In certain cases Alabedin et al (2011) and Xu et al (2011) have used
ANN in conjunction with other conventional MPPT techniques as an
optimizer and in some other cases researchers Jie & Ziran (2011), Veerachary
et al (2003) have used ANN to work with other soft computing techniques
such as Genetic Algorithm (GA), Fuzzy Logic Control (FLC) or Differential
Evolution (DE) producing improved performance.
Also Kulaksiz & Akkaya (2012) have given an excellent method of
MPPT for the PV modules wherein the ANN was optimised with the help of
Genetic algorithm. But they have stated the concern that the ANN works
correctly only for the PV module for which it is trained along with the
processing burden involved in implementing the ANN methods.
When ANN is used to track MPP of the PV modules, Salam et al
(2013) have clearly reported that the ability of the ANN to track the MPP
depends on the hidden layer’s algorithm and also depends on the meticulous
and extensive training given to the networks. More over in order to respond
appropriately for various atmospheric conditions, the ANN should be trained
for months or even years together which becomes a laborious and a gruelling
task. It is also reported by Esram & Chapman (2007), Safari & Mekhilef
(2011) and Mei et al (2011) that the neural network needs specific training for
each PV module.
Also Salam et al (2013) have essentially reported that once a
particular ANN is trained and designed for a specific PV module or climate, it
may not respond accurately if employed in a different condition. More over
for better accuracy the ANN requires more hidden nodes leading to longer
computation time. Hence in general ANN is unsuitable for low cost
MPPT with fuzzy logic control (FLC) techniques
A significant advantage in FLC, according to Mathew &
Selvakumar (2001) and Chiu (2010), is that a mathematical model of the
system is not required or does not require an accurate mathematical model. As
a consequence Ishaque et al (2010 & 2011a), Ishaque & Salam (2013a), have
stated that the uncertainties such as un-modelled physical quantities, nonlinearity and unpredictable changes in operating point can be excellently
taken care of. However, it does require the designer to have some prior
knowledge about the qualitative response of the system.
In FLC the assignment of the linguistic variables is mainly
dependent on the user and their design is solely based on the user’s skill for a
specific problem. More over the technical skills of the engineer who is
designing the rule base and the computation of error dictates the effectiveness
of the method (Salam et al 2013). Hence the designer requires ample
experience and dexterity.
Due to the difficulty in changing the rules table dynamically, Salam
et al (2013) have reported that a standalone FLC cannot handle the situation
of partial shading effect on the PV modules.
MPPT with evolutionary algorithm (EA) techniques
As the EA technique is a stochastic method having the capability to
optimize non-linear and multi-modal objective functions based on search
optimization, it is well suited for solving problems possessing local and global
maxima. Hence EA techniques are inherently suitable for tracking MPP of the
PV modules under partial shaded conditions. Among the EA techniques, the
most popular methods are Particle Swarm Optimization (PSO), Genetic
Algorithm and Differential Evolution. It is reported by Miyatake et al (2007)
that the particle swarm optimization (PSO) has gained significant importance
owing to its simple structure and easy implementation.
In the PSO techniques random numbers are used to optimize the
problem. The main disadvantage of this approach according to Ishaque &
Salam (2013b) is that the randomness tends to reduce the ef¿ciency of
searching signi¿cantly. Furthermore, they have stated that when the number
of particles is less the unpredictability of solution due to randomness is more
severe. Hence in order to improve the possibility of converging to a feasible
solution the number of particles is increased. However, this can only be
achieved at the expense of computation time. Consequently if the time taken
to locate the Global Peak is too long, practical implementation of the
algorithm may not be possible.
Further more Lian et al (2014) have stated that the time required for
convergence in the PSO method of tracking will be long if the range of the
search space is large. In order to reduce the search space for the PSO, the PSO
technique is hybridized with P&O technique and hence the time required for
convergence is greatly improved.
Also as reported by Salam et al (2013), GA is usually used to
optimize other algorithms like ANN or FLC. They have further stated that
Differential Evolution is similar to Genetic Algorithm in terms of
optimization procedure and used for dynamic PV modeling and designing
MPPT techniques.
Generally according to Chekired et al (2014) and Ishaque & salam
(2013b), invoking the Artificial intelligence techniques such as ANN and
FLC for MPPT leads to higher level of intricacies, difficulty in
implementation and expensive in cost. It is also evident from many literatures
that invariably a high power DSP processor is used to implement the artificial
intelligence based MPPT techniques which normally incur more cost.
Significant number of publications by numerous researchers in the
field of Power extraction optimization has revealed the importance of Solar
Photovoltaic in the future electric power generation. Many techniques of
optimized power extraction from the PV modules were proposed. Some of
them are simple and cost effective but not having good efficiency in tracking
and while some other techniques are good in tracking efficiency however they
may be complex and cost more. Hence it is aimed to contemplate a new
method which shall be simple, cost effective, and good in tracking efficiency
moreover can easily be constructed by any interested engineer. In this aspect a
humble effort has been endeavoured to develop new methods for tracking the
maximum power point of the photovoltaic modules.
In any method of maximum power point tracking, the intension of
the control is to make the PV module to operate at a unique operating point
almost in the curvature region of the I-V characteristics. Most of the control
techniques observe the variable to be optimized, either the PV power or its
derivatives, and invokes the control such that the control variable, either the
PV voltage or the duty cycle of the converter employed, is adjusted to make
the PV module to deliver optimized power output. Many techniques are
employed for the adjustment of control variable which invokes simple to
more complex algorithms. Almost in many algorithms proposed hitherto, the
operating point is moved to the optimized operating point not in a single step
and further requires more computation power for the controllers used. More
over as these types of algorithms continuously observe the variable to be
optimized directly, the tracking accuracy is always good.
There are few techniques which will not observe the variable to be
optimized rather they periodically optimize the variable through some known
approximate linear relationships which relates some of the typical PV
parameters such as open circuit voltage, short circuit current etc. and the
corresponding variable at the maximum power point condition. Such
techniques though simple in implementation and cost effective, the tracking
efficiencies are not guaranteed to be good all the times.
In this context the research has been mooted to include the
following aspects in the developed techniques. The aspects are:
Complexity – It should be simple in construction and
Affordability – The implementation should cost less and hence
affordable to almost everyone.
Dynamic performance – The response to dynamic variations
in the atmospheric parameters should be fast and should have
no divergence in tracking.
Steady state performance – The operation of the proposed
techniques should eliminate any oscillations in the operating
point at steady state and the consequential power loss
associated with it, which is an important aspect in improving
the efficiency of power extraction from the PV modules.
requirements should be less, thus enabling the use of low cost
digital controllers.
Stability – The method should be robust in control.
The major research contributions in the works presented in this
thesis are precisely listed below in a concise manner. In the contributions,
A method to determine the Power Region in the I-V
Characteristics is devised.
A Mathematical modelling of Power Region in the I-V
Characteristics is empirically discovered.
Determination of Maximum possible rectangle corresponding
to the maximum power point through online back ground
sweeping technique is proposed.
Study on the effects of degradation on the tracking efficiency
is made and suggestions to maintain the efficiency
consistently over a long period are presented.
Methods to tackle the effects of change in the levels of
irradiation and temperature are given.
A Method to estimate the maximum power point through
estimation process in two stages is proposed.
A hardware model is developed, which is simple and
affordable as it uses a low cost microcontroller, in consequent
to the low computational requirements of the proposed
This thesis is organised systematically into seven chapters.
Chapter 1 discusses the genesis of the research work, the need for
harnessing the PV energy, presents a detailed literature work related to the
submitted research work and finally the aims and contributions of the research
Chapter 2 provides a detailed study about the various characteristics
of the PV modules, the effect of change in the level of irradiation and
temperature, the influence of the parasitic resistances and at last the need for
maximum power point tracking in PV power extraction.
Chapter 3 highlights the various mathematical models of the PV
module and its selection for the simulation study of the presented research
works in this thesis.
Chapter 4 proposes a novel maximum power point tracking
technique by endeavouring a detailed power plane analysis and discovers a
method to determine the power region and an empirical mathematical model
of the power region and elaborates the design and development of the
complete hardware used to implement the proposed methods of MPPT.
Chapter 5 introduces a new maximum power point tracking
technique with the help of analysing in detail the mathematical model of the IV characteristic of the PV model and discovers a technique of estimating the
maximum power point in two stages taking into account the influence of the
parasitic resistances on the behaviour of the PV modules.
Chapter 6 summarizes the complete contributions of the research
work presented in this thesis and gives suggestions for future expansion of the
proposed methods of MPPT.