1 CHAPTER 1 INTRODUCTION 1.1 PREAMBLE 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 2 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. 1.2 THE GENESIS OF THE THESIS 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 3 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. 1.3 HARNESSING THE PV ENERGY 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 modules. 4 1.4 LITERATURE SURVEY 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 5 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: 1.4.1 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) I ­ ½ § V IN s Rse · § V IN s Rse · ° ° I ph I OS ®exp ¨ ¸ 1¾ ¨ ¸ ° ° © N s Rsh ¹ © n N sVT ¹ ¿ ¯ (1.1) 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. 6 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). ­ ½ § V IN s Rse · ° ° I # I ph I OS ®exp ¨ ¸ 1¾ ° © n N sVT ¹ ° ¯ ¿ (1.2) 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 n N V ° s T © ¹ ° ¯ ¿ (1.3) 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 · 1¸ ¹ (1.4) 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 § V I N R ·§ · 1 exp ¨ mpp mpp s se ¸¨1 mpp ¸ n N V n N V s T s T ¹ © ¹© (1.5) 7 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, Voc § V · nN sVT ln ¨ mpp 1¸ Vmpp I mpp N s Rse nN V © s T ¹ (1.6) 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 (1.7) 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. 8 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 9 188.8.131.52 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 10 because during the periodical PV module’s voltage sweep, the power extracted will not be at the optimum. 184.108.40.206 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. 1.4.2 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) (1.8) where ‘k’ is a proportionality constant and ‘G’ represents the level of insolation. 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 11 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. 1.4.3 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 12 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 13 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 14 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 15 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. 1.4.4 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). dP dV d VI dV I V dI dV (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 dP dV 0 at MPP, Equation (1.9) can be written as 16 dI dV I V (1.10) In practice, the condition stipulated in the incremental conductance method, dP dV 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 17 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. 'P 'G (1.11) 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. 18 1.4.5 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 conditions. 220.127.116.11 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 conditions. 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. 19 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 microprocessors. 18.104.22.168 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 20 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. 22.214.171.124 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 21 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. 1.5 AIM AND CONTRIBUTIONS IN THE THESIS 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 22 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. 23 In this context the research has been mooted to include the following aspects in the developed techniques. The aspects are: 1. Complexity – It should be simple in construction and implementation. 2. Affordability – The implementation should cost less and hence affordable to almost everyone. 3. Dynamic performance – The response to dynamic variations in the atmospheric parameters should be fast and should have no divergence in tracking. 4. 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. 5. Computational requirements – The computational requirements should be less, thus enabling the use of low cost digital controllers. 6. 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, 1. A method to determine the Power Region in the I-V Characteristics is devised. 24 2. A Mathematical modelling of Power Region in the I-V Characteristics is empirically discovered. 3. Determination of Maximum possible rectangle corresponding to the maximum power point through online back ground sweeping technique is proposed. 4. 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. 5. Methods to tackle the effects of change in the levels of irradiation and temperature are given. 6. A Method to estimate the maximum power point through estimation process in two stages is proposed. 7. 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 techniques. 1.6 ORGANIZATION OF THE THESIS 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 work. 25 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.