- Email: [email protected]
Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/he
Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter Saban Ozdemir a, Necmi Altin b,*, Ibrahim Sefa b a b
Vocational School of Technical Sciences, Gazi University, Ostim, Ankara, Turkey Department of Electrical-Electronics Eng., Faculty of Technology, Gazi University, 06500 Besevler, Ankara, Turkey
article info
abstract
Article history:
In this study, a maximum power point tracking DCeDC quadratic boost converter for high
Received 14 November 2016
conversion ratio required applications is proposed. The proposed system consists of a
Received in revised form
quadratic boost converter with high step-up ratio and fuzzy logic based maximum power
28 January 2017
point tracking controller. The fuzzy logic based maximum power point tracking algorithm
Accepted 26 February 2017
is used to generate the converter reference signal, and the change in PV power and the
Available online xxx
change in PV voltage are selected as fuzzy variables. Determined membership functions and fuzzy rules which are design to track the maximum power point of the PV system
Keywords:
generates the output signal of the fuzzy logic controller's output. It is seen from MATLAB/
Quadratic boost converter
Simulink simulation and experimental results that the quadratic boost converter provides
MPPT
high step-up function with robustness and stability. In addition, this process is achieved
Fuzzy logic controller
with low duty cycle ratio when compared to the traditional boost converter. Furthermore,
PV system
simulation and experimental results have validated that the proposed system has fast response, and it is suitable for rapidly changing atmospheric conditions. The steady state maximum power point tracking efficiency of the proposed system is obtained as 99.10%. Besides, the output power oscillation of the converter, which is a major problem of the maximum power point trackers, is also reduced. © 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction PV based electricity generation is increasing exponentially every year because of some reasons such as reduction of fossil fuel reserves, negative effects on environment of fossil fuels and increasing awareness on environment. In parallel to this development, the researchers are concentrating on developing new and advanced PV system technologies [1,2]. Mainly studies are focused on two major topics in order to obtain the maximum benefit from the PV system investment. The first research topic is designing new, high efficiency and low cost
PV cells and modules, and contains studies on modules structure and materials. The second research topic covers studies on power electronics converter topologies and their control techniques. Different converter topologies and control strategies have been proposed for this aim [1e9]. PV panels generate specific power at certain operation conditions. The PV output voltage and current vary with environmental effects such as the solar irradiation, the ambient temperature, the pollution of the PV module surface, shadowing etc. As it is known, environmental conditions vary seasonally and on a daily basis. If these parameters change, also the amount of produced power changes. Therefore, the
* Corresponding author. Fax: þ90 312 212 13 38. E-mail addresses: [email protected] (S. Ozdemir), [email protected] (N. Altin), [email protected] (I. Sefa). http://dx.doi.org/10.1016/j.ijhydene.2017.02.191 0360-3199/© 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
2
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
PV output parameters, according to the changing environmental conditions, must be continuously monitored. In addition, the generated power form a PV module is also related with the load level. Consequently, the PV module has nonlinear power versus voltage (PeV) and current versus voltage (IeV) characteristics and there is a unique operation point on these characteristics that provide the possible maximum power. This point is called as maximum power point (MPP) [1,2]. Since, the load level of the practical PV system and environmental conditions such as solar irradiation and ambient temperature continuously change, a proper control algorithm and power converter should be used to control the operation point and to keep the system at MPP. This control algorithm is called as maximum power point tracking (MPPT) algorithm. Many algorithms have been suggested for MPPT action [2e8]. In some studies, these control algorithms are grouped as online and offline methods. Some researchers group these methods as seeking and true seeking methods, while others group them as direct and indirect methods. The fundamental difference between these two groups is related to performing way of the MPPT process [3,4]. Online methods measure some parameters form the PV system continuously and control the system to track the maximum available power according to these measured data. Besides, offline methods use some predefined formulas, measurements or tables to perform same action. Although offline methods are usually fast, online methods perform more realistic MPPT process than offline methods. The most well-known online methods are incremental conductance (IC) and perturb and observe (P&O) techniques [1e4]. The fuzzy logic control based methods are the new approach, and they have become popular in recent years. Among the different intelligent controllers, fuzzy logic controller (FLC) stands out with its simple structure [5]. Furthermore, other artificial intelligence methods such as artificial neural networks, genetic algorithms, particle swarm optimization are also used in MPPT studies [6]. Soft switching MPPT converters are also proposed to achieve higher total system efficiency [9]. The output voltage of the PV panel is usually lower than required in typical energy applications such as motor drives, inverters, etc. Therefore, this voltage level must be increased for these type of applications. This required higher voltage level may be accomplished by series connection of PV panels. However, the number of series-connected PV panels must be within certain limits in practice due to some limitations such as PV voltage isolation, efficiency, shadowing effect, etc. On the other hand, conversion ratio of the conventional DCeDC converters is usually not suitable for the required voltage level of the aforementioned inverters for PV applications. (The conversion ratio is defined as the ratio of the output voltage to the input voltage.) Moreover, it is well known that, in conventional DCeDC boost converters, increasing duty cycle decreases the stability and increases the control difficulty. Therefore, while output voltage of boost converter increases exponentially with duty cycle, in practice the voltage conversion ratio between output and input voltage of the converter is recommended to be selected as a maximum four [10]. Although, another alternative to increase the conversion ratio is using the isolated DCeDC converter topology, this structure causes some problems such as cost, complexity, etc. [11]. Different DCeDC
converter topologies with high voltage step-up capability have been investigated. The combination of the conventional boost converter with switched capacitors have been proposed to provide high conversion ranges. In this system, the output voltage level is related to the number of capacitors used in the circuit. However, voltage regulation action decreases the efficiency of the converter dramatically. Therefore, this topology is suitable and provides high efficiency, if an additional converter is used for voltage regulation [12]. In addition, the power switch suffers from high charge current. The DCeDC multilevel boost converter topology is proposed to overcome this drawback. This topology also combines the boost converter and switched capacitor action. The boost converter charges several capacitors in series with its output (same) voltage [13]. Thus, output voltage can be easily controlled with a number of series connected capacitors. Although this structure is very suitable to supply neutral point clamped multilevel inverters, the requirement that output capacitors should provide the whole load current limits its usage. The coupled-inductor technique is also used to obtain high step-up converter [14]. However, the efficiency of this technique is low, and the leakage-inductor energy of the coupled inductor will cause voltage spike on the switch and increase switching losses [15]. Active and passive clamp circuits are utilized to recycle the leakage inductor energy, but clamp circuits increase the cost of the system. High step-up voltage gain can also be achieved with two cascaded boost converters. But this topology requires two controllers and two active switches. The quadratic boost converter (QBC) which is structurally similar to cascaded two boost converters has been proposed to provide high voltage conversion ratio. The QBC circuit is given in Fig. 1. The output voltage of the QBC is given as a quadratic function of the duty cycle of switching signal [16]. Since the QBC has only one active switch, additional driver circuit requirement is removed and more reliable and efficient converter is obtained. Therefore, the QBC is used in several applications where high voltage conversion ratio is required such as power factor correction applications and PV applications [17e19]. The output voltage of the fuel cell or PV module is usually low, and this low voltage should be increased to supply conventional AC loads or to export generated energy to the grid. Therefore, a robust, reliable and high efficiency converter design with high voltage conversion ratio is an important requirement. Although some studies have been presented on QBC control, the number of studies on MPPT quadratic boost converters is limited and a few simulation studies have been proposed [17,20,21]. In this study, a DCeDC quadratic boost converter with MPPT capability for PV systems which requires high voltage
Fig. 1 e The PV supplied quadratic boost converter.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
step-up ability is proposed. The proposed converter contains high conversion ratio QBC and the FLC based MPPT algorithm. The FLC has two inputs and one output. The change in output power (dP/dt) and the change in output voltage (dV/dt) of the PV system are selected as inputs of the FLC, and change in duty cycle is selected as the output variable of the FLC. Therefore, the FLC based MPPT algorithm uses principles of the IC algorithm, but it has adaptive nature and variable step size advantages. The duty cycle of the QBC is obtained by integrating the output of the FLC based MPPT algorithm. Hence, the operation point of the quadratic boost converter is adjusted according to the PV system parameters. Results of MATLAB/Simulink simulations and experimental studies show that, the QBC provides high voltage conversion ratio even with low duty cycle values, and ensures robust and stable operation. Additionally, the simulation and experimental results indicate that, the MPPT efficiency is obtained as 99.10% and the oscillation of the converter output power at the MPP is very small. Besides, the proposed converter has fast response and reaches the MPP in 180 ms. It is seen that, the proposed converter and MPPT method is convenient for quickly changing atmospheric condition.
The quadratic boost converter Quadratic converters can be implemented as buck or boost type. In the past literature, it is possible to see a variety of these structures [22e24]. Also some control algorithms are
3
proposed in recent studies [25e27]. The quadratic converter is implemented by two series-connected converter with elimination of the second switch. Although the conversion ratio of the converter is same as the cascaded converter, the number of components and system cost are lower than the traditional cascaded converter. However, it can be noted as a drawback that, the efficiency of this converter is lower than the conventional buck or boost converter [22]. But, if the conventional DCeDC boost converter voltage gain increases, which means that it requires high duty cycle level, then the efficiency of the converter will drop dramatically. In addition, the voltage stress on the switch will also increase [28,29]. Furthermore, high conversation ratio with the conventional converter causes electromagnetic interference due to the high duty cycle level [30]. So, the QBC seems better solution for high step-up applications. The voltage stress on power switch and efficiency values of the QBC are within the acceptable levels due to lower duty ratio values while the voltage conversion ratio is high. The schematic diagram of the quadratic boost converter is shown in Fig. 1. As seen from the converter structure, it looks like to two cascaded boost converters. The circuit analysis can be explained according to Fig. 1. If the controlled switch S1 turned on (ON state), then D1 and D3 diodes pass to the OFF state. In this situation, input supply current flows L1 and D2. In this condition inductor L1 gathers energy from the power supply and inductor L2 gathers energy from capacitor C1. At the same time, load supplied by the output capacitor C2. After that, the controlled switch turns off (OFF state). In this condition, states of diodes are completely
Fig. 2 e The quadratic boost converter circuit a) When S1 is ON; b) When S1 is OFF. Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
4
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
where u is the control signal which is “1” when S1 is turned on (ON state), and “0” when S1 is turned off (OFF state). Here, i0 is used as load disturbances. In steady state conditions, all the derivative terms are equal to zero. Eq. (5) is obtained by substituting the control signal D instead of u in Eqs. (1)e(4): vc1 vc2 1 : ¼ ¼ vpv vc1 1 D
(5)
Finally, the conversion ratio of the converter (M(D)) can be obtained as given below:
Fig. 3 e The one diode model of the PV cell.
MðDÞ ¼
contrary; D1 and D3 are ON state, and D2 is OFF state. At the same time, C1 and C2 capacitors are charged by L1 and L2 inductors, respectively. In addition, inductors supply the load energy demand. Circuit diagrams for both operation conditions are depicted in Fig. 2. The equation of the converter conversion ratio can be obtained from differential equations obtained according to the control signal [31]. diL1 vpv vc1 ¼ ð1 uÞ dt L1 L1
(1)
diL2 vc1 vc2 ¼ ð1 uÞ dt L2 L2
(2)
dvc1 iL2 iL1 ¼ þ ð1 uÞ dt C1 C1
(3)
dvc2 vc2 iL2 i0 ¼ þ ð1 uÞ dt Rload $C2 C2 C2
(4)
vc2 vc1
vc1 vpv
¼
1 ð1 DÞ2
(6)
As can be seen from Eq. (6) that, the conversion ratio of the quadratic boost converter is quadratic expression and, this ratio provides a high conversation ratio even though lower duty cycle level is applied.
The PV model and MPPT algorithms The PV cell converts sun light directly to electrical energy. Different models and equivalent circuits for PV cell have been proposed to investigate its performance for different operation conditions. In addition, artificial neural network based models have been proposed for PV systems [32,33]. Similarly, various online and offline MPPT algorithms have been proposed for PV systems to obtain fast and high accurate MPPT [1e7].
The PV model PV cells can be modeled by a current source, a diode and a high-value resistor connected in parallel to the current source,
Fig. 4 e Proposed fuzzy logic controller based MPPT quadratic boost converter. Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
Fig. 5 e Basic illustration of the FLC. and low-value resistance connected in series to the all circuit. A diagram of this model called one diode model is given in Fig. 3 [34]. In addition, the double-diode model is also common in the literature. According to the model, the following equation can be written for the PV cell output current: qðVout þ Iout $RS Þ ðVout þ Iout $RS Þ 1 Iout ¼ IG I0 exp n:k:T RP
(7)
where, Iout is the cell output current, IG is the light-produced current, I0 is the cell darkness current, q is the electronic
5
charge value (1,6.1019C), Vout is the cell output voltage, n is the ideality factor, k is Boltzmann's constant (8.65 105 eV/K) and T is the cell temperature (K). As can be seen from the equation, the PV output current shows a nonlinear characteristic. Efficiency values of commercially available PV panels are around 9e21% according to their technology and materials [35]. This efficiency value is only applicable at a certain current and voltage values. The current and voltage values, that it is specific to the PV panel, are also related to atmospheric conditions such as temperature level, solar irradiation, etc. Because of this nonlinear relation, the PV current and voltage, so the output power should be continuously tracked to obtain maximum energy conversion efficiency at different atmospheric conditions. Otherwise, maximum available power cannot be get from the PV array.
MPPT algorithms Many MPPT algorithms have been suggested in past studies. Generally, offline methods estimate the maximum power point (MPP) using some mathematical equations, measurements and look-up tables, etc. Performances and accuracies of
Fig. 6 e a) Membership functions of input variables b) Membership functions of the output variable. Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
6
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
Table 1 e The rule base of the FLC. Change in PV power (dP/dt) Change in PV voltage (dV/dt)
NL NM NS Z PS PM PL
NL NL NL NL Z PS PM PL
NM NL NM NM Z PS PM PL
NS NM NM NS Z PS PS PM
Z Z Z Z Z Z Z Z
PS PM PM PS Z NS NS NM
PM PL PM PM Z NM NM NL
PL PL PL PL Z NL NL NL
these methods are related to many conditions, and usually decrease with time because of some factors such as pollution, aging, etc. Therefore, their efficiency values are lower than online methods while their responses are fast. The most wellknown examples of offline methods are pilot cell (PC), lookup-table (LUT), constant voltage (CV) and constant current (CV) methods. Online control methods have been performing MPPT process more realistic than offline methods. The PV voltage, current or power is continuously monitored and questioned whether the operation point is the MPP or not. The P&O technique, the IC technique, the ripple correlation control (RCC) method, the current sweep technique (CST), the parasitic capacitance method common on line methods. Recently, artificial intelligence based methods become popular such as FLC, artificial neural networks, genetic algorithm, evolutionary algorithm, particle swarm intelligence, etc [1e4]. The FLC provides high performance, even if the load and parameter changes and removes the system model requirement [36]. The main advantage of the FLC based methods over other online control techniques is its lower dependency of the mathematical model and system parameters. With this feature, it has been reported to be more suitable for the MPPT process especially for rapidly changing atmospheric conditions [37]. Most of these methods are effective under uniform insolation conditions. However, PV systems do not receive uniform insolation because of partial shading of PV modules. This
condition affects the PV system performance and PeV curve of PV system of has multiple peak points (some local peak points and a global peak point). The conventional methods do not guarantee MPPT under non-uniform insolation condition, thus some global maximum power point tracking methods have been proposed [38].
Proposed quadratic boost converter with fuzzy logic controller based MPPT algorithm QBCs and their topologies have been studying for applications require high voltage step-up. Although the FLC seems as a mature method, application of the FLC for MPPT is still hot topic and it is widely studied and discussed. In this study, the FLC based MPPT technique is applied with the QBC to combine advantages of high step-up nature of the QBC, and fast, robust and stable operation ability of the FLC based MPPT method. The block diagram of the proposed system including both power and control systems is given in Fig. 4. The proposed system consists of PV panels, the QBC and the FLC based MPPT unit. Basic structure of the FLC is shown in Fig. 5. As shown in the figure, the FLC consists of a fuzzifier, an inference engine, a knowledge base and a defuzzifier. The fuzzifier transforms the crisp input data that obtained from the real word to linguistic labels and membership values by using the knowledge base. After this process is completed, inputs are called fuzzy inputs and used in fuzzy inference engine to generate verbal judgments. The fuzzy inference engine uses the fuzzy inputs and “IF e THEN” rules that are in knowledge base to generate fuzzy outputs. The defuzzifier converts these fuzzy outputs to crisp values. The knowledge base consists of the input and output membership functions and rules which define the relation between inputs and outputs. In this study, a FLC with two inputs and one output is proposed to track the MPP of the PV system. Input variables of the FLC have been selected as change in the PV power (dP/dt) and change in the PV voltage (dV/dt). The output
Fig. 7 e PV model parameters.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
7
Fig. 8 e QBC output characteristic according to the different irradiation level.
variable of the FLC has been selected as change in the duty ratio (cD). The duty ratio of the QBC is obtained by integrating the output signal of the FLC. In one aspect, this approach is similar to IC algorithm which determines the MPP according to derivative of the PeV curve (at the MPP, dP dV ¼ 0), because of its input variables. However, its step size is variable. Input and output membership functions of the FLC are given in Fig. 6. Seven membership functions are determined for both input and output variables, and as it is seen, linguistic labels such as positive large (PL), positive medium (PM), positive small (PS), zero (Z), negative small (NS), negative medium (NM), negative large (NL) are used for these membership functions. The rule base has been determined as given in Table 1 to obtain fast tracking speed and reduced oscillations at steady state. The minemax inference method, which it is widely used and well-known method applied in this study. In this method, the minimum (min) operator is used as fuzzy implication function and fuzzified inputs are combined to obtain rule strength. The maximum (max) operator is used to combine outputs of the rules. The center of gravity defuzzification method which is given in Eq. (8) is used to obtain crisp values of the output: P mðzÞ:z : z* ¼ P mðzÞ
(8)
The center of gravity method calculates the center of gravity of the area determined by the fuzzy output. Its output varies continuously while inputs of the fuzzy logic controller is varying continuously.
Simulation and experimental results The proposed fuzzy logic controller based MPPT quadratic boost converter is modeled and simulated in MATLAB/Simulink program. The proposed system consists of the PV array, the quadratic boost converter and the fuzz logic controller based MPP tracker. Sharp ND-167U1 type PV panel is used in this study. The maximum power point voltage, current and power of this panel at 25 C are given as VMP ¼ 23 V, IMP ¼ 7.27 A and PMP ¼ 167.21 W, respectively. The PV array consists of two strings and each one has 3 series-connected PV panels. So, 1 kW total PV array power is provided. All parameters of the PV panel are given in Fig. 7. The figure also shows the IeV and the PeV curves of the PV panel for two different temperature levels. The proposed fuzzy logic controller based MPPT quadratic boost converter is simulated in different solar irradiation conditions to test the performance of the proposed MPPT converter system. The variation of the solar irradiation level is given in Fig. 8(a). It can be seen that figure, irradiation level is
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
8
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
Fig. 9 e PPPE interface program, the PeV curve of one string of the PV plant and the operation point.
changed 1000 W/m2 to 500 W/m2 at 0.15 s. Then, irradiation level is ramped up to 1000 W/m2 level with a specific positive slope. After that, the irradiation level is stepped-down to 500 W/m2 level at 0.45 s, and stepped-up to 1000 W/m2 level again at 0.6 s. Afterward, the irradiation level is ramped-down
to 500 W/m2 with a negative slope. Finally, the irradiation level is stepped-up to 1000 W/m2 level at 0.9 s. The FLC based MPPT algorithm calculates the required change in duty cycle to track the MPP of the PV system. The duty cycle of the QBC converter is generated by integrating the
Fig. 10 e Variations of the PV voltage (Ch. 1), the switching signal (Ch. 2), output voltage (Ch. 3), the PV current (Ch. 4), and the PV power (Ch. Math). Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
9
Fig. 11 e The performance of the proposed system at startup.
output of the FLC. The output power, voltage and duty cycle of the QBC are given in Fig. 8(b)e(d), respectively. As it is seen in Fig. 8(b), the output power of the QBC tracks the variation of solar irradiation which is directly related producible PV power level. This figure also indicates that the FLC based MPPT algorithm has good performance not only under slow changing and steady state conditions but also rapidly changing atmospheric conditions. It can be easily seen from Fig. 8(c) that, the ripple of the QBC output voltage is very low, and thus the power oscillations are also very limited. There is no unexpected behavior in this figure such as voltage spike, etc. Fig. 8(d) indicates the duty cycle of the QBC. The duty cycle always changes in order to track maximum power point of the PV array according to the solar irradiation level. The proposed FLC based MPPT quadratic boost converter is also implemented and experimental studies are performed. New generation SiC Mosfet (CAS300M12BM2 SiC based MOSFET module) is used in QBC circuit as an active switch. In addition, TMS320F28335 DSP is used to implement the FLC based MPPT algorithm and to perform analog to digital conversions and PWM generation. The experimental set up of the proposed system is supplied by MAGNA-POWER TDS III 600-8 model PV Power Emulator (PPPE). This PPPE is a DC power supply which can act like PV modules. In this study, the PPPE is programmed to operate as a PV plant which has two strings each has 3 series-connected Sharp ND-167U1 type PV panels through its interface program by using parameters given in datasheets such as Voc, Isc, Imppt, Vmppt, Tn,
Tw, operation irradiation level, catalogue irradiation level, irradiation coefficients, temperature, etc. In this study, two parallel connected PPPE are used to emulate the PV plant and supply the system (the interface program shows values of one string). Thus, same PV plant (around 1000 W) is used in both simulation and experimental studies. The PPPE generates a PeV curve according to the programmed conditions, and changes its output current and voltage according to defined PV characteristic. The irradiation and temperature levels can be changed with computer interface during the operation, and thus, different atmosphere conditions can be tested. The interface program of the PPPE is given in Fig. 9. The parameters of the used PV module, the PeV curve of one string of the PV plant and the operation point of the system for 1000 W/m2 irradiation level are shown in the figure. It is seen that the proposed FLC based MPPT algorithm tracks the MPP of the PV system and energy conversion efficiency of the PV system is improved. The MPPT efficiency is obtained from interface program as 99.10%. This value is higher than the values presented in literature such 98.84, 98.78, 97.12, 95.00%, and 90.8% [1,2,39e41]. In addition, the load and the solar irradiation levels are changed to test the dynamic performance of the proposed FLC based MPPT algorithm. The switching signal, the PV voltage, the PV current, the output voltage and the PV power waveforms for this transition are shown in Fig. 10. It is seen that, the proposed MPPT quadratic boost converter has fast
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
10
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
Fig. 12 e The PV voltage (Ch. 1), the switching signal (Ch. 2), output voltage (Ch. 3), the PV current (Ch. 4), and the PV power (Ch. Math) waveforms, a) for 500 W power level b) for 1000 W power level.
transient response and reaches the MPP in about 180 ms. The transient response of the proposed system is also visualized by PPPE interface program. The PeV curve of the PV system for actual solar irradiation level, the MPP and operation points of the system, the PV voltage, PV current and the PV power waveforms can be seen and tracked form interface program screens. The PPPE interface program screen for the startup moment is given in Fig. 11. As it is seen that, the proposed system determines and tracks the MPP of the system fast, and steady state oscillations are removed. Thus high efficient MPPT algorithm is obtained. Also the switching signal, the PV voltage, the PV current, the output voltage and the PV power waveforms for low power (about 500 W) and high power (about 1000 W) operation
conditions are given in Fig. 12. As can be easily seen from figures, the proposed system is stable for both operation conditions. Furthermore, screen of the PPPE interface program for variation of the solar irradiation conditions are given in Fig. 13. The solar irradiation level is changed from 500 W/m2 to 1000 W/m2 and vice versa periodically. The PeV curve of the PV system for actual solar irradiation level, the MPP and operation points of the system for both two irradiation levels, the PV voltage, the PV current and the PV power curves are depicted in the PPPE interface program screen. This test also prove that the proposed system has fast transient response, stable steady state operation characteristics. As it can be seen that, the power oscillation around the MPP is greatly reduced.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
11
Fig. 13 e The performance of the proposed system for change in solar irradiation level.
Conclusions In this study, a quadratic DCeDC boost converter with MPPT ability for high step-up ratio applications is proposed. The QBC provides high voltage conversion ratio with lower duty cycle lower than the conventional boost converters, and provide lower voltage stress and higher efficiency values. The QBC is also more simple, reliable and highly efficient than the cascaded converters because the second active switch is eliminated. The proposed system also includes a FLC based MPPT controller. The proposed FLC has two inputs and one output. The output power and output voltage of the PV panel are selected as input variables, and the change in duty cycle is determined as output variable of the FLC. The duty cycle of the QBC is obtained by integrating the FLC output. Results obtained from MATLAB/Simulink simulations and experimental studies indicate that the QBC ensures robust and stable operation with low duty cycle even though high step-up is required. The MPPT efficiency for steady state operation condition is obtained as 99.10%. The proposed FLC based MPPT
quadratic boost converter has fast transient response and reaches the MPP in about 180 ms. Additionally, the FLC based MPPT algorithm reduced the oscillation of the converter output power at the MPP.
references
[1] Altin N, Ozdemir S. Three-phase three-level grid interactive inverter with fuzzy logic based maximum power point tracking controller. Energy Convers Manag 2013;69:17e26. [2] Ozdemir S, Altin N, Sefa I. Single stage three level grid interactive MPPT inverter for PV systems. Energy Convers Manag 2014;80:561e72. [3] Salas V, Olias E, Barrado A, Lazaro A. Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems. Sol Energy Material Sol Cells 2006;90(11):1555e78. [4] Esram T, Chapman PL. Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transaction Energy Convers 2007;22(2):439e49.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
12
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
[5] Khateb AE, Rahim NA, Selvaraj J, Uddin MN. Fuzzy-logiccontroller-based SEPIC converter for maximum power point tracking. IEEE Trans Industry Appl 2014;50(4):2349e58. [6] Anurag A, Bal S, Sourav S, Nanda M. A review of maximum power-point tracking techniques for photovoltaic systems. Int J Sustain Energy 2016;35(5):478e501. [7] Bizon N. Global extremum seeking control of the power generated by a photovoltaic array under partially shaded conditions. Energy Convers Manag 2016;109(1):71e85. [8] Bouzelataa Y, Kurt E, Chenni R, Altin N. Design and simulation of a unified power quality conditioner fed by solar energy. Int J Hydrogen Energy 2015;40(44):15267e77. [9] Oncu S, Nacar S. Soft switching maximum power point tracker with resonant switch in PV system. Int J Hydrogen Energy 2015;41(29):12477e84. [10] Alcazar YJA, Oliveira DS, Tofoli FL, Torrico-Bascope RP. DCeDC nonisolated boost converter based on the three-state switching cell and voltage multiplier cells. IEEE Trans Industrial Electron 2013;60(10):4438e49. [11] Lopez-Santos O, Martinez-Salamero L, Garcia G, ValderramaBlavi H, Zambrano-Prada D. Steady-state analysis of inductor conduction modes in the quadratic boost converter. IEEE Trans Power Electron 2017;32(3):2253e64. [12] Abutbul O, Gherlitz A, Berkovich Y, Ioinovici A. Step-up switching-mode converter with high voltage gain using a switched-capacitor circuit. IEEE Trans Circuits SystemsdI Fundam Theory Appl 2003;50(8):1098e102. [13] Rosas-Caro JC, Ramirez JM, Peng FZ, Valderrabano A. A DCeDC multilevel boost converter. IET Power Electron 2010;3(1):129e37. [14] Axelrod B, Berkovich Y, Ioinovici A. Switched coupledinductor cell for DC-DC converters with very large conversion ratio. In: IEEE 32nd Annual Conference on Proc. Industrial Electronics, IECON; 2006. p. 2366e71. [15] Hsieh Y-P, Chen J-F, Liang T-J, Yang L-S. Analysis and implementation of a novel single-switch high step-up DC eDC converter. IET Power Electron 2012;5(1):11e21. [16] Leyva-Ramos J, Ortiz-Lopez MG, Diaz-Saldierna LH, MoralesSaldana JA. Switching regulator using a quadratic boost converter for wide DC conversion ratios. IET Power Electron 2009;2:605e13. [17] Altin N, Ozturk E. Maximum power point tracking quadratic boost converter for photovoltaic systems. In: ECAI 2016International Conference e 8th Edition Electronics, Computers and Artificial Intelligence, 1e4; 30 Junee02 July, ^ nia]. 2016 [Ploiesti, Roma [18] Kadri R, Gaubert J-P, Champenois G, Mostefaı¨ M. Performance analysis of transformless single switch quadratic boost converter for grid connected photovoltaic systems. In: XIX International Conference on Electrical Machines (ICEM); 2010. p. 1e7. [19] Yan T, Xu J, Dong Z, Shu L, Yang P. Quadratic boost PFC converter with fast dynamic response and low output voltage ripple. Int Conf Commun Circuits Syst (ICCCAS) 2013;2:402e6. [20] Ghamrawi A, Gaubert J-P, Mehdi D. New dual-mode variable step-size control strategy for quadratic boostconverter used in solar energy system. In: 18th European Conference on Power Electronics and Applications (EPE'16 ECCE Europe); 2016. p. 1e10. [21] Ghamrawi A, Gaubert J-P, Mehdi D. New control strategy for a quadratic boost converter used in solar energy system. In: IEEE International Energy Conference (ENERGYCON); 2016. p. 1e6. [22] Wijeratne DS, Moschopoulos G. Quadratic power conversion for power electronics: principles and circuits. IEEE Trans Circuits SystemsdI Regul Pap 2012;59(2):426e38. [23] Huusari J, Suntio T. Dynamic properties of current-fed quadratic full-bridge buck converter for distributed
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
photovoltaic mpp-tracking systems. IEEE Trans Power Electron 2012;27(11):4681e9. ~ a JA, Palacios-Herna ndez E. Loera-Palomo R, Morales-Saldan Quadratic step-down DCeDC converters based on reduced redundant power processing approach. IET Power Electron 2013;6(1):136e45. ~ a JA, Loera-Palomo R, Palacios-Herna ndez E, Morales-Saldan lez-Martı´nez JL. Modelling and control of a DCeDC Gonza quadratic boost converter with R2P2. IET Power Electron 2014;7(1):11e22. pez-Santos O, Martı´nez-Salamero L, Garcı´a G, ValderramaLo Blavi H, Mercuri DO. Efficiency analysis of a sliding-mode controlled quadratic boost converter. IET Power Electron 2013;6(2):364e73. Morales-Saldana JA, Galarza-Quirino R, Leyva-Ramos J, Carbajal-Gutierrez EE, Ortiz-Lopez MG. Multiloop controller design for a quadratic boost converter. IET Electr Power Appl 2007;1(3):362e7. Saadat P, Abbaszadeh K. A single switch high step up dc-dc converter based on quadratic boost. IEEE Trans Industrial Electron 2016;63(12):7733e42. Zhang N, Sutanto D, Muttaqi KM, Zhang B, Qiu D. Highvoltage-gain quadratic boost converter with voltage multiplier. IET Power Electron 2015;8(12):2511e9. Patidar K, Umarikar AC. High step-up converters based on quadratic boost converter for micro-inverter. Electr Power Syst Res 2015;119:168e77. Lopez-Santos O, Martinez-Salamero L, Garcia G, ValderramaBlavi H, Sierra-Polanco T. Robust sliding-mode control design for a voltage regulated quadratic boost converter. IEEE Trans Power Electron 2015;30(4):2313e27. Lopez-Guedea JM, Ramos-Hernanzb JA, Zuluetaa E, Fernadez-Gamizc U, Oterinod F. Systematic modeling of photovoltaic modules based on artificial neural networks. Int J Hydrogen Energy 2016;41(29):12672e87. ~ a M, Ionescu V. Lopez-Guede JM, Ramos-Hernanz JA, Gran ANN based model of PV modules. Int Jt Conf SOCO'16CISIS'16-ICEUTE’16 Adv Intelligent Syst Comput 2016;527:147e55. Ozdemir S, Altin N, Sefa I, Bal G. PV supplied single stage MPPT inverter for induction motor actuated ventilation systems. Elektron Ir Elektrotech 2014;20(5):116e22. Green AM, Emery K, Hishikawa Y, Warta W, Dunlop ED. Solar cell efficiency tables (Version 45). Prog Photovoltaics 2015;23(1):1e9. Sefa I, Altin N, Ozdemir S, Kaplan O. Fuzzy PI controlled inverter for grid interactive renewable energy systems. IET Renew Power Gener 2015;9(7):729e38. Messai A, Mellit A, Massi Pavan A, Guessoumd A, Mekki H. FPGA-based implementation of a fuzzy controller (MPPT) for photovoltaic module. Energy Convers Manag 2011;52:2695e704. Patel H, Agarwal V. Maximum power point tracking scheme for PV systems operating under partially shaded conditions. IEEE Trans Industrial Electron 2008;55(4):1689e98. Kanimozhi K, Shunmugalatha A. Optimal choice of DC-DC converter for enhancement of power tracking efficiency in photovoltaic system. In: IEEE international conference on computational intelligence and computing research (ICCIC); 2015. p. 1e7. Meekhun V, Boitier J-M, Dilhac S, Petibon C, Alonso B, Estibals. Buck converter design for photovoltaic generators with supercapacitor energy storage. Int Conf Renew Energies Power Qual 2011;1(9):682e5. Zainuri MAAM, Radzi MAM, Soh AC, Rahim NA. Development of adaptive perturb and observe-fuzzy control maximum power point tracking for photovoltaic boost dcedc converter. IET Renew Power Gener 2014;8(2):183e94.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/he
Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter Saban Ozdemir a, Necmi Altin b,*, Ibrahim Sefa b a b
Vocational School of Technical Sciences, Gazi University, Ostim, Ankara, Turkey Department of Electrical-Electronics Eng., Faculty of Technology, Gazi University, 06500 Besevler, Ankara, Turkey
article info
abstract
Article history:
In this study, a maximum power point tracking DCeDC quadratic boost converter for high
Received 14 November 2016
conversion ratio required applications is proposed. The proposed system consists of a
Received in revised form
quadratic boost converter with high step-up ratio and fuzzy logic based maximum power
28 January 2017
point tracking controller. The fuzzy logic based maximum power point tracking algorithm
Accepted 26 February 2017
is used to generate the converter reference signal, and the change in PV power and the
Available online xxx
change in PV voltage are selected as fuzzy variables. Determined membership functions and fuzzy rules which are design to track the maximum power point of the PV system
Keywords:
generates the output signal of the fuzzy logic controller's output. It is seen from MATLAB/
Quadratic boost converter
Simulink simulation and experimental results that the quadratic boost converter provides
MPPT
high step-up function with robustness and stability. In addition, this process is achieved
Fuzzy logic controller
with low duty cycle ratio when compared to the traditional boost converter. Furthermore,
PV system
simulation and experimental results have validated that the proposed system has fast response, and it is suitable for rapidly changing atmospheric conditions. The steady state maximum power point tracking efficiency of the proposed system is obtained as 99.10%. Besides, the output power oscillation of the converter, which is a major problem of the maximum power point trackers, is also reduced. © 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Introduction PV based electricity generation is increasing exponentially every year because of some reasons such as reduction of fossil fuel reserves, negative effects on environment of fossil fuels and increasing awareness on environment. In parallel to this development, the researchers are concentrating on developing new and advanced PV system technologies [1,2]. Mainly studies are focused on two major topics in order to obtain the maximum benefit from the PV system investment. The first research topic is designing new, high efficiency and low cost
PV cells and modules, and contains studies on modules structure and materials. The second research topic covers studies on power electronics converter topologies and their control techniques. Different converter topologies and control strategies have been proposed for this aim [1e9]. PV panels generate specific power at certain operation conditions. The PV output voltage and current vary with environmental effects such as the solar irradiation, the ambient temperature, the pollution of the PV module surface, shadowing etc. As it is known, environmental conditions vary seasonally and on a daily basis. If these parameters change, also the amount of produced power changes. Therefore, the
* Corresponding author. Fax: þ90 312 212 13 38. E-mail addresses: [email protected] (S. Ozdemir), [email protected] (N. Altin), [email protected] (I. Sefa). http://dx.doi.org/10.1016/j.ijhydene.2017.02.191 0360-3199/© 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
2
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
PV output parameters, according to the changing environmental conditions, must be continuously monitored. In addition, the generated power form a PV module is also related with the load level. Consequently, the PV module has nonlinear power versus voltage (PeV) and current versus voltage (IeV) characteristics and there is a unique operation point on these characteristics that provide the possible maximum power. This point is called as maximum power point (MPP) [1,2]. Since, the load level of the practical PV system and environmental conditions such as solar irradiation and ambient temperature continuously change, a proper control algorithm and power converter should be used to control the operation point and to keep the system at MPP. This control algorithm is called as maximum power point tracking (MPPT) algorithm. Many algorithms have been suggested for MPPT action [2e8]. In some studies, these control algorithms are grouped as online and offline methods. Some researchers group these methods as seeking and true seeking methods, while others group them as direct and indirect methods. The fundamental difference between these two groups is related to performing way of the MPPT process [3,4]. Online methods measure some parameters form the PV system continuously and control the system to track the maximum available power according to these measured data. Besides, offline methods use some predefined formulas, measurements or tables to perform same action. Although offline methods are usually fast, online methods perform more realistic MPPT process than offline methods. The most well-known online methods are incremental conductance (IC) and perturb and observe (P&O) techniques [1e4]. The fuzzy logic control based methods are the new approach, and they have become popular in recent years. Among the different intelligent controllers, fuzzy logic controller (FLC) stands out with its simple structure [5]. Furthermore, other artificial intelligence methods such as artificial neural networks, genetic algorithms, particle swarm optimization are also used in MPPT studies [6]. Soft switching MPPT converters are also proposed to achieve higher total system efficiency [9]. The output voltage of the PV panel is usually lower than required in typical energy applications such as motor drives, inverters, etc. Therefore, this voltage level must be increased for these type of applications. This required higher voltage level may be accomplished by series connection of PV panels. However, the number of series-connected PV panels must be within certain limits in practice due to some limitations such as PV voltage isolation, efficiency, shadowing effect, etc. On the other hand, conversion ratio of the conventional DCeDC converters is usually not suitable for the required voltage level of the aforementioned inverters for PV applications. (The conversion ratio is defined as the ratio of the output voltage to the input voltage.) Moreover, it is well known that, in conventional DCeDC boost converters, increasing duty cycle decreases the stability and increases the control difficulty. Therefore, while output voltage of boost converter increases exponentially with duty cycle, in practice the voltage conversion ratio between output and input voltage of the converter is recommended to be selected as a maximum four [10]. Although, another alternative to increase the conversion ratio is using the isolated DCeDC converter topology, this structure causes some problems such as cost, complexity, etc. [11]. Different DCeDC
converter topologies with high voltage step-up capability have been investigated. The combination of the conventional boost converter with switched capacitors have been proposed to provide high conversion ranges. In this system, the output voltage level is related to the number of capacitors used in the circuit. However, voltage regulation action decreases the efficiency of the converter dramatically. Therefore, this topology is suitable and provides high efficiency, if an additional converter is used for voltage regulation [12]. In addition, the power switch suffers from high charge current. The DCeDC multilevel boost converter topology is proposed to overcome this drawback. This topology also combines the boost converter and switched capacitor action. The boost converter charges several capacitors in series with its output (same) voltage [13]. Thus, output voltage can be easily controlled with a number of series connected capacitors. Although this structure is very suitable to supply neutral point clamped multilevel inverters, the requirement that output capacitors should provide the whole load current limits its usage. The coupled-inductor technique is also used to obtain high step-up converter [14]. However, the efficiency of this technique is low, and the leakage-inductor energy of the coupled inductor will cause voltage spike on the switch and increase switching losses [15]. Active and passive clamp circuits are utilized to recycle the leakage inductor energy, but clamp circuits increase the cost of the system. High step-up voltage gain can also be achieved with two cascaded boost converters. But this topology requires two controllers and two active switches. The quadratic boost converter (QBC) which is structurally similar to cascaded two boost converters has been proposed to provide high voltage conversion ratio. The QBC circuit is given in Fig. 1. The output voltage of the QBC is given as a quadratic function of the duty cycle of switching signal [16]. Since the QBC has only one active switch, additional driver circuit requirement is removed and more reliable and efficient converter is obtained. Therefore, the QBC is used in several applications where high voltage conversion ratio is required such as power factor correction applications and PV applications [17e19]. The output voltage of the fuel cell or PV module is usually low, and this low voltage should be increased to supply conventional AC loads or to export generated energy to the grid. Therefore, a robust, reliable and high efficiency converter design with high voltage conversion ratio is an important requirement. Although some studies have been presented on QBC control, the number of studies on MPPT quadratic boost converters is limited and a few simulation studies have been proposed [17,20,21]. In this study, a DCeDC quadratic boost converter with MPPT capability for PV systems which requires high voltage
Fig. 1 e The PV supplied quadratic boost converter.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
step-up ability is proposed. The proposed converter contains high conversion ratio QBC and the FLC based MPPT algorithm. The FLC has two inputs and one output. The change in output power (dP/dt) and the change in output voltage (dV/dt) of the PV system are selected as inputs of the FLC, and change in duty cycle is selected as the output variable of the FLC. Therefore, the FLC based MPPT algorithm uses principles of the IC algorithm, but it has adaptive nature and variable step size advantages. The duty cycle of the QBC is obtained by integrating the output of the FLC based MPPT algorithm. Hence, the operation point of the quadratic boost converter is adjusted according to the PV system parameters. Results of MATLAB/Simulink simulations and experimental studies show that, the QBC provides high voltage conversion ratio even with low duty cycle values, and ensures robust and stable operation. Additionally, the simulation and experimental results indicate that, the MPPT efficiency is obtained as 99.10% and the oscillation of the converter output power at the MPP is very small. Besides, the proposed converter has fast response and reaches the MPP in 180 ms. It is seen that, the proposed converter and MPPT method is convenient for quickly changing atmospheric condition.
The quadratic boost converter Quadratic converters can be implemented as buck or boost type. In the past literature, it is possible to see a variety of these structures [22e24]. Also some control algorithms are
3
proposed in recent studies [25e27]. The quadratic converter is implemented by two series-connected converter with elimination of the second switch. Although the conversion ratio of the converter is same as the cascaded converter, the number of components and system cost are lower than the traditional cascaded converter. However, it can be noted as a drawback that, the efficiency of this converter is lower than the conventional buck or boost converter [22]. But, if the conventional DCeDC boost converter voltage gain increases, which means that it requires high duty cycle level, then the efficiency of the converter will drop dramatically. In addition, the voltage stress on the switch will also increase [28,29]. Furthermore, high conversation ratio with the conventional converter causes electromagnetic interference due to the high duty cycle level [30]. So, the QBC seems better solution for high step-up applications. The voltage stress on power switch and efficiency values of the QBC are within the acceptable levels due to lower duty ratio values while the voltage conversion ratio is high. The schematic diagram of the quadratic boost converter is shown in Fig. 1. As seen from the converter structure, it looks like to two cascaded boost converters. The circuit analysis can be explained according to Fig. 1. If the controlled switch S1 turned on (ON state), then D1 and D3 diodes pass to the OFF state. In this situation, input supply current flows L1 and D2. In this condition inductor L1 gathers energy from the power supply and inductor L2 gathers energy from capacitor C1. At the same time, load supplied by the output capacitor C2. After that, the controlled switch turns off (OFF state). In this condition, states of diodes are completely
Fig. 2 e The quadratic boost converter circuit a) When S1 is ON; b) When S1 is OFF. Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
4
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
where u is the control signal which is “1” when S1 is turned on (ON state), and “0” when S1 is turned off (OFF state). Here, i0 is used as load disturbances. In steady state conditions, all the derivative terms are equal to zero. Eq. (5) is obtained by substituting the control signal D instead of u in Eqs. (1)e(4): vc1 vc2 1 : ¼ ¼ vpv vc1 1 D
(5)
Finally, the conversion ratio of the converter (M(D)) can be obtained as given below:
Fig. 3 e The one diode model of the PV cell.
MðDÞ ¼
contrary; D1 and D3 are ON state, and D2 is OFF state. At the same time, C1 and C2 capacitors are charged by L1 and L2 inductors, respectively. In addition, inductors supply the load energy demand. Circuit diagrams for both operation conditions are depicted in Fig. 2. The equation of the converter conversion ratio can be obtained from differential equations obtained according to the control signal [31]. diL1 vpv vc1 ¼ ð1 uÞ dt L1 L1
(1)
diL2 vc1 vc2 ¼ ð1 uÞ dt L2 L2
(2)
dvc1 iL2 iL1 ¼ þ ð1 uÞ dt C1 C1
(3)
dvc2 vc2 iL2 i0 ¼ þ ð1 uÞ dt Rload $C2 C2 C2
(4)
vc2 vc1
vc1 vpv
¼
1 ð1 DÞ2
(6)
As can be seen from Eq. (6) that, the conversion ratio of the quadratic boost converter is quadratic expression and, this ratio provides a high conversation ratio even though lower duty cycle level is applied.
The PV model and MPPT algorithms The PV cell converts sun light directly to electrical energy. Different models and equivalent circuits for PV cell have been proposed to investigate its performance for different operation conditions. In addition, artificial neural network based models have been proposed for PV systems [32,33]. Similarly, various online and offline MPPT algorithms have been proposed for PV systems to obtain fast and high accurate MPPT [1e7].
The PV model PV cells can be modeled by a current source, a diode and a high-value resistor connected in parallel to the current source,
Fig. 4 e Proposed fuzzy logic controller based MPPT quadratic boost converter. Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
Fig. 5 e Basic illustration of the FLC. and low-value resistance connected in series to the all circuit. A diagram of this model called one diode model is given in Fig. 3 [34]. In addition, the double-diode model is also common in the literature. According to the model, the following equation can be written for the PV cell output current: qðVout þ Iout $RS Þ ðVout þ Iout $RS Þ 1 Iout ¼ IG I0 exp n:k:T RP
(7)
where, Iout is the cell output current, IG is the light-produced current, I0 is the cell darkness current, q is the electronic
5
charge value (1,6.1019C), Vout is the cell output voltage, n is the ideality factor, k is Boltzmann's constant (8.65 105 eV/K) and T is the cell temperature (K). As can be seen from the equation, the PV output current shows a nonlinear characteristic. Efficiency values of commercially available PV panels are around 9e21% according to their technology and materials [35]. This efficiency value is only applicable at a certain current and voltage values. The current and voltage values, that it is specific to the PV panel, are also related to atmospheric conditions such as temperature level, solar irradiation, etc. Because of this nonlinear relation, the PV current and voltage, so the output power should be continuously tracked to obtain maximum energy conversion efficiency at different atmospheric conditions. Otherwise, maximum available power cannot be get from the PV array.
MPPT algorithms Many MPPT algorithms have been suggested in past studies. Generally, offline methods estimate the maximum power point (MPP) using some mathematical equations, measurements and look-up tables, etc. Performances and accuracies of
Fig. 6 e a) Membership functions of input variables b) Membership functions of the output variable. Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
6
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
Table 1 e The rule base of the FLC. Change in PV power (dP/dt) Change in PV voltage (dV/dt)
NL NM NS Z PS PM PL
NL NL NL NL Z PS PM PL
NM NL NM NM Z PS PM PL
NS NM NM NS Z PS PS PM
Z Z Z Z Z Z Z Z
PS PM PM PS Z NS NS NM
PM PL PM PM Z NM NM NL
PL PL PL PL Z NL NL NL
these methods are related to many conditions, and usually decrease with time because of some factors such as pollution, aging, etc. Therefore, their efficiency values are lower than online methods while their responses are fast. The most wellknown examples of offline methods are pilot cell (PC), lookup-table (LUT), constant voltage (CV) and constant current (CV) methods. Online control methods have been performing MPPT process more realistic than offline methods. The PV voltage, current or power is continuously monitored and questioned whether the operation point is the MPP or not. The P&O technique, the IC technique, the ripple correlation control (RCC) method, the current sweep technique (CST), the parasitic capacitance method common on line methods. Recently, artificial intelligence based methods become popular such as FLC, artificial neural networks, genetic algorithm, evolutionary algorithm, particle swarm intelligence, etc [1e4]. The FLC provides high performance, even if the load and parameter changes and removes the system model requirement [36]. The main advantage of the FLC based methods over other online control techniques is its lower dependency of the mathematical model and system parameters. With this feature, it has been reported to be more suitable for the MPPT process especially for rapidly changing atmospheric conditions [37]. Most of these methods are effective under uniform insolation conditions. However, PV systems do not receive uniform insolation because of partial shading of PV modules. This
condition affects the PV system performance and PeV curve of PV system of has multiple peak points (some local peak points and a global peak point). The conventional methods do not guarantee MPPT under non-uniform insolation condition, thus some global maximum power point tracking methods have been proposed [38].
Proposed quadratic boost converter with fuzzy logic controller based MPPT algorithm QBCs and their topologies have been studying for applications require high voltage step-up. Although the FLC seems as a mature method, application of the FLC for MPPT is still hot topic and it is widely studied and discussed. In this study, the FLC based MPPT technique is applied with the QBC to combine advantages of high step-up nature of the QBC, and fast, robust and stable operation ability of the FLC based MPPT method. The block diagram of the proposed system including both power and control systems is given in Fig. 4. The proposed system consists of PV panels, the QBC and the FLC based MPPT unit. Basic structure of the FLC is shown in Fig. 5. As shown in the figure, the FLC consists of a fuzzifier, an inference engine, a knowledge base and a defuzzifier. The fuzzifier transforms the crisp input data that obtained from the real word to linguistic labels and membership values by using the knowledge base. After this process is completed, inputs are called fuzzy inputs and used in fuzzy inference engine to generate verbal judgments. The fuzzy inference engine uses the fuzzy inputs and “IF e THEN” rules that are in knowledge base to generate fuzzy outputs. The defuzzifier converts these fuzzy outputs to crisp values. The knowledge base consists of the input and output membership functions and rules which define the relation between inputs and outputs. In this study, a FLC with two inputs and one output is proposed to track the MPP of the PV system. Input variables of the FLC have been selected as change in the PV power (dP/dt) and change in the PV voltage (dV/dt). The output
Fig. 7 e PV model parameters.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
7
Fig. 8 e QBC output characteristic according to the different irradiation level.
variable of the FLC has been selected as change in the duty ratio (cD). The duty ratio of the QBC is obtained by integrating the output signal of the FLC. In one aspect, this approach is similar to IC algorithm which determines the MPP according to derivative of the PeV curve (at the MPP, dP dV ¼ 0), because of its input variables. However, its step size is variable. Input and output membership functions of the FLC are given in Fig. 6. Seven membership functions are determined for both input and output variables, and as it is seen, linguistic labels such as positive large (PL), positive medium (PM), positive small (PS), zero (Z), negative small (NS), negative medium (NM), negative large (NL) are used for these membership functions. The rule base has been determined as given in Table 1 to obtain fast tracking speed and reduced oscillations at steady state. The minemax inference method, which it is widely used and well-known method applied in this study. In this method, the minimum (min) operator is used as fuzzy implication function and fuzzified inputs are combined to obtain rule strength. The maximum (max) operator is used to combine outputs of the rules. The center of gravity defuzzification method which is given in Eq. (8) is used to obtain crisp values of the output: P mðzÞ:z : z* ¼ P mðzÞ
(8)
The center of gravity method calculates the center of gravity of the area determined by the fuzzy output. Its output varies continuously while inputs of the fuzzy logic controller is varying continuously.
Simulation and experimental results The proposed fuzzy logic controller based MPPT quadratic boost converter is modeled and simulated in MATLAB/Simulink program. The proposed system consists of the PV array, the quadratic boost converter and the fuzz logic controller based MPP tracker. Sharp ND-167U1 type PV panel is used in this study. The maximum power point voltage, current and power of this panel at 25 C are given as VMP ¼ 23 V, IMP ¼ 7.27 A and PMP ¼ 167.21 W, respectively. The PV array consists of two strings and each one has 3 series-connected PV panels. So, 1 kW total PV array power is provided. All parameters of the PV panel are given in Fig. 7. The figure also shows the IeV and the PeV curves of the PV panel for two different temperature levels. The proposed fuzzy logic controller based MPPT quadratic boost converter is simulated in different solar irradiation conditions to test the performance of the proposed MPPT converter system. The variation of the solar irradiation level is given in Fig. 8(a). It can be seen that figure, irradiation level is
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
8
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
Fig. 9 e PPPE interface program, the PeV curve of one string of the PV plant and the operation point.
changed 1000 W/m2 to 500 W/m2 at 0.15 s. Then, irradiation level is ramped up to 1000 W/m2 level with a specific positive slope. After that, the irradiation level is stepped-down to 500 W/m2 level at 0.45 s, and stepped-up to 1000 W/m2 level again at 0.6 s. Afterward, the irradiation level is ramped-down
to 500 W/m2 with a negative slope. Finally, the irradiation level is stepped-up to 1000 W/m2 level at 0.9 s. The FLC based MPPT algorithm calculates the required change in duty cycle to track the MPP of the PV system. The duty cycle of the QBC converter is generated by integrating the
Fig. 10 e Variations of the PV voltage (Ch. 1), the switching signal (Ch. 2), output voltage (Ch. 3), the PV current (Ch. 4), and the PV power (Ch. Math). Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
9
Fig. 11 e The performance of the proposed system at startup.
output of the FLC. The output power, voltage and duty cycle of the QBC are given in Fig. 8(b)e(d), respectively. As it is seen in Fig. 8(b), the output power of the QBC tracks the variation of solar irradiation which is directly related producible PV power level. This figure also indicates that the FLC based MPPT algorithm has good performance not only under slow changing and steady state conditions but also rapidly changing atmospheric conditions. It can be easily seen from Fig. 8(c) that, the ripple of the QBC output voltage is very low, and thus the power oscillations are also very limited. There is no unexpected behavior in this figure such as voltage spike, etc. Fig. 8(d) indicates the duty cycle of the QBC. The duty cycle always changes in order to track maximum power point of the PV array according to the solar irradiation level. The proposed FLC based MPPT quadratic boost converter is also implemented and experimental studies are performed. New generation SiC Mosfet (CAS300M12BM2 SiC based MOSFET module) is used in QBC circuit as an active switch. In addition, TMS320F28335 DSP is used to implement the FLC based MPPT algorithm and to perform analog to digital conversions and PWM generation. The experimental set up of the proposed system is supplied by MAGNA-POWER TDS III 600-8 model PV Power Emulator (PPPE). This PPPE is a DC power supply which can act like PV modules. In this study, the PPPE is programmed to operate as a PV plant which has two strings each has 3 series-connected Sharp ND-167U1 type PV panels through its interface program by using parameters given in datasheets such as Voc, Isc, Imppt, Vmppt, Tn,
Tw, operation irradiation level, catalogue irradiation level, irradiation coefficients, temperature, etc. In this study, two parallel connected PPPE are used to emulate the PV plant and supply the system (the interface program shows values of one string). Thus, same PV plant (around 1000 W) is used in both simulation and experimental studies. The PPPE generates a PeV curve according to the programmed conditions, and changes its output current and voltage according to defined PV characteristic. The irradiation and temperature levels can be changed with computer interface during the operation, and thus, different atmosphere conditions can be tested. The interface program of the PPPE is given in Fig. 9. The parameters of the used PV module, the PeV curve of one string of the PV plant and the operation point of the system for 1000 W/m2 irradiation level are shown in the figure. It is seen that the proposed FLC based MPPT algorithm tracks the MPP of the PV system and energy conversion efficiency of the PV system is improved. The MPPT efficiency is obtained from interface program as 99.10%. This value is higher than the values presented in literature such 98.84, 98.78, 97.12, 95.00%, and 90.8% [1,2,39e41]. In addition, the load and the solar irradiation levels are changed to test the dynamic performance of the proposed FLC based MPPT algorithm. The switching signal, the PV voltage, the PV current, the output voltage and the PV power waveforms for this transition are shown in Fig. 10. It is seen that, the proposed MPPT quadratic boost converter has fast
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
10
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
Fig. 12 e The PV voltage (Ch. 1), the switching signal (Ch. 2), output voltage (Ch. 3), the PV current (Ch. 4), and the PV power (Ch. Math) waveforms, a) for 500 W power level b) for 1000 W power level.
transient response and reaches the MPP in about 180 ms. The transient response of the proposed system is also visualized by PPPE interface program. The PeV curve of the PV system for actual solar irradiation level, the MPP and operation points of the system, the PV voltage, PV current and the PV power waveforms can be seen and tracked form interface program screens. The PPPE interface program screen for the startup moment is given in Fig. 11. As it is seen that, the proposed system determines and tracks the MPP of the system fast, and steady state oscillations are removed. Thus high efficient MPPT algorithm is obtained. Also the switching signal, the PV voltage, the PV current, the output voltage and the PV power waveforms for low power (about 500 W) and high power (about 1000 W) operation
conditions are given in Fig. 12. As can be easily seen from figures, the proposed system is stable for both operation conditions. Furthermore, screen of the PPPE interface program for variation of the solar irradiation conditions are given in Fig. 13. The solar irradiation level is changed from 500 W/m2 to 1000 W/m2 and vice versa periodically. The PeV curve of the PV system for actual solar irradiation level, the MPP and operation points of the system for both two irradiation levels, the PV voltage, the PV current and the PV power curves are depicted in the PPPE interface program screen. This test also prove that the proposed system has fast transient response, stable steady state operation characteristics. As it can be seen that, the power oscillation around the MPP is greatly reduced.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
11
Fig. 13 e The performance of the proposed system for change in solar irradiation level.
Conclusions In this study, a quadratic DCeDC boost converter with MPPT ability for high step-up ratio applications is proposed. The QBC provides high voltage conversion ratio with lower duty cycle lower than the conventional boost converters, and provide lower voltage stress and higher efficiency values. The QBC is also more simple, reliable and highly efficient than the cascaded converters because the second active switch is eliminated. The proposed system also includes a FLC based MPPT controller. The proposed FLC has two inputs and one output. The output power and output voltage of the PV panel are selected as input variables, and the change in duty cycle is determined as output variable of the FLC. The duty cycle of the QBC is obtained by integrating the FLC output. Results obtained from MATLAB/Simulink simulations and experimental studies indicate that the QBC ensures robust and stable operation with low duty cycle even though high step-up is required. The MPPT efficiency for steady state operation condition is obtained as 99.10%. The proposed FLC based MPPT
quadratic boost converter has fast transient response and reaches the MPP in about 180 ms. Additionally, the FLC based MPPT algorithm reduced the oscillation of the converter output power at the MPP.
references
[1] Altin N, Ozdemir S. Three-phase three-level grid interactive inverter with fuzzy logic based maximum power point tracking controller. Energy Convers Manag 2013;69:17e26. [2] Ozdemir S, Altin N, Sefa I. Single stage three level grid interactive MPPT inverter for PV systems. Energy Convers Manag 2014;80:561e72. [3] Salas V, Olias E, Barrado A, Lazaro A. Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems. Sol Energy Material Sol Cells 2006;90(11):1555e78. [4] Esram T, Chapman PL. Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transaction Energy Convers 2007;22(2):439e49.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191
12
i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y x x x ( 2 0 1 7 ) 1 e1 2
[5] Khateb AE, Rahim NA, Selvaraj J, Uddin MN. Fuzzy-logiccontroller-based SEPIC converter for maximum power point tracking. IEEE Trans Industry Appl 2014;50(4):2349e58. [6] Anurag A, Bal S, Sourav S, Nanda M. A review of maximum power-point tracking techniques for photovoltaic systems. Int J Sustain Energy 2016;35(5):478e501. [7] Bizon N. Global extremum seeking control of the power generated by a photovoltaic array under partially shaded conditions. Energy Convers Manag 2016;109(1):71e85. [8] Bouzelataa Y, Kurt E, Chenni R, Altin N. Design and simulation of a unified power quality conditioner fed by solar energy. Int J Hydrogen Energy 2015;40(44):15267e77. [9] Oncu S, Nacar S. Soft switching maximum power point tracker with resonant switch in PV system. Int J Hydrogen Energy 2015;41(29):12477e84. [10] Alcazar YJA, Oliveira DS, Tofoli FL, Torrico-Bascope RP. DCeDC nonisolated boost converter based on the three-state switching cell and voltage multiplier cells. IEEE Trans Industrial Electron 2013;60(10):4438e49. [11] Lopez-Santos O, Martinez-Salamero L, Garcia G, ValderramaBlavi H, Zambrano-Prada D. Steady-state analysis of inductor conduction modes in the quadratic boost converter. IEEE Trans Power Electron 2017;32(3):2253e64. [12] Abutbul O, Gherlitz A, Berkovich Y, Ioinovici A. Step-up switching-mode converter with high voltage gain using a switched-capacitor circuit. IEEE Trans Circuits SystemsdI Fundam Theory Appl 2003;50(8):1098e102. [13] Rosas-Caro JC, Ramirez JM, Peng FZ, Valderrabano A. A DCeDC multilevel boost converter. IET Power Electron 2010;3(1):129e37. [14] Axelrod B, Berkovich Y, Ioinovici A. Switched coupledinductor cell for DC-DC converters with very large conversion ratio. In: IEEE 32nd Annual Conference on Proc. Industrial Electronics, IECON; 2006. p. 2366e71. [15] Hsieh Y-P, Chen J-F, Liang T-J, Yang L-S. Analysis and implementation of a novel single-switch high step-up DC eDC converter. IET Power Electron 2012;5(1):11e21. [16] Leyva-Ramos J, Ortiz-Lopez MG, Diaz-Saldierna LH, MoralesSaldana JA. Switching regulator using a quadratic boost converter for wide DC conversion ratios. IET Power Electron 2009;2:605e13. [17] Altin N, Ozturk E. Maximum power point tracking quadratic boost converter for photovoltaic systems. In: ECAI 2016International Conference e 8th Edition Electronics, Computers and Artificial Intelligence, 1e4; 30 Junee02 July, ^ nia]. 2016 [Ploiesti, Roma [18] Kadri R, Gaubert J-P, Champenois G, Mostefaı¨ M. Performance analysis of transformless single switch quadratic boost converter for grid connected photovoltaic systems. In: XIX International Conference on Electrical Machines (ICEM); 2010. p. 1e7. [19] Yan T, Xu J, Dong Z, Shu L, Yang P. Quadratic boost PFC converter with fast dynamic response and low output voltage ripple. Int Conf Commun Circuits Syst (ICCCAS) 2013;2:402e6. [20] Ghamrawi A, Gaubert J-P, Mehdi D. New dual-mode variable step-size control strategy for quadratic boostconverter used in solar energy system. In: 18th European Conference on Power Electronics and Applications (EPE'16 ECCE Europe); 2016. p. 1e10. [21] Ghamrawi A, Gaubert J-P, Mehdi D. New control strategy for a quadratic boost converter used in solar energy system. In: IEEE International Energy Conference (ENERGYCON); 2016. p. 1e6. [22] Wijeratne DS, Moschopoulos G. Quadratic power conversion for power electronics: principles and circuits. IEEE Trans Circuits SystemsdI Regul Pap 2012;59(2):426e38. [23] Huusari J, Suntio T. Dynamic properties of current-fed quadratic full-bridge buck converter for distributed
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
photovoltaic mpp-tracking systems. IEEE Trans Power Electron 2012;27(11):4681e9. ~ a JA, Palacios-Herna ndez E. Loera-Palomo R, Morales-Saldan Quadratic step-down DCeDC converters based on reduced redundant power processing approach. IET Power Electron 2013;6(1):136e45. ~ a JA, Loera-Palomo R, Palacios-Herna ndez E, Morales-Saldan lez-Martı´nez JL. Modelling and control of a DCeDC Gonza quadratic boost converter with R2P2. IET Power Electron 2014;7(1):11e22. pez-Santos O, Martı´nez-Salamero L, Garcı´a G, ValderramaLo Blavi H, Mercuri DO. Efficiency analysis of a sliding-mode controlled quadratic boost converter. IET Power Electron 2013;6(2):364e73. Morales-Saldana JA, Galarza-Quirino R, Leyva-Ramos J, Carbajal-Gutierrez EE, Ortiz-Lopez MG. Multiloop controller design for a quadratic boost converter. IET Electr Power Appl 2007;1(3):362e7. Saadat P, Abbaszadeh K. A single switch high step up dc-dc converter based on quadratic boost. IEEE Trans Industrial Electron 2016;63(12):7733e42. Zhang N, Sutanto D, Muttaqi KM, Zhang B, Qiu D. Highvoltage-gain quadratic boost converter with voltage multiplier. IET Power Electron 2015;8(12):2511e9. Patidar K, Umarikar AC. High step-up converters based on quadratic boost converter for micro-inverter. Electr Power Syst Res 2015;119:168e77. Lopez-Santos O, Martinez-Salamero L, Garcia G, ValderramaBlavi H, Sierra-Polanco T. Robust sliding-mode control design for a voltage regulated quadratic boost converter. IEEE Trans Power Electron 2015;30(4):2313e27. Lopez-Guedea JM, Ramos-Hernanzb JA, Zuluetaa E, Fernadez-Gamizc U, Oterinod F. Systematic modeling of photovoltaic modules based on artificial neural networks. Int J Hydrogen Energy 2016;41(29):12672e87. ~ a M, Ionescu V. Lopez-Guede JM, Ramos-Hernanz JA, Gran ANN based model of PV modules. Int Jt Conf SOCO'16CISIS'16-ICEUTE’16 Adv Intelligent Syst Comput 2016;527:147e55. Ozdemir S, Altin N, Sefa I, Bal G. PV supplied single stage MPPT inverter for induction motor actuated ventilation systems. Elektron Ir Elektrotech 2014;20(5):116e22. Green AM, Emery K, Hishikawa Y, Warta W, Dunlop ED. Solar cell efficiency tables (Version 45). Prog Photovoltaics 2015;23(1):1e9. Sefa I, Altin N, Ozdemir S, Kaplan O. Fuzzy PI controlled inverter for grid interactive renewable energy systems. IET Renew Power Gener 2015;9(7):729e38. Messai A, Mellit A, Massi Pavan A, Guessoumd A, Mekki H. FPGA-based implementation of a fuzzy controller (MPPT) for photovoltaic module. Energy Convers Manag 2011;52:2695e704. Patel H, Agarwal V. Maximum power point tracking scheme for PV systems operating under partially shaded conditions. IEEE Trans Industrial Electron 2008;55(4):1689e98. Kanimozhi K, Shunmugalatha A. Optimal choice of DC-DC converter for enhancement of power tracking efficiency in photovoltaic system. In: IEEE international conference on computational intelligence and computing research (ICCIC); 2015. p. 1e7. Meekhun V, Boitier J-M, Dilhac S, Petibon C, Alonso B, Estibals. Buck converter design for photovoltaic generators with supercapacitor energy storage. Int Conf Renew Energies Power Qual 2011;1(9):682e5. Zainuri MAAM, Radzi MAM, Soh AC, Rahim NA. Development of adaptive perturb and observe-fuzzy control maximum power point tracking for photovoltaic boost dcedc converter. IET Renew Power Gener 2014;8(2):183e94.
Please cite this article in press as: Ozdemir S, et al., Fuzzy logic based MPPT controller for high conversion ratio quadratic boost converter, International Journal of Hydrogen Energy (2017), http://dx.doi.org/10.1016/j.ijhydene.2017.02.191