Maximum power point tracking in photovoltaic systems under different operational conditions by using ZAINC algorithm
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Abstract
Today, environmental pollution has increased due to excess use of fossil resources. Amidst, renewable energy resources, solar energy is one of the infinite, clean and accessible options. Each solar cell has a unique operating point which is called maximum power point. Considering nonlinearity of I–V and P–V curves of Photovoltaic (PV) resources, their delivered power depends on operating point of the PV. That is, for changes in temperature or irradiation, some actions should be taken to obtain maximum operating point which is called maximum power point tracking (MPPT). This paper tries to configure power circuit using distributed maximum power point tracking (DMPPT) with a flyback converter, optimize power and reduce fluctuations around maximum power point. MPPT is analyzed through simulation using P&O, INC and zero fluctuation, adaptive step, increasing (ZAINC) guidance algorithms in two steps including variable irradiation with constant temperature and variable temperature with constant irradiation using DMPPT and centralized MPPT. In DMPPT scheme using ZAINC algorithm, power is optimized, losses are reduced, fluctuations around maximum power point are reduced, gain and tracking speed are increased.
Keywords
Photovoltaic MPPT ZAINC algorithm Weather conditions Flyback converter1 Introduction
Today, environmental pollution has decreased due to renewable energy resources such as solar energy improved. PV systems have improved due to use of power electronic converters; that is why, such systems are so popular. Weak performance of PV systems under different operational conditions might be because of: shade of objects on solar panels, shading while sunset and sunrise, fault in generation process, aging and failure of solar panels, dust layer on panels, improper selection of solar panels’ location, weather conditions, irradiation and temperature. Optimizing location of solar panels depends on mechanical factors and imposes heavy costs. Considering nonlinearity of I–V and P–V curves of PV source, their delivered power depends on operating point of the PV. That is, for changes in temperature or irradiation, some actions should be taken to obtain maximum operating point which is called MPPT [1, 2]. In summary, MPPT system has to determine operating point and adjust voltage and current of solar array to reach maximum power point. One of the low cost and effective methods for getting maximum power from PV is electrical tracking of maximum power point which tries to obtain maximum possible power from the cell under different weather conditions. In the following, studies on MPPT methods are reviewed briefly.
First class includes the methods which follow a basic algorithm among which perturbation and observation (P&O), hill climbing (HC) and incremental conductance (INC) can be mentioned [2, 3]. P&O is based on creating perturbation on voltage and observing output power. If power is increased, perturbation is continued along the same path and if power is decreased, perturbation is reversed. This class tracks maximum power point without requiring parameters of the solar cell. Main disadvantage of this class is fluctuations around maximum power point and low tracking speed [2, 4, 5, 6].
Second class includes the methods based on modelling solar cell. These methods are designed and implemented through modelling solar cell and establishing relationships governing the model. Main disadvantage of this class is that they are not flexible, if a solar cell is replaced with another cell. Such that each implementation is specific to one type of solar cells which it has been designed. In addition, finding the model and parameters of the solar cell before designing is another problem [7, 8].
Third class includes the methods based on relationship between operating point and parameters of the solar cell among which two mentioned methods can be named. Method which employs linear relationship between short circuit current and operating point. Another method called open circuit voltage employs linear relationship between voltage of operating point and open voltage of the cell. Shortcoming of this class is that effect of variations in temperature and irradiation is not considered [9].
Fourth class includes the intelligent control methods among which fuzzy logic control and artificial neural networks can be mentioned. Intelligent methods require modeling solar cell which limits using control systems in the designed solar cell. In other words, it cannot be ensured that it performs well under glide gradient and its efficiency depends on knowledge and ability of the design [10, 11, 12, 13, 14].
In general, there are different measures for selecting a MPPT system which include manufacturing cost, gain factor, tracking speed, accuracy of the power point and simple implementation. Each method which can deliver maximum power from the solar cell is better and more efficient. So, best converter and MPPT algorithm which has lower fluctuations, higher tracking speed and higher gain factor compared to previous algorithms should be selected.
The contribution of this paper is ZAINC method in two cases (CMPPT and DMPPT). It should be noted that from simulation results advantages of DMPPT include isolation, loss reduction, power increase, gain coefficient increase, simple implementation, voltage increase using flyback converter and using the same algorithm for all modules. MPPT is simulated using P&O, INC and ZAINC algorithm under two conditions: (1) variable irradiation with constant temperature, (2) variable temperature with constant irradiation.
2 Mathematical formulation of the PV cell
3 MPPT control algorithms
3.1 P&O control algorithm
3.2 Incremental conductance control algorithm
3.3 ZAINC control algorithm (zero fluctuation, adaptive step, incremental conductance)
4 Flyback converter

More than one output.

Output might be either positive or negative.

Electric isolation between input and output is very high.
5 Parameters proposed for implementing the design
Electric characteristics of SX3200 W module under STC standard
Parameter  Module 1  Module 2  Module 3 

P_{mpp}  200 W  200 W  200 W 
Voltage at P_{mpp}, V_{mpp}  24.5 V  24.5 V  24.5 V 
Current at P_{mmp}, I_{mpp}  8.16 A  8.16 A  8.16 A 
Open circuit voltage, V_{oc}  30.8 V  30.8 V  30.8 V 
Short circuit current, I_{sc}  8.6 A  8.6 A  8.6 A 
Parameters required for simulation
Parameter  Value 

RC (in flyback)  100 µF, 0.01 Ω 
RC (out flyback)  10 µF, 0.01 Ω 
Magnetizing inductance (flyback)  0.11937 H 
Input voltage (flyback)  30 V 
Output voltage (flyback)  30 V 
Power (flyback)  MPPT700 W/DMPPT200 W 
Switching frequency load  28.5 kHZ 50 Ω 
When irradiation intensity of the module is zero, module receives voltage from the network and the module is burnt; this problem is resolved using a diode. Flyback converter with unit conversion ratio is used to prevent collection of nonisolated voltages at one point and burning.
6 Simulation results
6.1 Constant temperature with variable irradiation
Details of the three modules used for simulation in constant temperature and variable irradiation
Module at constant temperature  Irradiation (W/m^{2})  Mpp voltage (V)  Mpp current (A)  Mpp power (W) 

Module l  1000  24.5  8.16  200 
Module 2  500  24.1  4.31  104 
Module 3  600  24.3  5.3  129 
6.2 Variable temperature and constant irradiation intensity
simulation details of the three modules (variable temperature and constant irradiation intensity)
Module at constant irradiation  Temperature of the module  Mpp voltage (V)  Mpp current (A)  Mpp power (W) 

Model 1  25  24.5  8.16  200 
Model 2  15  25.74  8.16  210 
Model 3  35  23.9  8.16  195 
6.3 Distributed maximum power point tracking
6.4 Centralized maximum power point tracking
Analysis of trackers under constant temperature and variable irradiation
Scheme with constant temperature  Algorithm  Reliability  Cost  Delivered power (W)  Real value (W)  Gain factor (%)  Recovery time (s)  Fluctuations around maximum power point (W)  Number of current and voltage sensors 

DMPPT  P&O  Low  High  402  433  92.8  0.24  46  7 
DMPPT  INC  Low  High  408  433  94.2  0.20  15  7 
DMPPT  ZAINC  Low  High  412  433  95.1  0.035  10  7 
MPPT  P&O  High  Low  385  433  89  0.25  60  3 
MPPT  INC  High  Low  395  433  91.2  0.23  21  3 
MPPT  ZAINC  High  Low  400  433  92.3  0.04  15  3 
Analysis of trackers under constant irradiation and variable temperature
Scheme with constant irradiation  Algorithm  Reliability  Cost  Delivered power (W)  Real value (W)  Gain factor (%)  Recovery time (s)  Fluctuations around maximum power point (W)  Number of current and voltage sensors 

DMPPT  P&O  Low  High  570  605  94.2  0.26  47  7 
DMPPT  INC  Low  High  578  605  95.5  0.32  25  7 
DMPPT  ZAINC  Low  High  584  605  96.5  0.05  12  7 
P&O  MPPT  High  Low  550  605  90  0.28  76  3 
MPPT  INC  High  Low  560  605  92.5  0.35  40  3 
MPPT  ZAINC  High  Low  568  605  93.9  0.08  17  3 
7 Conclusion
Advantages of DMPPT include isolation, loss reduction, power increase, gain coefficient increase, simple implementation, voltage increase using flyback converter and using the same algorithm for all modules. In DMPPT, power of each module is almost maximum power point. Power obtained using INC algorithm is higher than P&O algorithm and MPPT is performed with higher speed and accuracy under variable irradiation intensity. When temperature varies, power delivered by the array is inversely proportional to the temperature; in such condition, P&O reaches first maximum power point faster than INC. In ZAINC, fluctuations around maximum power point are reduced, tracking speed is increased, efficiency is increased and MPPT is adjusted close to maximum power. ZAINC can be used under environmental and weather changes.
Notes
Compliance with ethical standards
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
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