Boost Chopper Implementation Eased on Variable MPPT Duty Cycle Control Applied to Photovoltaic Systems Salah 1HFDLELD, Mounia Samira .HODLDLD, Hocine LDEDU Department of Electrical Engineering, Badji Mokthar University, Annaba, Algeria
[email protected]
Abstract — Photovoltaic systems have a slow dynamic of convergence towards the maximum power point (MPP), especially those equipped with boost converters based on Perturb and Observe (P&O) algorithm. The target of this paper is the definition of a faster maximum power point tracking (MPPT) method involving less ripples based on a modified algorithm of the most popular Perturb and Observe technique. An adaptive duty cycle is proposed and implemented in the μ-controller (PIC16F877A). The detailed study of the soft and hardware parts show the achieved improvements. Indeed, the experimental results are closely similar to the simulation ones and confirm the faster convergence of the proposed method. Keywords - Perturb and Observe (P&O) algorithm; Maximum Power Point Tracking (MPPT); Photovoltaic (PV) System.
I.
INTRODUCTION
Ammar 1HFDLELD Unité de Recherche en Energies Renouvelables en Milieu Saharien, CDER, Adrar, Algeria. *Pierre-Olivier /RJHUDLV Université Paris-Est, CERTES, IUT de Sénart – Fontainebleau, 36 rue Georges Charpak, 77567 Lieusaint, France,
[email protected] developed and implemented. These ways change according to their complexity, required sensors, time convergence, costs, range of effectiveness, implementation hardware and popularity. Owing to these reasons, they have been categorized from great simplicity to most creativity in [5]. MPPT methods have been classified into two categories, indirect and direct methods. On one hand, the indirect methods, such as open-circuit and short-circuit methods which focus on mathematical concepts cannot track the MPP for all meteorological conditions [5]. On the other hand, direct methods which enable to track the MPP for all meteorological conditions such as Perturb and Observe (P&O) and Incremental Conductance (IncCond) approaches are the most used ones [6]. Commercial products are generally equipped with perturbative MPPT methods which determine the optimal voltage value for each moment, so that the PV panel supplies its maximum power [7].
Photovoltaic (PV) energy plays an essential role in electric power generation and becomes highly necessary because of the environmental impacts of conventional fuels. For the latter reasons, greater importance will be granted to photovoltaic power in the upcoming years. According to the energy [r]evolution scenario, by 2050, at least 70% of the electricity produced worldwide will come from renewable energy including wind, solar thermal energy and PV electricity generation. [1,2]. Besides, photovoltaic solar power is expected to be the main source of renewable energy because of the decreased costs of PV panels observed in the last decade [3].
Direct methods are either fast but inaccurate or vice versa. To overcome these drawbacks many researchers use artificial intelligence [8]. To apply these techniques, a deep knowledge of the modeling parameters of the PV panels should be undertaken along with the aging effect in the PV panel model. In this paper, the proposed technique is classified within the indirect methods where a deep knowledge of the PV cells model is not necessary. Only the input action (the PV voltage controlled by the MOSFET duty cycle) and the output reaction (the current delivered by the PV panel) are the main control parameters.
The photovoltaic generation systems have two main issues: the conversion effectiveness of electric energy is low, and the electric power generated by PV panels changes continuously with meteorological conditions. Furthermore, the power output changes with meteorological conditions due to the non-linear I–V and P–V curves of the PV panel and the dynamic load profile which both cause a mismatch between the load characteristics and the maximum power point (MPP). To solve these issues, maximum power point tracking (MPPT) is involved in order to force the system to track the optimal operating point [4]. Many ways to track the MPP have been
A PV system with MPP tracking is studied to give an upturn to the conversion capability power from sun irradiance to electrical energy. First, the model of the PV system is described. Then, a new approach to track the MPP is shown. Simulation results are compared to experimental ones obtained with an experimental bench in order to distinguish between the effect of the execution time of the program loaded in the microcontroller and the effect of the response time of the semiconductors.
II.
MODELING OF THE PV SYSTEM SUPPLY
A. PV cell The photovoltaic overall system consists of several elements, including the PV panel, which is considered as the key element of this structure. The PV panel is constituted of PV cells, which are able to convert directly the energy of the sunlight into electricity. In general, a photovoltaic cell produces voltages between 0.5 and 0.8 V and currents between 2 and 5 A based on the semiconductor technology and the materials used. The gathering of the photovoltaic cells forms the PV module to produce power under a rated voltage and current [8]. The mathematical expression of the PV panel output current I can be written as (Fig. 1) [9]: =
( +
−
)
−1 −
( +
)
The cell photocurrent depending on the temperature irradiation is as: =[ +
( −
)]
.
∗
×
.
×
+
Switch
Vpv
-
(3)
In Eq (1) to (3), is the dimensionless junction material the band-gap energy of a cell semiconductor (eV); factor; the reverse saturation current at (A); the Boltzmann the short-circuit current constant 1.28×10-23 J.K-1; and the number of cell temperature coefficient (%/°C); strings connected in parallel and in series respectively; q the and are the solar cell series electron charge 1.6×10-19 C; and parallel resistances (Ω) respectively; the solar irradiation the cell reference (W/m²); the temperature (K); temperature (K). The solar energy delivered by the photovoltaic panel is related to the solar irradiation and the solar temperature. The increasing of sunlight intensity causes an augmentation in the short-circuit current but the raise of temperature occasions a rundown of the open-circuit voltage . Practical PV Device Ideal PV Cell
Figure 1. Equivalent circuit of a photovoltaic cell.
B. DC–DC boost converter Among DC-DC converters used in PV applications, DCDC chopper is the most popular one, because it has a simple structure which allows to easily change the linked impedance between the PV panel and the load by modifying the duty cycle which affects the operating point of the PV module. The boost
Vout
Load
-
Control Signal
Figure 2. Boost converter.
and the
where Is is the cell reverse saturation current: =
Iout
Ipv +
(1)
(2)
100
converter has been selected to play an effective role based on its simplicity and to enhance higher conversion efficiency. The boost converter shown in Fig. 2 is very adapted (the PV panel voltages are very low so they need to be boosted) for a broad scale of input voltage values. In fact, the output/input voltage rate is in the Continuous Inductor Mode (CIM) [10].
The switching device used in this converter is a MOSFET, which is faster than an IGBT, with less power losses at high frequencies. The MOSFET is controlled by PWM (Pulse Width Modulation), influencing the duty cycle. The mathematical expressions needed for the boost converter are as follows [11]. = =
where
1 1− (1 − )
=
(4) (5)
and
=
is the chopper switch on time (s), the chopper cycle with time (s) and the chopper frequency sample (Hz). and are the output voltage and the current of the PV and are the output voltage and panel respectively, current of the boost converter and is the duty ratio of the switch controller. III.
MAXIMUM POWER POINT SEARCH ALGORITHM
The use of the MPPT can maximize the energy produced by the PV module of any converter [12]. In this work, the boost converter is the main impedance adaptation circuit between the load and the PV panel. The output power of the boost converter is controlled by the output voltage to maximize the delivered power. Knowing that the voltage of the output converter is related to the duty cycle, it is calculated so that the voltage is maximized. A. Conventional Perturb and Observe Algorithm The P&O approach is widely used by researchers and in commercial PV installations because of its simple implementation compared to other methods. However, some improvements are still required in order to achieve the advantages given by the other methods. This type of tracking is executed by perturbing the operating voltage point Vpv and observing the power variation Ppv. This algorithm measures for each instant k the current Ipv(k) and the voltage Vpv(k) of the PV panel, then calculates the power Ppv(k) and compares it with Ppv(k-1), where Ppv(k-1) is calculated with Ipv(k-1) and
Vpv(k-1). During the operating points the algorithm continues to perturb the system and uplifts the voltage when the variations of the power and the voltage are positive. Otherwise, if the variations of the power and the voltage are negative, the algorithm perturbs the system in the opposite direction [13]. The flow chart of the conventional P&O approach is presented in Fig. 3.
reduce the ripples around the MPP compared to the conventional variable step-size method. The flow chart of the modified P&O algorithm is depicted in Fig. 4. To diminish ripples at steady-state obtained with a conventional variable step-size method, an acceptable error given by Eq 7 is used: ∆ + ∆
< 0.04
(7) START
START
MEASURES PV VOLTAGE V(k) PV CURRENT I(k)
MEASURES PV VOLTAGE V(k) PV CURRENT I(k)
PV POWER P(k) = V(k) * I(k) ∆P = P(k) - P(K-1) ∆V = V(k) - V(K-1) ∆I = I(k) -I(K-1) STEP=N*abs(∆P/∆V)
PV POWER P(k) = V(k) * I(k) ∆P = P(k) - P(K-1) ∆V = V(k) - V(K-1) ∆D = D(k) -D(k-1)
∆I/∆V + I/V = Yes
∆P > 0
No No
No
∆V < 0
Yes
No
∆V > 0
No
D(k) = D(k) ∆D
D(k) = D(k) + ∆D
D(k) = D(k) + ∆D
D(k) = D(k) ∆D
∆V < 0
D(k) = D(k) STEP
∆I/∆V > - I/V
D(k) = D(k) + STEP
No
D(k) = D(k) STEP
UPDATE P(k-1) = P(K) V(k-1) = V(k) I(k-1) = I(k)
Figure 3. Flow chart of the conventional P&O Approach.
Figure 4. Flow chart of modified P&O algorithm.
Therefore, a satisfactory compromise between the dynamic response and the oscillations around the MPP has been made by the conventional P&O approach. The ratio of the derivative of the power with respect to the voltage DP/DV of a PV module is used here as a fit variable for regulating the step size of the conventional P&O approach [12]. So the development of this method relies on observing the DP/DV by taking into consideration the P-V characteristic of the photovoltaic panel as displayed in Fig. 5 [15]. The difference between the conventional P&O approach and the variable step-size algorithm is the calculation of the step size as shown in Eq 6 where N is the scaling factor. The conventional variable stepsize method permits the system to respond very quickly to reach the MPP but it becomes confused at steady-state [16]: (6)
The modified P&O [17] which is suggested allows to make the system respond very fast to reach the MPP and likewise to
2
100
MPP
PV panel Power Step
B. Modified Perturb and Observe Algorithm The conventional P&O approach has two main issues. The first one, when the step size is fixed at a high value, it makes the system respond very rapidly, but unfortunately, a large oscillation around the MPP is noted. Against, for the second one, when the step size is fixed at a small value, it is observed that the system is stable with less oscillations around the MPP but with a slowed-down response of the system [14].
∗
Yes
Yes
D(k) = D(k) + STEP
UPDATE P(k-1) = P(K) V(k-1) = V(k) I(k-1) = I(k)
=
Yes
∆P > 0
Yes
1
00 0
50
Step-size duty cycle 5
5
10
10
15
PV Voltage15
20
20
PV Power
No
Yes
epo
0 25
25
Figure 5. P and adaptive duty cycle control vs. voltage.
IV.
SYSTEM DESIGN AND SIMULATION RESULTS
The components of the boost converter are: = 274 μ , _ = _ = 2200 μ with a switching frequency of 10 and a selected load _ = 20Ω. The latter power resistance is chosen because in DC voltage only active power is considered. The PV module which is studied is of ISOFOTON I-75/12 type. Table I is used for simulations and experimental tests. The model is represented under ISIS PROTUES which also permits to launch the simulations (Fig. 6).
TABLE I.
PV MODULE PARAMETERS (ISOFOTON I-75/12). Parameters Maximum power Maximum voltage Current at max power Open-circuit voltage Short-circuit current
Variable
Value 75 W 17.3 V 4.34 A 21.6 V 4.67 A
In order to test the whole PV system behavior, various types of solar irradiation are accounted for (Fig. 10). The response time is upgraded under any irradiance variation (Fig. 11). P&O technique is sensitive to the irradiance profile, as can be seen on the current ripples magnification (Fig. 12). The proposal is more accurate because it offers a delivered power almost close to the irradiance profile (Fig. 13).
Figure 6. The whole PV MPPT system under ISIS PROTUES.
Simulation results are presented for both the conventional and the modified P&O approaches in Fig. 7 to 9 under an irradiance of 504.95 W/m² and a temperature of 10.3°C. As can be noticed in these figures, the convergence time towards the MPP with the proposed algorithm makes the system progress very speedily with a time reduction from 0.6 s to 0.34 s compared to the conventional P&O approach. On the other hand, the amplitude of the ripples of the proposed algorithm around the MPP is strongly lowered in the different stages of the conversion (Table II).
Figure 8. PV current and output boost current (G=504.95W/m² T=10.3°C): (a) conventional P&O approach (b) modified P&O approach.
Figure 9. PV delivered power and output boost power (G=504.95W/m² T=10.3°C) (a) conventional P&O approach (b) modified P&O approach.
Figure 7. PV voltage and boost output voltage (G=504.95W/m² T=10.3°C): (a) conventional P&O approach (b) modified P&O approach.
Figure 10. Different types of irradiance change.
TABLE II.
SIMULATION RESULTS COMPARISON Conventional P&O PV panel
Proposed algorithm
16.13% Voltage ripples Fig. 7 10.64% Current ripples Fig. 8 5.26% MPP error Fig. 9 Chopper output 7.85% Voltage ripples Fig. 7 10.64% Current ripples Fig. 8 7.01% MPP error Fig. 9
6.25% 4.17% 2.59% 3.84% 3.62% 4.16%
Figure 11. PV voltage and boost output voltage (different irradiances, T=25°C): (a) conventional P&O approach (b) modified P&O approach.
Figure 14. The PV system experimental test bench (December 22 2016 at 10:30 am).
V.
Figure 12. PV current and output boost current (different irradiances, T=25°C): (a) conventional P&O approach (b) modified P&O approach.
Figure 13. PV delivered power and output boost power (different irradiances, T=25°C): a./conventional P&O approach, b./modified P&O approach.
HARDWARE IMPLEMENTATION AND EXPERIMENTAL RESULTS
The overall system consists of a PV panel, a boost converter, a PIC16F877A microcontroller, a gate drive and the load. The switch of the boost converter is controlled by the duty cycle, so that, the operating point of the PV array progresses towards the MPP. The PIC16F877A microcontroller is programmed with the conventional perturb and observe algorithm and also with the proposed one to generate the fit PWM controlling the duty cycle. The ACS712 (current sensor with an incertitude of about 1.5% at 25°C) is used to measure the current for both the PV module and the boost converter. Both the voltages of the output converter and the PV module are measured with a voltage divider (with an uncertainty of about 0.5% at 25°C). The used wattmeter has an uncertainty of about 2.75%). The experiment is tested under an irradiance of 504.95 W/m² and a temperature of 10.3°C. Once the experimental test with the device of Fig. 14 is performed, the simulation results can be validated. The experimental results show clearly that the expected objectives of this paper are reached. In Fig. 15 and 16, the proposal allows less ripples of currents and voltages with a very fast time response (0.6 s) compared to the ones obtained with the conventional Perturb and Observe technique (2.9 s). From Fig. 15.a, the voltage ripple is evaluated at about 4 V whereas in Fig. 14.b the voltage ripple is lowered to 1 V. The convergence time of the conventional algorithm is of 2.9 s as displayed in Fig. 14.a, in contrast with the proposed algorithm (0.6 s) as observed in Fig. 15.b. From Fig. 16.a, it is obvious that the conventional technique surfing in tracking the MPP exhibits a large oscillation at steady-state. On the contrary, the suggested method diminishes the amplitude perturbation to the minimum level (Fig. 16.b). The results obtained by simulations and experiments are almost alike, except for those got for the
PV panel. Because the PV panel model used in the simulation is highly nonlinear, many iterations are necessary and the computation time gets longer. But for experiments, the PV panel reacts naturally. The outputs of the PV panel require more time for iterations in simulation due to its nonlinear characteristics, but no iterative calculations are needed in the experimental case. The proposal offers the load a diminution of 400% of current ripples compared to the conventional one and a push down of 200% of voltage ripples, which signify a better power quality delivery.
making the system more stable, decreasing the energy losses and ameliorating the conversion efficiency to ensure the proper use of the PV system. Hence, this algorithm is focused on both adapting the step size to get a very rapid system response and to adapt the oscillations around the MPP. These achievements have been successfully tested by the simulation and the experimental results. The PV panel is linked to the load through a converter. So a description of the used chopper is presented and the latter can be implemented in any further works. ACKNOWLEDGMENT Many thanks to Mrs. Wilhelmina Logerais, a mother tongue speaker, for proofreading this article in English. [1] [2]
[3] [4] [5] [6] Figure 15. PV voltage and boost output voltage (G=504.95W/m², T=10.3°C): (a) conventional P&O approach (b) modified P&O approach.
[7]
[8] [9] [10]
[11] [12] [13] Figure 16. PV current and output boost current (G=504.95W/m², T=10.3°C): (a) conventional P&O approach (b) modified P&O approach.
VI. CONCLUSION DC/DC choppers are used within PV installations to reach high yields. Unfortunately, the most popular P&O algorithm can neither control the response time nor the ripple amplitude. It is proved that the proposed method can effectively and accurately track the MPP compared to the conventional algorithm. This proposed algorithm can spot quickly and precisely the MPP with very smooth oscillations of the current (2.17% of PV panel against 13.33% and 3.3% of the chopper against 12%) and voltage at their steady-state (6.25% of PV panel against 29% and 3% of the chopper against 7.7%),
[14]
[15]
[16] [17]
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