2012 Andean Region International Conference
Fuzzy Control System for Maximum Power Point Tracking in Solar Panels Based On DC-DC Converter PI Current Control Alberto Berzoy, Erick Baethge, Jose Restrepo and Julio Viola Departamento de Electrónica y Circuitos Universidad Simón Bolívar Caracas, Venezuela e-mail:
[email protected];
[email protected];
[email protected];
[email protected] “Positive Output Elementary Super Lift Luo Converter” (POESLC) [15] working in continuous mode. Fig. 1 shows a block diagram of the proposed system: solar panel, DC-DC converter, PI controller, fuzzy inference system (FIS) and load.
Abstract—A new controfller for maximum power point tracking, using a fuzzy inference system, is presented. This control has an external loop with a classical PI controller providing the output current reference in a DC-DC converter. The control system is simulated in Matlab Simulink 2010b and experimentally tested in a Microchip microcontroller to verify the dynamic behavior, versatility and robustness of the proposed controller. Keywords-MPPT, Fuzzy Inference System, DC-DC converter.
I.
INTRODUCTION
Nowadays solar energy is still expensive due mainly to the low efficiency of the conversion from photovoltaic (PV) to electrical energy [1]. The efficiency of the PV is affected by the panel’s irradiance and temperature which are stochastic and unpredictable. For this reason, it is not possible to connect the load directly to the PV to obtain the maximum power, so it is necessary to include a balance of system (BOS). Typically this BOS is a DC-DC converter to adjust the properties of the load. This converter has the advantage of managing the power delivered to the load. In order to obtain the maximum energy from the PV it is necessary to operate the PV in the maximum power point where the product of current and voltage is maximized. Recently, several algorithms have been developed to optimize the extraction of energy like the maximum power point tracking (MPPT) systems. The most common algorithm to track the maximum power point is the “Perturb and Observe” method (P&O) [2][9]. Fuzzy logic has also been used in the implementation of the controller for maximum power point tracking [10]-[12]. P&O does not depend on any estimator and it has a very good dynamic response. In addition, it does not need any characterization of the solar panel, in neither irradiance nor temperature. This work presents a fuzzy P&O technique with an internal current loop controller in a DC-DC converter. The system is similar to the one presented in [13] with the added value of fuzzy control based on zero order Takagi-Sugeno fuzzy inference system [14]. This system is simulated in Matlab and implemented in a prototype test rig. The control algorithm drives, through a PWM stage, the DC-DC converter’s power switch. The DC-DC converter is a
978-0-7695-4882-1/12 $26.00 © 2012 IEEE DOI 10.1109/Andescon.2012.36
Figure 1. Block diagram of the proponed system to MPPT control.
II.
FUZZY P&O CONTROL
The FIS rules are based on “perturb and observe” algorithm and the FIS inputs are the PV power difference and the output DC-DC current difference. The difference are calculated between the current control period instant (k) and the next one (k+1). Δp(k ) = p(k + 1) − p(k ) (1) where p(k) is the difference between the power at the actual control period p(k) and the power of the next control period p(k+1). Applying the same procedure to the current: Δi (k ) = i (k + 1) − i (k ) (2) The linguistic rules are expressed as: • If i(k) > 0 and p(k) > 0 then, the current reference is increased. • If i(k) > 0 and p(k) < 0 then, the current reference is decreased. • If i(k) < 0 and p(k) > 0 then, the current reference is decreased. • If i(k) < 0 and p(k) < 0 then, the current reference is increased. Table I shows the rule matrix for the corresponding TSFIS. The output of the fuzzy MPPT control is the current reference for the inner loop controller of the DC-DC converter. As is shown in table I, the universe of discourse is divided in 3 membership functions. Triangular membership functions are used for both input variables. 101 119
TABLE I.
FIS TAKAGI-SUGENO RULE MATRIX.
Δi (k ) \ Δp (k ) NH Z PH III.
NH PH Z -PH
Z Z Z Z
where T is the control period, f is the control frequency and RL is the load resistance. The inductor must guarantee continuous operation mode of the converter in the worst case. The zero of the derivative of (6) with respect to the duty cycle yields an expression that indicates which is the duty cycle that leads to the largest value of Lmin. dLmin R ª 2 − 8d + 8d 2 − 2d 3 º (7) = L « »=0 dd 2. f ¬ (2 − d ) 2 ¼
PH -PH Z PH
DC-DC CONVERTER TOPOLOGY
The “Positive Output Elementary Super Lift Luo DC-DC Converter” [15] operating in continuous mode has some advantages: it has a good relation between input voltage and output voltage when compared to the “Boost” converter, it doesn’t use transformer as the “Forward” converter and it uses fewer components: one power switch, one inductor, two diodes and two capacitors. Fig. 2 shows the electrical circuit of the POESLC using a MOSFET.
From (7) d = 0.382 and combining with (6) the minimum inductance is: 0.382 * (1 − 0.832) * 200 Lmin = = 902 μ H (8) 2 *10000 * (2 − 0.382) It was chosen Lmin = 1000 μ H and with (6) it is possible to find the maximum load to ensure the continuous mode. Then RL < 221.8033 , and the load value was chosen as RL = 200 .
Figure 2. Electrical circuit of Positive Output Elementary Super Lift Luo Converter
The transfer function in continuous conduction mode is: VO 2 − d = (3) Vi 1 − d where d is the duty cycle. IV.
Figure 3. Complete system with control.
SYSTEM DESIGN
The Luo converter is designed to manage a maximum power of 180 W (180 V at 1 A). The input voltage will be 36.5 V with a current of 5.1 A, which is the point of maximum power transfer when the irradiance is 1 sun, as reported in the solar panel BP7185 datasheet. The control frequency is established in f = 5 kHz. The capacitor C1 is chosen heuristically to reduce the peak voltage in it. The value is determined as 100 μ F . The capacitor C2 is chosen to control the ripple at the output. The determined value is 1000 μ F which guarantees a 2% output ripple. The minimal inductance to hold continuous operation mode is: Δi I L − LON > 0 (4) 2 Vo V .d .T − i
d .(1 − d ) 2 .RL 2. f .(2 − d )
V.
RESULTS
The simulation was done with the SimpowerSystem toolbox of Simulink Matlab 2010b and the block diagram is shown in Fig. 3. The solar panel was simulated using the models in [16] and [17]. The power controller block is the FIS and the current controller block is a PI controller where the constants Kp and Ki were found heuristically as 1 and 10 respectively. The experimental results were implemented in a prototype test rig developed using a microcontroller Microchip PIC 16F877 to embed the fuzzy P&O algorithm and the PI controller. A. Simulated Results 1) Step of Irradiance and fixed temperature (25 °C) This simulation shows the response of the system for an irradiance step from 0.8 suns to 1sun. The fuzzy MPPT control manages to get the maximum power of the solar panel for these irradiances, producing the current reference shown in Fig. 4a. Fig. 4b shows the input power of the DCDC converter where for 0.8 suns the maximum power is 150W and for 1 sun is around 185 W as reported in the BP7185 panel datasheet. The duty cycle of the PWM was about 70% in average.
(5)
(6)
120 102
photovoltaic current are proportional. Moreover, the irradiance can be estimated using the instantaneous power’s expression and assuming a constant voltage. The BP7185 panel datasheet report 185 W at 1 sun, then the value of the irradiance for 120 W is 0.6486 suns.
(a)
(a)
(b) Figure 4. Step irradiance response of the system.(a) Reference current and output current in the DC-DC converter. (b) Input and output power.
2)
Sinusoidal Irradiance and fixed temperature
In this simulation the irradiance is set to have sinusoidal behavior with amplitude of 0.1 suns and a DC component of 0.9 suns with 1 Hz, to demonstrate that the power is almost proportional with the irradiance. Fig. 5a shows the reference current is sinusoidal between the same values of the step irradiance simulation. Fig. 5b shows that the maximum power is get in the peak of the sinusoidal irradiance. The duty cycle of the PWM for this test was about 75% in average.
(b) Figure 5. Sinusoidal irradiance response of the system. (a) Reference current and output current in the DC-DC converter. (b) Input and output power.
In order to verify the experimental results, and due to the lack of an irradiance meter in the power electronics group, it was carried a simulation of the photovoltaic panel that shows the characteristic curve power versus voltage (Fig. 7). The measurements of short circuit current and open circuit voltage are not enough to determine the operation point of the solar panel. However, it can be estimated using the maximum transfer theorem. When the solar panel’s Thevenin resistance and the equivalent input resistance of the POESLC are equal, then the panel is in the MPP and the duty cycle is approximately 75% at 1 sun irradiance and 30°C. The same procedure was done for 0.9, 0.8, 0.7, 0.6 and 0.5 suns, and the results are shown in Fig. 7. It is shown that for 0.7 suns the duty cycle for MPP should be 69% which is close to 75%.
B. Experimental Results This test was performed using a solar panel BP7185. Table I shows the characteristics of the panel. The experiment was performed at Simon Bolivar University Laboratory of Power Electronics at midday. TABLE II.
ELECTRIC CHARACTERISTICS OF SOLAR PANEL BP7185.
Voltage at MPP (VMPP) Current at MPP (IMPP) Voltage in open circuit (VOC) Current in short circuit (ISC)
36.5 V 5.1 A 44.8 V 5.5 A
Fig. 6 was captured with a Tektronix digital oscilloscope TDS2014B. The average input power is 110W and its maximum reach is 120W. The duty cycle is around 75%. In the simulations was demonstrated that the irradiance and
VI.
CONCLUSION
This work has introduced the design of a controller for maximum power point tracking of solar panel using fuzzy
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[5] Figure 6. Experimental results, input power, current and voltage at DC-DC converter.
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Figure 7. Simulated characteristic curves of the photovoltaic panel.
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