New P&O MPPT Algorithm For FPGA Implementation. EL BASRI YOUSSEF1, PETIBON STEPHANE1, ESTIBALS BRUNO1,2, ALONSO CORINNE1,2

1 CNRS; LAAS; 7 AVENUE DU COLONEL ROCHE, F-31077, TOULOUSE, FRANCE. 2 UNIVERSITE DE TOULOUSE; UPS, INSA, INP, ISAE; LAAS; F-31077, TOULOUSE, FRANCE.

Abstract— This paper proposes an FPGA

implementation of a new Maximum Power Point Tracking (MPPT) technique for Photovoltaic (PV) applications. The main idea is to develop an improved Perturb and Observe (P&O) MPPT control for an easy FPGA implementation. This new algorithm consists of a constant perturbation with an offset control which will make easier the FPGA PV power data acquisition process. This strategy of control has, in first time, been validated by simulations. After, some experimental results are obtained using Boost converter and FPGA MPPT control in real conditions for battery charging.

I. INTRODUCTION Currently, research on solar power generates great

interest in achieving the only aim to get the maximum energy that PV can provide. Generally, a power converter associated to a function called Maximum Power Point Tracking (MPPT) is introduced between the photovoltaic module (PV) and the load, constituting a photovoltaic conversion chain, to achieve this goal. The literature proposes many MPPT strategies which guarantee to work to the Maximum Power Point (MPP) of the I-V curve of the PV array. But, in practice there are numerous factors which cause the actual operating point to vary from the true MPP. For example, the MPPT commands that use search algorithms which oblige the operating point to oscillate around the MPP (Perturb & Observe). These oscillations induced by a voltage or duty cycle steps [1] cause in the finite time a power transfer loss. The same goes for the MPPT command that use the proportionality relations based to the short-circuit current (ICC) [2,3] and to open circuit voltage (VOC) [4], in order to obtain the MPP. In this case, the different parameters are specific to electrical PV characteristics and defined in the constant conditions. Thus, when atmospheric conditions variations appear (irradiance level, temperature, fluctuation (clouds),…), these parameters values are not well adapted to localize with accuracy the new MPP. These MPPT inaccuracies conspire to reduce the conversion efficiency of the PV array and therefore the overall system efficiency. In this perspective, control systems become ever more complex and multitasking, hence the need to use control systems by FPGAs. For the MPPT control, we can see in the literature several techniques for finding the maximum-power point as P&O, Hill-Climbing, Fuzzy Logic or Neural Methods. But these sequential techniques are not yet well adapted for FPGA implementation. So, the aim of our works is to explore and develop an accurate and robust MPPT control whit a minimum FPGA implementation size. The P&O MPPT algorithm, based on an Extremum Seeking control, has been chosen for this application for its stability, good accuracy and good Efficiency/Price ratio[5]. So in Section II, a conventional P&O MPPT control is studied and implemented in a FPGA for final

experimental results. Then, the principle of the proposed new P&O MPPT control is exposed in Section III. Moreover, this section contains simulation results and experimental tests of the global power management photovoltaic system in real conditions in order to evaluate this new control strategy. Finally, conclusions are drawn in Section IV.

Fig. 1 : Elementary photovoltaic conversion chain.

II. CLASSICAL P&O MPPT CONTROL.

A. MPPT Concept Initial works realized in the LAAS-CNRS laboratory about MPPT controls are focused on extremum seeking control developed by Ramon Leyva in 2004 [6]. Gradually, the control became more stable and more accurate with an MPPT efficiency of 99% thanks to a digital processing evolution [7]. The principle of this command has been theoretically and experimentally demonstrated in [6]. The different steps of this MPPT method are depicted in Fig 2 on a PPV=f(VPV) photovoltaic module characteristics.

Fig 2: Illustration of Extremum Seeking principle

The Extremum Seeking control applied to photovoltaic application shows that when the derivative of the PV power function of the PV voltage is equal to zero (dPPV / dVpv= 0), the operating power point corresponds to the MPP. Consequently,

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the algorithm senses the PV voltage (Vpv) and PV current (Ipv) to calculate the power extracted by the solar panel and find the derivative of power. If the derivative is positive, it means that the system approaches the PPM, if the derivative is negative the system moves away from the MPP. The TRACK signal lets you know how the meaning DPWM (Digital-Pulse Width Modulation) must vary to oscillate around the MPP. The inhibition delay establishes, after a fixed time interval, if the direction of maximum searching has to be maintained or should be changed. The waiting interval insures that the power converter is operating in steady-state when the decision on the change or maintenance of the control law sign is made. The detailed algorithm Fig 3 shows this principle.

Fig 3: MPPT Algorithm

In summarize: - When the derived power is positive, the Track signal

remains unchanged. - When the derived is negative and the authorized

change occurred (delay finished), the Track signal state is modified.

- Each Track signal inversion initializes the delay.

B. MPPT Implementation.

The algorithm, in the first time developed to be implemented in a microcontroller, has to be modified in order to be implemented in a FPGA. Fig. 4 shows the different functions created for this FPGA control achievement. After scanning the state variable IPV and VPV, signals are filtered in

order to calculate the power. The following block calculates the derivative based on the power variation. The signal Track is generated in function of the derivative and the timer. Finally the signal Track is integrated and the DPWM is created.

Fig. 4 : The schematic propose for MPPT

C. Experimental Results.

The problem encountered, during the implementation

of this algorithm in the FPGA, is that the system is function of the derivative accuracy and so power signal accuracy. More the derivative is true more the command is successful. The acquisition of signal voltage and current is very important because when we multiply the current with voltage, noises is multiplied and becomes difficult to remove. (Switching frequency of the converter induced noises on signals sensors) The solution used to overcome this problem and use a digital filter to smooth the sampled signals and limit the influence of noises. We chose FIR filter because its implementation in FPGA is easier (Finite Impulse Response). Equation (1) defines the output of an FIR filter (y[n]) function of its input (x[n]) thanks to a convolution of the coefficient sequence bi with the input signal for a N filter order.

∑ (1)

Fig. 5 : Experimental result of FIR filter

The experimental results have demonstrated the effectiveness of using a 16 order FIR filter. Fig. 5 shows the

Ipv

Vpv

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voltage and current sampled at input and output of the filter. The use of filter adds a delay, but greatly improves the signal and therefore improves the detection of MPP. Fig. 6 is an experimental result in real condition using a BP585 solar panel and a DC-DC Boost converter. We see PV current, PV voltage, PV power, derivate signal and Track signal. We note that the derivative following the correct power system is stable and oscillates around the MPP.

Fig. 6 : experimental result of MPPT Based of extremum Seeking

control.

However the use of a FIR filters has a default when we implement this system in a FPGA, occupation is very important. Fig. 7 is a detailed inventory of occupation of the different functions contained in this algorithm in number of logic gates (4629 total gates).

Fig. 7: FPGA occupation gates diagram for a classical P&O MPPT

control.

The filter occupies 60% of the control. In this case we use a low power micro FPGA AGL250V2 which contains 6144 logic cells. The fact is that if we want to complicate the management of converters used in solar energy while minimizing the consumption of the control system. We must develop an efficient, stable and unobtrusive MPPT control. The next of this article speak about a new MPPT control strategy based on P&O which used a constant perturbation, FPGA implementation and practical results.

III. NEW P&O MPPT

A. New P&O MPPT Principal.

The aim of this new P&O MPPT is to realize a constant perturbation of the PWM in order to find the MPP. As we can see in Fig. 8, the first step consists of a constant triangular perturbation which allows a variation of the PV voltage and consequently, the PV power. So also two PV power measurements are needed (P1, P2) to realized this P&O technique. If P1 is upper than P2, the MPP must be on the left of operating point. Therefore, we have to move the operating point (in step 2 and step 3) to achieve equality between P1 and P2. In this condition, the disturbance oscillates around the MPP.

Fig. 8 : Principle of new MPPT.

The Fig. 9 presents the general schematic circuits of the complete photovoltaic conversion chain. We can see the power part composed of DC-DC converter, a solar panel and a load. The FPGA control is divided in different simple functions. A switch block recovers the value of P1 on the rising edge and the value of P2 on falling edge of perturbation signal. The control block has its principle algorithm represented in Fig. 10. When P1> P2, the OFFSET signal is incremented, when P2> P1 the signal is decremented. The perturbation signal is integrated to produce a triangular waveform that is added to the OFFSET to create the order signal. Finally, the DPWM block creates duty cycle which controls the converter.

Fig. 9 : The schematic propose for new MPPT.

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Fig. 10 : Details of Controller blo

B. New P&O MPPT Simulations.

This part presents the first PSIM electric

order to validate the new P&O MPPT algorithoperating conditions. We can see in Figschematic of the conversion chain with the control. This control is composed of an MPcontrol (in C code) which defined the Vref incThen, a perturbation of 100Hz is inserted becontrol and the PWM function in order tosignal Vc. This perturbation frequency is castability analysis depicted in reference [6].Fincontrol Vc is compared with a triangular wavto generate the pulse width modulation requiconverter operation. Fig. 12-a) presents simthe photovoltaic power and MPPT control steady state. As we can see, this control stroscillatory behavior around the equilibrium Mefficiencies of over 99%.

Fig. 11 : PSIM schematic of a Boost photovoltaic

Simulation results, during a rapid v

photovoltaic array power, are shown in Fig. 1the maximum power point has changed, aMPPT have to adapt the system operation to Thus, the array voltage increases linearly unew maximum power point. These results allothat the accuracy and the dynamical behavsystem is quite good, with an MPPT efficiency

ock

cal simulations in hm under different g. 11 the PSIM

proposed MPPT PPT Script Block crease or decrease. etween the MPPT create the P&O

alculated from the nally, this voltage veform of 250kHz ired by the Boost

mulation results of variables during

rategy induces an MPP, with MPPT

c conversion chain.

variation of the 12-b). In this case, and therefore, the the new situation.

until reaching the ow us to conclude

vior of the global y of 99%.

a) Steady State B

b) Transient BehFig. 12 : PSIM Simulation Results. Ph

with the improved P&O M

C. Experimental Results.

To verify the proposed algorithprototype, presented in Fig. 13, of inserted between the solar panel abattery. The proposed MPPT algoriIgloo nano AGLN250 FPGA of Aclock speed. A capture card composedigital converters ADC124S101. (Sample-Per-Seconde) for new impfor classical implementation, allowtwelve bits electrical signals, as IPV, V

a) b) Fig. 13 : Prototype a) Boost converter b

board

ehavior

havior otovoltaic conversion Chain MPPT Control

hm, we have developed a a DC-DC Boost converter

and a 24 Volts Lead-Acid ithm is implemented in an

Actel operated at 20 MHz ed of 4 series Analogical to

Operating at 200 Sps plementation and 51.2k Sps ws us to scan 4 channels VPV, IBAT and VBAT.

c)

b) Acquisition board c) FPGA

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The DC-DC converter is controlled by a duty cycle (D) at a frequency of 250 kHz, with a resolution of 8 bits or 256 values of duty cycle. As it can be seen in Fig. 14, this frequency has been chosen for optimum efficiency on any day of operation. In the usual case defined for photovoltaic applications, a solar panel works 48% of its time at 50% of its maximal power, see PV European efficiency calculation in [8]. In this working condition, a switching frequency of 250 kHz is the best frequency for our application.

Fig. 14: Efficiency of Boost converter function of input power for

different frequencies.

The perturbation created by varying the voltage of 1V in 5ms. We can determine the variation of duty ratio required starting from the equations of type boost converter (1) & (2). D (1)

∆V V ∆D (2) This gives us a variation of 4.16% of the duty cycle for an input voltage variation of ΔVin = 1V and Vout=24V. You can see in Fig. 15 the power extracted from the solar panel. The system oscillates around the MPP in steady state when the error between P1 and P2 is low. For a maximum power of 53Watts, the mean power is 52.4Watts so a MPPT efficiency is 99%. Fig. 16 also shows a transient response of MPPT, too. When there is a variation of the PV system stabilizes quickly. The system has the same behavior which can be observed in the simulation.

Fig. 15 : Experimental Results in steady state. Photovoltaic array

power(Ppv), Voltage control (Vc), Current (Ipv) and Voltage (Vpv).

Fig. 16 : Experimental Results in transient mode. Photovoltaic array power(Ppv), Voltage control (Vc), Current (Ipv) and Voltage (Vpv).

The performances of our MPPT control have been

evaluated by means of experimental trial obtained thanks to a solar simulator AGILTENT E4360A on 4 hours of operation (One operating day reduce to 4h). Experimental results are exposed in the Fig. 17 where the top curve is the MPPT efficiency and the other, the PV power production. During this test, the proposed MPPT control has proved to be suitable thanks to the MPPT efficiencies between 96% and 99% for PV power from 10W to 55W. This variation of MPPT efficiency exists because the perturbation is constant. A possible evolution of this control, in order to increase the MPPT efficiency on a wide range of power, is to adapt the amplitude of the perturbation (Vpertub.) which will induce a variation of the PV power oscillation function of the operating MPP as we can see in the reference [7,9].

Fig. 17 : Experimental Results. MPPT Efficiency & PV Power

We have seen through experimental tests that the performances between the two commands were similar in terms of efficiency. The major difference lies in the fact that,

Vpv

Ipv

Ppv

Vc

Vpv

Ipv

Ppv

Vc

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for the same performance, the new architectfilter, the occupation of architecture in the reduced. Fig. 18 is a summary of the differethe block. The MPPT occupies 1105 logic capacity FPGA Actel against 75% for the prev

Fig. 18: FPGA occupation gates diagram for thecontrol.

IV. CONCLUSIONS

This paper treats the problematic of timplementation in FPGA. Since controlphotovoltaic applications is constantly increadevelop accurate MPPT control for implementation. That’s why we have develoP&O MPPT control with an accurate trackingpower for an easy FPGA implementation.

In the classical P&O technique, an asamples of the array power is used to find a gthe power function of the voltage. So, this thigh frequency ADC at 51.2ksps and compleFIR filter, for an accurate calculation of the de

The innovation of the new P&O coinserting a constant triangular perturbation order to find the MPP. With a perfectsynchronization between the signal pertumeasurements acquisition process, the frequecan be reduced to 200 sps without digital filterthe FPGA implementation size is reduced by same MPPT performances. From 4629 logclassical P&O technique against 1105 gates fcontrol, as depicted in Fig. 19.

Simulation and experimental tests ehave been completed demonstrating high pernew P&O algorithm. We have validated this conditions with DC-DC converter and MPPand also shown its correct working in steadytransients.

ture does not use FPGA is greatly

ent dimensions of cells or 18% of

vious version.

e New P&O MPPT

the MPPT control l complexity of asing, we need to

easier FPGA oped an improved g of maximum PV

average of several great derivative of echnique requires

ex digital filter, as erivative. oncept, consist in on the PWM in

t control of the urbation and the ency of the ADC r need. Moreover, a factor 4 for the

gic gates for the for this new P&O

employing FPGA rformances of this

technique in real PT digital control y-state and during

Fig. 19: FPGA occupation GatMPPT and the New Imp

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