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How to extract the Maximum Power available from Photovoltaic Systems - By : Ali Chikh, Ambrish Chandra,

How to extract the Maximum Power available from Photovoltaic Systems


Ali Chikh
Ali Chikh is conpleting a Ph.D. in electrical engineering at the ÉTS. His research includes renewable energy resources, power quality and active power filters.

Ambrish Chandra
Ambrish Chandra Author profile
Ambrish Chandra is a professor in the Electrical Engineering Department at ÉTS. His research interests focus on the quality of voltage, power factor correction, control and integration of renewable energy sources.

Editor’s Note

Thinking about a photovoltaic (PV) system for your home or business one of these days?  Having one already? This article presents a new method to extract the maximum power available in PV systems with reduced hardware. The proposed technique developed by researchers from the the Power Electronics and Industrial Control Research Group (GREPCI) lab at the École de technologie supérieure (ÉTS) of Montreal helps to reduce the hardware set-up and response time while increasing the efficiency.

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Introduction

Nowadays, environmental problems produced by the use of classical power generation seem to acquire more attention around the world. The extensive use of these types of energies increases the earth temperature and warming. Currently, renewable energies, especially PV energy, considered as palliative energy resource, are growing rapidly and are used in several applications. They have long life, require minimum maintenance and produce no noise or disturbing effects. Therefore, they are environmental friendly. However, they suffer from a relatively low conversion efficiency, which makes their optimization necessary. This is done by extracting their maximum power for fluctuating climatic environments and known as the “maximum power point tracking” (MPPT) which is done through a particular control of a converter.

It is noticed, from Fig. 1, that the array power has a unique maximum, which varies with the solar radiation. Therefore, it is necessary to track constantly this point.

Figure 1

Fig. 1 Array power voltage curve

Literature review

Various techniques have been proposed depending on their complexity, sensors used, convergence, setup and in further aspects [1-7][19-22]. The most common methods are the Perturb and Observe (P&O) and the Incremental Conductance (InCond) [2-4][6][8][17-21][23]. For this method a perturbation is made (a tension change) and the corresponding power is measured: If it is higher, an higher tension is made till the system reached the peak in tension in a trial and error mode. Other methods based on sliding mode control and model-based control are proposed [9-11]. In this method, an incremental conductance is created and the corresponding power is measured similar to the Perturb and Observ method.

Nowadays, intelligent systems are progressively used due to their humanlike capability and ability to adapt and improve their performance. Many neural networks (NN) and fuzzy logic (FL) based techniques are proposed. [8] [12-14] [16] ][23-24][25]. The combination of NN and FL seems to be more attractive since it associates the learning capabilities of the ANN with the ability of the FL to trait inaccurate data which makes it suitable for PV applications [15]. Many methods are presented based on radial basis NN function (RBFNN) [26], on a generalized dynamic FNN (GD-FNN) [27] and on adaptive Neuro-Fuzzy Inference System (ANFIS) [28-29].

It can be noticed from the literature that all the proposed MPPT methods need information on the array voltage, array current and, in some cases, environmental parameters which is a difficult task resulting in an increased hardware with high failure probability and noise measurements.

Proposed research

The research deals with an efficient MPPT method based on the computation of the instantaneous and junction array conductances using an ANFIS cell model presented in a recent paper of the authors [15]. Moreover, climatic parameters are obtained using a de-noising based wavelet algorithm which helps reducing the hardware by using only one voltage sensor (Fig. 2).

Figure 2

Fig. 2 Proposed MPPT algorithm

The operation of this technique starts by measuring  Vpv and estimating Ipv by using the ANFIS model. Next, the thermal voltage Vt is computed. Finally, the parameters  (G,Tc) are estimated for the algorithm to operate continuously.

Proposed experimentation

The experimental setup developed includes a PV emulator, a boost converter connected to a resistive load, a gate drive circuitry, a voltage sensor, an MPPT controller implementation based on DS1104 DSP of dSPACE. Fig. 3 and 4 show the setup of the system realized in the laboratory of GREPCI at École de technologie supérieure de Montréal (ÉTS).

Figure 3

Fig. 3 Schematic experimental setup

 

Fig. 3 Hardware setup: 1. LC Filter; 2. Grid-Side; 3. Nonlinear Load; 4. Sensors; 5.Phase Resistive Load; 6. IGBT Driving Circuits; 7. PV-Side; 8. dSpace; 9. DC-Link Capacitor; 10. Inverter; 11. PC.

Fig. 4 Hardware setup: 1. LC Filter; 2. Grid-Side; 3. Nonlinear Load; 4. Sensors; 5.Phase Resistive Load; 6. IGBT Driving Circuits; 7. PV-Side; 8. dSpace; 9. DC-Link Capacitor; 10. Inverter; 11. PC.

To highlight the capability of the proposed scheme, the PV emulator is programmed to make three step changes in its output current corresponding to a variation in solar radiation from 250 W/m2, 500 W/m2 , 750 W/m2 to 1000 W/m2. The corresponding extracted power is shown in Fig. 5 and Table 1.

Figure 4

Fig. 5 Extracted PV power

Table 1

Table 1 Extracted power with error

From Fig. 5, it is noticed that the PV power follows the solar radiation changes instantly with a minimum mean error of  2% (Table 1).
All the proposed MPPT techniques in the literature use at least two sensors which increases their hardware and noise measurements with losses varying between 4.46% to  8% at 1000 W/m2.
On the other hand, these methods show diverse results in terms of response time that varies from 5 s to 10 ms. However, the proposed methodology demonstrates fast response time which is around 1.7 ms.

Conclusion and possible applications

The proposed method has four major advantages over the techniques reported in the literature, which are:
1. reduced hardware implementation (only voltage sensor used) (Fig. 6),
2. fast tracking capability,
3. very low error in the tracked power and finally,
4. estimation of the environment parameters,
5. universal so that it can be used as well as for stand-alone and grid-connected PV applications.

Figure 5a

Fig. 6.a Hardware of the existing methods: They use at least 2 sensors (4 in this example)

Figure 5b

Fig. 6.b Hardware of the proposed method: It uses only one sensor

Fig. 6 Hardware comparison between the proposed method (b) and the existing ones (a)

Additional Information

We invite you to read the following research paper to get more information regarding this project :

Chikh, A. and Chandra A. 2015. “An Optimum Maximum Power Point Tracking Algorithm for PV Systems With Climatic Parameters Estimation”. IEEE Transactions on Sustainable Energy, vol. 6, No 2, April 2015.

GREPCI logoConsult our web site for more information on the other projects conducted in the Power Electronics and Industrial Control Research Group (GREPCI) and the students wanted for our projects .

 

 

Ali Chikh

Author's profile

Ali Chikh is conpleting a Ph.D. in electrical engineering at the ÉTS. His research includes renewable energy resources, power quality and active power filters.

Program : Electrical Engineering 

Research laboratories : GREPCI – Power Electronics and Industrial Control Research Group 

Author profile

Ambrish Chandra

Author's profile

Ambrish Chandra is a professor in the Electrical Engineering Department at ÉTS. His research interests focus on the quality of voltage, power factor correction, control and integration of renewable energy sources.

Program : Electrical Engineering 

Research laboratories : GREPCI – Power Electronics and Industrial Control Research Group 

Author profile


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