Implementation of improved Perturb & Observe MPPT

Journal of King Saud University – Engineering Sciences xxx (2018) xxx–xxx

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Original article

Implementation of improved Perturb & Observe MPPT technique with confined search space for standalone photovoltaic system Muhammad Kamran a,⇑, Muhammad Mudassar a, Muhammad Rayyan Fazal a, Muhammad Usman Asghar a, Muhammad Bilal b, Rohail Asghar b a b

College of Engineering and Technology, Riphah International University, Faisalabad, Pakistan Centre for Energy Research and Development, University of Engineering and Technology, Lahore, Pakistan

a r t i c l e

i n f o

Article history: Received 4 November 2017 Accepted 29 April 2018 Available online xxxx Keywords: Perturb and Observe MPPT Solar tracker Solar PV

a b s t r a c t Solar photovoltaic technology has been adopted by various global PV markets with 227 GW cumulative globally installed PV capacity in 2015 replacing the conventional fossil fuel energy resources. However, efficiency is still a big challenge for researchers and PV industry. This paper proposes a solar tracker and modified Perturb and Observe (P&O) algorithm for the standalone solar photo-voltaic system. Proposed algorithm confines the search space of the power curve to 10% area that contains Maximum Power Point (MPP) and starts perturbation and observation within that limited search space. The proposed P&O algorithm was simulated in MATLAB/Simulink. Solar tracker makes sure the availability of uniform and maximum irradiance to the solar module throughout the course of the sun during the day. Confinement of the algorithm’s search space lessened the response time to the changing weather conditions that in return decreases the steady-state oscillations at the MPP. Integration of the solar tracker and improved P&O MPPT algorithm provided the better quality and conditioned electricity to the load. The proposed system was experimentally steered whose results verified the effectiveness of the proposed P&O algorithm. Ó 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction Among renewables, solar Photovoltaic (PV) has the capacity to compete the conventional fossil fuel-based electricity market. Till 2015, global cumulative PV capacity was about 227 GW, out of which 50 GW record capacity was added in 2015 (REN21, 2016). In various power sectors, PV has achieved solar PV grid parity: a situation when the cost of electricity from grid becomes equal to the cost of electricity from solar PV (Karneyeva and Wüstenhagen, 2017). Solar photovoltaic has been subjected to efficiency improvement since the invention of the photovoltaic cell. Once the photovoltaic cell is made in the laboratory or industry, its efficiency improvement measures cannot be taken. However, ⇑ Corresponding author. E-mail address: [email protected] (M. Kamran). Peer review under responsibility of King Saud University.

Production and hosting by Elsevier

solar trackers and Maximum Power Point Trackers (MPPT) are used to getting the maximum out of the solar modules what they can provide (Ahmed and Salam, 2015). As the sun moves through the sky from east to west, solar radiations accomplished by the solar panel are continuously varied resulting the degraded performance of the solar panel; the PV cell starts operating below its maximum power. Similarly, at higher temperatures performance of the solar module is degraded (Almasoud and Gandayh, 2015; Ashfaq et al., 2017) Varying insolation changes all the parameters (PMAX, VMAX, IMAX, VOC, ISC) of the solar cell shown in characteristics curves of the PV cell in Fig. 1. Solar trackers are used to orienting the PV modules towards the sun to maximize the solar irradiance coupling. Conversely, PV module performance is always vulnerable to the insolation variations because of the cloudy season. MPPT is used to let the photovoltaic cell function at its maximum power point by properly adjusting the duty cycle of the converter. Till now, various MPPT algorithms have been implemented by the researchers in various papers. Incremental Conductance (INC): it compares the slope of the power curve and determines whether to increment or decrement the duty ratio (Shahid et al., 2018), Perturb & Observe: it observes the voltage level and perturbs the voltage till it grasps the maximum power point, Artificial Neural

https://doi.org/10.1016/j.jksues.2018.04.006 1018-3639/Ó 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article in press as: Kamran, M., et al. Implementation of improved Perturb & Observe MPPT technique with confined search space for standalone photovoltaic system. Journal of King Saud University – Engineering Sciences (2018), https://doi.org/10.1016/j.jksues.2018.04.006

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M. Kamran et al. / Journal of King Saud University – Engineering Sciences xxx (2018) xxx–xxx

The paper is structured as: Section 1 introduces the topic and the literature review. Section 2 presents the methodology adopted to perform the study. Section 3 presents the results and discussion of the proposed algorithm. Section 4 concludes the paper. 2. Methodology 2.1. Modeling of photovoltaic cell

Fig. 1. I-V and P-V characteristics of solar PV cell.

Network (ANN), Constant voltage (CV), Fuzzy Logic (FL) and Particle Swarm Optimization (PSO) are some of the MPPT algorithms that are widely used. All these algorithms have their own characteristics regarding complexity, convergence speed, step response, steady state oscillations about MPP and required electronics equipment (de Cesare et al., 2006; Roy Chowdhury and Saha, 2010). Kamarzaman and Tan (2014), Saravanan and Ramesh Babu (2016) presented a detailed review of various MPPT algorithms. Alik et al. (2015) reviewed P&O algorithm for PV systems. Alik and Jusoh (2017), Huynh et al. (2013a), Liu and Lopes (2004), Liu Chun-xia and Liu Li-qun (2009), Razali and Rahim (2011), Sridhar et al. (2010), Youngseok Jung et al. (2005) have presented modified and improved versions of the P&O algorithm with little bit improvements in results reducing the steady state oscillations. INC is complex than P&O algorithm because of the involvement of the measurement of the slope of the power curve. Improvements and modifications in INC algorithm have been studied by various authors (Kumar et al., 2014; Putri et al., 2015; Radjai et al., 2014; Tey and Mekhilef, 2014; Visweswara, 2014). Dzung et al. (2010), Ramaprabha et al. (2011) have implemented the MPPT using ANN algorithms. Fuzzy Logic Control (FLC) has been used by (Padmanabhan et al., 2012) to implement the MPPT algorithm for solar PV systems. Particle Swarm Optimization (PSO) has been studied and improved by (Huynh et al., 2013b; Ishaque et al., 2012) by removing steady-state oscillations in solar PV systems. P&O is the least complex algorithm but its conventional versions carry the steady state oscillations at the maximum power point which have been removed in the proposed algorithm by limiting the searching area of the maximum power point containing region. Tey et al. (2018) introduced differential evolution algorithm by improving global search space that tracked the maximum power point within 2 s with 99% accuracy. Priyadarshi et al. (2018) proposed an intelligent Fuzzy Particle Swarm Optimization (FPSO) under varying atmospheric and load conditions. Alik and Jusoh (2018) simulated and implemented an enhanced P&O MPPT algorithm and stated that the presented modified algorithm was 99.96% efficient. Mao et al. (2017) introduced a novel MPPT based on two-stage Particle Swarm Optimization. The implemented algorithm reduced the steady-state oscillations and improved the output power as compared to the conventional PSO algorithm. This paper proposes a confined search space based improved P&O algorithm integrated with a solar tracker. Solar tracker makes sure the maximum coupling of the irradiance with the solar panel by keeping the panel always normal to the sun. The proposed algorithm first divides the power curve into three regions: Area 1 and area 3 are the left and right regions to the MPP respectively. Area 2 is the intermediate region of the power curve containing 10% area of the power curve and MPP lies in this area 2. This reduction in the search space reduces the step response time to reach the maximum power and the steady-state oscillations at the maximum power point.

The solar photovoltaic cell is fabricated of the semiconductor materials with rear side positive and sun facing side as negative. Whenever the sunlight falls on the PV materials, it generates electrons flowing in the external circuit known as photocurrent (Meskani et al., 2015) or short circuit current and is calculated by equation (i). In modeling of the PV cell, it is indicated by a current source as shown in Fig. 2. There appears a voltage at the output terminal if it is open circuited and called open circuit voltage ðV oc Þ calculated by equation (ii). This voltage causes a current through the P-N junction just like a diode. This diode and the current source (Iph) are put in parallel as depicted in Fig. 2. As the photogenerated current starts flowing, some of the electron-hole recombination occurs that reduces the originally generated electrons; this loss of current is presented by a shunt resistance (Rsh). Series resistance (Rs) indicates the resistance faced by the current as it flows through the bulk material, external metal contacts and to the load. Manufacturers always manage to keep the effects of both these resistances as low as possible to improve the working of the PV module. Fig. 2 indicates the single diode model of the solar photovoltaic cell The photocurrent, also known as short-circuit current is generated when the solar rays strike the solar module and is calculated by Eq. (1)

Isc ¼

G ½Iscr þ K i ðT c T r Þ 1000

ð1Þ

where G solar irradiance, Isc photocurrent, Iscr reverse saturation current, Ki temperature coefficient, Tc cell temperature, Tr reference temperature The open circuit voltage as generated at the output of the single diode model of the solar photovoltaic cell is calculated by Eq. (2)

V oc ¼ ln

Isc nkT c þ1 Io q

ð2Þ

where Voc open circuit voltage, Isc photo current, I0 saturation current, n ideality factor, k Boltzmann constant, Tc cell temperature, q Electron charge Eq. (3) gives the relation between the voltage and current of the solar cell.

qV 1 Iph I ¼ Is exp kT

ð3Þ

where Is saturation current, q Electron charge, V voltage across the diode, k Boltzman constant, T absolute temperature (K), Iph light generated current

Fig. 2. Single diode model of solar PV cell.


M. Kamran et al. / Journal of King Saud University – Engineering Sciences xxx (2018) xxx–xxx Table 1 Specification of solar module TTB12W. Characteristic

Value

Rated Power (Pmax) Tolerance Voltage Maximum Power (Vmax) current at Maximum power (Imax) voltage at Open circuit (VOC) Photo Current (ISC)

12 W 0 ± 3% 17.8 V 0.68A 21.6 V 0.81A

Experimental verification of the proposed algorithm was validated on a low voltage solar module. Specifications of solar module TTB12W are presented in Table 1. The solar module provides 12 W with VMPP 17.8 V and IMPP 0.68A at standard test conditions (250C and 1 kW/m2). Fig. 3 shows the characteristics curves of the module used in the experimental setup. Characteristics curve of the PV module stipulates a distinctive point where the module provides maximum power and highest efficiency as shown in Fig. 1; this maximum power point is always prone to two important factors: solar irradiance and the temperature. It is necessary to keep the solar module vertical to the sun for maximum solar coupling. Fig. 4 displays the influence of varying solar irradiance on the P-V curve of the solar panel. 2.2. Solar tracker The solar rays obtained by the solar module subject to a continuous variation. A solar tracker is used to track the sun to enhance the solar irradiance coupling. In PV systems, PV modules are

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mounted on solar trackers so that PV modules are always directed normal toward the sun. As the sun travels from east to west throughout the day, the solar trackers make sure the consistent and maximum production of electricity that evenly charges the batteries and hence increases their lifetime. In indoor lighting systems, solar trackers are used to focusing the sunlight at Fresnel lens’ focal length to be collected by the optical fiber. In the proposed solar system, the twin axis tracker is implemented that tracks the sun and generates maximum electricity. In the implemented solar tracker, light sensors were used to measure the light intensity. An opaque obstacle was placed between those light sensors to avoid the light coming from other directions to accurately and quickly track the sun. Light Dependent Resistors (LDRs) were used as light sensors: Two for the East-West and two for the North-South orientation. Light intensity was measured by using a resistor in series with each LDR making a voltage dividing circuit as shown in Fig. 6. Whenever the intensity of light on LDR changes, its resistance and hence the output voltage are changed; change in intensity is translated into a change in voltage. Tracker under the shadow and in a tracking mode is shown in Fig. 5. Under the following four conditions, tracker decides the direction to be moved. If light intensity on both LDRs is same, it means their resistance would be equal (RE ¼ RW ) and hence the same voltage to the controller; solar tracker will maintain its present position. If intensity on LDRE is larger than intensity on LDRW, the resistance of LDRE will be lower than the resistance of LDRW resulting in V E > V W ; solar tracker will move toward the east .

Fig. 3. Characteristic curves of the TTB12W solar module.

Fig. 4. PV curves under varying solar irradiance.

Fig. 5. (a) Solar tracker in shadow mode. (b) solar tracker in tracking mode.


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Fig. 6. Light intensity measurement by LDR.

If intensity on LDRW is larger than intensity on LDRE, the resistance of LDRW will be lower than the resistance of LDRE resulting in V W > V E ; solar tracker will move toward the west. Based on the above-stated conditions, solar tracker flowchart is shown in Fig. 7. The light intensity of east and west LDRs are compared and accordingly decision is made whether to move the panel in east or west direction. After proper orientation in the east–west direction, south-north orientation is checked and implemented. Each time the position of the sun is changed, the controller repeats the same flowchart algorithm and orients the panel in the sun’s direction. 2.3. Conventional P&O algorithm Conventional Perturb & Observe algorithm has been extensively used due to ease of implementation as explained in the flowchart in Fig. 8. This is a continuous process of observation and perturbation till the operating point converges at the MPP. The algorithm compares the power and voltages of time (K) with the sample at a time (K-1) and predicts the time to approach to MPP. A small voltage perturbation changes the power of the solar panel if the power alteration is positive, voltage perturbation is continued in the same track. But if delta power is negative, it indicates that the MPP is far away and the perturbation is decreased to reach the MPP. Table 2 shows the summary of the P&O algorithm. Thus, in this way the whole PV curve is checked by small perturbations to find the MPP that increases the response time of the algorithm. Conversely, if the perturbation size is enlarged, it generates steady state oscillations about the MPP. Many researchers have proposed modifications in the P&O algorithm to overcome the response time problem and steady state oscillations.

Fig. 7. Flowchart of dual axis solar tracker.

2.4. Modified P&O algorithm The problems confronted in the conventional algorithm as identified above can be eliminated by the proposed modifications. The proposed algorithm limits the search space to only 10% area of the power curve that not only decreases the response time but also diminishes the steady state oscillations. Enslin et al. (1997), Huynh and Dunnigan (2016) states that the VMPP is about 76% of the open circuit voltage (VMPP = 76% of VOC). So, the P-V curve

Fig. 8. Conventional flowchart of the P&O algorithm.



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Table 2 Scheme of the P&O algorithm. Perturbation

Delta P

Resulting Perturbation

+ve +ve ve ve

+ve ve +ve ve

+ve ve ve +ve

Table 3 Area distribution of power curve.

Area 1 Area 2 Area 3

Starting (% of Voc)

Ending (% of Voc)

Total area (% of Voc)

0 70 80

70 80 100

70 10 20

has been segregated into three regions named Area1, Area 2, and Area 3 as shown in Fig. 10. Specifications of each of these areas are given in Table 3. Area1 and area 3 contain 90% area of the power curve that has been excluded from the search space. Area 2 is the MPP containing region restricting to only 10% of the PV curve and improved algorithm needs to search the maximum power point only in area 2 that reduces the step response time of the algorithm and eliminates the steady state oscillations about the MPP. Flowchart of the modified confined search spaced Perturb and Observe algorithm is shown in Fig. 9. It first measures the voltages V1 and V2 to find the MPP containing region to restrict the operating point of the solar panel into area 2 region which is only 10% of the power curve and then starts perturbation and observation. In few perturbations, MPP is achieved and maintained. Under uniform weather conditions, it sticks to the maximum power point while as the irradiance changes it finds new local maxima in the same way as described for the constant irradiance and then maintains it.

Fig. 9. Flowchart of modified P&O algorithm.

Fig. 10. Search space limitation of the power curve.

3. Results and discussion 3.1. Simulation results To authenticate the enactment of the recommended algorithm, a MATLAB/Simulink model was developed as depicted in Fig. 11. PV module simulated in this study was based on the characteristic equations of the solar cell as explained in Section 2. The MPPT algorithm was implemented by using coding of the flowchart that made it easy to understand and implement the modifications in the conventional algorithm. The IGBT based DC-DC boost converter was controlled by the gate signal which was decided by the proposed P&O algorithm. The switching frequency of the gate signal was 30 kHz and the boost converter consisted of 23 mH of the inductor and 120 mF of the capacitor. The tracking routine of the conventional and the proposed perturb and observe MPPT algorithm in constant solar irradiance can be analyzed by Figs. 12 and 13 respectively. The conventional P&O generated so many oscillations at the maximum power point with such an amplitude that it may lose the locus point under varying atmospheric conditions. In the proposed P&O algorithm, it tracked the maximum power point with the elimination of the steady state oscillations about the MPP giving a smooth PV output as shown in Fig. 13. The enlarged view of the power of both the algorithms is also shown. To confirm the enactment of the proposed Perturb and Observe algorithm, it was simulated under varying atmospheric conditions like irradiance and temperature. Fig. 14 shows the outputs under fluctuating solar irradiance and temperature. Solar irradiance was varied from 1 kW/m2 to 0.25 kW/m2 in various stages and the temperature was varied from 0 °C to 50 °C. Power, voltage and duty cycle profile exhibited the same pattern as the irradiance profile. Reduction in oscillations as the irradiance changed can be viewed in the power profile. A sudden variation in temperature created disturbance in the power and voltage profile. Duty cycle abruptly decreased as the irradiance and PV cell temperature increased. The results established the fact that under uniform atmospheric conditions all the algorithms tracked the MPP accurately but the conventional algorithm carried the problem of steady-state oscillations under abruptly changing weather conditions. The proposed algorithm accurately tracked the MPP under both uniform and varying atmospheric situations without any steady state oscillation at the maximum power point. A comparison of the proposed confined search spaced P&O algorithm with the conventional MPPT techniques is given in Table 4. Fig. 15 indicates the inductor current of the boost converter, load current and load voltage showing a varying pattern in response to the variation in solar irradiance.


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Fig. 11. Simulation model of the whole system.

Fig. 12. PV power for conventional P&O algorithm under STC (1000 W/m2 and 25 °C).

3.2. Hardware setup Solar tracker and modified P&O algorithm were implemented on hardware to validate the usefulness of the proposed PV system. For the alignment recognition of the solar module, LDRs were used as light sensors whose changing resistance was translated into a change in voltage. Two servo motors were used for the movement of the panel; one for the east-west orientation and other for the north-south orientation. Arduino Uno was used for implementing solar tracker algorithm and control of the motors. Output voltages from four LDRs circuits were given to four analog pins of the Ardu-

ino and two analog pins were used for two servo motors. The controller of the Arduino board senses the voltage and after implementing the solar tracker flow chart decides in which direction the panel is to move; control signal dictates the motor to move. The proposed modified P&O algorithm was implemented for low voltage solar system using Arduino Uno. To visualize and analyze the outputs from Arduino Uno, output data was exported to MegunoLink software through the serial port. MegunoLink receives the data from Arduino’s output pins; draws a graph of the received data and saves it in MS Excel sheet. Experimental setup of the



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Fig. 13. PV power for proposed P&O algorithm under STC (1000 W/m2 and 25 °C).

Fig. 14. PV Power, voltage and duty cycle under varying atmospheric weather conditions.

proposed MPPT technique is shown in Fig. 16. Fig. 3 shows the characteristic curves of the PV module used in hardware setup under varying solar irradiance; Maximum power that it can deliver is 12 W with VMPP 17.8 V and IMPP 0.68 A. Though the solar tracker tracks and adjust the maximum solar coupling position, solar irra-

diance continuously vary due to cloudy weather conditions and change in altitude of the sun; solar panel never can deliver maximum constant power. MPPT tracked the maximum power on the P-V curve of the solar panel and continued to operate the PV module at that point. Figs. 17 and 18 shows the PV voltage and PV


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Table 4 Comparison of different MPPT techniques from literature survey. Ref.

MPPT technique

Control variable

Converter type

Observations

Proposed

Confined search spaced P&O

Duty cycle

Boost converter

(Killi and Samanta, 2015)

Drift-free P&O algorithm

Duty ratio, dynamic perturbation

SEPIC converter

(Tang et al., 2017)

Model predictive control

MPC duty cycle

Boost converter

(Kchaou et al., 2017)

Second order sliding mode control

Duty ratio

Boost converter

(Chaieb and Sakly, 2018)

Simplified Accelerated Particle Swarm Optimization Fractional order control based incremental conductance

Duty cycle

Buck converter

Duty cycle

Boost converter

Finite Time Sliding Mode Control (FTSMC) Variable step size P&O

Duty cycle

DC-DC converter Boost converter

This paper presents a modified P&O algorithm integrated with dual axis solar tracker that confines the search space of the algorithm within the maximum power point containing area. Confinement of the algorithm’s search space lessened the response time to the changing weather conditions that in return decreases the steady-state oscillations at the MPP. The authors state that in high solar insolation, conventional P&O algorithm suffers from drift because of the wrong decision taken by the algorithm. The paper presents a modified P&O algorithm that avoids the drift in suddenly changing irradiance and accurately tracks the MPP. The paper proposes an MPC based MPPT algorithm for the large-scale marine PV system. The system maximizes the solar energy utilization by overcoming the dynamic partial shadings. The paper proposes a robust MPPT technique that uses second order sliding mode control strategy. The results prove that the algorithm provides fast response and less chattering under varying atmosphere. The paper presents a modified version of the particle swarm optimization. The results reveal that the proposed algorithm is able to track the global maximum, especially under the partial shading conditions. The paper presents a fractional order control based incremental conductance MPPT algorithm. The results show high tracking accuracy for remarkable climate changes. The integration of the fractional order control with the conventional INC algorithm increases the tracking speed by 41. 67%. The proposed FTSMC MPPT technique ensures the fast error tracking capability for the PV pumping system. The paper presents a modified P&O algorithm that increases the efficiency of the system by 16% in partial shading conditions.

(Al-Dhaifallah et al., 2018)

(El Khazane and Tissir, 2018) (Alik and Jusoh, 2018)

Duty cycle

Fig. 15. Inductor current, load current and load voltage under varying weather conditions.

power waveforms of the modified P&O algorithm respectively. Power graph in Fig. 18 shows the improvement in the modified algorithm as there are no oscillations at MPP and step response time has also been reduced to less than 1 s. The hardware results validate the simulated results (Fig. 19).

4. Conclusions Solar photovoltaic technology has been adopted by various global PV markets with 227 GW cumulative globally installed PV

capacity in 2015 replacing the conventional fossil fuel energy resources. However, efficiency and conditioned output power is still a big challenge for researchers and PV industry. Power optimization strategy plays a vibrant role in the performance of the solar photovoltaic systems. This strategy is the integration of the solar tracker and the MPPT to harness the maximum solar power as it moves through the sky from east to west in a whole day. Dual axis solar tracker makes sure the maximum coupling of the sun with the solar module that maximizes the solar productivity. The proposed Perturb and Observe algorithm is the modification in the conventional algorithm that confines the search space of the



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Fig. 16. Hardware implementation of the proposed MPPT technique.

Fig. 19. PV power under varying solar irradiance.

Fig. 17. PV voltage of modified P&O algorithm.

mum power point and reduction in step response verified the improvements in the conventional Perturb and Observe algorithm for standalone solar photovoltaic systems.

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Fig. 18. PV power of modified P&O algorithm.

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