Zakaria Mohamed Salem Elbarbary (Electrical Engineering, King Khalid University , Abha, Saudi Arabia ) (Electrical Engineering, Faculty of Engineering, Kafrelsheikh University , Kafr el-Sheikh, Egypt )
Mohamed Abdullrahman Alranini ( King Khalid University , Abha, Saudi Arabia )Article publication date: 6 May 2021
Issue publication date: 6 July 2021
Silicon photovoltaics technology has drawbacks of high cost and power conversion efficiency. In order to extract the maximum output power of the module, maximum power point (MPP) is used by implying the nonlinear behavior of I-V characteristics. Different techniques are used regarding maximum power point tracking (MPPT). The paper aims to review the techniques of MPPT used in PV systems and review the comparison between Perturb and Observe (P&O) method and incremental conductance (IC) method that are used to track the maximum power and gives a comparative review of all those techniques.
A study of MPPT techniques for photovoltaic (PV) systems is presented. Matlab Simulink is used to find the MPP using P&O simulation along with IC simulation at a steady temperature and irradiance.
MATLAB simulations are used to implement the P&O method and IC method, which includes a PV cell connected to an MPPT-controlled boost converter. The simulation results demonstrate the accuracy of the PV model as well as the functional value of the algorithms, which has improved tracking efficiency and dynamic characteristics. P&O solution gave 94% performance when configured. P&O controller has a better time response process. As compared to the P&O method of tracking, the incremental conductance response rate was significantly slower.
In PV systems, MPPT techniques are used to optimize the PV array output power by continuously tracking the MPP under a variety of operating conditions, including cell temperature and irradiation level.
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Copyright © 2021, Zakaria Mohamed Salem Elbarbary and Mohamed Abdullrahman Alranini
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The maximum power point tracking (MPPT) is a control system-based method that enables PV module to generate all possible power they are capable of MPPT. Mechanical tracking device can be merged with to find MPPT but the control system adjusts the electrical operating point of PV modules to ensure optimal efficiency and, as a result, optimum output. Based on differences in irradiation and temperature, MPPT algorithms are used to derive the full power from the solar array. The highest power point of a PV module is the voltage at which it can output the most power (or peak power voltage). Ashok Kumar et al. (2015) mentioned that solar radiation, atmospheric temperature and solar cell temperature all influence maximum power.
The charge controller used to accommodate for the fluctuating voltage current. Gergaud et al. (2002) showed that more power is extracted from the PV module as the charge controller behaves as it is changing the load continuously when it is not. The MPPT controls the solar panel's output voltage and current and calculates the optimum operating point for supplying the maximum amount of power to the load. If the MPPT version can precisely control the continuously changing operational point where the maximum power is available, the solar cell's efficiency will be raised. Beriber and Talha (2013) and Bollipo et al. (2021) proposed several algorithms, including P&O, IC and the fuzzy logic control (FLC) method. These algorithms differ in terms of their efficacy, complexity, convergence speed, needed sensors and cost.
A typical grid-connected PV system consists mainly of the boost converter, inverter and PV module was proposed by Gonzalez et al. (2012). Figure 1 shows the configuration of grid-connected PV system configuration, boost converter and a DC-AC inverter link the PV panel to the grid. The voltage and current from the solar panel are fed into the boost converter and MPPT controller in such a system; the first goal is to impose a desired voltage to the PV panel that ensures maximum power output, known as MPP voltage.
According to the electrical scheme the PV panel and DC–DC converter can be thought of as a single unit that must be controlled to reject disturbances like load and irradiance.
The power decreases over time, with maximum power available at lower temperatures was presented in (Beriber and Talha, 2013). Furthermore Younis et al. (2012) state that when a PV module is directly linked to a load, the load impedance determines the working state of the PV module, and only the optimum load, allows the PV module to collect the maximum power. A comparative Analysis between P&O method and IC method under steady and dynamic weather conditions was presented by Lodhi et al. (2017). Under the dynamic condition, IC algorithm shows the best proficiency among these two techniques. However, IC hardware design is more complex as compared to the P&O method. While P&O methods are simple and operating point oscillates from around MPP and some power will be lost. The MPPT applications was shown by Subudhi and Pradhan (2012) which includes solar water pumping systems, satellite power supply and off-grid and grid-tied power supply systems. The purpose of this paper is to review the various techniques of MPPT used in solar systems, as well as to compare and evaluate P&O and IC methods through theoretical analysis and MATLAB simulation under steady weather condition.
The P&O algorithm enables the PV panel to achieve the MPP by varying the PV panel output voltage (Beriber and Talha, 2013). The module voltage is periodically perturbed in this method, and the output power is compared to the previous perturbing cycle (Atallah et al., 2014). As seen in Figure 2, increasing (decreasing) the voltage increases (decreases) the power on the left side of the MPP while decreasing (increasing) the power on the right side of the MPP. As a result, if the power is increased, the perturbation must remain constant to obtain the MPP. If the power reduces the perturbation reverses (Esram and Chapman, 2007).
This strategy essentially searches for a difference in PV cell power ( d P ) and then a change in PV cell voltage ( d V ) . As a function of the obtained values, D is perturbed. The actual point appears to be in the left half of the MPP if the d P / d V is positive; if the d P / d V is negative, the actual point appears to be in the right half. Additionally, this step continues until ( d P / d V ) equals zero.
At the extreme point of any P-V curve, MPP is defined by (2), (3) and (4) are used to determined MPP's location (to the left or right).
(1) d P P V d V P V = 0 At MPP (2) d P P V d V P V > 0 Left side of MPP (3) d P P V d V P V < 0 Right side of MPPFigure 3 shows the A flowchart of P&O technique.
The merits of P&O are high tracking capability, simple and fast dynamic. The de-merits are oscillations around the MPP, unable to track exact MPP under PSCs and high power loss in stable conditions (Bollipo et al., 2021).
Can be rewritten as
(6) < d I d V = − I V , At MPP d I d V >− I V , Left of MPP d I d V < − I V , Right of MPPThe flowchart is shown in Figure 4.
The merit of IC technique is low oscillations around the MPP. The de-merits are different steps require complex and expensive controls (Bollipo et al., 2021).
It is called voltage ratio method. The control technique requires contrasting the PV voltage with a defined reference voltage equal to V M P P .
Nevertheless, the V O C / V M P P ratio is influenced by the solar cell temperature. Any minor variation of the V O C after the sample will cause a significant shift in the follow-up time of the MPP.
The merit of CV is best implying where temperature varies very little. The de-merits are oscillations around the MPP and slow tracking (Atallah et al., 2014; Bollipo et al., 2021).
V MPP can be estimated using the analytical relationship as shown here:
(7) V MPP = K v * V o cThe constant K v is between 0.78 and 0.92. At the load end, the PV unit is opened for a fraction of a second, and V o c is determined, after which V MPP is estimated. V o c is sampled every few seconds during this process, and the value of V MPP is changed (Subudhi and Pradhan, 2012).
The merits of OCV are that requires less no of sensors and complexity of the circuit is less. The de–merit is higher power loss under PSCs. (Bollipo et al., 2021).
It uses the PV module's current. In usual circumstances, current at MPP I MPP occurs close short circuit current I s c under some random environmental conditions (Bollipo et al., 2021; Kota and Bhukya, 2017). Based on these V-I characteristics, a mathematical relationship between I MPP and I s c is
(8) I MPP = K i * I s cThe merit of SCC is simple and precise with less hardware computation. The de-merits are short circuit current must be calculated on a regular basis, and high power loss at dynamic weather conditions (Bollipo et al., 2021).
The switching process of the converter produces voltage and current ripple on the PV array if a PV array is connected to a converter. The PV system uses this ripple to execute MPPT in the RCC technique. (Subudhi and Pradhan, 2012; Esram et al., 2006).
The merit of RCC is that there is no need for artificial perturbation. The de-merit is that accurate mathematical calculations are required.
FLC consists of fuzzy rule, fuzzification and defuzzification (Reisi et al., 2013). The controller achieves high efficiency regardless of whether the information is correct or not (Bollipo et al., 2021; Esram and Chapman, 2007).
The flowchart of this method is appeared in Figure 5.
The merit of FLC is that no need of mathematical model and knowledge of the PV system. The de-merit is the tuning complexity of the membership function, scaling factor, and control rules that is presented by FLC. (Mohapatra et al., 2017; Bollipo et al., 2021).
Require no detailed information about the system. The ANN MPPT can be denoted by a directed graph, with the nodes and edges representing neurons and synapses, accordingly.
The merits of ANN are once trained with input sets, can able to track any PSC and, it is fast tracking and handle more complex problems. The demerits are the requirement of PV system information for training, storage of enormous data makes the technique a bit costly, and parameter tuning. (Bahgat et al., 2005; Bollipo et al., 2021; Mohapatra et al., 2017).
A highly sophisticated intelligence-based SMC is designed for quickly tracking the MPP without compromising in its efficiency. Two modes of operation: approaching mode and sliding mode. The methodology's basic concept is to use the current of the DC link capacitor to control the DC–DC converter.
The merits of SMC are very precise in tracking and well applicable for non-linear systems. The de-merit is the sliding surface choices have a strong impact on the efficiency of the SMC (Bollipo et al., 2021; Mohapatra et al., 2017).
It will locate the MPP with the help of the centered differentiation which is newer of its type. It's most widely used to solve nonlinear problems involving the least square approximation.
The merits of this method are the tracking is accurate with less time and no need for PV system knowledge. The de-merit is complex calculation (Mohapatra et al., 2017; Subudhi and Pradhan, 2012; Bollipo et al., 2021).
Table 2 shows the comparison of MPPT techniques based on intelligent algorithms (Reisi et al., 2013; Bollipo et al., 2021; Yu, 2018).
Cuckoo's nature is used as a metaphor for the representation of choosing the best solution during the process of MPP tracking.
The merits of CS are high convergence speed along with higher efficiency, bulk randomization and more robust in performance with lesser variables. The de-merit is composite mathematical modelling (Mohapatra et al., 2017; Bollipo et al., 2021).
This PSO also a bio-inspired algorithm is taken from the analogy of bird flocking. It takes a few assumptions for the process of obtaining the best solution.
The merits of PSO are the bio-inspired nature of tracking is helpful for accurate tracking of global maximum power point (GMPP) and fast-tracking in variable conditions. The de-merit is that the objective function is a little complicated since it is dependent on the particle's velocity. (Bollipo et al., 2021).
The merits of GWO are the decrement in both transient and steady-state oscillations, robust with better tracking efficiency and along with fewer variables. The de-merit is higher cost and computational time due to the large search space (Mohapatra et al., 2017; Bollipo et al., 2021).
The merits of ACO are convergence is independent of initial sample position, simple control strategy, low cost and can handle the various PSCs due to robustness. The de-merits are simultaneous optimization of four variables is to be at a time which is a tough task for the controller and complex estimations (Dorigo et al., 1996; Mohapatra et al., 2017).
This food-finding method is well-used in PV systems to find the optimum point by employing the correct activation function. Its efficiency is almost 99.99%. When the shading patterns changes instantaneously, efficiencies decrease.
The merit of ABC is that it uses fewer control parameters. The de-merits are slow tracking, complex and local maximum point tracking (LMPP) may be affected by less control parameters (Bollipo et al., 2021; Mohapatra et al., 2017).
Table 3 shows the comparison of MPPT techniques based on optimization algorithms (Mohanty et al., 2017).
The merit of FPSO is the switching losses are decreased. PSO avoids the conventional usage of PI controller by tuning the membership functions and control rules by itself. The de-merit is that a portion of approximation and trial and error have to be included while designing the fuzzy rules and rule base on human intelligence (Bollipo et al., 2021).
The merits of GWO-P&O are faster convergence speed, no oscillations, high efficiency and neglects the process of tuning and its process complexity. The de-merit is a high level of mathematical computations (Bollipo et al., 2021).
The merits of PSO-P&O are much simpler in algorithm modelling and their hardware implementation and it achieves better transient performance than the conventional method. The de-merit is oscillations around the MPP.
Table 4 shows the comparison of MPPT techniques based on hybrid algorithms (Wan et al., 2019; Bollipo et al., 2021).
MATLAB Simulink software is used to model and simulate the system to verify the control technique and measure system output (photovoltaic generator, boost converter, and MPPT Tracking algorithm P&O).
The simulation results of the output power of the PV panel using the P&O process controller at steady temperature (T = 25 °C) and irradiance (E = 1,000 w/m 2 ) indicate that the P&O solution provides 94% performance. As the irradiation switches quickly, however, the P&O controller has a better time response process.
After implementing the algorithm in Simulink, voltage level, current level was improved. Figure 6 shows the output voltage, current and power of PV panel. Initial surge was observed in millisecond which was balanced by the P&O converter to obtain the constant voltage over the course of time.
The simulated model shows a steady (T = 25 °C) temperature and irradiance (E = 1,000 w/m 2 ). IC algorithm was successfully implemented in MATLAB Simulink, the output voltage was improved but response time was slower as compared to P&O algorithm. In Figure 7, the overall, performance can be observed through graph of voltage, current and the output power.
The paper reviewed the different techniques of MPPT and comparatively reviewed P&O and IC techniques used to track the MPP. Both techniques were implemented on Simulink MATLAB and results were compared. Simulink model of both MPPT techniques consist of solar panel, algorithm block and load. The P&O technique is relatively easy to implement but gets difficult to collect data during oscillation. Under conditions of steady temperature (T = 25) and irradiance (E = 1,000 w/m 2 ) the P&O solution provides 94% performance. From the data it has been demonstrated that P&O controller has a better time response process than the IC controller. Voltage, Current and Power output graph of Solar Panels were obtained and concluded that P&O method has better performance than IC and response time is quicker.
Grid-connected PV power system