ARID ZONE JOURNAL OF ENGINEERING, TECHNOLOGY & ENVIRONMENT AZOJETE December 2023. Vol. 19(4):747-758 Published by the Faculty of Engineering, University of Maiduguri, Maiduguri, Nigeria. Print ISSN: 1596-2490, Electronic ISSN: 2545-5818 www.azojete.com.ng Corresponding author’s e-mail address: amusatramoni@gmail.com 733 MAXIMUM POWER POINT TRACKING IN PARTIAL SHADED PHOTOVOLTAIC SYSTEM USING SMELL AGENT OPTIMIZATION ALGORITHM R. O. Amusat1, S. Shodiya2, Y. H. Ngadda1 and D. I. Malgwi1 1Department of Physics, University of Maiduguri, Borno Nigeria 2Department of Mechanical Engineering, University of Maiduguri, Borno Nigeria *Corresponding author's email address: amusatramoni@gmail.com ARTICLE INFORMATION Submitted 27 Aug, 2023 Revised 9 Oct, 2023 Accepted 11 Oct, 2023 Keywords: PV array Maximum Power Point Tracking Partial Shading Conditions and Booster Converter ABSTRACT With partial shading conditions, it is essential to acquire Maximum Power Point at which the Photovoltaic systems (PV) operate effectively despite the variation in the cell temperature and incident angle of sunlight rays on the panels. This study explores the use of a Smell Agent Optimization (SAO) algorithm for Maximum Power Point Tracking (MPPT) in partial shaded PV systems. The proposed MPPT system is composed of a PV model, a DC-DC converter model and a control part. The Smell Agent Algorithm (SAA) was adopted in the control part of the MPPT system to implement the optimization algorithm using four different shading patterns (SPs) and to calculate the optimal switching duty cycle of the DC-DC converter. The effectiveness of the proposed system was verified using simulations in the MATLAB/Simulink environment. The SAO respectively track maximum values for Power, Voltage and Current as 845.8476 W, 211.7308 V, 3.99492 A while the maximum values for Power, Voltage and Current for Perturb and Observe (P and O) are 845.0465 W, 211.6305 V, 3.993028 A respectively during SP1. The results showed that the SAO algorithm has excellent tracking results in terms of convergence speed, accuracy, power extracted stability, and dynamic response in reaching the optimum point. 1.0 Introduction Solar energy is the pathway to an environmentally friendly future energy. Every single day, the sun generates far greater quantities of energy than we require for our daily activities. This allows researchers to create models that can predict the future prospects of renewable energy (Amusat et al., 2020). Using renewable energy (Solar energy) to generate electric power has an advantage over other traditional power source of energy owing to its ability to transform directly into electrical energy using the photovoltaic (PV) solar cells. A photovoltaic system is made of semiconducting materials that convert incident photon into electric energy with its conducting qualities adjusted by changing the type and amount of impurities added to the initial semiconducting substance. Solar cells or photovoltaic cells can be combined together to make panels or modules. Large photovoltaic arrays can be created by grouping panels’ together (Amusat et al., 2020). A solar panel (with numerous cells connected in series and/or parallel) or a set of panels is commonly referred to as an array (Surya and Babu, 2012). Many efforts have been made in the field of modeling and simulation of diode-based photovoltaic cells but, tracking of the maximum power point at which solar panel operates effectively, incorporating a shading parameter into the conventional PV-array model that will account for varying degrees of shading across individual cells of a given PV-module are the http://www.azojete.com.ng/ mailto:amusatramoni@gmail.com mailto:amusatramoni@gmail.com mailto:amusatramoni@gmail.com Arid Zone Journal of Engineering, Technology and Environment, Dec, 2023; Vol. 19(4):747-758. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: amusatramoni@gmail.com 748 major concern (Claude and Nazih, 2015). Maximum Power Point Trackers are devices that maximize the output power of Photovoltaic Systems for different ambient conditions and maintain the operation of PVs at their maximum power points (Derbeli et al., 2021). Many studies on MPPT techniques have been conducted (Claude and Nazih, 2015). The MPPT techniques: The Perturbed and Observation (P and O) and Climbing Hill (H and C) techniques are widely used due to their ease of implementation and low sensor requirements (Attou et al., 2014; Parween, 2019; Jiang et al., 2017; Srushti and Uttam 2013; Lokanadham and Vijaya, 2012). The incremental conductance algorithms compare PV array incremental and instantaneous conductance which track a PV system’s maximum power point and transfer high PV energy to the load (William and Ramesh, 2013; Hsieh et al., 2013; Rajaram and Sharma, 2015). Short circuit current (Isc) and open circuit voltage (Voc) can be used to determine the MPP current (IMPP) and MPP voltage (VMPP) (Hadji et al., 2013; Subudhi and Pradhan, 2012). The main disadvantage of this MPPT is the uncertainty that exists in this tracking approach, as demonstrated by the approximate relationship of Isc to IMPP and Voc to VMPP. Also, most research does not consider the effect of partial shading (PSC) on the PV system when determining the MPPT. Research on PV maximum power point tracking has been conducted in recent years and the effect of partial shading has been considered. Researchers discovered that conventional methods have been very poor in tracking the performance with many of them failing to track the true MPP under partial shading of PV arrays. (Nur and Chee, 2014). As a result of the shortcomings of traditional MPPT algorithms, many research studies have used artificial intelligence (AI) techniques to address the stated limitations. Such as Fuzzy Logic Controller (FLC) and Neural Network (NN) (Seyedmahmoudiana et al., 2016; Ramana and Jena, 2015; Lalam and Kalpana, 2013; Zaki et al., 2012; Younis et al., 2012). The FLC which is based on rational data interpretation can find means of retrieving the maximum power under partially shaded and rapidly varying atmospheric conditions. Even though these techniques are efficient when dealing with the nonlinear characteristic I - V curves, they require a significant amount of computation. FLC, for example, deals with operations such as fuzzification, rule- based storage, inference mechanisms, and defuzzification. The enormous amount of data needed for training is a major constraint for NN. More so, since the operational conditions of the PV system change continuously, MPPT must respond instantaneously to changes in irradiance and temperature. Therefore, low -cost processors cannot be employed in such a system. Their tracking efficiencies are affected by high control circuit complexity and large data processors for training the system. There are new MPPT algorithms, inspired by nature and biological structure that were developed to track PV array output power. Among these are Particle Swarm Optimization and Grasshopper Optimization algorithms (Sadegh et al., 2022; Mansoor et al., 2020; Saxena et al., 2018; Saremi et al., 2017; Kashif et al., 2012; Miyatake et al., 2011; Chen et al., 2010). Some bio-inspired optimizations were introduced to tackle the limitations of conventional techniques based on the literature review. But, handling the most extreme environmental changes such as changing irradiance and PSCs, require an improvement in the aspects of computational burden, precision and accuracy of the result, ease and the convergence of the implementation. Therefore, efforts are made nonstop to look for more current algorithms with good performance. The SAO, which has not been exploit in PV is a current introduced meta-heuristic algorithm, recognized for its robust characteristics in tackling different optimization problems mentioned above. In this study, SAO was explored in tracking the maximum power point of the PV system under partial shading conditions. file:///C:/user/Downloads/azojete143/www.azojete.com.ng file:///C:/Users/Engr.%20Samuel/Documents/Engr%20Oyeniyi/azojete/AZOJETE%20ARCHIVE/UPLOAD/VOL%2019%20NO%204/engr.ibraheem1@gmail.com Amusat et al: Maximum Power Point Tracking in Partial Shaded Photovoltaic System using Smell Agent Optimization Algorithm. AZOJETE, 19(4):747-758. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: amusatramoni@gmail.com 749 2 Materials and Method 2.1 Materials The materials used for this study are the Solar panel of model Tala Power Solar Systems TP250MBZ with specification shown in Table 1, booster converter of the specification shown in Table 2, sensors, MPPT controller and four different SPs of solar irradiance at constant cell temperature of 25oC as shown in Figure 3 Table 1. Key specification of Tala Power Solar Systems PV array Model TP250MBZ type Polycrystalline silicon (156mm×156mm) Model Tala Power Solar Systems TP250MBZ Cell type Polycrystalline silicon Maximum Power Pmp [W] 249 Open Circuit Voltage Voc [V] 36.8 Short Circuit Current Isc [A] 8.83 Maximum Voltage Vmp [V] 30 Maximum Current Imp [A] 8.3 Temperature Coefficient of Voc [%/deg. C] -0.33 Temperature Coefficient of Isc [%/deg. C] 0.063805 Diode ideality factor 0.94812 Diode Saturated Current Io [A] 1.0132e-10 Shunt Resistance Rsh [Ω] 314.7646 Series Resistance Rsh [Ω] 0.2914 Light Generated Current IL [A] 8.8382 Number of Cell Ncell 60 Table 2. Specification of the Booster Symbol Parameters Values L Inductance 1.1478 X 10-3 Cin Input Capacitance 10 μF Co Output Capacitance 0.467 X 10-3 R Load Resistance 53 Ω 2.2 Method Figure 1 is the schematic diagram of the proposed system. An intelligent control technique based on SAO algorithm for MPPT during unexpected changes in atmospheric conditions was used. The algorithm in the system is used to track the maximum operating point of the solar panel. The SAO is a new meta-heuristic algorithm based on the evaporation of smell molecules in the form of gas and the perception capability of a smell agent. The mathematical model of the SAO consists of three modes- Sniffing, trailing and Random modes (Salawudeen et al., 2021). This is used to update the duty cycle a DC – DC converter, there by tracking the global maximum point while updating the position and velocity continuously as shown in Figure 1. This in turn gives a signal to boost the converter that maintains the operating voltage of the maximum operating point irrespective of the solar irradiance and temperature. To evaluate the performance of the algorithms, the proposed SAO-based MPPT method is implemented on a DC-DC boost converter and its performance was compared with that of the P and O algorithm using Matlab Simulink environment. http://www.azojete.com.ng/ file:///C:/Users/Engr.%20Samuel/Documents/Engr%20Oyeniyi/azojete/AZOJETE%20ARCHIVE/UPLOAD/VOL%2019%20NO%204/engr.ibraheem1@gmail.com Arid Zone Journal of Engineering, Technology and Environment, Dec, 2023; Vol. 19(4):747-758. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: amusatramoni@gmail.com 750 Figure 1: Schematic Diagram of the Proposed PV System (adopted: Attou et al., 2014). 2.2.1 Mathematical Modeling of PV Cell Photovoltaic cell models have long been a means of describing photovoltaic cell behavior. The most commonly used model for predicting energy production in photovoltaic cell is the single- diode lumped circuit model (Dominique et al., 2013). The performance of photovoltaic systems, vis - a-vis, the output current/voltage curve (I-V curve) and Power/Voltage (P-V curve) are studied using an equivalent circuit model. This equivalent circuit consists of a current source with two resistors, one connected in parallel and the other in series. Based on these electronic components, equations (1-5) were used for the photovoltaic systems (Jobeda and Simon, 2018). Figure 2 shows an equivalent circuit of a Photovoltaic with one diode. Figure 2: Equivalent Circuit of a Photovoltaic with one diode (adopted: Jieming et al., 2013). The Mathematical equations of PV from used in the program are shown in equations (1) to (5) (Jobeda and Simon, 2018). The module Ipv (photon current) under the different environmental conditions are related as Eq. (1): 𝐼𝑝𝑣(𝑇, 𝑆) = [𝐼𝑠𝑐 + 𝐾𝑖(𝑇𝑟 − 𝑇𝑜) ∗ ( 𝑆 1000 )] (1) The terms in equation (1) are: Ki is the photon current temperature coefficient; S is solar irradiance (Watt/meter square), Tr is the cell reference temperature in degree (Kelvin), ISC Short-circuit current (Ampere), To is the cell operating temperature (Kelvin) which is 25oC at STC file:///C:/user/Downloads/azojete143/www.azojete.com.ng file:///C:/Users/Engr.%20Samuel/Documents/Engr%20Oyeniyi/azojete/AZOJETE%20ARCHIVE/UPLOAD/VOL%2019%20NO%204/engr.ibraheem1@gmail.com Amusat et al: Maximum Power Point Tracking in Partial Shaded Photovoltaic System using Smell Agent Optimization Algorithm. AZOJETE, 19(4):747-758. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: amusatramoni@gmail.com 751 The reverse saturated current at standard testing condition is expressed as Eq. (2): 𝐼𝑟𝑠 = 𝐼𝑠𝑐 [𝑒𝑥𝑝 ( 𝑞𝑉𝑜𝑐 𝑁𝑠𝐴𝐾𝑇) − 1] (2) The diode saturated current varies as a cubic function of the temperature and 𝐼𝑜 can be expressed as Eq. (3): 𝐼𝑜 = 𝐼𝑟𝑠 ( 𝑇𝑜 𝑇𝑟 ) 3 𝑒𝑥𝑝 [ 𝑞 ∗ 𝐸𝑔𝑜 𝐴𝐾 ( 1 𝑇𝑟 − 1 𝑇𝑜 )] (3) In order to assure the parameters required on the consumers, a photovoltaic system has to be made a sufficient number of PV cells interconnected in series or parallel usually called module of array. Then the photon current can be expressed as Eq. (4): Ipv = Np ∗ Iph − Np ∗ I0 [exp ( qVpv NsAKT ) − 1 ] (4) Where Np and Ns are the number of PV cells connected in parallel and series respectively Iph is Photocurrent (Ampere). The output power of the PV at any given point as Eq. (5): 𝑃 = Ipv ∗ Vpv (5) 2.2.2 Mathematical Model of the Smell Agent SAO algorithm is a meta-heuristic algorithm that mimics the process of smell perception (Muhammad et al., 2023; Salawudeen et al., 2021). SAO is classified into 3 modes; these include: 2.2.2.1 Sniffing Mode The total number of molecules evaporating from the smell source is used in modeling the smell agent algorithm. Each member of the total population is assigned a vector position using Eq. (6) (Salawudeen et al., 2021): ,1 ,2 ,[ , ,... ]t t t t i q q q nX x x x= , (6) where i=1,2, 3…, n and q is the population of the smell molecule A molecule is said to obey Newton’s first law of motion (uniform velocity) as it evaporates and moves toward the direction of the smell. The rate of displacement of the smell molecule is denoted by V using Eq. (7): ,1 ,2 ,[v , v ,..., v ]t t t t i q q q nV = , (7) where i=1,2, 3…, n The equation (7) offers an appropriate velocity for molecule in the proposed SAO. Since the movement of smell molecules is non-uniform in six-dimensional coordinates, then, velocity is obtained as in Eq. (8): 𝑉(𝑢.𝑣.𝑤) = ∆𝑋(𝑥.𝑦.𝑧) ∆𝑡 (8) http://www.azojete.com.ng/ file:///C:/Users/Engr.%20Samuel/Documents/Engr%20Oyeniyi/azojete/AZOJETE%20ARCHIVE/UPLOAD/VOL%2019%20NO%204/engr.ibraheem1@gmail.com Arid Zone Journal of Engineering, Technology and Environment, Dec, 2023; Vol. 19(4):747-758. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: amusatramoni@gmail.com 752 where, ∆𝑋(𝑥.𝑦.𝑧) is the change in the displacement, x,y,z are the displacement coordinates ∆𝑡 is the time interval and 𝑉(𝑢.𝑣.𝑤) is the velocity in the u,v,w coordinates. Using equation (8) the initial position of the smell molecule relative to the Cartesian Coordinate can be represented by Eq. (9): 𝑋(𝑥,𝑦,𝑧) (𝑡+1) = 𝑉(𝑥,𝑦,𝑧) (𝑡) ∆𝑡 + 𝑋(𝑥,𝑦,𝑧) (𝑡) (9) If ∆𝑡 = 1 and the iteration increases continuously until all the iterations get exhausted. Then, the initial position of the smell molecule relative to the Cartesian coordinate can be represented as in Eq. (10): 𝑋(𝑗) (𝑡+1) = 𝑉(𝑢,𝑣,𝑤) (𝑡) + 𝑋(𝑥,𝑦,𝑧) (𝑡+1) (10) As the gas molecule moves in different directions, its velocity can be expressed from the static pressure of the gas as Eq. (11): 𝑉 = √ 3𝐾𝑇 𝑚 (11) The velocity of the smell molecule from equation (7) is updated as indicated in Eq. (12) 𝑉(𝑡) (𝑡+1) = 𝑉(𝑖) (𝑡) + 𝑟𝑜√ 3𝐾𝑇 𝑚 (12) where, 𝑉(𝑡) (𝑡+1) is the updated velocity, 𝑉(𝑖) (𝑡) is the previous velocity, K is Boltzmann’s constant, T is the temperature of the environment of the smell molecules, m is the mass of the molecules and 𝑟𝑜 is the random number. Thus, the updated position of the agent of the smell molecules is given as in Eq. (13): 𝑋𝑡 (𝑡+1) = 𝑋𝑖 𝑡 + (𝑉(𝑖) (𝑡) + 𝑟𝑜√ 3𝐾𝑇 𝑚 ) (13) where, 𝑋(𝑖) (𝑡) is the previous position of the molecule 2.2.2.2 Trailing mode This is the movement of the agent continuously toward the region with the highest concentration until the molecule with the overall best is found. This depends on the strength of the olfaction (olf) of the agent (Muhammad et al., 2023; Salawudeen et al., 2018). The movement is done through Eq. (14): 𝑋𝑡 (𝑡+1) = 𝑋𝑖 (𝑡) + 𝑟1𝑜𝑙𝑓(𝑋𝑎𝑔𝑒𝑛𝑡 (𝑡) − 𝑋𝑖 (𝑡) ) − 𝑟2𝑜𝑙𝑓(𝑋𝑤𝑜𝑟𝑠𝑡 (𝑡) − 𝑋𝑖 (𝑡) ) (14) 𝑟1and𝑟2 are random numbers generated at different intervals, 𝑋𝑎𝑔𝑒𝑚𝑡 (𝑡) and 𝑋𝑤𝑜𝑟𝑠𝑡 (𝑡) are the fitness of the present position of the agent and the position with the worst smell fitness respectively. file:///C:/user/Downloads/azojete143/www.azojete.com.ng file:///C:/Users/Engr.%20Samuel/Documents/Engr%20Oyeniyi/azojete/AZOJETE%20ARCHIVE/UPLOAD/VOL%2019%20NO%204/engr.ibraheem1@gmail.com Amusat et al: Maximum Power Point Tracking in Partial Shaded Photovoltaic System using Smell Agent Optimization Algorithm. AZOJETE, 19(4):747-758. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: amusatramoni@gmail.com 753 2.2.2.3 Random Mode If there is variation in the intensity of the smell molecule from one point to another over a certain period, the agent may be trapped by local minima resulting in failure in subsequent trailing, then, the agent will execute random mode in searching for the smell molecule source. The random mode is described as in Eq. (15): 𝑋𝑖 𝑡+1 = 𝑋𝑖 𝑡 + 𝑟3𝑆𝑀 (15) where, SM is a constant indicating the step movement, and 𝑟3 is a random number that stochastically penalizes the value of the step movement (Muhammad et al., 2023; Salawudeen et al., 2018). 2.2.3 Partial Shading Condition The PSC has a greater impact on reducing the power generated by solar systems, and this depends upon the shading pattern, the number of bypass diodes, and the solar system arrangement. The partially shaded PV module functions as a load and not as a source of power. This is known as the hot-spot effect, and if the heat produced exceeds a certain threshold, it leads to disintegration and damages the cell, resulting in an open circuit (Al-wesabi et al., 2020).To safeguard against such a hazardous scenario, the bypass diode is installed in the PV system, as shown in Figure 3.The bypass diodes will remain in the reverse cut-off state if the photovoltaic panels are subjected to normal conditions. In the event the PV modules are exposed to the PSC, the bypass diode conducts to form a short circuit; thereby preventing it from getting reversed by the reverse leakage current and enhancing the whole output power. To examine the efficiency of these algorithms, the 4S configuration (Figure 3) was employed in the simulation. This is a well-known configuration for the PV system which consists of four series connected PV modules and has four different power peaks under PSCs. The four different shading patterns (SPs) used in this model correspond to these radiations Pattern 1( 1000, 950,900,850 W/m2), pattern 2 (950,900,850,800 W/m2), pattern 3 (900,850,800,750 W/m2) and pattern 4 (850,800,750,700 W/m2). Each of the four elements of irradiance in each pattern corresponds to the irradiance falling on the PVs. Pattern 1 Pattern 2 Pattern 3 Pattern 4 Figure 3: PV Patterns for Partial Shading Conditions (adopted: Sadegh et al.,2022) 900 W/m2 Ipv + Vpv - PV 3 PV 2 850 W/m2 950 W/m2 PV 1 1000 W/m2 PV 4 850 W/m2 Ipv + Vpv - PV 3 PV 2 800 W/m2 900W/m2 PV 1 950 W/m2 PV 4 800 W/m2 Ipv + Vpv - PV 3 PV 2 750 W/m2 850 W/m2 PV 1 900 W/m2 PV 4 750 W/m2 Ipv + Vpv - PV 3 PV 2 700 W/m2 800 W/m2 PV 1 850 W/m2 PV 4 Bypass diode http://www.azojete.com.ng/ file:///C:/Users/Engr.%20Samuel/Documents/Engr%20Oyeniyi/azojete/AZOJETE%20ARCHIVE/UPLOAD/VOL%2019%20NO%204/engr.ibraheem1@gmail.com Arid Zone Journal of Engineering, Technology and Environment, Dec, 2023; Vol. 19(4):747-758. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: amusatramoni@gmail.com 754 3. Results and Discussion The results for the PV system under a temperature of T _ 25 °C and different shading patterns of power for SAO and P and O algorithms are shown in Table 3.These results are for transition from pattern 1 to pattern 4 and consist of several local maximum power points (LMPPs) and only one global maximum power point (GMPP) under PSCs. As shown in Table 4, SAO tracks maximum values for Power as 845.8476 W while the maximum values for P and O as 845.0465 W during SP1. The performance of SAO and P and O algorithms in four patterns have been compared in Table 4which shows that all algorithms succeed in tracking GMPP but the convergence speed of SAO is higher, its efficiency is slightly improved and has less tracking energy losses compared to P and O MPPT algorithms. Table 3: Shading patterns (SP1, SP2, SP3 and SP4) for Maximum Power of SAO and P and O techniques in Watt (W) S/N Pattern 1 SAO P and O Pattern 2 SAO P and O Pattern 3 SAO P and O Pattern 4 SAO P and O 1 0 0 0 0 0 0 0 0 2 688.2668 826.0345 675.2183 772.7223 650.2169 720.7421 593.3545 659.3255 3 628.2921 845.0079 731.9725 794.727 709.6505 744.1879 670.7713 693.1686 4 756.3685 845.0458 764.4782 794.7547 735.3934 744.241 685.2611 693.2556 5 818.0000 845.0452 783.9713 794.7576 741.5228 744.2793 692.9182 693.2257 6 842.2819 845.0456 792.309 794.7556 741.9805 744.244 691.2745 693.2411 7 842.4592 845.0455 792.4423 794.7553 742.1281 744.2403 691.4015 693.2564 8 790.5823 845.0453 776.2829 794.7576 742.6341 744.2794 692.1432 693.2261 9 843.3691 845.0453 793.2386 794.7575 742.7758 744.2463 691.9284 693.2404 10 843.5959 845.0456 793.4373 794.7568 742.9336 744.2439 692.0617 693.2556 11 844.1987 845.0457 793.9608 794.7558 743.3733 744.2789 692.4209 693.2273 12 844.3433 845.0457 794.0848 794.7557 743.4716 744.2441 692.5025 693.2416 13 842.0566 845.0458 793.9811 794.7552 743.6923 744.2435 693.1189 693.2557 14 845.1664 845.0452 794.7898 794.7571 744.0636 744.2436 692.9823 693.226 15 845.5824 845.0452 795.1522 794.7576 744.351 744.2796 693.2221 693.2404 16 845.6837 845.0452 795.242 794.7574 744.418 744.2465 693.2805 693.2544 17 845.7514 845.0452 795.2922 794.7574 744.4675 744.244 693.3165 693.2259 18 845.7999 845.0461 795.3297 794.7555 744.5033 744.2779 693.3434 693.2419 19 845.295 845.0463 795.0421 794.7559 744.5278 744.2446 693.3761 693.2562 20 845.8476 845.0463 795.3871 794.7551 744.5419 744.2419 693.3672 693.2281 21 845.6934 845.0465 794.9709 794.7548 744.5614 744.2765 693.3928 693.2421 Table 4: Comparative Analysis of SAO and P and O Techniques in 4 SP Configuration Pattern Target Power (W) SAO Tracking Power (W) P and O Tracking Power (W) 1 923.1130 845.8476 845.0465 2 874.3670 795.3871 794.7575 3 825.4070 744.5614 744.2796 4 776.2320 693.3928 693.2564 Figures (4-7) are the detailed simulations of results for the PV system under different shading patterns. The power curves for SAO and P and O algorithms have been shown in Figure (4-7) for a transition from pattern 1 to pattern 4. Figure 4 shows the power curve for both SAO and P and O corresponding to the partial shading conditions of pattern (1000, 950,900,850 W/m2), in this case, SAO and P and O respectively track 845.8476 W and 845.0465W as the GMPP and several LMPPs as depicted in Table 3. file:///C:/user/Downloads/azojete143/www.azojete.com.ng file:///C:/Users/Engr.%20Samuel/Documents/Engr%20Oyeniyi/azojete/AZOJETE%20ARCHIVE/UPLOAD/VOL%2019%20NO%204/engr.ibraheem1@gmail.com Amusat et al: Maximum Power Point Tracking in Partial Shaded Photovoltaic System using Smell Agent Optimization Algorithm. AZOJETE, 19(4):747-758. ISSN 1596-2490; e-ISSN 2545-5818, www.azojete.com.ng Corresponding author’s e-mail address: amusatramoni@gmail.com 755 Figure 4: Output Power curve of SAO and P and O models for pattern 1 at 25oC Figure 5 shows the power curve for both SAO and P and O corresponding to the partial shading conditions of pattern (950,900,850,800) W/m2. The SAO and P and O respectively track 795.3871 W and 794.7576 W as the GMPP and several LMPPs as depicted in Table 3. The two algorithms track the GMPP but the SAO has a higher value under the same tracking time and partial shading condition as shown in Table 4. Figure 5: Output Power curve of SAO and P and O models for pattern 2 at 25oC Figure 6 shows the power curve of the SAO and P and O, this corresponds to the partial shading conditions of the pattern (900,850,800,750 W/m2). The SAO and P and O respectively track 744.5614 W and 744.2796 W as the GMPP and several LMPPs as depicted in Table 3. In respective of their tracking efficiency. The SAO has a higher value under the same tracking time and partial shading conditions as shown in Table 4 Figure 6: Output Power curve of SAO and P and O models for pattern 3 at 25oC Figure 7 shows the power curve corresponding to the partial shading conditions of pattern the (850,800,750,700 W/m2) of both SAO and P and O, in this case, SAO and P and O respectively track 693.3928 W and 693.2564 W as the GMPP and several LMPPs as depicted 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0 100 200 300 400 500 600 700 800 900 Time (Seconds) P o w e r (W a tt s ) SAO P and O 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0 100 200 300 400 500 600 700 800 Time (Seconds) P o w e r (W a tt s ) SMO P and O 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0 100 200 300 400 500 600 700 800 Time (Seconds) P o w e r (W a tt s ) SAO P and O http://www.azojete.com.ng/ file:///C:/Users/Engr.%20Samuel/Documents/Engr%20Oyeniyi/azojete/AZOJETE%20ARCHIVE/UPLOAD/VOL%2019%20NO%204/engr.ibraheem1@gmail.com Arid Zone Journal of Engineering, Technology and Environment, Dec, 2023; Vol. 19(4):747-758. ISSN 1596-2490; e-ISSN 2545-5818; www.azojete.com.ng Corresponding author’s e-mail address: amusatramoni@gmail.com 756 in Table 3. Though both algorithms track the GMPP, the SAO has a higher value under the same tracking time and partial shading conditions as shown in Table 4. Figure 7: Output Power curve of SAO and P and O models for pattern 4 at 25oC 4. Conclusion The MPPT based on smell agent optimization was developed using MATLAB/Simulink environment. This was used to track maximum power under partial shading conditions. The Power curves of the shaded PV array have several local peaks and a single maximum power. The effectiveness of the proposed method was determined by comparing its results with conventional techniques (P and O). 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Maximum Power Point Tracking for PV System using Advanced Neural Networks Technique. International Journal of Emerging Technology and Advanced Engineering, 2(12): 58 -63. file:///C:/user/Downloads/azojete143/www.azojete.com.ng file:///C:/Users/Engr.%20Samuel/Documents/Engr%20Oyeniyi/azojete/AZOJETE%20ARCHIVE/UPLOAD/VOL%2019%20NO%204/engr.ibraheem1@gmail.com