Multiple Maximum Power Points Research Articles

Design and Analysis of a Triple-Input Three-Level PV Inverter with Minimized Number of MPPT Controllers

Photovoltaic (PV) energy has been a preferable choice with the rise in global energy demand, as it is a sustainable, efficient, and cost-effective source of energy. Optimizing the power generation is necessary to fully utilize the PV system. Harvesting more power uses cascading of impedance source converters taking input from low-voltage PV arrays which requires multiple maximum power point tracking (MPPT) controllers. To solve this problem, a three-level inverter topology with a proposed PV arrangement, offering higher voltage boosting and a smaller size with a lower cost suitable for low-voltage panels, is designed in this article. The design criteria for parameters are discussed with the help of the small signal analysis. In this paper, three PV arrays are used to harvest maximum energy, which require only one MPPT controller and employ an extended perturb and observe (P&O) algorithm, being faster, highly efficient, and reducing the computational burden of the controller. Moreover, a three maximum power points tracker algorithm, which perturbs one parameter and observes six variables, is designed for the selected converter topology. Finally, the designed 1.1 kVA grid-connected PV system was simulated in MATLAB (R2023a) which shows that the MPPT algorithm offers better dynamics and is highly efficient with a conversion efficiency of 99.2% during uniform irradiance and 97% efficiency during variable irradiance conditions.

Optimizing photovoltaic systems performance under partial shading using an advanced cuckoo search algorithm

Partial shading negatively impacts power output in photovoltaic systems (PVs), causing multiple local maximum power points (LMPP) instead of a single global maximum power point (GMPP). The cuckoo search (CS) technique utilizes the maximum power point tracking (MPPT) technique to extract the global maximum power (GMP) from shaded PVs. CS is a metaheuristic technique that has gained widespread recognition. Moreover, the CS algorithm is associated with several challenges, including a failure rate, long response time, and noticeable oscillations during steady-state operation. To address these limitations, our proposed advanced cuckoo search (ACS) algorithm is designed to overcome the shortcomings of the standard CS algorithm. The algorithm iteratively evaluates individual solar panels and collectively explores the solution space using levy flight operations. Persistent variables are used to store and track the current state and previous iterations. Where the duty cycles of the solar panels are optimally set to enhance the overall power generation efficiency. We also evaluate and analyze the results obtained from the performance of our proposed technique and compare them to the performance of the four most recent CS optimization techniques. for all test cases, the tracking efficiency was improved to 99.98% with a fast-settling time of <44 ms.

Comparing MPPT Algorithms for Improved Partial-Shaded PV Power Generations

olar energy, accepted as an alternative energy source, is attracting commercial interest and scholars and researchers for improving efficiency and lowering the losses within the system. One of these significant losses is due to partial and complex shading. This study concentrates on reducing losses to enhance the efficiency of solar systems. Maximum Power Point Tracking (MPTT) uses several alternative algorithms for efficient operations. We have selected four algorithms supporting MPPT, namely P&O, PSO, Adaptive cuckoo, and Dragonfly. These algorithms are applied on photovoltaic (PV) systems in four different scenarios: uniform irradiance, partial shading, complex partial shading, and multiple local maximum power points. According to this study, results show that the algorithms' performance vary significantly based on these scenarios. It has been shown that PSO has the longest tracking time compared to other but tracks the maximum power best when exposed to uniform irradiance. In contrast, DFO takes the shortest tracking time and performs best in I-V curves but do not have a maximum power point at the knee. Both adaptive cuckoo and PSO perform well in tracking the global maximum power point, particularly in partial shadings. The study provides insights into the strengths and weaknesses of each algorithm in different scenarios and can guide the selection of an appropriate algorithm for a given PV system.

Fast Tracking of Maximum Power in a Shaded Photovoltaic System Using Ali Baba and the Forty Thieves (AFT) Algorithm

Photovoltaic (PV) generation systems that are partially shaded have a non-linear operating curve that is highly dependent on temperature and irradiance conditions. Shading from surrounding objects like clouds, trees, and buildings creates partial shading conditions (PSC) that can cause hot spot formation on PV panels. To prevent this, bypass diodes are installed in parallel across each panel, resulting in a global maximum power point (GMPP) and multiple local maximum power points (LMPPs) on the power-voltage (P-V) curve. Traditional methods for maximum power point tracking (MPPT), such as perturb and observe (P&O) and incremental conductance (INC), converge for LMPPs on the P-V curve, but metaheuristic algorithms can track the GMPP effectively. This paper proposes a new, efficient, and robust GMPP tracking technique based on a nature-inspired algorithm called Ali Baba and the Forty Thieves (AFT). Utilizing the AFT algorithm for MPPT in PV systems has several novel features and advantages, including its adaptability, exploration-exploitation balance, simplicity, efficiency, and innovative approach. These characteristics make AFT a promising choice for enhancing the efficiency of PV systems under varied circumstances. The performance of the proposed method in tracking the GMPP is evaluated using a simulation model under MATLAB/Simulink environment, the achieved simulation results are compared to particle swarm optimization (PSO). The proposed method is also tested in real-time using the Hardware-in-the-loop (HIL) emulator to validate the achieved simulation results. The findings indicate that the proposed AFT-based GMPP tracking method performs better under complex partial irradiance conditions than PSO.

Atomic Orbital Search Algorithm for Efficient Maximum Power Point Tracking in Partially Shaded Solar PV Systems

The efficient extraction of solar PV power is crucial to maximize utilization, even in rapidly changing environmental conditions. The increasing energy demands highlight the importance of solar photovoltaic (PV) systems for cost-effective energy production. However, traditional PV systems with bypass diodes at their output terminals often produce multiple power peaks, leading to significant power losses if the optimal combination of voltage and current is not achieved. To address this issue, algorithms capable of finding the highest value of a function are employed. Since the PV power output is a complex function with multiple local maximum power points (LMPPs), conventional algorithms struggle to handle partial shading conditions (PSC). As a result, nature-inspired algorithms, also known as metaheuristic algorithms, are used to maximize the power output of solar PV arrays. In this study, we introduced a novel metaheuristic algorithm called atomic orbital search for maximum power point tracking (MPPT) under PSC. The primary motivation behind this research is to enhance the efficiency and effectiveness of MPPT techniques in challenging scenarios. The proposed algorithm offers several advantages, including higher efficiency, shorter tracking time, reduced output variations, and improved duty ratios, resulting in faster convergence to the maximum power point (MPP). To evaluate the algorithm’s performance, we conducted extensive experiments using Typhoon HIL and compared it with other existing algorithms commonly employed for MPPT. The results clearly demonstrated that the proposed atomic orbital search algorithm outperformed the alternatives in terms of rapid convergence and efficient MPP tracking, particularly for complex shading patterns. This makes it a suitable choice for developing an MPP tracker applicable in various settings, such as industrial, commercial, and residential applications. In conclusion, our research addresses the pressing need for effective MPPT methods in solar PV systems operating under challenging conditions. The atomic orbital search algorithm showcases its potential in significantly improving the efficiency and performance of MPPT, ultimately contributing to the optimization of solar energy extraction and utilization.

Hill climbing MPPT performance analysis for photovoltaic system under partial shading

The HC approach is developed in this work with the intention of tracking maximum power from the PV array. Multiple local maximum power points are displayed on the power–voltage characteristic of photovoltaic (PV) arrays when all of the modules are not receiving uniform solar irradiation, which is another way of saying that they are operating under partial shading conditions (PSCs). Under conditions of consistent sun irradiation, it has been demonstrated that conventional maximum power point tracking (MPPT) approaches are effective. On the other hand, it is possible that they will miss the worldwide peak caused by PSCs. In this thesis, a new approach for maximum power point tracking (MPPT) of photovoltaic arrays is proposed for use under both PSCs and uniform circumstances. The proposed method locates all of the regional maximum power locations by doing an analysis of the sun irradiance pattern and then applying the well-known Hill Climbing method. Simulations run in a MATLAB/SIMULINK environment are used to assess how well the suggested approach performs.

Solar irradiance estimation and optimum power region localization in PV energy systems under partial shaded condition

The efficient operation of PV systems relies heavily on maximum power point tracking (MPPT). Additionally, such systems demonstrate complex behavior under partial shading conditions (PSC), with the presence of multiple maximum power points (MPP). Among the existing MPPT algorithms, the conventional perturb and observe, and incremental conductance stand out for their high simplicity. However, they are specialized in single MPP problems. Thus, due to the existence of multiple MPPs under PSC, they fail to track the global MPP. Compared with the conventional schemes, the modified conventional algorithms, and several existing MPPT variants introduce a trade-off between complexity and performance. To enhance the simplicity of the PV system, it is crucial to adapt the operation of the simple conventional algorithm to scenarios under PSC. To achieve such an adaptation, the power-voltage curve that conventionally admits multiple MPPs under PSC must be converted to an equivalent curve having only a single MPP. To address such a requirement, this paper introduces a novel approach to the fast determination of the MPP. A consistent methodology for reducing the complex multiple MPP problem of PV systems under PSC, to a single MPP objective, is put forward. Thus such reduction enhances the tracking environment for simple conventional MPPT algorithms under partial shading. Studies of the PV array behavior for 735 partial shading patterns revealed an interesting possibility of reducing the classical PV curve to 8.2620% of its actual area. The newly established area is an optimum power region that accommodates a single MPP. To arrive at such a reduction, an intelligent neural network-based predictor, incorporating a cost-effective and reliable solar irradiance estimator is put forward. Unlike existing methods, the approach is free from the direct and expensive measurement of solar irradiance. The predictor relies on the PV array current and voltage only to precisely determine the optimum power region of the PV system.

Global Maximum Power Point Tracking in Dynamic Partial Shading Conditions Using Ripple Correlation Control

Under partial shading conditions (PSC), a photovoltaic (PV) system may produce multiple local maximum power points (LMPPs). Traditional maximum power point tracking (MPPT) techniques are not able to distinguish between LMPPs and the global maximum power point (GMPP), leading to sub-optimal PV array power outputs. This article proposes a two-level algorithm for tracking the GMPP. The first level is a discretized global search, allowing the system to hone in on the neighborhood containing the GMPP. In the second level, the well-known ripple correlation control (RCC) technique is used to swiftly converge to the GMPP. Using the proposed two-level algorithm, it can be guaranteed that the GMPP is successfully found and tracked. A benchmark analysis involving other state of the art algorithm reveals that the proposed method is the most superior algorithm in terms of accurately and swiftly tracking the global maximum power point in dynamic partial shading conditions. The algorithm is implemented with a simple inexpensive microcontroller, and therefore can be readily adopted in a myriad of dynamic partial shading applications.Therefore, this work allows the penetration of future photovoltaic power conversion systems to be more efficient.

Modified Artificial Hummingbird Algorithm-Based Single-Sensor Global MPPT for Photovoltaic Systems

Recently, a swarm-based method called Artificial Hummingbird Algorithm (AHA) has been proposed for solving optimization problems. The AHA algorithm mimics the unique flight capabilities and intelligent foraging techniques of hummingbirds in their environment. In this paper, we propose a modified version of the AHA combined with genetic operators called mAHA. The experimental results show that the proposed mAHA improved the convergence speed and achieved better effective search results. Consequently, the proposed mAHA was used for the first time to find the global maximum power point (MPP). Low efficiency is a drawback of photovoltaic (PV) systems that explicitly use shading. Normally, the PV characteristic curve has an MPP when irradiance is uniform. Therefore, this MPP can be easily achieved with conventional tracking systems. With shadows, however, the conditions are completely different, and the PV characteristic has multiple MPPs (i.e., some local MPPs and a single global MPP). Traditional MPP tracking approaches cannot distinguish between local MPPs and global MPPs, and thus simply get stuck at the local MPP. Consequently, an optimized MPPT with a metaheuristic algorithm is required to determine the global MPP. Most MPPT techniques require more than one sensor, e.g., voltage, current, irradiance, and temperature sensors. This increases the cost of the control system. In the current research, a simple global MPPT method with only one sensor is proposed for PV systems considering the shadow conditions. Two shadow scenarios are considered to evaluate the superiority of the proposed mAHA. The obtained results show the superiority of the proposed single sensor based MPPT method for PV systems.

Algorithm to extract model parameters of partially shaded photovoltaic modules

Uneven irradiation, due to partial shading, can produce hot spots in photovoltaic modules. A classical solution to avoid hot spot consists in using bypass diodes in antiparallel to series-connected cell groups. This solution brings a new problem: the presence of multiple local maximum power points. We present a simple algorithm for fast extraction of the model parameters of partially shaded photovoltaic panels with bypass diodes. An example of the application of the proposed algorithm is illustrated using the data from a real monocrystalline silicon technology photovoltaic module measured under uniform illumination and partial shading conditions. The possibility of using the algorithm as a practical approximate solution is also discussed. The simulations, using only four parameters, represent reasonably well the measured data.

An enhanced arithmetic optimization algorithm for global maximum power point tracking of photovoltaic systems under dynamic irradiance patterns

ABSTRACT The photovoltaic power system is highly dependent on the quantity of solar irradiance incident on it and its temperature. These parameters are dynamically changing with time due to climatic changes, shadows formed by clouds, trees, buildings, and so on, resulting in multiple maximum power points (MPP). Out of which one is global MPP (GMPP) and the rest are local MPP (LMPP). The GMPP varies dynamically along with the weather conditions and connected load. Many GMPP tracking (GMPPT) algorithms were developed which are inefficient and ineffective under dynamic irradiance conditions. This paper proposes a new enhanced arithmetic optimization algorithm based on the levy flight (AOA-LF) as a GMPPT method, which improves the tracking efficiency and tracking speed because of its good exploration and exploitation due to the long jump with a variable step size. The suggested AOA-LF GMPPT method is implemented in MATLAB/SIMULINK and tested with eight different dynamic irradiance patterns whose tracking curves are compared with the existing GMPPT algorithms like JAYA-LF, DFO, AOA, PSO, and P&O, which reveals that it is excellent in the tracking of GMPP with increment in efficiency as high as 19% when compared with P&O method and the least time to reach the GMPP with percentage decrement as high as 69% as compared to the PSO method. Also, the proposed AOA-LF GMPPT method is validated with the help of OPAL-RT OP4510 Real-Time Digital Simulator. Hence, the proposed algorithm shows superior performance with zero steady-state oscillations and also enhanced exploration and exploitation.

Modeling and experimental validation of matrix structure photovoltaic array reconfiguration technique to harvest maximum power under continuous dynamic shading condition

This paper puts forth an analysis about the effects of continuous dynamic shading scenarios upon various photovoltaic (PV) array configurations. The detailed examination documents the extent of mismatch power losses (MPLs) that creep in PV arrays (PVAs) due to non-uniform solar irradiations. Along with MPLs, these partial shading conditions (PSCs) are also responsible for non-linearity and multiple local maximum power points on the P-V and the I-V characteristics. These examinations propose for an optimal PVA design to draw the maximum power from the PVA upon exposure to partial irradiance. A novel static reconfiguration-based matrix structure (MS) PVA and two new conventional PVAs such as alternative bridge link and short cross tied are proposed in this paper. Comparisons are also made between the proposed PVAs and conventional PVAs like bridge link, series parallel, total cross tied and honeycomb. These PVAs are examined under dynamic shading and the comparisons are drawn for MPLs, P-V curves and % fill factors (FFs). Experimental Test workbench and MATLAB/Simulink environment is adopted for validating all the PVA configurations (PVACs) under consideration. Among the analysed configurations, the MS PVAC have lesser MPLs and better % FF under continuous PSCs and, thus, found to be the superior configuration.

Modified flower pollination algorithm for an off-grid solar photovoltaic system

This Operating the solar photovoltaic (PV) system at its maximum power point (MPP) under numerous environmental conditions to extract the maximum power is a challenging task. The challenge is to track the MPP, especially under partial shading conditions (PSC), where the formation of multiple MPP occurs in the characteristic curve of a PV array. Nevertheless, achieving this would benefit us with optimal power production, reducing the payback time and initial cost of the PV system. To perform this duty, an electronic circuit ruled by an algorithm is employed. The MPP tracking (MPPT) algorithms can be categorized into conventional and nature inspired. The conventional algorithms can successfully track the MPP under uniform weather conditions (UWC), and unable to identify the global MPP (GMPP) under PSC. However, the nature inspired algorithms possess the ability to perform efficiently under all weather conditions. Considering this strength of nature inspired algorithms, one of the top performing algorithms named as Flower pollination algorithm (FPA) is selected based on its brilliant searching strategy in adjacent and distant locations. In this paper, some structural modifications have been proposed in the FPA to further improve its searching capability and get more quick, accurate and efficient results for the MPPT of solar PV system. Results have proven the superiority of the proposed Modified FPA (MFPA) over the FPA in terms of efficiency, accuracy, tracking speed, energy conservation, economic saving, and payback time. Simulation is performed in MATLAB/Simulink.

Game theory based strategy to reconfigure PV module arrangements for achieving higher GMPP under PSCs: Experimental feasibility

Due to shading conditions, solar photovoltaic (PV) arrays deliver less power compared to ideal operating conditions. Moving clouds, nearby telecom towers, and trees, among other things, are the basic causes that come under partial shading conditions (PSCs). Furthermore, PV array characterization carries multiple maximum power points (MPP) on the P–V characteristics. Distinguish the electrical connections of PV modules present in literature to show the diminishing effect of PSCs. In the present manuscript, existing Series–parallel (SP), bridge-link (BL), honey-comb (HC), total-cross-tied (TCT), and proposed Game Theory (PGT) puzzle-based rearrangements of PV array systems are employed and analysed during shading instances. The PGT puzzle-based arrangement shows higher shade-dispersion capability, which accounts for power enhancement (PE), improved fill factor (FF), and minimum power loss (PL). Over all, the considered performance indices are identified based on the behaviour of global and local maxima power point (LMPP and GMPP) locations on the obtained power–voltage (P–V) curves behaviour. The experimental system is developed and tested to obtain the performance of PGT higher side as 52.92ImVm, 59.67ImVm, 52.65ImVm, and 44.1ImVm compared to existing approaches.

A Fast Parabolic-Assumption Algorithm for Global MPPT of Photovoltaic Systems Under Partial Shading Conditions

Photovoltaic (PV) arrays exhibit multiple local maximum power points (MPPs) in their I-V and P-V characteristic curves when different modules are subjected to different radiations at the same time, known as a partial shading condition (PSC). Hence, tracking the global MPP is crucial to increase the PV system efficiency. Conventional global MPP tracking (GMPPT) algorithms suffer from slow convergence, while some can fail to track the global MPP during PSCs. This article proposes a totally new approach to finding the global MPP during PSC using a single current sensor: the parabolic assumption GMPPT algorithm uses a fixed number of current scans (steps) equal to the number of solar modules connected in series to directly and immediately calculate the global MPP. The proposed algorithm uses simple parabolic equations to calculate the global MPP near-exactly during PSCs. The performance of the proposed algorithm is first evaluated by simulation in MATLAB/Simulink, and then by experimental verification. The results show very fast global MPP tracking and negligible tracking energy loss with an experimental tracking efficiency over 99.6% for the four PSC patterns tested.

Technical Review of MPPT Algorithms for Solar Photovoltaic System: SWOT Analysis of MPPT Algorithms

To continuously operate the Photovoltaic (PV) system at its Maximum Power Point (MPP) under changing weather is a challenging task. To accomplish this, multiple MPP Tracking (MPPT) algorithms have been proposed, which can be portioned into two: 1) Conventional algorithms, have the strengths of a simple structure, fewer computations, and low memory requirement, and cheap implementation. Whereas, trapping under Partial Shading Conditions (PSC), steady-state oscillations, and system dependency are the associated drawbacks. Conversely, 2) Soft computing algorithms, perform efficiently under all weather conditions with zero steady-state oscillations, and are system independent. The structural complexities, giant computations, huge memory requirements, and expensive implementation, are the accompanying concerns. The core contribution of this study is to present a deep analysis of all the MPPT algorithms at the standard benchmarks defined in the published literature, for the readers so they could decide which algorithm to choose under certain circumstances.

Generalized regression neural network and fitness dependent optimization: Application to energy harvesting of centralized TEG systems

The thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition, several maximum power points (MPPs) appear on the P/V curve. In multiple MPPs, the true global maximum power points (GMPP) are very important for optimum action. The existing conventional technologies have slow tracking speed, low productivity, and unwanted fluctuations in voltage curves. To overcome the TEG system behavior and shortcomings, A novel control technology for the TEG system is proposed, which utilizes the improved generalized regression neural network and fitness dependent optimization (GRNNFDO) to track the GMPP under dynamic operating conditions. Conventional TEG system control techniques are not likely to trace true GMPP. Our novel GRNNFDO can trace the true GMPP for NUTD and under varying temperature conditions In this article, some major contributions in the area of the TEG systems are investigated by solving the issues such as NUTD global maxima tracking, low efficiency of TEG module due to mismatch, and oscillations around optimum point. The results of GRNNFDO are compared with the Cuckoo-search algorithm (CSA), and grasshopper optimization (GHO) algorithm and particle swarm optimization (PSO) algorithm. Results of GRNNFDO are verified with experiments and authenticated with MATLAB/SIMULINK. The proposed GRNNFDO control technique generates up to 7% more energy than PSO and 60% fast-tracking than meta-heuristic algorithms.

Arithmetic optimization algorithm based MPPT technique for centralized TEG systems under different temperature gradients

Since centralized thermoelectric power generation (TEG) system presents multiple local maximum power points (LMPPs) at different temperature gradients (DTG), thus its optimal power harvesting is difficult to realize via conventional approaches. Therefore, an efficient arithmetic optimization algorithm (AOA) is applied to realize maximum power point tracking (MPPT) of centralized thermoelectric power generation system at different temperature gradients to improve the energy exploitation and utilization. AOA is utilized to efficiently and reliably identify unique global maximum power point (GMPP) in multiple LMPPs, which replicates the distribution mechanism of main arithmetic operators during mathematic calculation to find the best solution from a set of randomly generated candidate solutions. Compared with other well-known algorithms, AOA owns the distinctive superiorities of simple implementation structure, as well as few control parameters need to be tuning. Meanwhile, its own random and adaptive parameters selection principle greatly boost the AOA convergence performance. In addition, AOA shows strong ability to avoid falling into local optima thanks to its proper balancing between global exploration (diversification) and local exploitation (intensification). For further validation of the practicability of AOA based MPPT, two case studies are conducted, e.g., start-up test and step change of temperature. Experimental results show that AOA can achieve the optimal energy harvesting with minimal power fluctuations in comparison with the other three optimization algorithms, e.g., the energy produced by AOA is 34.44%, 6.03%, and 8.17% higher than that of perturb and observe (P&O), particle swarm optimization (PSO) and grey wolf optimization (GWO) respectively under the step change of temperature.

Energy harvesting and stability analysis of centralized TEG system under non-uniform temperature distribution

Thermoelectric generator (TEG) systems are gaining much attraction due to utility in heat recovery, surface cooling, concentrated solar thermal, and sensor applications. In series–parallel configurations, TEG modules due to Heterogeneous temperature difference (HeTD) show the non-linear behavior. Due to non-uniform temperature distribution (NUTD), multiple maximum power points (MPP) appears on the P-V curve. It is crucial to drive the system at true global maximum power point (GMPP) among multiple MPP’s. Existing classical techniques exhibit slow tracking, low efficiency, and undesired fluctuation in output voltage transients. To address these shortcomings our control technique based on improved Moth Flame Optimization (IMFO) is employed for the maximum power point tracking (MPPT) control under dynamic operating conditions. Comparison of the proposed technique is made with other well-known meta-heuristic techniques including particle swarm optimization (PSO), cuckoo search (CS), Artificial Bee Colony (ABC), and recently developed Dragon Fly Optimization (DFO). The comprehensive case studies with statistical and quantitative analysis are performed to confirm the superior performance of IMFO for NUTD condition, fast varying temperature condition, and stochastic operations. To experimentally validate the performance of the IMFO algorithm a low-cost TEG emulator setup is designed. The IMFO based control is implemented on a low-cost microcontroller achieving effective real-time control application in hardware. The proposed IMFO algorithm attains up to 6 W more power and takes 59% less time to track and settle at GMPP with minimum fluctuation. Results also validate that IMFO extracts 5.2% more electrical energy in comparison to competing techniques. In light of comprehensive analysis, it is safe to conclude that the proposed IMFO performs excellently for TEG MPPT control.

Characteristic Study of Solar Photovoltaic Array Under Different Partial Shading Conditions

Photovoltaic (PV) systems are frequently exposed to partial or complete shading phenomena. Partial shading has a profound impact on the performance of solar power generation. The operational performance of PV arrays under partial shading shows multiple maximum power point peaks, therefore it is challenging to identify the actual maximum power point. This paper investigates the impact of partial shading location on the output power of solar photovoltaic arrays with various configurations. Multiple photovoltaic strings, in both parallel and series configurations, are considered. Different random shading patterns are considered and analyzed to determine which configuration has higher maximum power point. The sensitivity of the partial shading can change according to the partial shading types, shading pattern, and the configuration used to connect all PV modules. Moreover, the study also investigates the output of the PV array with shading two random models, two consecutive models, and three random and consecutive modules. Experimental results validate the analysis and demonstrate the effect of various partial shading on the eficiency and performance of the PV system.