Partial Shading Scenarios Research Articles

Smart hybrid algorithm for global maximum power point tracking under partial shading conditions

ABSTRACT Maximum power point tracking (MPPT) is a complex task due to the nonlinear behavior of the photovoltaic (PV) array. Moreover, partial shading (PS) is a major problem that significantly decreases the overall system’s efficiency, whereas traditional algorithms are mostly local searches and thus cannot guarantee global optimality. Therefore, Swarm Intelligence (SI) algorithms are developed to circumvent this problem. Existing SI algorithms like Cuckoo Search (CS) and particle swarm optimization (PSO) have been used to address partial shading issues; nevertheless, there is a research gap in developing hybrid methods that combine these two algorithms to enhance MPPT performance under various environmental conditions. This paper fills this research gap by providing a novel hybrid algorithm called Smart Hybrid Algorithm (SHA). The developed optimizer avoids the common disadvantages of conventional MPPT techniques and provides a simple and robust MPPT tracker to handle PS in PV systems effectively. The proposed optimizer features a reset function to restart the search process for the new GMPP at any irradiance change. Multiple performance tests were conducted using 4S, 8S, 10S, 12S, and 4S2P PV array configurations to investigate the performance of our optimizer. The results obtained are compared to those of well-known recent algorithms. The proposed system demonstrates a convergence time of less than one second and a steady state power efficiency exceeding 99.79% across various complex partial shading scenarios. It also offers a convergence time reduction of at least 14% compared to other techniques. The findings demonstrate the robustness and suitability of SHA to handle complex partial shading conditions (CPSCs) and uniform irradiance with high dynamic and steady-state efficiencies and minor transient power fluctuations.

Improved voltage scanning algorithm based MPPT algorithm for PV systems under partial shading conduction

Maximum Power Point Tracking (MPPT) algorithms are crucial for maximizing power extraction from photovoltaic (PV) systems. Traditional MPPT methods often exhibit suboptimal performance under partial shading conditions. Hence, advanced MPPT algorithms have been developed to enhance efficiency in such scenarios. The voltage scanning-based MPPT algorithm is notable for its superior performance under partial shading, characterized by high tracking speed and efficiency. This study introduces a novel enhancement to this method—namely, a voltage skipping algorithm designed to further improve tracking speed. Unlike conventional scanning techniques, this approach dynamically calculates skipping voltages during operation, eliminating the need for prior knowledge of PV panel characteristics. Performance evaluation was conducted using a MATLAB/Simulink model of a PV system comprising 4 series panels and a boost converter, subjected to simulations under 5 distinct partial shading scenarios. Comparative analyses with established optimization algorithms such as particle swarm optimization, cuckoo search algorithm, and grey wolf optimization highlight the proposed method's effectiveness in terms of tracking speed and maximum power output. The proposed algorithm has worked with high efficiency of 99.28 %, 99.61 %, 99.13 %, 99.16 % and 99.58 % in 5 different scenarios, respectively. By using the proposed method, a significant superiority has been achieved over other methods, with tracking speeds of 0.26s, 0.22s, 0.2s, 0.22s and 0.26s, respectively.

A genetic algorithm approach for flexible power point tracking in partial shading conditions

A novel Flexible Power Point Tracking (FPPT) algorithm based on Genetic Algorithms (GAs) is presented in this study. The algorithm is designed to effectively handle fluctuating solar irradiation levels and partial shading scenarios. The main objective is to enhance power output and tracking accuracy under dynamic environmental conditions, based on extensive simulations. The results showed superior performance for power output, tracking accuracy, and convergence speed than traditional techniques. Its adaptability to changing environmental conditions renders it suitable for real-time implementation, thereby contributing to efficient power generation in PV systems. Simulations demonstrate the efficacy of the algorithm in maximizing power extraction and meeting grid-code requirements under dynamic conditions. The comparative analyses underscore the superiority of the GA-based FPPT algorithm, highlighting its potential to significantly improve power point tracking in photovoltaic systems. This shows significance of adaptive algorithms in modern PV systems, particularly for reducing the effects of partial shading on energy yield. More research is required on the refinement of the algorithm's performance for long-term reliability and scalability in larger PV installations fostering advancements in photovoltaic power point tracking toward sustainable energy systems.

Comparison and classification of photovoltaic system architectures for limiting the impact of the partial shading phenomenon

This article proposes a comparison and classification of PV system architectures with the aim of limiting the impact of the partial shading phenomenon which remains one of the most harmful defects during the production of electrical energy with significant consequences on output power, current and voltage. the methodological approach used consists of analyzing in depth the main most recent architectures developed in the current literature with their advantages and disadvantages; then define five partial shading scenarios for different irradiance levels (1000W/m2; 900W/m2; 700W/m2; 500W/m2; 300W/m2), which will then be immediately applied to five other proposed architectures: SP (serial-parallel), BL (Bridge-Link); HC (Honey Comb); TCT (Total-Cross-Tied); TSPL (Triple Series Parallel Ladder); the values obtained at the output with each of its architectures will be used for an in-depth descriptive analysis with PCA (principal component analysis) in statistics but to carry out a comparative analysis between the architectures. All with a Matlab Simulink 2022.b software environment. The results obtained offer a strong positive correlation for TCP and TSPL architectures with better weight compared to other architectures. All in accordance with the IEEE-519-2022 standard. This work is therefore positioned as a contribution to the optimization of the performance of electrical energy production through the use of PV systems which today represent widely used alternatives in the renewable energy register.

Adaptive metaheuristic strategies for optimal power point tracking in photovoltaic systems under fluctuating shading conditions

In recent years, there has been a growing interest in photovoltaic (PV) systems due to their capacity to generate clean energy, reduce pollution, and promote environmental sustainability. Optimizing the operational efficiency of PV systems has become a critical goal, particularly under challenging conditions like partial shading. Traditional maximum power point tracking (MPPT) methods face limitations in addressing this issue effectively. To tackle these challenges, this study introduces an enhanced MPPT approach based on the grey wolf optimizer (GWO), tailored to excel in GMPP tracking even under partial shading conditions. The algorithm harnesses adaptive and exploratory capabilities inspired by the behaviour of grey wolves in the wild. To comprehensively evaluate the proposed GWO-based MPPT algorithm's effectiveness, we conduct a comparative analysis with established metaheuristic algorithms, including particle swarm optimization (PSO) and the Pelican optimization algorithm (POA). Through this comparison, our study provides valuable insights into the algorithm's efficiency, behavior, and adaptability in addressing the complex challenges posed by partial shading scenarios in PV systems, thereby contributing to the advancement of efficient solar energy conversion.

A Comparative Study of PSO, GWO, and HOA Algorithms for Maximum Power Point Tracking in Partially Shaded Photovoltaic Systems

Abstract Solar energy harnessed through photovoltaic technology plays a crucial role in generating electrical energy. Maximising the power output of solar modules requires optimal solar radiation. However, challenges arise due to obstacles such as stationary objects, buildings, and sand-laden winds, resulting in multiple points of maximum power on the P–V curve. This problem requires the use of maximum power point tracking algorithms, especially in unstable climatic conditions and partial shading scenarios. In this study, we propose a comparative analysis of three MPPT methods: particle swarm optimisation (PSO), grey wolf optimisation (GWO) and Horse Herd Optimization Algorithm (HOA) under dynamic partial shading conditions. We evaluate the accuracy of these methods using Matlab / Simulink simulations. The results show that all three methods solve partial shading problems effectively and with high precision. Furthermore, the Horse Herd Optimization approach has superior tracking accuracy and faster convergence compared with the other proposed methods.

Performance Analysis of Novel Solar PV Array Configurations with Reduced Tie Interconnection to Extract Maximum Power under Partial Shading

The non-uniform irradiation pattern causes the partial shading effects and decreases the performance of the solar Photovoltaic (PV) array. The effect of uneven shading condition depends on its pattern and area. The PV array’s performance is determined by the cross ties that are connected within the array. This research proposes eight different PV array tie interconnection configurations to obtain the maximum power output under uneven insolation conditions. Experimental analysis is done in the PV array consists of 16 numbers of 10 W PV panels which has a total capacity of 160 W. The practical results reveal that the losses due to partial shading are influenced by the shade pattern, shade area, and the placement of the cross ties. Overall, among the conventional configurations, the hybrid bridge link total cross tied interconnection pattern produced the best results, followed by the hybrid series parallel total cross tied and bridge link patterns. In the proposed configurations, the plus tie configuration outperformed the others in maximum of the partial shading scenarios. In general, the PLT configuration demonstrated an average power efficiency increase of 19.17%, 8.37%, and 8.28% compared to the series parallel, hybrid series parallel total cross-tied, bridge link, and bridge link total cross-tied configurations, respectively. The effectiveness of the proposed configurations is validated by the practical verifications.

Investigation of single and multiple MPPT structures of solar PV-system under partial shading conditions considering direct duty-cycle controller

Partial shading of solar panels diminishes their operating efficiency and energy synthesized as it disrupts the uniform absorption of sunlight. To tackle the issue of partial shading in photovoltaic (PV) systems, this article puts forward a comprehensive control strategy that takes into account a range of contributing factors. The proposed control approach is based on using multi-string PV system configuration in place of a central-type PV inverter for all PV modules with a single DC-DC converter. This adaptation enhances overall efficiency across varying radiation levels. Also, the proposed technique minimizes the overall system cost by reducing the required sensors number by utilizing a radiation estimation strategy. The converter switching strategy is synthesized considering direct duty-cycle control method to establish the maximum power point (MPP) location on the P–V curve. The direct duty-cycle tracking approach simplifies the control system and improves the system’s response during sudden partial shading restrictions. To validate the effectiveness of the suggested MPPT method, two system configurations were constructed using MATLAB/SIMULINK software and assessed under various partial shading scenarios. Additionally, a multi-string system was subjected to real irradiance conditions. The sensor-less MPPT algorithm proposed achieved an impressive system efficiency of 99.81% with a peak-to-peak ripple voltage of 1.3V. This solution offers clear advantages over alternative approaches by reducing tracking time and enhancing system efficiency. The system findings undoubtedly support the theoretical scrutiny of the intended technique.

Optimizing Photovoltaic Power Production in Partial Shading Conditions Using Dandelion Optimizer (DO)-Based MPPT Method

This research proposes the dandelion optimizer (DO), a bioinspired stochastic optimization technique, as a solution for achieving maximum power point tracking (MPPT) in photovoltaic (PV) arrays under partial shading (PS) conditions. In such scenarios, the overall power output of the PV array is adversely affected, with shaded cells generating less power and consuming power themselves, resulting in reduced efficiency and local hotspots. While bypass diodes can be employed to mitigate these effects by redirecting current around shaded cells, they may cause multiple peaks, making MPPT challenging. Therefore, metaheuristic algorithms are suggested to effectively optimize power output and handle multiple peaks. The DO algorithm draws inspiration from the long-distance movement of a dandelion seed, which relies on the force of the wind. By utilizing this bioinspired approach, the DO algorithm can successfully capture the maximum power point (MPP) under different partial shading scenarios, where traditional MPPT algorithms often struggle. An essential contribution of this research lies in the examination of the performance of the proposed algorithm through simulation and real-time hardware-in-the-loop (HIL) results. Comparing the DO algorithm with the state-of-the-art algorithms, including particle swarm optimization (PSO) and cuckoo search (CS), the DO algorithm outperforms them in terms of power tracking efficiency, tracking duration, and the maximum power tracked. Based on the real-time HIL results, the DO algorithm achieves the highest average efficiency at 99.60%, surpassing CS at 96.46% and PSO at 94.74%. These findings demonstrate the effectiveness of the DO algorithm in enhancing the performance of MPPT in PV arrays, particularly in challenging partial shading conditions.

An adjustable machine learning gradient boosting-based controller for PV applications

Traditional Proportional, Integral, and Derivative (PID) controllers are employed in most industrial control applications because they are simple and easy to implement. However, they are fallible and exhibit poor performance in complicated, non-linear, time-delayed systems, and noisy feedback loops. In photovoltaic systems applications such as maximum power point (MPP) tracking, PI controller performance in low irradiance levels, varying load conditions, and partial shading scenarios is considerably inefficient in tracking the corresponding reference current, which generates MPP and causes considerable steady-state error. Machine learning can be used with selective input/output data from the PI controller to build a model that has the ability to adjust in case of steady-state error. Thus, an adjustable Machine Learning Gradient Boosting-based (MLGB) controller with a PV module and a DC-DC boost converter is proposed in this work. To create the raw dataset, a PI controller was simulated, and the input/output signals were recorded. Data preprocessing, utilizing feature engineering and Shapley Additive Explanations (SHAP) values, was used to explain the dynamic behavior of each feature, the inter-feature dependability, and their significance with reference to the model output. Hyperparameters were tuned with cross-validation while the model was being created using the CatBoost method. To assess the adjustment of each feature to minimize the error, particularly for lower irradiance values, varying load conditions, and partial shading, a regression model was built. Using the MATLAB Simulink Environment, a complete system controlled by both the PI and the MLGB controller was developed and tested. A random irradiance level fluctuation profile, along with varying load and partial shading scenarios, was used to compare three controllers, namely the PI, conventional MLGB, and adjusted MLGB controllers, to see how they respond to rapidly changing environmental conditions. It has been proven through a thorough simulation analysis that the novel adjustable MLGB controller exhibits good tracking performance to follow the DC-DC boost converter's inductor current. When compared to the traditional PI controller, the new controller exhibits better results in terms of steady-state behavior and transient responsiveness in general, with a much lower mean (3.059E-03), median (1.908E-03), and RMS value (2.168E-02) of the signal error under various conditions.

Enhancing the Hybrid Microgrid Performance with Jellyfish Optimization for Efficient MPPT and THD Estimation by the Unscented Kalman Filter

Power management in advanced grid systems requires the seamless integration of diverse renewable energy sources. This study investigates the optimization of a grid-connected system comprising a photovoltaic (PV) solar panel, energy storage system, fuel cell (FC), and diesel generator (DG) using the bioinspired metaheuristic technique called jellyfish optimization (JF). The objective is to maximize power generation from the PV system under normal and partial shading conditions. The performance of JF is compared against particle swarm optimization (PSO) using various parameters. As India heavily relies on solar PV, the results highlight JF’s exceptional effectiveness in extracting maximum power during partial shading scenarios. Inspired by the active and passive motions of jellyfish in the ocean, the JF algorithm is utilized. To further optimize the power output, the system is integrated with an efficient battery management system, PEM fuel cell stacking, and diesel generators. The system’s performance is analyzed using fast Fourier transform (FFT) to evaluate harmonic distortions, which consistently meet the limits specified in IEEE STD 1547-2018. Furthermore, unscented Kalman filter-based analysis is employed to assess total harmonic distortion (THD) and power rating for the grid system across various renewable energy scenarios. The contribution of the jellyfish optimization (JF) algorithm lies in its ability to efficiently and effectively maximize power generation from the PV system, regardless of normal or partial shading conditions. JF, a bioinspired metaheuristic optimization technique, successfully emulates the collective behavior of jellyfish in the ocean to identify optimal solutions. In this study, JF outperforms particle swarm optimization (PSO) in terms of power generation under partial shading conditions. Notably, JF exhibits remarkable capability in exploring the search space and discovering the global optimum, even when the system operates under challenging conditions. Overall, this study demonstrates the tremendous potential of JF in maximizing power generation in grid-connected systems with renewable energy sources while also highlighting the benefits of integrating additional components to further enhance the system performance.

Enhancing MPPT Performance in Partially Shaded PV Systems under Sensor Malfunctioning with Fuzzy Control

The shift towards sustainable energy sources is gaining momentum due to their environmental cleanliness, abundant availability, and eco-friendly characteristics. Solar energy, specifically harnessed through photovoltaic (PV) systems, emerges as a clean, abundant, and environmentally friendly alternative. However, the efficacy of PV systems is subjective depending on two critical factors: irradiance and temperature. To optimize power output, maximum power point tracking (MPPT) strategies are essential, allowing operation at the system’s optimal point. In the presence of partial shading, the power–voltage curve exhibits multiple peaks, yet only one global maximum power point (GMPP) can be identified. Existing algorithms for GMPP tracking often encounter challenges, including overshooting during transient periods and chattering during steady states. This study proposes the utilization of fuzzy sliding mode controllers (FSMC) and fuzzy proportional-integral (FPI) control to enhance global MPPT reference tracking under partial shading conditions. Additionally, the system’s performance is evaluated considering potential sensor malfunctions. The proposed techniques ensure precise tracking of the reference voltage and maximum power in partial shading scenarios, facilitating rapid convergence, improved system stability during transitions, and reduced chattering during steady states. The usefulness of the proposed scheme is confirmed through the use of performance indices. FSMC has the lowest integral absolute error (IAE) of 946.94, followed closely by FPI (947.21), in comparison to the sliding mode controller (SMC) (1241.6) and perturb and observe (P&O) (2433.1). Similarly, in integral time absolute error (ITAE), FSMC (56.84) and FPI (57.06) excel over SMC (91.03) and P&O (635.50).

Maximum Power Point Tracking Achievements and Challenges in Photovoltaic Systems

The ever-increasing demand for electrical energy in recent decades has necessitated the exploration of alternative energy sources, one of which is solar energy. The most practical means of utilizing solar energy is through the use of a Photovoltaic (PV) system. Nevertheless, the energy harvested by PV modules is constrained by low conversion efficiency, nonlinearity, and susceptibility to weather conditions, such as temperature and irradiance levels. To address these limitations, Maximum Power Point Tracking (MPPT) techniques have been developed to optimize the output of PV systems under specific circumstances. This academic article provides an in-depth analysis of the most widely used MPPT techniques, utilizing both traditional and soft computing methods. The article discusses the fundamental principles and practical applications of these techniques, as well as the challenges associated with MPPT, such as coping with rapidly changing irradiance and partial shading scenarios.

A new MPPT design using arithmetic optimization algorithm for PV energy storage systems operating under partial shading conditions

Arithmetic optimization is a new metaheuristic algorithm that has shown great strength and good performance in handling and solving complex problems. In photovoltaic systems, and especially in the case of a partial shading condition, the power-voltage curve of the photovoltaic array exhibits multiple peaks of which only one is global, making the tracking of the global maximum power point more complex, and thus requiring a powerful metaheuristic maximum power point tracking algorithm. Therefore, to address this problem, this paper introduces a new design of an arithmetic optimization based maximum power point tracking controller for battery charging schemes through photovoltaic systems operating under partial shading conditions. The main objective of the arithmetic optimization based maximum power point tracking proposed here is to maximize the power extracted from the photovoltaic array under all types of climatic conditions in order to efficiently charge the battery through the maximization of its charging current. The feasibility and effectiveness of the proposed arithmetic optimization based maximum power point tracking method are verified through MATLAB simulations and processor-in-the-loop test using the low-cost STM32F429 embedded board. Moreover, a comparison of its performance with existing maximum power point tracking methods such as, hybrid of grey wolf optimization and particle swarm optimization, particle swarm optimization and perturb and observe is also carried out while considering real climatic conditions as well as uniform and partial shading scenarios. Simulation and processor-in-the-loop results have demonstrated the good performance of the proposed method in terms of battery charging efficiency as well as the ability to track the global maximum power point with an efficiency of 99.99% and a tracking time of 0.85 s.

Production enhancement of long string photovoltaic plants during partial shading through switching capacitor power optimizer circuit

Long series connections are widely preferred by photovoltaic (PV) power plants because they offer advantages such as higher voltage, independent operation of modules at optimal voltage, ease of installation, and lower losses. However, PV power plants are highly susceptible to partial shading and suffer high power losses, resulting in large financial losses. The existing solutions have numerous weaknesses, such as multiple peaks in the characteristic’s curves, erroneous tracking losses, lower power generation and extraction, and limited applications. To overcome the above limitations, a power optimizer based on a switching capacitor circuit has been proposed in this paper to improve the reliability of long strings in power plants under partial shading scenarios. The power optimizer provides higher power output and a single peak in the characteristics curves by minimizing the voltage differences between modules in the string to enable smoother operation of PV systems under partial shading. To evaluate the application and performance of the proposed power optimizer, two PV system sizes are considered and compared with the conventional techniques under static, moving, and progressive partial shading scenarios. From the in-depth analysis, it is found that the power optimizer minimizes the mismatch between modules by reducing the power losses from 70.83 % in the conventional system to 35.13 %. By implementing the power optimizer in the string, a power extraction of up to 68.68 %, generation efficiency of more than 99 %, and power improvement of 54.21 % are achieved along with single-peak characteristics curves.

Alternate panel interchange technique to improve the power production of PV array operating under various shading situations

ABSTRACT This work presents an improved shade dispersion technique named as Alternate Panel Interchange (API) technique to get an enhanced power output of the PV system under partial shading scenarios. In this proposed technique, Total Cross Tied forms the electrical connections, whereas the physical position of the module is as per the API technique. The proposed technique distributes the effects of mismatching simplified with two stepped dispersion procedures. This paper considers a 6 × 6 array for various shading scenarios. For comparative investigation of the proposed technique with hybrid configurations, Bridge Link-Total Cross Tied, Series Parallel-Total Cross Tied, Honey Comb-Total Cross Tied, and conventional Total Cross Tied are taken. The instantaneous maximum power of the proposed technique improves over TCT by 19.58%, 9.52%, 7.92%, and 1.36% in uneven rows, corner, L-shaped, and triangular shading scenarios. The reduction in mismatch losses is a substantial cause for improved power output with the proposed technique and eventually results in the fill-factor enhancement.

Implementation of the Bio-Inspired Metaheuristic Firefly Algorithm (FA) Applied to Maximum Power Point Tracking of Photovoltaic Systems

In this paper, an algorithm for the maximum extraction of energy generated by photovoltaic (PV) systems was presented. The tracking of the global maximum point of the system is complex due to the non-linearity of the current-voltage (I-V) characteristic curve of the photovoltaic modules, as they vary according to the temperature and solar irradiation in the module. To obtain the best energy efficiency in these systems, it is important that the generation is delivering the maximum power available through the arrangement. In order to solve such problems, in this work an efficient MPPT-FA method was proposed, which showed good traceability when compared to traditional methods. Most traditional MPPT techniques are not able to find the global maximum point to extract the maximum power provided by the PV system. Finally, the Firefly Metaheuristic MPPT method proved to be robust against several partial shading scenarios. Simulations were presented to demonstrate the effectiveness of the proposal when compared to the traditional MPPT-PO method.

A new hybrid BIPV array for enhancing maximum power with reduced mismatch losses under extreme partial shading scenarios

ABSTRACT Building integrated photovoltaic (BIPV) systems are gaining major attention in modern cities due to the absence of free space to install ground-mounted photovoltaic (PV) plants. A large interconnected BIPV array (liBA) is essential for standalone and grid-connected BIPV systems. The major problem with liBA is the partial shading (PS) phenomenon that increases the mismatch losses (MmLs) and reduces the maximum power (MP) output. Modelling, simulation, and performance analysis of a novel 7 × 5 and 7 × 8 hybrid triple-tied total-cross-tied (TrTd-TtCrTd) fixed BIPV array for increasing MP with reduced MmLs under extreme PS conditions are the main goals of this research paper. In addition, existing hybrid arrays such as series-parallel total-cross-tied (SePl-TtCrTd), bridge-linked total-cross-tied (BdLk-TtCrTd), and honey-combed total-cross-tied (HnCb-TtCrTd), as well as conventional triple-tied (TrTd) and total-cross-tied (TtCrTd), are compared. The simulation results are also validated experimentally using two PS situations. Finally, the global maximum power point (GlMxPoPt), MmLs, fill factor (FiFa), and efficiency (Effy) are used to evaluate the performance. Under six different extreme PS scenarios, the simulation results show that the proposed 7 × 5 hybrid TrTd-TtCrTd array increases GlMxPoPt by 4.84 %, decreases MmLs by 3.46 %, and increases FiFa and Effy by 2.20 % and 0.34 %, respectively. Under two severe PS scenarios, simulation results show that the proposed 7 × 8 large size hybrid TrTd-TtCrTd array increases GlMxPoPt by 3.43 %, decreases MmLs by 2.29 %, and increases FiFa and Effy by 1.26 % and 0.23 %, respectively. Under two extreme PS scenarios, experimental results indicate that the proposed 7 × 8 hybrid TrTd-TtCrTd solar array increases GlMxPoPt by 2.84 %, decreases MmLs by 2.12 %, and increases FiFa and Effy by 1.82 % and 0.22 %, respectively. The proposed hybrid TrTd-TtCrTd array improves GlMxPoPt, reduces MmLs and improves FiFa and Effy with low wiring requirements under different PS cases.

Static and Dynamic Reconfiguration Strategies for Reducing Partial Shading Effects in Photovoltaic Array: A Comprehensive Review

The output power generated by a photovoltaic (PV) cell decreases remarkably because of the irregular irradiance pattern. Thus, the overall system performance is degraded. The decrease in the power output is not dependent on the shaded area but is rather dependent on the array configuration and the shading pattern. To eliminate partial shadings, many strategies have been documented in the state‐of‐the‐art literature. These strategies may not improve the maximum output power to the required level but definitely do so to a certain extent. Thus, to minimize these losses, a promising reconfiguration approach is needed, i.e., reconfiguring the PV modules inside the PV array to improve the maximum power output. These techniques are broadly divided into two categories, viz., static and dynamic reconfiguration methods. The advanced PV array reconfiguration solutions to improve global power output under mismatch and partial shading scenarios are presented in this article. According to the results of the review article studied in this research, dynamic approaches are costlier as compared to static reconfiguration strategies, but are more effective to reduce the partial shading impact in comparison to the static techniques.

Power losses mitigation through electrical reconfiguration in partial shading prone solar PV arrays

The susceptibility of solar photovoltaic (PV) modules towards partial shading is one of the major demerits encountered by PV arrays installed in the field. The partial shading among the modules leads to a serious reduction in the array power generation as it introduces several losses due to mismatch among modules. These power losses are mainly countered by implementing various electrical interconnecting configurations such as bridge-linked (BL), honeycomb (HC), and total-cross-tied (TCT) rather than using the conventional series-parallel (SP) connection. However, the configurations fail to generate maximum power during all partial shading scenarios. Hence, in this paper, a new module electrical reconfiguration technique is proposed to disperse the effect of partial shading power generation improvement and reduce the losses in the PV arrays. The technique is a one-time fixed electrical reconnection strategy for modules based on the algorithm proposed and requires no sensors and switches. The efficacy of the proposed electrical reconfiguration is investigated for two array sizes in MATLAB/Simulink and a real-time experimental environment whereas, the algorithm is programmed in the Python language. Also, a comparison is done with the conventional electrical configurations under various shading scenarios using the characteristics curves analysis, power generation, losses, efficiencies and performance ratio. The proposed reconfiguration enhances the power output of 26.92% than SP, 24.07% than BL, 25.17% than HC, and 22.96% than TCT during shading.