Partial Shading Conditions Research Articles

Maximum power enhancement in solar PV modules through modified TCT interconnection method

This article presents a narrativemove towards for maximal power augmentation in poly-crystalline solar photovoltaic (PV) modules through the modified Total Cross Tied (TCT) interlinkconnection method. Worldwide, the cost of coal has increased after a pandemic rebound in industrial activity and it leads to demand for electricity. The maximum power extraction from sustainable energy sources plays a vital role as they are ample, eco-friendly, harmless, and won’t run out. Due to shading effects like the transition of clouds and tree shadows, the output power generated from the solar photovoltaic array is reduced uptill 60%. Several methodologies of array formation and reconfiguration strategies were developed to overcome the partial shading effects. In this proposed method, PV cells in modules are tied via modified TCT fashion with a bypass diode through each row. Real-time experimental analysis is performed with 4 × 4 cell connections. The current (I)-(V)voltage and Power(P)-(V) voltage characteristics are obtained under normal and various partial shading conditions. According to our proposed method, the percentage power enhancement achieved by row-wise partial shading increases from 10% to 60%, approximately 83.64, 82%,80%, 74.29%, 70%, and 64%. According to the conventional topology, the maximum fill factor is 0.28 in columnwise 25% shading ratio, whereas the proposed modified TCT PV array has a fill factor of 0.53, 89% higher. Compared to the proposed pattern, which has a maximum performance ratio of 0.86 for 10% shading condition. The modified TCT interconnected PV module connection extracts a maximum of 50% increased output power when compared to conventional PV modules. The performance of the projected modules is also examined in a one kW PV array.

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.

Moth flame optimization for the maximum power point tracking scheme of photovoltaic system under partial shading conditions

The dwindling reserves of fossil fuels have spurred the expansion of photovoltaic power systems, widely regarded as an alluring solution. Yet, a formidable challenge arises when it comes to optimizing the output of PV systems exposed to irregular irradiance stemming from external environmental factors. Consequently, this research endeavors to advocate the use of the Moth Flame Optimization (MFO) algorithm to search for the highest power output of a solar energy harvesting system. The distinctive behavior of moths proves instrumental in thorough searching of the feasible space, mitigating the risk of entrapment in local optima. To evaluate its efficacy, this algorithm’s performance is validated by comparing its outcomes with those of the Butterfly Optimization Algorithm (BOA). Both algorithms are subjected to experimentation to search for the Global Maximum Power Points (GMPPs) of the PV system under two distinct Partial Shading Condition (PSC) scenarios: Case 1 and Case 2. The results indicate that BOA tends to produce outcomes with a broader data dispersion range relative to the mean, unlike MFO. Specifically, for Case 1, the standard deviation values for MFO and BOA are 2.327040E−02 and 5.777913E−02, respectively, while for Case 2, they are 5.0567340E−02 and 8.519362E−02, respectively. Hence, the proposed approach demonstrates faster and more precise convergence.

Development of rapid and reliable cuckoo search algorithm for global maximum power point tracking of solar PV systems in partial shading condition

Development of rapid and reliable cuckoo search algorithm for global maximum power point tracking of solar PV systems in partial shading condition

Performance improvement of reconfiguration strategies in photovoltaic array by replacement of blocking diodes with MOSFETs

ABSTRACT The power from photovoltaic array (PVA) is affected by temperature, solar irradiation, dust concentration, wind velocity, photovoltaic array configuration (PVAC) and shading pattern. The problem of partial shading (PS) is recognized as a major concern which decreases the PVA output power and represents multiple peak power points in the PV characteristic. Different PVACs respond differently with regard to their mismatch power loss (MPL) and fill factor (FF) under partial shaded condition (PSC). The conventional PVACs, such as SP-diode (Series-Parallel), TCT-diode (Total-Cross-Tied), BL-diode (Bridge-Link) and HC-diode (Honey-Comb), use blocking diodes (B-diodes) to block the reverse current. On the other hand, the proposed PVACs, including SP-MOSFET, TCT-MOSFET, BL-MOSFET and HC-MOSFET, replace the B-diodes with Blocking P-MOSFETs (BP-MOSFETs). The improvement in power output of the PVA is examined for four different configurations with B-diode and BP-MOSFET under changing illumination conditions. It is observed that TCT-MOSFET configuration shows better performance with least power losses and maximum efficiency under PSCs.

Solar Power Prediction Modeling Based on Artificial Neural Networks under Partial Shading

Photovoltaic systems are emerging as an important device to address the environmental pollution generated from conventional energy production. The objectives of this study are to accurately predict the power of photovoltaic systems under partial shading conditions and to model high-efficiency photovoltaic systems. First, the power loss under partial shading conditions was addressed using a bypass diode. In previous studies, for the power prediction, one or two parameters were trained through artificial neural networks. In this study, we employ five main parameters to improve the accuracy: the photo-current (Iph), diode saturation current (I0 ), diode idealization factor (n), series resistance (Rs), and shunt resistance (Rsh). Compared to the results of previous studies, the proposed model yielded consistent results. As a result, more accurate power predictions are possible with variations in temperature and irradiation.

An Adaptive Perturb and Observe Algorithm With Enhanced Skipping Feature for Fast Global Maximum Power Point Tracking Under Partial Shading Conditions

Solar energy exposed its prominence to diminish the growing energy demands. But the formation of multiple peaks under partial shading conditions causes the conventional maximum power point tracking controllers to fail to track the global maximum power point (GMPP). Scanning the whole power-voltage curve to locate the GMPP takes extreme time and reduces algorithm effectiveness. Thus, the proposed method devotes the skipping and scanning mechanism incorporated with the perturb and observe (P&O) algorithm to reduce the scanning area drastically. The skipping mechanism skips over the scanning areas between successive peaks based on a specific formula that narrows down the scanning zone. Along with a buck-boost converter, the system includes a voltage control loop controlled by a proportional-integral controller. The proposed algorithm's effectiveness is tested in both simulation and experimental environments on discretized partial shading patterns while GMPP is deployed at different locations. According to the experimental outcomes, the proposed algorithm outperforms the modified maximum power trapezium, skipping fast GMPP, modified cuckoo search, and the conventional P&O algorithm with an average convergence time of 1.02 sec and average efficiency of 99.44 percent.

Metaheuristics Assisted Efficiency Maximizing Flexible Power Point Tracking of a Photovoltaic Array Under the Partial Shading

The vast installation of the photovoltaic (PV) plants has necessitated bringing more flexibility to the PV power generation instead of the simple maximum power point tracking (MPPT). Certain flexible power point tracking (FPPT) techniques have already emerged to operate the PV array not only at the maximum power point (MPP) but also below the MPP. Similarly to the MPPT, the FPPT also needs special control techniques to operate under the partial shading condition. Apart from the local peak blockage problem, there is an issue of solution multiplicity in the FPPT. As there may be multiple equilibrium points for a specific power level, a procedure is needed to dynamically select the equilibrium at the highest voltage level so that the thermal stress on semiconductor devices can be minimized by maximizing the power conversion efficiency. The present work is undertaken to meet the particular objective. A metaheuristics assisted efficiency maximizing (MAEM) control algorithm is suggested to attain the desired power regulation under partial shading. A specific implementation of the MAEM control is shown for a two-stage PV system with due consideration for the duty ratio quantization effect. The proposed MAEM-FPPT control technique is thoroughly verified via both MATLAB simulations and practical experimentation.

Comparative Study Of Five Mppt Controls In Two-Stage Three-Phase Grid-Connected Photovoltaic Systems Under Partial Shading Conditions

Abstract One of the maximum popular renewable electricity assets is photovoltaics. Grid-connected photovoltaic structures are designed to generate as a lot strength as possible. Photovoltaic systems have nonlinear traits imposed with the aid of environmental factors consisting of radiation and temperature, making it hard to operate at the most power factor. It may additionally be tough to extract the maximum amount of electricity due to the formation of nearby maxima because of other factors which include shading or degradation. There are several MPPT algorithms and approaches that may be used for this. Publications with comparative analyses have also been released. However, in most of these works, comparisons are based on simulations or literature review. From the simplest to the most complex MPPT methods, empirical validation remains important. The two simplest and most widely used MPPT techniques are the perturbation, open-loop, and incremental conductance algorithms. The three most challenging ones are sliding mode control, backstepping controller, and particle swarm optimization. Therefore, five MPPT algorithms are empirically studied in this paper. Under test settings, Matlab/Simulink was used to conduct experimental experiments. The findings demonstrate that under ordinary operating circumstances, the backstepping algorithm is the only one capable of finding the global MPP under the influence of local shadowing.

Optimal PV array reconfiguration under partial shading condition through dynamic leader based collective intelligence

This paper applies the innovative idea of DLCI to PV array reconfiguration under various PSCs to capture the maximum output power of a PV generation system. DLCI is a hybrid algorithm that integrates multiple meta-heuristic algorithms. Through the competition and cooperation of the search mechanisms of different metaheuristic algorithms, the local exploration and global development of the algorithm can be effectively improved to avoid power mismatch of the PV system caused by the algorithm falling into a local optimum. A series of discrete operations are performed on DLCI to solve the discrete optimization problem of PV array reconfiguration. Two structures (DLCI-I and DLCI-II) are designed to verify the effect of increasing the number of sub-optimizers on the optimized performance of DLCI by simulation based on 10 cases of PSCs. The simulation shows that the increase of the number of sub-optimizers only gives a relatively small improvement on the DLCI optimization performance. DLCI has a significant effect on the reduction in the number of power peaks caused by PSC. The PV array-based reconstruction system of DLCI-II is reduced by 4.05%, 1.88%, 1.68%, 0.99% and 3.39%, when compared to the secondary optimization algorithms.

An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions.

Global maximum power point (GMPP) tracking under shading conditions with low tracking time and reduced startup oscillations is one of the challenging tasks in photovoltaic (PV) systems. To cope with this challenge, an improved grasshopper optimization algorithm (IGOA) is proposed in this work to track the GMPP under partial shading conditions (PSC). The performance of the proposed approach is compared with well-known swarm intelligence techniques (SITs) such as gray wolf optimization (GWO), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), improved SSA based on PSO (ISSAPSO), and GOA in terms of tracking time, settling time, failure rate, and startup oscillations. For a fair comparison, the PV system is analysed under uniform irradiance and three PSCs having four to six peaks in the power-voltage characteristic curves and using three to six search agents for each SIT. For this purpose, a PV system containing six solar panels has been built using MATLAB/SIMULINK software, and statistical analysis is performed in detail. The results show that the IGOA tracks the GMPP in 0.07 s and settles the output in 0.12 s which is 25% to 96% faster than its counterparts. Moreover, IGOA proves its consistency with a minimal tracking failure rate of 0% for four to six search agents with negligible startup oscillations. This work is expected to be helpful to PV system installers in obtaining maximum benefits from the installed system.

An innovative grey wolf optimizer with Nelder–mead search method based MPPT technique for fast convergence under partial shading conditions

Partial shading condition (PSC) is an unavoidable problem faced by photovoltaic arrays, where multiple peaks appear on the power–voltage (P–V) curve. The conventional techniques are unable to track the global power under PSCs, and thus, various optimization techniques aim to reduce the impact of PSCs. This paper proposes a hybrid Grey wolf optimization with Nelder–mead (GWO-NM) algorithm for tracking the maximum power point. The GWO algorithm assures achieving the GMPP through search space exploration. In contrast, NM is a direct search technique that aids all particles in rapidly converging on the GMPP. Consequently, the proposed hybrid method has the ability to track actual GMPP with high efficiency, providing an enhanced convergence rate and fast response time, and is highly capable of operating under varying irradiance. The proposed algorithm avoids unnecessary exploration of particles and also effectively reduces steady-state oscillations. The proposed algorithm is experimentally validated using a DSP controller, showing an average tracking time of 0.54 s with a steady-state efficiency of 99.91% under different shading conditions. Finally, the effectiveness of the proposed GWO-NM algorithm is compared to recent meta-heuristic algorithms in terms of tracking power, speed, and efficiency.

A novel harbor seal whiskers optimization algorithm

A novel optimization algorithm, namely Harbor Seal Whiskers Optimization Algorithm (HSWOA) is proposed in this work. Harbor seals use their whiskers to find underwater disturbances which are in the form of oscillating spheres and track prey even though they lack lateral-line systems. HSWOA mimics the high-level sensing that seal whiskers possess. As such, HSWOA has an excellent exploration capability for the search space and a high exploitation capacity for exploiting the all-optimum solutions to reach the most optimum solution. To validate these abilities, the proposed HSWOA utilizes two sets of test functions: 33 benchmark functions and five IEEE Congress on Evolutionary Computation (CEC2019) benchmark functions. The results of HSWOA are compared with ten well-established optimization algorithms. The comparison results show that HSWOA offers superior performance indices to reach an optimum solution while requiring less control variables. The results also show that HSWOA is more efficient regarding computational demand and resolution accuracy. Finally, the HSWOA is employed to track Maximum Power Point (MPP) of Photovoltaic (PV) array with partial shading conditions (PSCs) for two case studies. The results show that HSWOA extracts maximum power in minimum tracking time and high average power capturing capability compared to other optimization techniques.

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.

A Comprehensive Evaluation of Up-to-Date Optimization Algorithms on MPPT Application for Photovoltaic Systems

ABSTRACT Maximum Power Point Tracking (MPPT) plays a significant role in obtaining maximum power at PV system outputs. Recent research has focused on minimizing the adverse effects of partial shading and dynamic environmental conditions on MPPT. Metaheuristic optimization algorithms have attracted attention with their success in these issues. The growing body of literature lacks a study that comprehensively evaluates current metaheuristic algorithms. In this study, the performances of 20 metaheuristic algorithms under five different shading conditions, nonuniform temperature distribution, and variable irradiance conditions have been investigated. The successes of the algorithms in the convergence of the global maximum value and their convergence rates have been calculated through various statistical metrics with different aspects. This study provides a novel approach to objectively evaluating the performances of the algorithms by using the three-dimensional Pareto Front method. As the result of this multicriteria evaluation, RKO, MPA and CGO algorithms are able to provide non-dominated results. These three algorithms are further tested using case analysis designed for dynamic operating conditions, and the RKO algorithm exhibited the most favorable results. Additionally, the RKO algorithm exhibits remarkable performance by reaching the LMPP/GMPP point within an average time of 3.2 milliseconds in all cases. Moreover, it demonstrates an average efficiency value exceeding 0.999.

Improving Photovoltaic Grid Integration under Partial Shading by Equilibrium Slime Mould Optimization

In the realm of photovoltaic grid integration with Shunt Active Power Filters operating under partial shading conditions, this study introduces an innovative approach aimed at minimizing both power consumption from the electrical grid and associated costs. The primary objective of this research is to maximize the efficiency of photovoltaic system output by implementing a novel algorithm known as the Equilibrium Slime Mould Optimization technique. This algorithm is employed to precisely track the global power point of the photovoltaic array under partial shading conditions, resulting in increased photovoltaic power injection and decreased grid-side consumption. The choice of the Equilibrium Slime Mould Optimization technique is motivated by its exceptional ability to efficiently explore the search space and avoid falling into local extrema. Additionally, this article incorporates Predictive Direct Power Control, one of the most contemporary Shunt Active Power Filter control techniques, to effectively eliminate harmonics and enhance overall system efficiency. To validate this proposed approach, a simulation setup was meticulously developed. The obtained results demonstrate a remarkable enhancement in the efficiency of photovoltaic power injection compared to the conventional sliding mode technique, which tends to get trapped at local maximum power point, thereby resulting in diminished power injection. This pioneering approach heralds a new era in the application of metaheuristic algorithms within practical systems, leading to enhanced productivity and reduced costs for consumers. Furthermore, it holds the potential to advance various categories of interconnected photovoltaic systems, ensuring improved performance across diverse operational scenarios.

A Hybrid Particle Swarm Optimization with Butterfly Optimization Algorithm Based Maximum Power Point Tracking for Photovoltaic Array under Partial Shading Conditions

The key objective of this paper is to develop a photovoltaic (PV) maximum power point tracking (MPPT) algorithm based on particle swarm optimization–butterfly optimization algorithm (PSO-BOA) that is adapted for partial shading conditions (PSCs). Generally, conventional MPPT techniques are often unable to accurately locate the global maximum power point (GMPP) generated by partial shading in PV systems. As a result, a significant decrease in power output occurs. The traditional particle swarm optimization (PSO) algorithm traps the local maxima point easily, while the butterfly optimization algorithm (BOA) has slow convergence speed and large oscillations during its use in research. To address the limitations of the aforementioned PSO and BOA algorithms, the MPPT strategy of PV systems combining PSO-BOA is presented, which can ameliorate the efficiency and accuracy in PSCs. In this paper, the control parameter of sensory modality in the BOA can be acquired based on logistic mapping, and the self-adaptive adjustment of the inertial weight of the PSO algorithm is designed. According to the simulation findings, the suggested method is more suitable than PSO and BOA with respect to intricate shading-induced variations in irradiance and changes in external temperatures. The average tracking time is less than 0.5 s, and the tracking accuracy is not less than 99.94%. Especially under sudden variations in irradiance and temperature conditions, the tracking time of the PSO-BOA algorithm is only 49.70% of that of the PSO algorithm and 55.63% of that of the BOA. Therefore, the MPPT method presented has the ability to improve the oscillations and result in less convergence speed, which in turn accurately tracks the GMPP.

One-step adaptive reconfiguration technique for partial shaded photovoltaic array

The performance of a photovoltaic (PV) array greatly depends on partial shading conditions, which can be diminished by careful planning and positioning of PV modules during installation to avoid self-shading. However, partial shading caused by nearby buildings, dust, and snow cannot be completely avoided. One of the approaches to reduce the effects of partial shading is the reconfiguration technique. However, the reconfiguration increases the length of the wire and system complexity. This study proposes an adaptive reconfiguration technique for a 4x4 PV array with reduced complexity and better shade dispersion. The effectiveness of the proposed reconfiguration technique is implemented and tested on a 4x4 total-cross-tied PV array in a MATLAB Simulink environment. In the proposed configuration, the switching matrix connects the fixed and adaptive part of the PV array, in such a way that the switches can connect the PV module of the next column by shifting downwards and shifting upwards. A comprehensive analysis of proposed reconfiguration is carried out and its performance is compared with existing fixed and dynamic reconfiguration techniques in terms of performance parameters. The study result shows that the proposed reconfiguration technique delivers superior performance compared to existing fixed and dynamic reconfiguration techniques in terms of better shade dispersion, enhanced performance and reduced complexity. For the LC shade pattern, the proposed BSSU technique has obtained improved PE of 48%, 31%, 15% and 9% compared to CMA, TCT, ADR and SS configurations. Experimental verification is carried out to demonstrate the superiority of the proposed reconfiguration technique.

Differential power processing architecture to increase energy harvesting of photovoltaic systems under permanent mismatch

Differential power processing (DPP) architectures are a prominent solution for mitigating undesirable issues caused by mismatch that often occurs in photovoltaic (PV) strings. In this scenario, the association of DPP converters in parallel with PV strings is a common approach for enhancing energy harvesting. However, most topologies presented so far focus on temporary mismatch caused by partial shading conditions only, with little attention given to permanent mismatch due to the connection of modules with distinct ratings. Given the above, this article presents a novel DPP topology capable of handling both temporary and permanent mismatch in PV strings. Important advantages include lower component count; reduced voltage stresses on the active switches; operation under zero-current switching (ZCS) condition, with a direct impact on the converter efficiency; and greater design flexibility in terms of a modular solution that can be implemented at the PV module level. Experimental tests on a string composed of six modules are presented and the results are thoroughly discussed to validate the theoretical assumptions. It is effectively demonstrated that the architecture allows for increasing the power extracted from the string under two severe operating conditions.

A novel benchmark shading pattern for PV maximum power point trackers evaluation

Partial shading conditions (PSC) drive the P-V characteristics of the PV system to have multiple local peaks (LPs) and one global peak (GP). Conventional maximum power point tracker (MPPT) techniques may be trapped in one of the LPs and miss the GP, which substantially reduces the efficiency of the PV systems. Metaheuristic optimization algorithms (MOAs) have been widely used recently to avoid this limitation. Although, the robustness of the MOAs in tracking the GP, still they have many challenges that should be overcome. This paper shed light on the most important problems and solutions provided associated with the use of MOAs as an MPPT of PV systems. Moreover, this paper introduced benchmark shading patterns (BSP) that contain most of the important challenges that may occur in the PSC of PV systems. This BSP can be used as an evaluation benchmark for all MPPT techniques. The problem of stagnation of search agents within a certain LP is investigated and solutions are provided and evaluated. A novel flying search agent (FSA) strategy is used to detect any change in GP position without sacrificing the stability or the ripple contents in the PV system waveforms. The proposed FSA sends a search agent to the position of the anticipated peaks and compares its power with the current GP power. If the power generated from the FSA is higher than the current GP, it will move the particles to search around the position of the FSA.