Partial Shading Conditions Research Articles

Performance Enhancements and Modeling of Photovoltaic Panel Configurations During Partial Shading Conditions

The growing focus on solar energy has led to an expansion of large solar energy projects in the world. Though the most widely adopted Total Cross Tie (TCT) interconnection patterns will reduce the loss of mismatch, the appearance of shades in large-scale photovoltaic arrays drastically decreases the output power as well as several peaks of power in the P-V characteristics. To overcome these problems, the PV panels are organised using either electrical or physical reconfiguration methods. In this respect, it has been established that physical relocation methods are both cost-effective and efficient to disperse the shadow. In this context fits this work, his goal is to study the magic square view (MSV) for the physical rearrangement of the PV module in a TCT scheme. The simulation results reveal the effectiveness of the MSV method efficiently scattering the shade over the whole photovoltaic array. For validation, four types of Partial Shading Conditions (PSCs) patterns are considered and are then compared with the TCT and the recently proved Competence Square (CS) techniques: Short and Wide (SW), Long and Wide (LW), Long and Narrow (LN), and Short and Narrow (SN) shading patterns. Overall, MSV method is important in achieving improved output power enhancement of PV array under shaded conditions. A very clear improvement is obtained in the Long and Wide partial shading pattern.

Performance enhancements and modelling of photovoltaic panel configurations during partial shading conditions

Performance enhancements and modelling of photovoltaic panel configurations during partial shading conditions

A Particle Swarm Optimization–Adaptive Weighted Delay Velocity-Based Fast-Converging Maximum Power Point Tracking Algorithm for Solar PV Generation System

Photovoltaic (PV) arrays have a considerably lower output when exposed to partial shadowing (PS). Whilst adding bypass diodes to the output reduces PS’s impact, this adjustment causes many output power peaks. Because of their tendency to converge to local maxima, traditional algorithms like perturb and observe and hill-climbing should not be used to track the optimal peak. The tracking of the optimal peak is achieved by employing a range of artificial intelligence methodologies, such as utilizing an artificial neural network and implementing control based on fuzzy logic principles. These algorithms perform satisfactorily under PS conditions but their training method necessitates a sizable quantity of data which result in placing an unnecessary demand on CPU memory. In order to achieve maximum power point tracking (MPPT) with fast convergence, minimal power fluctuations, and excellent stability, this paper introduces a novel optimization algorithm named PSO-AWDV (particle swarm optimization–adaptive weighted delay velocity). This algorithm employs a stochastic search approach, which involves the random exploration of the search space, to accomplish these goals. The efficacy of the proposed algorithm is demonstrated by conducting experiments on a series-connected configuration of four modules, under different levels of solar radiation. The algorithm successfully gets rid of the problems brought on by current traditional and AI-based methods. The PSO-AWDV algorithm stands out for its simplicity and reduced computational complexity when compared to traditional PSO and its variant PSO-VC, while excelling in locating the maximum power point (MPP) even in intricate shading scenarios, encompassing partial shading conditions and notable insolation fluctuations. Furthermore, its tracking efficiency surpasses that of both conventional PSO and PSO-VC. To further validate our results, we conducted a real-time hardware-in-the-loop (HIL) emulation, which confirmed the superiority of the PSO-AWDV algorithm over traditional and AI-based methods. Overall, the proposed algorithm offers a practical solution to the challenges of MPPT under PS conditions, with promising outcomes for real-world PV applications.

Maximum power point tracking of a partially shaded solar photovoltaic system using a modified firefly algorithm-based controller

Solar photovoltaic (PV) system is one of the most promising power systems based on renewable energy sources, with several advantages compared to others. However, solar PV systems have a challenge of low conversion efficiency because most of the irradiances of the sun, which are channelled to the PV panels, are not fully utilized for power consumption. A more challenging situation of the system occurs when some of its panels are obstructed from full reception of the solar irradiance, a case referred to as partial shading conditions (PSC) in solar PV systems. This leads to the generation of multiple, unequal power peaks in the system, from which the one with the highest power must be tracked for optimum utilization of the system. To this regard, this work presents a modified firefly algorithm-based controller, tied operationally with a DC–DC boost converter. A model was developed and simulated on MATLAB, for tracking the maximum power point of the system, both at constant solar irradiance and at PSC.

Enhancing photovoltaic system modeling and control under partial and complex shading conditions using a robust hybrid DE-FFNN MPPT strategy

Improving efficiency, reliability, and extending the life of Photovoltaic (PV) systems are some essential benefits of developing an effective Maximum Power Point Tracking (MPPT) control system. In this study, an intelligent MPPT approach based on the Differential Evolution (DE) algorithm, and an adaptive Feed-Forward Neural Network (FFNN) is developed to address various issues of the most commonly used MPPT algorithms that affect the performance of PV systems including, power generation efficiency, tracking time, and instabilities around the Global Optima (GO). Consequently, in order to determine the suitable hyper-parameters’ values for the optimization and training procedure, the suggested hybrid technique is evaluated in the first part of this study for a variety of the FFNN configuration’s possibilities. Afterwards, the best obtained neural model is implemented as an MPPT controller, and a performance appraisal has been made between the DE algorithm based Feed-Forward Neural Network (DE-FFNN) and the most efficient utilized MPPT techniques. The observed results in the optimization and training phases show that our proposed strategy with 11 neurons in the hidden layer presents the most optimal neural model with a Mean Square Error (MSE) of 1.4924 in 139.8613 s in the optimization phase, and 2.57e−14 in the training phase. Subsequently, the created DE-FFNN model based-MPPT with the proposed configuration illustrates excellent performance compared to the applied MPPT techniques with a tracking speed between 0.0237 and 0.0106 s, a remarkable reduction in instability, and a power efficiency that can go up to 99.9971% in four different irradiance scenarios.

Evaluation of electrical power losses of photovoltaic modules subjected to partial shading conditions in the three climatic regions of Cameroon

This paper studies the behavior of three types of photovoltaic panels operating under partial shading conditions in the three climatic regions of Cameroon. The phenomenon of partial shading constitutes one of the factors which drastically disrupt the electric power produced by the photovoltaic generators. It is to contribute to solve this problem that we modelled the PV panels operating in partial shading conditions under the climatic parameters of the study area. The analytical method combined with the iterative method are used for the determination of PV module parameters under partial shading conditions. Studies were carried out over a period of one year in three cities, respectively: Yagoua for the sudano-sahelian region, Tchollire in the sudanese region and Tibati in the southern equatorial region. Three types of PV panels were used {polycrystalline (MSX-60), monocrystalline (Shell SP70) and CIS thin film (Shell ST40)}. The electrical power losses of each type of panel were evaluated.

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.

A novel reduced-cross-tied configuration for extracting maximum power output from a symmetrical PV array under partial shading conditions

A novel reduced-cross-tied configuration for extracting maximum power output from a symmetrical PV array under partial shading conditions

Bounded Region for Tracking by Two Fuzzy Rules Based Perturb and Observe Technique with Fractional Order based Proportional Integral Controller for Photovoltaic Conversion System

Maximum power from PV system is tracked by using Maximum power point (MPP) tracking techniques. In this paper the Two fuzzy rules based perturb and observed (2F-P&O) with Fractional order based PI (FOPI) controller is developed to track the global MPP under Partial shading conditions (PSCs). The two rules based fuzzy systems are implemented to provide the voltage boundaries under which the P&O starts to track GMP. FOPI controller is used to minimize the error and gives an accurate response under PSCs. The performance analysis of 2F-P&O based MPP technique is showing along with cases as diagonal & column based shading patterns, irradiance change conditions under several PSCs. The 2F-P&O with FOPI based technique compared with traditional P&O, Artificial neural network and Particle swarm optimization, Moreover from the comparative analysis 2F-P&O has higher efficiency, less oscillations and convergence time. This paper is validated using Opal-RT simulator and MATLAB/ Simulink Software.

A matlab-based modelling to study and enhance the performance of photovoltaic panel configurations during partial shading conditions

Introduction: The utilization of solar energy in large-scale photovoltaic arrays has gained immense popularity on a global scale. However, shadows in the array lead to significant reductions in power output and create multiple power peaks in the P-V characteristics. To address this issue, the Total Cross Tie (TCT) interconnection pattern is commonly employed to minimize mismatch loss. Additionally, physical relocation methods have proven effective in dispersing shadows.Method: In this context, the Magic Square View (MSV) offers a physical rearrangement of PV modules within a TCT scheme, effectively scattering shadows across the entire photovoltaic array.Results: Simulation results confirm the MSV efficacy in enhancing the PV array’s output power under various Partial Shading Conditions (PSCs) patterns. Four PSCs patterns (Short and Wide, Long and Wide, Long and Narrow, and Short and Narrow) are considered and compared to the TCT and the recently validated Competence Square (CS) techniques. The MSV method is vital in improving the PV array’s power output, especially when confronted with Long and Wide shading patterns. The outcomes demonstrate that adopting the MSV configuration leads to a substantial increase of 33.78% and 29.83% in power output for LW and SW shading patterns, respectively, compared to the TCT setup. Even under LN and SN shading patterns, there is a notable power enhancement, achieving a remarkable 25.15% increase for the LW shading pattern compared to the TCT, surpassing enhancements achieved by SuDoKu, DS, and CS methods, which improved by 20.5%, 18.2%, and 21.6%, respectively. Overall, the MSV configuration presents a promising solution for enhancing the performance of photovoltaic arrays under shading conditions.

Attenuation of hotspot phenomenon in PV systems

ABSTRACT PV waste management is a looming problem and a growing concern for environmental engineers and energy policymakers, as the early PV installations are approaching their end-of-life. Hence, improvement of PV reliability and prevention of early failures has now become more important than ever. Notably, the formation of hotspots in PV cells under partial shading and other mismatch conditions remains a dominant cause of early failures in PV modules. In this study, we undertake an experimental investigation to assess the effectiveness of the extant bypass mechanism in preventing hotspot formation in PV modules. The findings reveal several limitations associated with the conventional bypass approach. Although the bypass diode partially fulfills its intended purpose within a string array configuration, its effectiveness is entirely compromised when connected in the configuration of real-world installations, such as in a series-parallel (SP) array. The hotspot temperature in the SP array increased up to 70.7°C under partial shading conditions at the operating point (i.e., at MPP), whereas no hotspot was created (below 46.1°C) in a string array, except in the single-cell shading scenario. To make the bypass circuits effective, in the present work, a novel bypass strategy is proposed.

Optimal PV array configurations for partial shading conditions

Increasing the effectiveness of photovoltaic (PV) power generation systems is a major barrier to their widespread use. Partial shading conditions (PSCs), which are caused by nearby objects like trees, buildings, and clouds, reduce the energy produced by PV systems and may be mitigated by the reconfiguration of PV arrays. A novel approach dubbed a modified series-parallel (MSP) setup, was suggested in this paper and compares its performance to four PV configuration strategies: series-parallel (SP), total-cross-tied (TCT), bridge-linked (BL), and honey-comb (HC). A 3×3 solar array is used in five partial shading arrangements. MATLAB/Simulink simulates all shading conditions. This study indicated that MSP delivers high performance and is superior to all other configurations in terms of maximum power in even and uneven row-shaded cases. in the case of an even column shaded, the performance of (SP, BL, HC, and TCT) was equal in terms of the maximum power obtained, while MSP was the least in comparison. the performance of the TCT configuration was the best compared to the other configuration when shading the PV system vertically unevenly. Finally, for diagonal shading, TCT and MSP configurations work best. Shading loss, mismatch loss and fill factor (FF) results were also compared.

Ultra-Fast and Accurate MPPT Control Structure for Mobile PV System Under Fast-Changing Atmospheric Conditions

Photovoltaic (PV) system efficiency is highly dependent on atmospheric conditions, which are continuously varying. The presence of an efficient maximum power point tracking (MPPT) method suitable for fast-changing environmental conditions, even under partial shading conditions (PSC) is required to optimize the PV system efficiency. This paper proposes a different MPPT control structure, where the MPPT is nested in the proportional-integral (PI) voltage controller routine. By using the proposed control structure. The Analog-to-Digital Converter (ADC) samples are shared between the MPPT algorithm and PI controller. As a result, extreme dynamic tracking improvements while a cost-effective implementation is ensured. Using the proposed control structure, a high-speed MPPT is achieved, making it suitable for mobile PV systems subjected to fast-changing atmospheric conditions. As the tracking accuracy is high, the P&O subroutine usually used in GMPPT methods to maintain the GMPP is eliminated. Results revealed that with the proposed implementation scheme, the tracking speed and the transient energy losses can be four times better than the traditional scheme.

Extraction of the Global Maximum Power for PV System under PSC Using an Improved PSO Technique

To get the most power out of photovoltaic (PV) panels, PV systems must utilize a maximum power point tracking (MPPT) controller. In partial shading conditions (PSC), the power-voltage (P-V) characteristic of the PV network may show a single global maximum power point (GMPP) and two or more local maximum power points (LMPP). This indicates that the PV cells and panels do not get uniform illumination. As they converge on the maximum power point (MPP) that makes contact first, which is often one of the LMPPs in this scenario, common MPPT approaches like incremental conductance (InC) and perturb and observe (P&O) are unable to distinguish between a GMPP and LMPPs. In this paper, the extraction of the GMPP of the PV system under PSC based on a suggested particle swarm optimization (PSO) approaches is presented. The particularity of the suggested approach is that it takes into account the calculation of the position of each particle as a function of the duty cycle and the global maximum power. Results on the performance of the suggested PSO method show an advantage over the conventional PSO and the commonly used traditional P&O method. The suggested PSO technique offer better performance in terms of global power extracted, ripple rate of the power and efficiency.

DMPPT control of photovoltaic systems under partial shading conditions based on optimized neural networks

DMPPT control of photovoltaic systems under partial shading conditions based on optimized neural networks

A novel global FPPT strategy for PV system under partial shading condition

AbstractFlexible power point tracking (FPPT) is a common and significate photovoltaic control problem under partial shading conditions. Based on CPP and MPP distribution analysis, both constant power point tracking and maximum power point tracking could be mathematically described by a single objective optimization. As one of the recent meta‐heuristic algorithms, which avoid the limitations of conventional scanning methods, the carnivorous plant algorithm (CPA) solves non‐convex optimization problems with fast speed, high accuracy, and simple calculation. However, it exhibits several drawbacks such as inappropriate parameters, over‐searching, and feasible solution ignored issues. Therefore, a global FPPT strategy based on modified CPA (mCPA) is proposed, which includes a search objective integration, efficient non‐convex search space skipping strategy, and adaptive tuning parameters according to sensitivity analysis. By statistical experiments compared with eight popular meta‐heuristic algorithms, the mCPA can correctly converge to the global MPP under various scenarios with the highest accuracy of 99.0%. Meanwhile, the observations of dynamic experiments demonstrate the effectiveness of the proposed strategy in terms of fast time response (0.5–3 s) for global FPPT under various uniform and non‐uniform insolation conditions.

Maximizing Energy Output of Photovoltaic Systems: Hybrid PSO-GWO-CS Optimization Approach

Photovoltaic (PV) systems suffer from partial shade and nonuniform irradiance conditions. Meanwhile, each PV module has a bypass shunt diode (BSD) to prevent hotspots. BSD also causes a series of a peak in the power-voltage characteristics of the PV array, trapping traditional maximum Power Point Tracking (MPPT) methods in local peaks. This study aims to address these challenges by combining cuckoo search (CS), gray wolf optimization (GWO), and particle swarm optimization (PSO) to enhance MPPT performance. The results compared the yield power by Tracking the MPP using only GWO, CS, or PSO MPPT techniques and combining them. Results show that in four cases: in case 1) Uniform Irradiation in three patterns (High, Medium, and Low), In case 2) Fixed Nonuniform Irradiation, While In case 3) Slow Dynamic Nonuniform Irradiation and case 4) ) Fast Dynamic nonuniform irradiation. The efficiency (PSO + CS) 97.86%, (PSO + GWO) 97.74%, and (GWO + CS) 98.55% were the highest performers in the case 1 results in (high, medium, and low), respectively. In Case 2, the efficiency (GWO + CS) is 98.62%, and it operates more effectively under fixed nonuniform irradiance. It has the highest efficiency in both Cases 3 and 4, even though its respective PSO + GWO efficiencies are 97.45% and 97.26%. Based on these results, a hybrid mode of merging algorithms based on weather radiation conditions is proposed.

A novel technique using stretching, repulsion, and chimp optimization algorithm to find global maximum power point considering complex partial shading conditions

SummaryThe demand for green energy sources has led to the exploration of photovoltaic (PV) systems as a sustainable and cost‐effective solution. However, PV systems have an uncertain nature that makes the tracking of global maximum power point a challenge. To get the optimal benefits from the PV source, the extraction of power is required in a precise manner, especially in the case of irregular complex partial shading conditions (CPSCs). In the case of CPSCs, the global maximum point tracking (GMPP) is a bit difficult to manage by the conventional algorithms presented so far. As the power peaks increase with CPSCs, the conventional algorithms are usually stuck in the local maxima. To avoid sticking at all the local maxima points and to find GMPP with better efficiency, stretching is used here along with the repulsion technique. Stretching and repulsion techniques are merged with the chimp optimization algorithm (SRCA). The tuning parameters of SRCA are so effective that remove all local points and lead to finding the GMPP accurately. The proposed algorithm is applied to the PV array to extract optimal power off‐grid and on‐grid as well. The average tracking time and output power efficiency in the case of off‐grid is observed as 0.4 s and 99.87% while in the case of on‐grid as 0.09 s and 98.88%, respectively.

Archimedes optimization algorithm (AOA)-Based global maximum power point tracking for a photovoltaic system under partial and complex shading conditions

During partial shading conditions (PSCs), the efficiency of power transfer in a Photovoltaic (PV) system decreases significantly, which can result in the formation of hotspots in the PV array. Although the insertion of bypass diodes can alleviate this issue, it can result in multiple power peaks on the power-voltage (P–V) characteristics and complicate the process of maximum power tracking. To tackle this problem, using metaheuristic algorithms for Maximum Power Point Tracking (MPPT) can yield favourable results by avoiding convergence to local power peaks and reducing computation stress on the microcontroller. However, continuous research is required in this area due to variations in the performance of metaheuristic algorithms. Therefore, this work introduces a novel MPPT approach based on the Archimedes Optimization Algorithm (AOA) for the successful capture of the Maximum Power Point (MPP) under various PS scenarios. The performance of AOA is evaluated against the other state-of-the-art algorithms such as particle swarm optimization (PSO), Jaya, and the newly proposed variant of Jaya called the adaptive Jaya (A-Jaya). The proposed MPPT method is analysed in MATLAB/Simulink software and validated using real-time results obtained from Typhoon Hardware-in-the-loop (HIL)-402 emulator. The proposed algorithm outperforms the previous algorithms, as evidenced by a comparison of the results in terms of power tracking efficiency, tracking time, and the quantity of power fluctuations.

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.