Shading Patterns Research Articles

Comparative Review and Performance Evaluation of Various Solar PV System Reconfigurations

In the current context, the most challenging and demanding research is to obtain maximum power from solar photovoltaic (SPV) power systems. Partial shadowing (PS) is a prominent cause of solar photovoltaic panels output power loss. The output decreases proportionally to the shaded area, the placement of shaded panels within the array, panel interconnections, shade pattern, etc. Numerous static and dynamic approaches for reducing the PS effect have been reported in the literature. Of these, the most common is the reconfiguration technique based on the static method, which distributes shaded panels more consistently and increases maximum power generation. The current study investigates the performance of common existing reconfiguration techniques based on static methods, including TCT, Sudoku, odd even (OE), and Latin square (LS) on a 4×4 PV array. The five different shadow patterns: short narrow (SN), short wide (SW), long narrow (LN), long wide (LW), and centre (CN) are implemented in this work. The performance analysis is carried out by comparing the maximum power generated (PM) and the results of panel simulations, as well as their theoretical conclusions, to ensure that these strategies are effectively implemented in hardware configuration.

A novel hybrid series salp particle Swarm optimization (SSPSO) for standalone battery charging applications

This paper presents a novel hybrid series salp particle swarm optimization (SSPSO) algorithm for tracking the global maximum power point (GMPP) of the standalone battery charging systems under partial shading conditions (PSC). The photovoltaic (PV) characteristics such as power current (P-I) and power voltage (P-V) have a global peak and various local peaks with complicated shapes under PSC. Some conventional techniques failed to track accurately the GMPP under shaded conditions, therefore, the proposed SSPSO with its accuracy and performance of tracking nearly the GMPP is used and compared with other existing algorithms. DC-DC buck boost converter is used to adjust the impedance and match the proposed model with the output of the system. The same PV shaded patterns are applied with other conventional algorithms and the simulations results show that the novel hybrid SSPSO can quickly track the GMPP within a minor time, with high efficiency and moreover presents a high quality of tracked power as compared to the existing selected techniques. The proposed SSPSO presents the average tracking efficiency of 99.99%, which is highly required to be applied in the suburb areas or remoted areas where the partial shading repeatedly occurs in PV array due to the environment effects.

Hyper-Spherical Search Algorithm for Maximum Power Point Tracking of Solar Photovoltaic Systems under Partial Shading Conditions

Maximum Power Point Tracking (MPPT) for photovoltaic systems has widely been studied. However, studying the impact of partial shading (due to buildings, trees, clouds, adjacent arrays, and so on) on the MPPT and achieving the global maximum is still a challenging topic. This is because not only there is a global maximum power point (GMPP) but also there are several local maximums due to the non-linear nature of the power-voltage curve after partial shading. Therefore, the conventional MPPT methods fail to track the GMPP and are commonly trapped at one of the local maximum power points. This study utilizes the Hyper-Spherical Search (HSS) algorithm in MATLAB to achieve the GMPP while improving the efficiency, convergence speed, dynamic response, and reducing the losses. To track the GMPP using the HSS algorithm, the output power for the array (PPV) is defined as the objective function, and the duty cycle of the DC-DC converter is selected as the particles’ position (control variable). The performance of the proposed algorithm has been studied in three different partial shading patterns, and the simulation results confirm the capability of the algorithm in the rapid tracking of GMPP points. In addition, if the GMPP position changes over time, it will track the new GMPP with minimal oscillations. The proposed method along with PSO, P&O, ABC, and Dragon algorithms has been applied for various scenarios, and the obtained results using HSS have been compared with the four mentioned algorithms, which confirmed the effectiveness of the proposed method. Briefly, the advantages of the HSS algorithm in finding GMPP can be stated as simple implementation with few parameters, strong exploration, and exploitation during the tracking process, fast-tracking and low fluctuations during tracking, low oscillation at steady-state, high dynamic efficiency, and non-convergence to LMPP points.

Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network

Partial shading conditions (PSC) have negative effects on the operation of photovoltaic (PV) systems. In this paper, a PV array reconfiguration method is developed to minimize power losses of PV arrays under partial shading conditions. The proposed reconfiguration method is based on equalizing the reduction of the short-circuit current of the PV modules in the PV array. Eight state-of-the-art Convolutional Neural Network models are employed to estimate the effect of shading on the short-circuit current of a PV module. These models include LeNet-5, AlexNet, VGG 11, VGG 19, Inception V3, ResNet 18, ResNet 34, and ResNet 50. Among eight models, the VGG 19 achieves the best accuracy on 1842 sample images. Therefore, this model is used to estimate the ratio of the actual short-circuit current and the estimated short-circuit current in four studied shading scenarios. This ratio decides the switching rule between PV modules throughout the PV array under PSC. A 2×2 experimental PV array shows that the proposed reconfiguration method improves the output power from 5.81% to 25.19% in four shading patterns. Accordingly, the power losses are reduced from 1.32% to 13.75%. The power improvement and the reduction of power losses of the proposed dynamic PV array reconfiguration system under four case studies demonstrates its effectiveness in addressing the effects of PSC on the PV array.

Four square sudoku approach for alleviating shading effect on total-cross-tied PV array

Partial shading on photovoltaic (PV) modules significantly affects their productivity. Therefore, different PV reconfiguration approaches were employed as solutions for distributing the shade across the array. This paper proposes an advanced four-square (FS) sudoku technique for configuring the 9 × 9 total-cross-tied (TCT) PV array via moving the modules physically for one time to enhance the harvested maximum power under all possible shading patterns. The proposed FS is validated under different shading classes, including deterministic and random shading patterns. The performance of the proposed strategy is assessed versus six different approaches representing the literature, including basic sudoku (SDU), optimal sudoku (OSDU), chaos map (CMP), skyscraper (Sky-S), odd-even prime (OEP), and ancient chinese magic square (ACMS). The assessment process is conducted using the global maximum power point, execution ratio (ER), fill factor (FF), percentage power losses (%PL), power enhancement (%PH), and array output power-voltage (P-V) characteristic. Moreover, an extensive analysis of the cost and the energy saving during a day is performed to investigate the reliability of the proposed FS strategy. Furthermore, a critical evaluation is performed among the FS and recent dynamic reconfiguration approaches, including particle swarm optimizer (PSO), harris hawk optimization algorithm (HHO), genetic algorithm (GA), butterfly optimization algorithm (BOA), coyote optimization algorithm (COA) and artificial ecosystem-based optimization (AEO). The results of this paper reveal that the rearranged scheme-based FS approach can disperse different categories of shading efficiently with a high generated energy from the entire system and high revenue compared to TCT and other state-of-the-art arrangements with achieving smooth uni-peak P-V characteristics.

Bypass diode effect and photovoltaic parameter estimation under partial shading using a hill climbing neural network algorithm

In recent decades, population growth and industrial evolution have led to a significant increase in the need to produce electricity. Photovoltaic energy has assumed a key role in responding to this need, mainly due to its low cost and reduced environmental impact. Therefore, predicting and controlling photovoltaic power is an indispensable task nowadays. This paper studies how photovoltaic power can be affected under non-uniform irradiance conditions, i.e., when the photovoltaic energy production system is under partial shading. Concretely, the effect of bypass diodes on the current-voltage characteristic curve, according to the shaded area, was studied and the power loss under partial shading was quantified. In addition, electrical characteristics and the temperature distribution in the photovoltaic module were analyzed. Furthermore, we propose a hill climbing neural network algorithm to precisely estimate the parameters of the single-diode and double-diode models under partial shading conditions and, consequently, predict the photovoltaic power output. Different shading scenarios in an outdoor photovoltaic system were created to experimentally study how partial shading of a photovoltaic module affects the current-voltage characteristic curve. Six shading patterns of a single cell were examined, as well as three shading patterns of cells located in one or more strings. The hill climbing neural network algorithm was experimentally validated with standard datasets and different shading scenarios. The results show that the hill climbing neural network algorithm can find highly accurate solutions with low computational cost and high reliability. The statistical analysis of the results demonstrates that the proposed approach has an excellent performance and can be a promising method in estimating the photovoltaic model parameters under partial shading conditions.

Mitigating mismatch power loss of series–parallel and total-cross-tied array configurations using novel enhanced heterogeneous hunger games search optimizer

The location of shaded or faulty Photovoltaic modules in the PV array has a negative impact on the harvested power from the entire array. To overcome this significant limitation, PV reconfiguration is a considerable technique developed via interchanging the PV modules’ location physically or electrically. By this inspiration, in this article, the authors propose a novel enhanced heterogeneous hunger games search optimizer (EHHGS) based PV reconfiguration. The innovated EHHGS introduces a modified variant for the basic hunger game search optimizer (HGS) to achieve a high diversity and robust exploitation of the optimal solutions. The EHHGS is applied to identify the optimal relocation for the shaded or faulty modules in two configurations of PV connected array: total-cross-tied array (TCT) and Series–parallel one (S–P). The proposed approach has applied symmetric and asymmetric connected PV arrays with dimensions of 9 × 9 and 10 × 8 throughout five different shade patterns. Moreover, for providing a flexible tool for the user/researcher to detect and observe the benefits achieved via the PV reconfiguration strategy, a simple graphical user interface (GUI) for the PV reconfiguration strategy of TCT or S–P PV connected array using meta-heuristic algorithms is designed. This implemented GUI can extend for any size of PV arrays, different optimization algorithms, and different connection schemes. The proposed EHHGS, HGS, and set of recent optimizers, including harris hawk optimizer (HHO), marine predators algorithm (MPA), and artificial ecosystem-based optimization (AEO), handle a new simplified objective function to boost the optimizer’s ability in catching the optimal modules’ location to alleviate the mismatched power in the studied arrays. Several statistical metrics are computed for providing an unbiased comparison. Through the comparisons, the proposed EHHGS exhibits superior performance. It achieves the best re-design for the considered arrays that helps in avoiding the mismatch losses in the cases of the partial shaded/faulty modules and enhances the power generated profiles. EHHGS enhances the power by percentages of 44.42%, 11.9%, 33.36%, 20. 86% and 13.17% compared to the TCT-connected system. In the case of the S–P connection, the proposed EHHGS generates 47.2% and 10.45%, 30.75%, 17.25%, and 26.27% higher power.

Metaheuristic Algorithm Based Maximum Power Point Tracking Technique Combined With One Cycle Control For Solar Photovoltaic Water Pumping Systems

Solar photovoltaic technology has become eminent in the world because of its clean and abundant nature and can be effectively used for water pumping applications. A maximum power point tracking method is indispensable to achieve the best benefit from photovoltaic systems. Conventional maximum power tracking methods become successful only under uniform irradiance conditions and fail to track the maximum power under partial shading conditions (PSC). Hence, nature-inspired metaheuristic algorithms were proposed to track the optimum power under varying environmental conditions. This study proposes a method for improving the performance of the nature-inspired maximum power point tracking algorithms by using a nonlinear control technique called one cycle control. Based on the duty cycle obtained from the tracking algorithm the one cycle control technique generates pulses for the DC–DC converter, which is connected to a brushless DC motor pump system through a voltage source inverter. The performance of the proposed system under various PSCs is validated using the Cuckoo search, particle swarm optimization, and grey wolf algorithms. Simulation results for various shading patterns prove the supremacy of the system with respect to convergence speed, tracking efficiency, robustness, steady-state oscillations at maximum power point, and initial exploration oscillations in comparison with systems without one cycle control. In addition, the introduction of a KY converter as a DC–DC converter reduces the output voltage ripple in the system. Thus, the proposed system with one cycle control overcomes the disadvantages of the existing methods and can be effectively utilized for water pumping applications.

Maximum Energy Extraction in Partially Shaded PV Systems Using Skewed Genetic Algorithm: Computer Simulations, Experimentation and Evaluation on a 30 kW PV Power Plant

This paper presents an improved Genetic algorithm (GA) for Maximum Power Point Tracking (MPPT) in shaded Photovoltaic (PV) power generation systems. The proposed GA uses shrinking population wherein fitter chromosomes are retained for next generations while lesser-performing chromosomes are removed from the population sequentially. This methodology reduces convergence time while retains major advantages of GA. The method is explained lucidly and then computer simulations and experimental results on a prototype fabricated in the laboratory are presented. The practical feasibility of the new method is then showcased by applying the new theory on a 30-kW Photovoltaic (PV) power plant established in an educational institution premise. The PV plant undergoes partial shading conditions (PSC) during morning and afternoon hours due to branches of tall trees grown around the school building. The MPPT algorithm employed in the PV plant is Perturb and Observe (P&O) which fails to track global power peak at several shading conditions leading to loss of energy. The realistic shading patterns occurring on the PV plant were recorded and the new method is shown to exhibit enhanced energy yield.

Hill Climbing Artificial Electric Field Algorithm for Maximum Power Point Tracking of Photovoltaics

In this paper, maximum power point tracking (MPPT) of a photovoltaic (PV) system is performed under partial shading conditions (PSCs) using a hill climbing (HC)–artificial electric field algorithm (AEFA) considering a DC/DC buck converter. The AEFA is inspired by Coulomb’s law of electrostatic force and has a high speed and optimization accuracy. Because the traditional HC method cannot perform global search tracking and instead performs local search tracking, the AEFA is used for a global search in the proposed HC-AEFA. The critical advantage of the HC-AEFA is that it is desirable performing local and global searches. The proposed hybrid method is implemented to derive an MPP by tuning the converter duty cycle, considering the objective function for maximizing the PV system extracted power. Its capability is evaluated and compared with well-known particle swarm optimization (PSO), considering standards, PSCs, and radiation changes conditions. The tracking efficiency for the most challenging shading pattern (third pattern) using the HC-AEFA, HC, AEFA and PSO is obtained at 99.93, 90.35, 98.85, 99.80%, respectively. The analysis of the population-based optimization process for different algorithms proved the HC-AEFA faster convergence at lower iterations than the other methods. So, the superiority of the proposed HC-AEFA subjected to different patterns is confirmed with higher tracking efficiency and global power peak, fewer fluctuations, higher convergence speed, and higher dynamic and Static-efficiency compared to the other methods.

A Novel Topology to Improve PV Module Efficiency under Partial Shading Conditions

In this paper, we investigate the power degradation in a novel photovoltaic (PV) cell reconfiguration named KenDoKu (KDK) topology under different shading patterns. We analyze how a modification in the linkage between the PV cells within a shaded PV module can affect its effectiveness. The proposed approach relocates the physical position of the PV cells within the PV module without any change in electrical connections and redistributes the shading effects over the PV module for the improvement of the power generation. To achieve this purpose, modeling and simulation are performed for a set of various shading patterns such as homogeneous, sectional, and scattered shadings. The simulation model used is a combination of two-diode model and Bishop’s model. This model is applied to a PV module and is implemented in LTSpice software to quantify the impact of shading on P-V characteristics. The performance of the KDK topology is compared to other optimized configurations such as total-cross-tied (TCT), bridge link-total cross tied (BL-TCT), honey comb-bridge link (HC-BL), series parallel-total cross tied (SP-TCT) and existing odd-even (OE) and Latin square (LS) schemes of interconnection. The effectiveness of the KDK approach is evaluated in terms of the characteristics of P-V curves, global maximum power (GMP), mismatch power loss MPL (%), fill factor FF (%), and performance ratio PR (%). The simulated results revealed that the KDK configuration scheme performs better in terms of generating maximum power under the considered partial shading conditions. The proposed approach reduces the maximum power losses (MPL) and improves the fill factor (FF) with respect to OE and LS configurations in the most of the cases. Moreover, experimental verification is also carried out. The obtained results show that the KDK configuration outperforms the other analyzed PV cell rearrangement in terms of increased power.

An effective maximum power point tracking technique for partially shaded photovoltaic system under rapidly changing atmospheric conditions

AbstractPartial shading is an unavoidable severe problem experienced by a photovoltaic system. The P‐V characteristics are highly non‐linear and exhibit multiple peaks under partial shading conditions (PSC). The conventional MPPT techniques are unable to track global maximum power point (GMPP) under PSC. Various bio‐inspired optimization methods employed for MPPT were presented in the literature. The operation of each algorithm differs from one another when tracking the GMPP. Therefore, this paper proposes a hybrid of adaptive particle swarm optimization (APSO) and Nelder Mead (NM) in searching for the global maximum power point. In this hybrid method, the unnecessary movement of APSO particles is minimized, leading to rapid convergence without the local trapping of the NM process. Thus, the proposed algorithm can balance both explorations by APSO and exploitation by NM. Extensive simulations are performed using Matlab/Simulink, which shows that this method successfully tracks global power under various shading patterns. The proposed method is compared with existing advanced optimization algorithms. Further validation is done via experimental analysis, and it confirms that the proposed method performs better in tracking maximum global power, less steady‐state oscillations, and fast‐tracking speed. The results demonstrate the effectiveness of proposed method in tracking GMPP with an average efficiency of 99.32% and an average tracking time of 0.513 s.

Hybrid Maximum Power Tracking Technique for Partially Shaded PV Arrays

Photovoltaic panels are the most important green energy source because of their inexhaustible moreover its clean. It is important to connect the PV panels to the maximum power point tracking (MPPT) controller to optimize its output power. The PV panels output power efficiency is related to the rapidly variation of the incident irradiance moreover the partial shading pattern. The variation of the incident irradiance and the partial shading pattern makes the tracking of the global maximum peak (GMP) through the local ones too difficult, which extremely decrease the efficiency of the PV panels. The problem is the GMP value varies as the sun irradiance varies so; the detection of the GMP needs an efficient and fast algorithm which cannot be done by the traditional MPPT. In order to solve the problem, proposed system a hybrid new algorithm can combine a traditional MPPT algorithm, such as perturb and observe, or incremental conductance, with the ANN (artificial neural network) This new algorithm can combine a traditional MPPT algorithm, The proposed hybrid MPPT algorithm is based on the ANN and used to predict the global MPP region by estimating its voltage boundaries. Consequently, the conventional MPPT algorithm searches for the MPP in the predicted region. The proposed technique is modeled and simulated using MATLAB/Simulink.

Optimal reconfiguration of shaded PV based system using African vultures optimization approach

The photovoltaic (PV) system operation faces great challenges as its performance depends on the weather conditions like irradiance and temperature. One of the phenomena that has negative effect on the PV array is operation under partial shade condition (PSC) as it causes hot spots, increases the power loss, and reduces the generated power. Therefore, this work proposes recent methodology incorporated metaheuristic approach named African vultures optimization algorithm (AVOA) that is applied for the first time to reconfigure the PV array operated at PSC for maximizing the generated power. The merit of AVOA is its high ability to escape from the local optima. Five shade patterns of short wide (SW), long wide (LW), short narrow (SN), long narrow (LN), and lower triangle are analyzed. Moreover, comparison to total cross tied (TCT), SudoKu, harris hawks optimizer (HHO), Aquila optimizer (AO), and antlion optimizer (ALO) is conducted. Furthermore, comparative analysis in terms of fill factor (FF), power enhancement (Pe) with respect to TCT arrangement, power loss, and performance ratio (PR) is conducted. The proposed AVOA outperformed the others in terms of the power enhancement and performance ratio. The best Pe obtained via the proposed AVOA is 39.91% in the fifth shade pattern while the best PR is 82.9125% in the third pattern. Additionally, Wilcoxon sign rank, Friedman, ANOVA table, and multiple comparison tests are performed. The results demonstrated that, AVOA results are significantly different from HHO over the five studied cases. The reported p-values based on Friedman and ANOVA illustrated the existence of significant differences among algorithms. The best p-values for Friedman and ANOVA are 9.4725e−08 and 7.3013e−13 in the fourth and fifth patterns respectively. The results confirmed the preference of the proposed AVOA in achieving the best reconfiguration of the PV array at PSC. Chaotic mapping is recommended to adaptively set the proposed AVOA parameters.

A novel global maximum power point tracking algorithm based on Nelder-Mead simplex technique for complex partial shading conditions

In this study, a novel global maximum power point tracking (GMPPT) algorithm for photovoltaic generation system (PGS) operating under complex partial shading conditions is proposed. The presented GMPPT technique is based on Nelder-Mead (NM) simplex technique, which is commonly used to solve complicated optimization problems and has advantages such as simple implementation, derivative-free nature, fast convergence, and high accuracy. In this study, simulation results of 252 shading patterns with non-repeatable irradiance levels and 243 shading patterns under repeatable irradiance levels are provided to validate the effectiveness of the proposed GMPPT methods. According to the simulated and experimental results, the presented NM-based GMPPT approach has the highest success rate and tracking accuracy under all the tested shading patterns compared with other GMPPT methods.

BIPV Modeling with Artificial Neural Networks: Towards a BIPV Digital Twin

Modeling the photovoltaic (PV) energy output with high accuracy is essential for predicting and analyzing the performance of a PV system. In the particular cases of building-integrated and building-attached photovoltaic systems (BIPV and BAPV, respectively) the time-varying partial shading conditions are a relevant added difficulty for modeling the PV power conversion. The availability of laser imaging detection and ranging (LIDAR) data to create very-high-resolution elevation digital models can be effectively used for computing the shading at high resolution. In this work, an artificial neural network (ANN) has been used to model the power generation of different BIPV arrays on a 5 min basis using the meteorological and solar irradiance on-site conditions, as well as the shading patterns estimated from a digital surface model as inputs. The ANN model has been validated using three years of 5-min-basis monitored data showing very high accuracy (6–16% of relative error depending on the façade). The proposed methodology combines the shading computation from a digital surface model with powerful machine learning algorithms for modeling vertical PV arrays under partial shading conditions. The results presented here prove also the capability of the machine learning techniques towards the creation of a digital twin for the specific case of BIPV systems that complements the conventional monitoring strategies and can be used in the diagnosis of performance anomalies.

Optimal reconfiguration strategy based on modified Runge Kutta optimizer to mitigate partial shading condition in photovoltaic systems

In this article, a new variant of a recent optimization algorithm namely Runge Kutta optimizer (RUN) is proposed to solve the partial shading condition in photovoltaic systems and global optimization. The RUN’s improvement is mainly to mitigate the lack of the solutions quality, the imbalance between the exploitation and exploration phases, and premature convergence of the RUN algorithm. The mRUN, in which the standard RUN is incorporated with a promising strategy namely Orthogonal learning (OL) strategy to address out the original RUN’s drawbacks. To estimate the proposed algorithm’s efficiency, two experimental series were performed. In the first experiment, the mRUN was compared with other state-of-art metaheuristics on IEEE CEC’2020 test suite. Moreover, the quantitative and qualitative approved the robustness of the suggested algorithm. As a second experimental series, the proposed approach is investigated on an application of renewable energy-based system which is reconfiguration of partially shaded photovoltaic (PV) array. The main target of this process is to enhance the generated power from the PV array. Two shade patterns are investigated on 9 × 9 array and the obtained results via the modified RUN are compared to total cross tied (TCT) and SudoKu and other metaheuristic-based arrangements of Aquila optimizer (AO), Harris hawks optimizer (HHO), and Runge Kutta optimizer (RUN). The generated power from the PV array, arranged via the proposed approach, is enhanced by 28.41% and 1.015% in the first shade pattern compared to the TCT and conventional RUN configurations, respectively. In the second shade pattern, the proposed mRUN succeeded in improving the extracted power by 40.32% and 0.29% compared to TCT and RUN configurations, respectively. The modified RUN performed well in both studied cases outperforming the others in achieving the most enhanced power from the array.

Shade tree pattern of highland coffee plantation and coffee production in Aceh Tengah, Indonesia

Coffee is one commodity has high economics market that cultivated by farmer mainly in the high elevation area. The economics and ecological function of coffee plantations have to be worked in the area plantation, so formulation combination between coffee and forest tree is important. The research aimed to identify the tree species composition of a shade tree in a coffee plantation. Some plot sized 50 m x 50 m was distributed based on lands properties of farmers. Density, frequency, and tree spacing were measured to get basal area, relative density, relative frequency, and important value index. A more in-depth statistical analysis was performed using SPPS to determine the significance of the relationship between density and frequency with coffee productivity. The relationship between tree spacing planting, basal area, and shade trees’ density with Arabica coffee production is a strong enough relationship is the density variable. While the relationship between regular and disordered planting spacing variables with coffee production is strong. The relationship between basal area, density, and coffee production is very weak.

Modified Current Control for Tracking Global Peak Under Fast Changing Partial Shading Conditions

The power voltage (P-V) characteristics of photovoltaic (PV) systems exhibit multiple power peaks under partially shaded conditions. Several global maximum power point tracking (GMPPT) algorithms in the literature recognize the irradiance change, only after the convergence of operating point to global peak, or use additional hardware to call GMPPT subroutine at definite time intervals to detect any insolation change, and thus track the global peak. However, during fast changing partial shading conditions, these methods are less effective, as they do not detect any irradiance change during the tracking phase of any shading pattern. This paper proposes a novel modified current control approach that uses current as a parameter to detect the insolation change during the tracking phase and track the global peak under fast changing partial shading conditions without any additional hardware. The proposed technique improves the tracking efficiency by as much as 39%, thus proving to be effective under fast changing partial shading conditions. The superior tracking performance of the proposed algorithm over the existing techniques in terms of its tracking efficiency, dynamic tracking capability, tracking speed, and convergence to the global peak is demonstrated with extensive simulations using MATLAB/Simulink and experimental results.

Intelligent series inducing switching scheme (ISISS) to enhance power delivery in shaded photovoltaic array

PurposeThe aim of this research study is to mitigate shading impact on solar photovoltaic array. Photovoltaic (PV) array when getting shaded not only results in appreciable power loss but also exhibits multiple power peaks. Due to these multiple power peaks, the maximum power point tracking (MPPT) controllers’ performance will be affected, as most of the times it ends up in tracking the local maximum power peak and not the global power peak.Design/methodology/approachThe PV panels in an PV array when getting shaded even partially would result in huge power loss. The pattern of shading also plays a crucial role, as it renders a cascaded impact on the overall power output because the cells/panels are connected in series and are parallel. Therefore, during shading, intelligent schemes are needed to appropriately connect and discard the unhealthy and healthy panels in right place with right combination. This research proposes one such scheme to mitigate the shading impact.FindingsTo mitigate the shading impact and also to have a smooth power-voltage (P-V) curve, a new series inducing switching scheme is introduced. The proposed scheme not only mitigates the shading impact and enhances the output power but also smoothens the P-V curve that facilitates the MPPTs to track the P-V appropriately.Originality/valueThe research findings are inventive in nature and not copied work. The reference works and the inspirations have been duly cited and credited.