Partial Shading Conditions Research Articles

An LCS-HB Algorithm Optimized MPPT Controller and DC–DC Boost Converter for PV System at Partial Shading Conditions

In this study, a hybrid localized crow search asserted honey badger (LCS-HB) algorithm is used to optimize the MPPT controller for PV systems operating under PSC with the goal of achieving MPP. The developed approach’s main objective is to track the solar PV system’s highest power point while operating under PSC. A PID controller’s duty cycle can be changed to regulate the output voltage of a DC–DC converter. The proposed LCS-HB algorithm is used to tune the PID controller variables for improved performance. The proposed work is modeled in MATLAB/Simulink to conduct the experimental analysis. By contrasting the results with those of the conventional methods, the effectiveness of the proposed model is confirmed. Based on these discoveries, the experimental outcomes supported the algorithm’s high level of precision in tracking the MPP of the solar array working under PSC. Future studies will address hardware validation of the proposed MPPT technique using the LCS-HB method.

Design of a Novel Chaotic Horse Herd Optimizer and Application to MPPT for Optimal Performance of Stand-Alone Solar PV Water Pumping Systems

A significant part of agricultural farms in the Kingdom of Saudi Arabia (KSA) are in off-grid sites where there is a lack of sufficient water supply despite its availability from groundwater resources in several regions of the country. Since abundant agricultural production is mainly dependent on water, farmers are forced to pump water using diesel generators. This investigation deals with the increase in the effectiveness of a solar photovoltaic water pumping system (SPVWPS). It investigated, from a distinct perspective, the nonlinear behavior of photovoltaic modules that affects the induction motor-pump because of the repeated transitions between the current and the voltage. A new chaotic Horse Herd Optimization (CHHO)-based Maximum Power Point Tracking technique (MPPT) is proposed. This algorithm integrates the capabilities of chaotic search methods to solve the model with a boost converter to maximize power harvest while managing the nonlinear and unpredictable dynamical loads. The analytical modeling for the proposed SPVWPS components and the implemented control strategies of the optimal duty cycle of the DC–DC chopper duty cycle and the Direct Torque Control (DTC) of the Induction Motor (IM) has been conducted. Otherwise, the discussions and evaluations of the proposed model performance in guaranteeing the maximum water flow rate and the operation at MPP of the SPVWPS under partial shading conditions (PSC) and changing weather conditions have been carried out. A comparative study with competitive algorithms was conducted, and the proposed control system’s accuracy and its significant appropriateness to improve the tracking ability for SPVWPS application have been proven in steady and dynamic operating climates and PSC conditions.

Performance Improvement of Grid-Tied PV System With Boost and Quadratic Boost Converters Using Innovatory Hybridized HPO-PO MPPT

Interconnecting photovoltaics (PV) to the grid reduces energy bills. But, Partial shading condition (PSC) limits the functionality of PV assembly due to mismatch losses, which can be mitigated with proper converter topology, Maximum Power Point Tracking (MPPT) methodology and PV array configuration. In this article, performance enrichment of grid tied 4080 W PV system with Boost Converter and Quadratic Boost Converter (QBC), operated with novel hybrid MPPT has been addressed. Simulated outcomes demonstrate that, both converter-based PV systems with suggested MPPT offers a tracking efficiency of greater than 94% in all analyzed cases and reimburse the investment in 4.709 years. To validate the performance improvement of the suggested system, a prototype of 24 V, 100 W QBC with proposed algorithm is developed using 16-bit dsPIC30F2010 and tested. It yields a maximum efficiency of 97.95%, accomplishing its goal. Further, these experimental results are compared with simulation outcomes for the same input conditions and rating.

A comprehensive technical analysis on optimal sizing and operating strategy for large-scale direct coupled PV–electrolyser systems, considering PV system faults, degradation and partial shading conditions

The paper presents an investigation of the performance of the proton exchange membrane (PEM) electrolyser directly connected with large-scale PV systems under faulty, degradation and partial shading conditions. With the direct coupled system, the power loss and external price on auxiliary devices are avoided. However, the adaptability of the direct coupled system to fluctuate solar irradiance and ambient temperature is reduced significantly compared to systems with DC-DC converters. To maximise the system efficiency, the optimal electrolyser (ELY) cell configuration is identified using an optimal sizing approach or operating strategy. This paper applied the Particle Swarm Optimisation (PSO) algorithm to find the optimal configuration for the two approaches, namely the Sizing technique and Operating strategy under different PV systems: faults, degradation and partial shading conditions. Additionally, a large-scale system is simulated to describe the characteristics of a large-scale direct coupled PV-electrolyser system with actual operating faults and phenomena. Simulations of the large-scale direct coupling system were conducted using measured irradiance and temperature data for a typical day period. Based on the collected output, it is found that compared to the optimal sizing approach, the optimal operating strategy allows the direct coupled system to increase its coupling efficiency from 0.17% to 4.47%. In contrast, the amount of hydrogen production when applying the two methods witnessed different trends, depending on the type of faults. The differences are even more significantly presented during multiple short-circuit fault scenarios.

A New Fast and Efficient MPPT Algorithm for Partially Shaded PV Systems Using a Hyperbolic Slime Mould Algorithm

The design of new efficient maximum power point tracking (MPPT) techniques has become extremely important due to the rapid expansion of photovoltaic (PV) systems. Because under shading conditions the characteristics of PV devices become multimodal having several power peaks, traditional MPPT techniques provide crappy performance. In turn, metaheuristic algorithms have become massively employed as a typical substitute in maximum power point tracking. In this work, a new optimizer, which was named the hyperbolic slime mould algorithm (HSMA), is designed to be employed as an efficient MPPT algorithm. The hyperbolic tangent function is incorporated into the optimizer framework equations to scale down large perturbations in the tracking stage and boost its convergence trend. Moreover, to provide a strong exploration capability, a new mechanism has been developed in such a way the search process is carried out inside the best two power peak regions along the initial iterations. This region inspection mechanism is the prime hallmark of the designed optimizer in avoiding local power peaks and excessive global search operations. The developed algorithm was examined through diverse complicated partial shading conditions to challenge its global and local search abilities. A comparative analysis was carried out against the well-regarded PSO, GWO, and the standard slime mould algorithm. In overall, the designed optimizer defeated its contenders in all aspects offering higher efficiency, superior robustness, faster convergence, and fewer fluctuations to the operating point. An experimental setup that consists of the DSpace microcontroller and a PV emulator was employed to validate the algorithm overall performance. The recorded outcomes outline that the developed optimizer can achieve a tracking time of 0.6 seconds and 0.86 seconds on average, with 99.85% average efficiency under complex partial shading conditions.

Experimental Investigation of Efficiency Enhancement in Solar Photovoltaic Systems Under Partial Shading Conditions Using Discrete Time Slime Mould Optimization

Experimental Investigation of Efficiency Enhancement in Solar Photovoltaic Systems Under Partial Shading Conditions Using Discrete Time Slime Mould Optimization

Optimized Maximum Power Point Tracking using Giza Pyramid Construction Algorithm for Photovoltaic Systems

Background: One of the key challenges in maximizing the performance of PV systems is the efficient tracking of the maximum power point (MPP) under varying operational conditions, including changes in solar irradiance and temperature. Accurate MPP tracking is essential for achieving optimal energy conversion efficiency and maximizing the electricity generation potential of the PV array, even during partial shading conditions. Traditional maximum power point tracking (MPPT) algorithms, such as the incremental conductance (INC) method, often struggle to efficiently handle partial shading conditions. As a result, there is a need for more sophisticated and robust optimization techniques that can effectively address this challenge. This study presents a novel and innovative Giza Pyramid Construction (GPC) algorithm to solve the partial shadinginduced MPP tracking problem. Objective: This study aims to apply the Giza Pyramid Construction (GPC) algorithm for optimized maximum power point tracking in photovoltaic systems under partial shading conditions, aiming to enhance energy conversion efficiency and overall system performance. Methods: The methodology involves implementing the Giza Pyramid Construction (GPC) algorithm as the core optimization technique for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The GPC algorithms are utilized to iteratively adjust the duty cycle of the boost converter, enabling efficient power extraction from the PV array under varying shading conditions. The performance of the GPC algorithm is evaluated through simulations in MATLAB/SIMULINK and compared against conventional MPPT methods like INC and DGO techniques. Results: The successful application of the Giza Pyramid Construction (GPC) algorithm for optimized maximum power point tracking in PV systems under partial shading led to significantly reduced optimization time, faster settling times, and minimized output ripples. With the proposed GPC MPPT, optimization time is reduced to 41ms, settling time is reduced to 93ms, and ripples are minimized to 0.092%. Conclusion: the Giza Pyramid Construction (GPC) algorithm demonstrates its effectiveness as a robust and efficient maximum power point tracking method in photovoltaic systems, particularly under partial shading conditions. The improved optimization speed, reduced settling times, and minimized output ripples underscore the GPC algorithm's potential to enhance the overall efficiency and reliability of PV systems, paving the way for its practical implementation in real-world renewable energy applications.

Maximum Power Point Extraction in PV Array Under Partial Shading Conditions Using GWO-Assisted Beta Method

This paper presents a hybrid maximum power point tracking (MPPT) algorithm, which combines the grey wolf optimization (GWO) technique and Beta method for extraction of maximum power from a photovoltaic (PV) system operating under partial shading conditions. The proposed algorithm named GWO-Beta-based MPPT uses the GWO technique to achieve the global maximum power point (GMPP), while, simultaneously, the MPPT reference using Beta method is calculated according to the PV voltage and current at GMPP. After that, the MPPT algorithm is switched to operate only with the Beta method that keeps tracked on the MPP reference, guarantying the GMPP in that specific operation point. By means of computational simulation results, the performance of the proposed MPPT algorithm is evaluated taking into account the convergence speed and power oscillations to achieve the GMPP.

Visual Re-Initialization Model Development Methodology for Solving Problems Regarding Metaheuristic Algorithm-Based MPPT Applications

Metaheuristic algorithms are particularly useful for maximum power point tracking (MPPT) applications, because they can adapt to changes in operating conditions and effectively handle partial shading conditions. However, metaheuristic algorithms also have some limitations that need to be addressed to make them suitable for MPPT applications. The problems associated with metaheuristic algorithm-based MPPT applications include being trapped in local optima, slow convergence speed, shading condition variability, computational complexity and robustness. These problems lead to reduced efficiency in MPPT applications. In the literature, the solution of the aforementioned problems is partially addressed and some of them are solved via an additional irradiation sensor. The motivation of this study is to develop a control algorithm that covers all problems that have been partially solved in the literature and includes an original re-initialization modeling method in accordance with visual programing concept, without using any additional radiation sensor. The proposed control algorithm has the flexibility to be easily adapted to other metaheuristic algorithms and does not require any radiation sensors. The re-initialization model created via Matlab/Simulink and “Embedded Coder Support Package for TI C2000 Processors” allows easy tracking of the global maximum power point (GMPP) by detecting variable radiation conditions. The proposed model was implemented on the Cuckoo Search Algorithm (CSA) and verified through experimental studies carried out with a TI-TMS320f28069 microcontroller and PV emulator. The experimental results confirm that issue 1 is solved with 100%, issue 2 is solved with 99.5%, issue 3 is solved with 99.84%, and issue 4 is solved with 100% MPPT efficiency.

A new metaheuristic-based MPPT controller for photovoltaic systems under partial shading conditions and complex partial shading conditions

A new metaheuristic-based MPPT controller for photovoltaic systems under partial shading conditions and complex partial shading conditions

A Coronavirus Optimization (CVO) algorithm to harvest maximum power from PV systems under partial and complex partial shading conditions

Power generation from solar photovoltaic (PV) systems is employed due to its recyclable nature, limitless source of energy, low maintenance, ease of installment, and eco-friendly potential. There is a major challenge in the PV system to harvest the maximal power in varying weather scenarios which decreases the overall efficiency and causes significant power loss. Maximum power point tracking (MPPT) methods are exploited to crop the maximum power from PV strings. Conventional algorithms do not find the Global Maxima (GM) and are trapped on any Local Minima (LM). However, bio-inspired methods find the GM but take more time to search the GM. This paper presents a Corona-Virus Optimization (CVO) algorithm to harvest maximum power against seven well-known conventional and bio-inspired MPPT algorithms. The proposed technique is estimated for Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS) conditions. Furthermore, the comparative analysis between seven already implemented and proposed techniques shows that the CVO algorithm lessens the unwanted oscillation and quicker searching the GM. Moreover, their numerical analysis is also conducted to evaluate the stability, robustness, performance, and sensitivity of the CVO algorithm. The proposed technique gives the highest efficiencies of 99.99%, 99.98%, 99.97%, and 99.98% in all cases of UI, PS-1, PS-2, and CPS respectively when comparing with P&O, InC, DFO, CS, FFO, PSO, and ACO techniques. Real-time data from the Beijing database is also used to verify the performance of the proposed algorithm.

A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions

At present, fossil fuel-based power generation systems are reducing drastically because of their less availability in nature. In addition, it produces hazardous gasses and high environmental pollution. So, in this work, the solar natural source is selected for generating the electricity. Due to the nonlinear behavior of PV, achieving maximum voltage from the Photovoltaic (PV) system is a more tough job. In this work, various hybrid optimization controllers are studied for tracing the working power point of the PV under different Partial Shading Conditions. The studied hybrid optimization MPPT methods are equated in terms of oscillations across MPP, output power extraction, settling time of the MPP, dependency on the PV modeling, operating duty value of the converter, error finding accuracy of MPPT, algorithm complexity, tracking speed, periodic tuning required, and the number of sensing parameters utilized. Based on the simulative comparison results, it has been observed that the modified Grey Wolf Optimization based ANFIS hybrid MPPT method provides good results when equated with the other power point tracking techniques. Here, the conventional converter helps increase the PV source voltage from one level to another level. The proposed system is investigated by using the MATLAB/Simulink tool.

Modified Odd–Even–Prime pattern for effective dispersion of shade over the PV array under partial shading conditions

Photovoltaic (PV) arrays are extensively used for power generation being the most abundant renewable energy sources available. Partial shading of the PV array leads to mismatch between the modules and decrease in output power. A new Modified Odd–Even–Prime (MOEP) method of module arrangement for total cross tied (TCT) connection is presented in this paper to mitigate the effects of partial shading. The proposed MOEP method is a physical relocation method where the modules of PV array are relocated without altering the electrical connections. MOEP method of module rearrangement is based on row positions of the PV array. In this method, the row positions of PV modules in the first column are filled with odd, even and prime numbers in ascending order. The row positions of further columns are formed based on MOEP method. The proposed MOEP method is implemented on 9 × 9 and 8 × 8 PV arrays under various shading patterns. Further, the results are compared with TCT, Odd Even (OE) and Odd–Even–Prime (OEP) patterns. MATLAB/Simulink is used to validate the results. An experimental setup of 9 × 9 and 8 × 8 PV array is developed and tested in real time environment to validate the effectiveness of MOEP method over TCT, OE and OEP methods. The performance of proposed MOEP method is evaluated in terms of GMPP, mismatch power loss (MPL), Fill Factor (FF), Efficiency (η) and Shade Dispersion Ratio (SDR). A detailed study on energy savings from the proposed MOEP method is made on hourly, daily and yearly basis. From the experimental results obtained, it is observed that MOEP method generates a significant improvement in GMPP, efficiency and reduces the mismatch loss compared to TCT, OE and OEP methods.

Estimating the potential maximum power point based on the calculation of short‐circuit current and open‐circuit voltage

AbstractThis paper presents a technique to quickly determine the potential maximum power point (MPP) of a photovoltaic system using the duty cycle (D) of the DC/DC converter. The proposed method depends on estimating the open‐circuit voltage (Voc) and short‐circuit current (Isc) values under different operating conditions. It is an adapted version derived from the conventional Perturb and Observe (P&O) algorithm, designed to restrict the search area for the MPP. The working conditions, including temperature variations, irradiance, or both, are simulated in the PSIM environment and experimented with a simulator with parameters satisfied according to the proposed operating conditions. The research objective of this study also involves operating under partially shaded conditions. When comparing the results of this research with those obtained from the traditional P&O method and the Variable Step Size Perturb and Observe (VSSP&O) approach, it becomes evident that the proposed solution excels in terms of both convergence speed and performance, particularly when operating under partial shade conditions. In addition, this study's methods of determining Isc, Voc, and Dmp can be widely applied in low‐voltage and power systems.

A novel intelligent optimization-based maximum power point tracking control of photovoltaic system under partial shading conditions

A novel intelligent optimization-based maximum power point tracking control of photovoltaic system under partial shading conditions

A novel simple GMPPT method based on probability distribution of global maximum power point under partial shading conditions

A photovoltaic (PV) array having multiple cells in series with bypass diodes may exhibit multiple power peaks under uneven irradiation, therefore an algorithm is required to reach the global maximum power point (GMPP). While a lot of methods have been proposed in the literature, they are usually quite complex and does not fully utilize the characteristics of the PV array. This paper first highlights a rapid and optimized mathematical simulation of the PV array using MATLAB to find the probability distribution of voltage at GMPP under multiple irradiation conditions and temperatures. The resulting GMPP distribution in 22E6 irradiation and temperature conditions checked for an array of 4 PV modules in series with bypass diodes is presented, as well as the optimizations in MATLAB necessary to keep simulation time in check. From the obtained result, we propose a simple and efficient probability-based algorithm capable of reaching GMPP up to 93.64% of the time.

Design of a Level-3 electric vehicle charging station using a 1-MW solar system via the distributed maximum power point tracking technique

Abstract Solar power is mostly influenced by solar irradiation, weather conditions, solar array mismatches and partial shading conditions. Therefore, before installing solar arrays, it is necessary to simulate and determine the possible power generated. Maximum power point tracking is needed in order to make sure that, at any time, the maximum power will be extracted from the photovoltaic system. However, maximum power point tracking is not a suitable solution for mismatches and partial shading conditions. To overcome the drawbacks of maximum power point tracking due to mismatches and shadows, distributed maximum power point tracking is utilized in this paper. The solar farm can be distributed in different ways, including one DC–DC converter per group of modules or per module. In this paper, distributed maximum power point tracking per module is implemented, which has the highest efficiency. This technology is applied to electric vehicles (EVs) that can be charged with a Level 3 charging station in <1 hour. However, the problem is that charging an EV in <1 hour puts a lot of stress on the power grid, and there is not always enough peak power reserve in the existing power grid to charge EVs at that rate. Therefore, a Level 3 (fast DC) EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this issue. Finally, the simulation result is reported using MATLAB®, LTSPICE and the System Advisor Model. Simulation results show that the proposed 1-MW solar system will provide 5 MWh of power each day, which is enough to fully charge ~120 EVs each day. Additionally, the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous pollutants. For example, instead of supplying EVs with power from coal-fired power plants, 1989 pounds of CO2 will be eliminated from the air per hour.

Research on the output characteristics of photovoltaic arrays under partial shading conditions based on peak point approximate calculation method

Research on the output characteristics of photovoltaic arrays under partial shading conditions based on peak point approximate calculation method

An Improved PSO-Based MPPT Technique Using Stability and Steady State Analyses Under Partial Shading Conditions

Partial shading (PS) conditions create challenging issues for the maximum power point tracking (MPPT) algorithms in photovoltaic (PV) systems such as getting stuck in the local maximum power points (LMPP), slow tracking time and fluctuations in the generated power during tracking the global maximum power point (GMPP). To address these issues, this paper brings contributions by proposing an improved PSO-based MPPT technique tailored for PS conditions. First, to highlight the weakness of the traditional PSO-MPPT technique, its stability and steady state behavior in the PS conditions is analyzed in depth. Second, the required criteria to achieve a stable response are obtained. Finally, a novel technique to estimate the convex area of the power versus voltage (P-V) curve is presented where the GMPP is located using two voltage boundaries. The performance of the proposed MPPT technique is experimentally validated. The results highlight the capabilities of the proposed technique in finding the GMPP with a rapid convergence and small fluctuations. The proposed MPPT is applicable to most PV inverters under any PS conditions.

Power enhancement in PV arrays under partial shaded conditions with different array configuration

Solar Photovoltaic systems are used for electrical power generation, and they provide an alternative source to non-renewable energy sources like coal, oil, natural gas and nuclear energy. Photovoltaic arrays used in PV systems may be subjected to partial shading conditions, thereby affecting power generation because of higher power mismatch losses. Due to an uneven distribution of irradiation condition, some of the bypass diodes turned on and affect the power generation in a photovoltaic array. The mismatch losses are due to the output from PV panels subjected to different irradiations because of non-uniform partial shading conditions. The power loss can be reduced by uniformly distributing the partially shaded condition over the entire PV array. In this work different shaped 4 × 4 array configuration is proposed to overcome the effect of partial shading condition, thereby providing lower mismatch losses. Simulations under different partial shading conditions are carried out using MATLAB Simulink, and the experimental setupis carried out for the proposed array configuration for 4 × 4 PV array and the results are discussed.