Efficiency Of Photovoltaic System Research Articles

Improving the photovoltaic thermal system efficiency with nature-inspired dolphin turbulators from energy and exergy viewpoints

The main purpose of this study is to enhance the efficiency of the photovoltaic thermal (PVT) system using nature-inspired dolphin turbulators designed based on thermal images of the dolphin dorsal fin. The turbulators are placed in the cooling channel of the PVT. The effects of the number and length scale of the turbulators are investigated on electrical, thermal, and overall efficiencies from energy and exergy perspectives, as well as the photovoltaic cell temperature at different times of the day. The dolphin turbulator enhances heat transfer to the coolant by preventing the boundary layer growth, accelerating the fluid flow, directing and impacting the coolant to the absorber plate, and improving the mixing of coolant. Consequently, turbulators reduce the photovoltaic cell temperature, increase the coolant temperature, and improve efficiencies. The enhanced PVT improves the electrical, thermal, and overall efficiencies by about 2.59 %, 16.9 %, and 14.78 % from the energy viewpoint and 2.59 %, 12.87 %, and 3.77 % from the exergy viewpoint compared to the conventional PVT. In addition, this study investigates how operational conditions, such as the coolant Reynolds number, the coolant inlet temperature, and ambient wind speed, affect the efficiencies from energy and exergy viewpoints.

Enhancing the efficiency of photovoltaic cells through the usage of dye concentrators

Over the past few years, there has been a growing interest in renewable energy sources. Among them, photovoltaic (PV) technology is advancing rapidly. Solar insolation is the most crucial factor for PV installations. Various solutions, such as tracking mechanisms, hybrid systems, and new materials, can enhance the efficiency of PV systems. Concentrators focus solar light onto the surface of solar modules, increasing production of electricity. Implementing such solutions can reduce the number of silicon cells in installations, leading to a decrease in waste generated during production. Dye concentrators have a positive impact on the performance of silicon systems. A two-stage study on the effect of dye concentrator application on PV cell efficiency is carried out. In the first stage, specific types of dye concentrators are tested for their interaction with the silicon system. Tinted and luminescent acrylic glass (polymethyl methacrylate, PMMA) in yellow and red are used as dye concentrators. The experiment included multiple measurement calibrations, such as the temperature of the tested silicon cell and the intensity of illuminance. Results showed absolute increase of efficiency in solar cells ranging from 0.05% to 1.42%, depending on the type of concentrator used. The most significant improvements were observed with luminescent red PMMA, averaging at 1.21%. The potential of this concentrator was further explored in the second stage of the study, investigating the relationship between the surface involvement of the silicon cell and the dye concentrator. Test results indicated the potential of dye concentrators for integrating luminescent dye concentrator technology into PV systems. The effect of this integration is increase in the efficiency of the PV cell. On the other hand, it should be noted that replacing the PV cell with a dye concentrator reduces the efficiency of the entire photovoltaic system. Hence, the use of a PV cell and concentrator system is recommended especially for photovoltaic systems with a large area. As dye concentrators have the ability to operate without direct irradiance, they are also recommended for regions where natural light is dispersed.

Optimal design, construction and evaluation of symmetric compound parabolic concentrator integrated with photovoltaics system (CPC-PV)

To enhance the production and efficiency of photovoltaic systems, research has been conducted on various techniques including the use of compound parabolic reflectors (CPCs). A mathematical model is developed using the governing solar radiation and CPC geometry equations to optimize the geometrical parameters required for the experimental setup. The optimal values for the half acceptance angle and tilt angle were found to be 25° and 35°, respectively. Based on these optimal parameters, a laboratory setup was fabricated and evaluated. To compare the performance of our CPC-based system, three different panels constructed: a simple PV panel without a concentrator, a PV panel with a concentrator but without cooling (CPC-PV), and a PV panel with a concentrator and water cooling (CPC-PVT). Results showed that the use of a CPC with a concentration ratio of 1.7 in both modes, with and without cooling, led to a 10 % and 20 % increase in the output power, respectively, compared to the simple PV panel. In summary, this study demonstrates the effectiveness of using a CPC in enhancing the production and efficiency of photovoltaic systems. The mathematical model developed in this research can be utilized for optimizing the geometrical parameters required for the design of CPC-based systems. The experimental results obtained from this study indicate that the use of a CPC with water cooling can significantly improve the energy output of the photovoltaic system. These findings can aid in the design of cost-effective and efficient photovoltaic systems.

Enhancing PV solar panel efficiency through integration with a passive Multi-layered PCMs cooling system: A numerical study

Photovoltaic (PV) solar cells are at the forefront of sustainable electricity generation technologies, yet they exhibit relatively low efficiency. Typically, less than 20 % of the solar energy absorbed is converted into electrical energy, with the remainder converts into heat. This heat increases the PV panel's operating temperature, negatively impacting its efficiency and life expectancy. To mitigate this, a novel approach using multi-layered phase change materials (PCMs) has been examined in this study. The approach involves integrating organic PCMs (OPCMs) with metallic PCMs (MPCMs), enhancing PV panel cooling efficiency by attaching the multi-layer PCM to the panel's rear. This study, employing ANSYS FLUENT software's solidification and melting model, explores the impact of multi PCM layers thickness and total multi layers thickness on PV cell temperature and electrical efficiency. The analysis reveals that a multi-layered PCMs configuration with a thickness ratio of 15:85 between MPCM (CERROLOW-117® alloy) and OPCM (RT44) significantly decreases temperature rise in PV cells, resulting in a temperature drop of 59.6 °C. This improvement boosts PV panel performance by an average of 35.8 % compared to panels lacking cooling systems during peak sun hours under hot climate conditions characterized by ambient temperatures of 40 °C and high solar radiation exceeding 1000 W/m². This suggests that a carefully optimized PCM layering approach could markedly improve PV system efficiency, offering a practical solution to the overheating problem and extending the operational life of PV installations.

Maximizing off-grid solar photovoltaic system efficiency through cutting-edge performance optimization technique for incremental conductance algorithm

The maximum power point tracking (MPPT) algorithms are required to deliver the optimal energy from solar photovoltaic cells/array (PV) under numerous weather conditions. Therefore, MPPT circuits driven by defined rules called algorithms are designed. These algorithms range from simple to complex in design and implementation and are selected based on the scenarios of the surroundings. However, the incremental conductance (InC) MPPT algorithm is one of the market's most simple, easy to implement, and demanding algorithms. The drawback associated with the InC algorithm is its tracking speed. To overcome this weakness various researchers have made multiple improvements. Although the performance became better but was not satisfied. Further, the improvements introduce steady-state oscillations of the operating point around the MPP. So, the user needs to pick and choose based on demand. Keeping the target in focus, we have introduced a couple of modifications in the structure of INC. that remain fruitful. The proposed structure named the augmented InC algorithm has shown marvelous improvement in tracking speed and steady-state oscillations. The results have been compared with the conventional InC algorithm, where the proposed augmented InC algorithm has outperformed the conventional InC algorithm in tracking speed and steady-state oscillations. We have used the MATLAB script to code the conventional InC algorithm and proposed augmented InC algorithms based on their designed flowchart. Both algorithms have been applied to the standalone solar photovoltaic system composed of a solar photovoltaic array, DC/DC boost converter, illumination and temperature inputs, MPPT algorithm, and a DC load. The model is designed in Simulink/MATLAB.

Numerical examination of expanding the effectiveness of thermal photovoltaic framework by changing the level of heat transfer conveyance

In photovoltaic systems, only a tiny portion of solar radiation reaches the module's surface and is converted to electrical energy. The remaining solar radiation is wasted, which raises cell temperature and reduces electrical efficiency. This research focused on examining the effects of different factors on nanofluids. In the simulations performed in this thesis, the inlet temperature of the water fluid changes from 5°C to 30°C. The radiation intensity equals 600W per square meter, and the input speed is 0.07452m per second. The innovation of this article is the use of two nanofluids of aluminum oxide and copper together with a mixture of water to investigate the effect of effective parameters on the electrical, thermal, and overall efficiency of photovoltaic systems, such as the amount of incoming radiation to the surface of the panel, the temperature of the fluid inlet in mountainous areas, the temperature of the absorber. , so that the thermal efficiency of copper and aluminum oxide is investigated and compared. As a result, copper nanofluid can increase the ratio more than aluminum oxide and pure water. There is a direct relationship between the output fluid temperature and the input temperature. With an increase in the input fluid temperature, the output temperature also increases proportionally. Increasing the inlet temperature affects the temperature of the absorber surface, which, in turn, reduces the electrical efficiency of the photovoltaic system. These changes are reduced by adding nanofluids to the photovoltaic system.Although the increase of nanoparticles causes a decrease in the temperature of the absorber plate, and this temperature decrease for copper nanofluid is 10% higher than that of aluminum oxide and pure water until the volume fraction is reached.

Energy Efficiency and Greenhouse Gas Emission Reduction Potential of Solar PV and Biomass-Based Systems for a Food Processing Plant

The industrial sector is one of the most energy-intensive sectors, with a share of 38% (156EJ) of all energy used globally in 2020. Historical energy demand and consumption data from the poultry processing plant have been analyzed to lower greenhouse gas emissions (GHG) and reliance on traditional energy sources. The study includes energy, economic, and emissions analyses for (a) solar photovoltaic (PV) systems, (b) biomass fuels, and (c) coal-fired with wood. Fixed, single, and two-axis tracking solar PV systems that are company-owned and funded by banks are considered. The historical energy consumption data shows that 52.36% of energy demand is met by electrical energy and 47.63% by thermal energy. The yearly electricity demand is 9,938 MWh, costs $1,192,560, and emits 5117.6 tCO2. Thermal energy demand is based on an annual use of 898 tonnes of coal and 71.6 tonnes of liquefied petroleum gas (LPG), which costs $ 143,681 and emits 2,977.7 tonnes of carbon dioxide (tCO2). Feasibility of 1.3 MW, fixed axis company-owned, solar PV plant shows an annual electricity production of 1,876 MWh, GHG emission reduction of 965.9 tCO2 with a payback period of 3.4 years. Biomass system delivers yearly fuel cost savings of $25,724, reducing 2,784 tCO2 with a 1.9-year payback period. Co-firing biomass offers a 3.58% fuel cost saving and 557 tCO2 with a 0.93-year payback period. It concluded that the wood fuel feedstock biomass option is the best renewable energy resource for large-scale poultry facilities and can reduce 34.08% of cumulative GHG emissions.

Energy enhancement in grid-connected photovoltaic generation systems using adaptive control technique

This research paper presents an innovative adaptive control technique for enhancing energy efficiency in grid-connected photovoltaic (PV) generation systems. By integrating an advanced high-gain DC-DC converter with adaptive and non-linear control methods, the proposed system ensures optimal power absorption from various renewable resources and maintains robust power transfer efficiency and reliability. Key results from the implementation at the Union Territory of Puducherry substation demonstrate significant improvements: the adaptive control system dynamically adjusts to changes in source voltage and load conditions, maintaining a constant output voltage with minimized steady-state error and low overshoot. The feedback controller effectively responds to fluctuations in duty cycle and output load, ensuring stable operation under varying environmental conditions. These enhancements contribute to a reliable and efficient integration of renewable energy sources into the power grid, addressing critical challenges in renewable energy technology.

Enhancing photovoltaic operation system efficiency and cost-effectiveness through optimal control of thermoelectric cooling

Solar energy has experienced a surge in utilization, primarily through the adoption of solar photovoltaic (PV) panels, tapping into its abundant renewable potential. Efficiently cooling the operational surfaces of these PV panels is paramount for optimizing their performance and extending their lifespan. Cooling not only boosts electrical efficiency but also mitigates cell degradation. Temperature plays a crucial role in panel efficiency, particularly when temperatures exceed 25 °C, highlighting the need for effective cooling methods. Previous research has struggled to optimize control without relying on traditional cooling mediums. One promising approach involves attaching thermoelectric coolers (TECs) to the rear of PV panels, effectively reducing surface temperatures. To evaluate the economic viability of this approach, a comparative analysis is conducted between conventional systems and PV-TEC hybrid systems. A mathematical model describing the cooling process of PV modules is formulated, and simulations are carried out using MATLAB and the SCIP (Solving Constrained Integer Programs) in OPTI toolbox. The outcomes illustrate that precise control of TECs can increase the electrical output power efficiency of PV modules by 9.27 % while efficiently regulating surface temperatures. Furthermore, life cycle cost comparisons reveal that the hybrid system may be more cost-effective, boasting a 10.56 % cost saving over a 20-year project lifespan. A break-even point analysis indicates that the proposed system may break even in 6.5 years, demonstrating its potential for long-term economic benefits. This research underscores the significance of thermoelectric cooling in enhancing both performance and economic viability in solar PV systems.

Improved Mathematical Modelling and Statistical Assessment of Performance of Photovoltaic System under Non-Linear Operational Condition

It is study presents an advanced mathematical model that captures the non-linear operational characteristics of photovoltaic (PV) systems, enhancing the understanding and optimization of their performance under varying environmental conditions. By integrating principles of non-linear dynamics and statistical analysis, the model precisely simulates the performance responses of PV systems to non-linear inputs such as irradiance fluctuations, temperature variations, and load changes. We utilized a comprehensive dataset collected from multiple PV installations to validate our model, ensuring robustness and applicability. The results demonstrate a significant improvement in predicting system behaviors, which facilitates more effective management and optimization strategies. Statistical tools were employed to assess the reliability and efficiency of PV systems, revealing key insights into their operational dynamics. Our findings contribute to the development of more resilient and efficient solar energy systems, offering a valuable resource for researchers and practitioners in the field of renewable energy technologies.

Solar Tracking Methods: A Comprehensive Survey

Abstract: In the pursuit of maximizing solar energy utilization, solar tracking systems have emerged as indispensable tools for enhancing the efficiency of photovoltaic (PV) systems. This comprehensive survey delves into the multifaceted landscape of solar tracking methods, elucidating the diverse techniques and strategies employed in optimizing solar panel orientation relative to the sun's position Solar tracking systems are classified into two primary categories: single-axis and dual-axis tracking. Single-axis tracking systems adjust solar panels along one axis, typically the azimuth axis, while dual-axis tracking systems dynamically alter both azimuth and elevation angles to accurately follow the sun's apparent motion across the sky.Within the realm of solar tracking, passive and active tracking methods are explored. Passive tracking mechanisms rely on natural phenomena such as gravity or thermal expansion to adjust panel orientation, whereas active tracking systems employ mechanical, electrical, or hydraulic actuators driven by motors or other means to actively adjust panel angles based on real-time tracking algorithms.A pivotal aspect of solar tracking systems is the selection and implementation of tracking algorithms. Various algorithms, including sun position algorithms, maximum power point tracking (MPPT) algorithms, and predictive algorithms, are analyzed for their effectiveness in accurately predicting and optimizing solar panel orientation.Furthermore, this survey scrutinizes the role of sensor technologies in solar tracking, encompassing GPS-based tracking, light-intensity-based tracking, weather-based tracking utilizing meteorological sensors, and horizon tracking techniques. These sensor-based methodologies offer different advantages and trade-offs in terms of accuracy, cost, and reliability. To provide a comprehensive understanding, comparative analyses are conducted, evaluating the efficiency, cost-effectiveness, and energy yield of different tracking methods. These comparative assessments offer valuable insights into the performance characteristics and suitability of various tracking methodologies across diverse applications and environmental conditions.In conclusion, this survey serves as an invaluable resource for researchers, engineers, and policymakers involved in the design, development, and optimization of solar tracking systems for renewable energy applications. By synthesizing the latest advancements and comparative analyses, this survey facilitates informed decision-making and fosters the advancement of sustainable solar energy technologies.

Improve The Conversion Efficiency and Electrical Output of The Solar PV System by Developing and Implementing a Cooling System

The performance of the grid-connected solar photovoltaic (PV) system in the present study is improved using cooling technology. This technology reduces the temperature of solar PV modules and thus increases their efficiency and lifespan. The current solar PV system has been installed in northern Baghdad in the city of Taji. This study included improving electrical power, conversion efficiency, and performance ratio. All of these improvements are achieved by cooling using water. The performance ratio (PR) and efficiency maximum values of the reference (unimproved) and improved solar PV modules are at 10:10 am at 88.30% & 96.30% and 13.4% & 14.6%, respectively. Improved and reference PV modules maximum solar radiation values occur at 12:00 pm at 1.91 W and 1.566 W, respectively. The maximum solar radiation values of the reference and improved PV modules are at 12:00 pm at 922.9 W/m2 and 1012.22 W/m2, respectively. The maximum daily gain of the power and solar irradiance are 22% and 10%, respectively. The significant innovation in the study is the successful performance optimization of the grid-connected solar PV system. Using different equations to study performance by following different behaviors. Along with improving and studying CIGS PV module performance.

Improved Incremental Conductance MPPT Technique Designed to Addressing Drift Problem in a Photovoltaic System

Many researchers have concentrated on improving the efficiency of photovoltaic (PV) systems by optimizing control mechanisms aimed at extracting the maximum power from PV panels. The variable step size incremental conductance control (VSS-INC) technique has been the primary focus of the majority of these studies. However, this strategy faces challenges in the form of drift when confronted with swift changes in solar irradiation, temperature variations, and resistive load fluctuations. In addressing this issue, the present study proposes an innovative VSS-INC method. It is suggested to utilize a buck-boost converter as an impedance adaptor in achieving maximum power point tracking (MPPT). This involves controlling the duty cycle and aligning its input with that of the PV module. The efficiency of the suggested approach was evaluated through MATLAB software, and the outcomes were compared with traditional algorithms across various operational scenarios. Simulation results demonstrate the notably satisfactory and efficient performance of the proposed method when compared to conventional approaches.

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Enhancing photovoltaic systems using Gaussian process regression for parameter identification and fault detection

In recent years, significant efforts have been made to enhance the power-harnessing capacity of photovoltaic (PV) systems. Despite advancements, PV systems remain less efficient. Faults occurring in the PV system are one of the major reasons for reduced output power. Improving PV system efficiency reduces overall costs and extends the system’s operational lifespan. This research utilizes a machine learning-based non-linear Gaussian posterior distribution (GPR) algorithm for detecting, classifying, and localizing various faults in PV systems. The proposed technique uses solar cell parameters to classify the faults based on the severity levels. The experimental results validate the effectiveness of the proposed approach, emphasizing its potential to improve PV system performance and reliability. The model statistics show that training time for squared exponential GPR is shorter than exponential and rational quadratic GPR models, with squared exponential GPR taking 2771.5 s, while exponential GPR and rational quadratic GPR took 4227.6 s and 6660.5 s, respectively. Despite longer training time, rational quadratic GPR has the lowest root mean squared error (RMSE) validation, ranging from 0.016598 to 0.025616, among the three approaches. Exponential GPR, with an RMSE range of 363.59 to 397.1 across various faults, was chosen for its superior performance when compared to the other approaches.

A novel dynamic/adaptive K-nearest neighbor model for the prediction of solar photovoltaic systems’ performance

The inherent variability in solar Photovoltaic (PV) systems motivates developing dynamic and adaptive data-driven models capable of accurately predicting the systems’ performance. A dynamic/adaptive K-Nearest Neighbor (K-NN) model to estimate the conversion efficiency of a solar PV system is proposed in this work. The proposed model estimates the conversion efficiency of a test pattern based on three steps: (i) identifying the similar historical operational conditions experienced by the system, (ii) assigning a weight to each condition based on its similarity to the test pattern at hand, and (iii) computing the weighted average conversion efficiency of the test pattern. Thus, the proposed model is adaptive and dynamic as the nearest neighbors and the associated weights vary from one test pattern to another. The proposed model is applied to a 7.98 kWp rooftop grid-connected solar PV system at the Hashemite University in Jordan. Its effectiveness is evaluated by resorting to the Mean Square Error (MSE), Accuracy, Coefficient of Determination (R2), and Adjusted R2 (R2adjusted), and comparing the results with the traditional K-NN and other techniques adequately developed in the same context, such as Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Multiple Linear Regression (MLR). Results show that the proposed model exhibited superior performance across all metrics, with a performance gain reaching up to 45.2 % and 6.8 % for the MSE and R2adjusted, respectively, compared to the base MLR. Further, the simplicity and computational efficiency of the proposed model validate its practical deployment in predicting PV system performance.

Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions

This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes.

Influence of Selected Weather Conditions on the Photovoltaic System Efficiency in Central Poland – Case Study

Influence of Selected Weather Conditions on the Photovoltaic System Efficiency in Central Poland – Case Study

Falcon optimiser for optimal design of Even-Odd column for shaded PV array reconfiguration

ABSTRACT The operation of Photo-Voltaic (PV) arrays is often negatively impacted by the presence of shadows, which significantly reduce the generated power. Shadows create multiple local Maximum Power Points (MPP) and a unique global MPP within the shaded array. Therefore, it is crucial to distribute shadows evenly across the PV array surface to optimise power extraction by reconfiguring the shaded modules. This paper presents a novel approach for reconfiguring Solar Photovoltaic (PV) systems using the Falcon optimiser, specifically focusing on Even-Odd column reconfiguration. The objective is to optimise the performance of the PV system by strategically rearranging the columns of PV modules. The proposed technique aims to minimise shading effects and maximise power generation efficiency by identifying the optimal placement of modules. The Falcon optimiser, known for its efficient and effective optimisation capabilities, is utilised to determine the optimal configuration. Extensive simulations and comparisons with other reconfiguration methods are conducted to evaluate the performance of the proposed approach. The results demonstrate that the Even-Odd column reconfiguration based on the Falcon optimiser significantly improves the overall power output and efficiency of the solar PV system compared to alternative methods.

A novel approach to evaluate dynamic performance for photovoltaic system using software platform

With the growing demand for renewable energy, solar photovoltaic (PV) systems have gained popularity as a reliable source of clean electricity. However, the performance of these systems can be limited by factors such as suboptimal maximum power point tracking (MPPT) algorithms. In order to improve the power generation efficiency of PV systems, it is important to evaluate the performance of dynamic MPPT algorithms that can adapt to varying operating conditions. Traditionally, such evaluations have been time consuming and expensive, often requiring extensive testing and measurement equipment. In this paper, we propose a novel approach to evaluate dynamic MPPT performance very quickly and simply using PSIM software. This approach enables accurate and efficient evaluation of MPPT performance under a wide range of operating conditions, while minimizing the cost and time involved in traditional testing methods. When applying the proposed method to a 3.7 kW inverter using the traditional perturbation and observation (P and O) method, we found that the highest average efficiency was 98.92% at an MPPT control period of 0.1s and a voltage perturbation of 1 V. This evaluation technique provides valuable insights into the design and optimization of more efficient MPPT control algorithms, leading to improved power generation efficiency and increased adoption of solar PV systems.