Analysis of P&amp;O and INC MPPT Techniques for PV Array Using MATLAB

Rana, Md. Masud; Ali, Md. Rayhan; Ajad, Abul Kalam; Moznuzzaman, Md.

doi:10.9790/1676-104028086

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…They introduced a modified Incremental Conductance theorem to reduce steady-state oscillations [22].Azad et al (2016) explored the application of Perturb and Observe and Incremental Conductance in PV systems connected directly to the grid. Their results highlighted the effectiveness of Incremental Conductance under varying atmospheric conditions [23]. Ilyas et al (2017) provided a detailed explanation of the Incremental Conductance algorithm, integrating PV modules with DC-DC converters.…”

Section: Introductionmentioning

confidence: 94%

Mathematical Modelling and Statistical Analysis of Improved Grey Wolf Optimized Maximum Tracking for Solar Photovoltaic Energy System Under Non Linear Operational Conditions

Sunil Kumar Gupta

2024

anvi

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IAs the utilization of solar photovoltaic (PV) energy systems continues to expand, the efficient extraction of energy under non-linear operational conditions becomes paramount. This research focuses on the development and enhancement of a Maximum Power Point Tracking (MPPT) algorithm, specifically tailored for solar PV systems, through the integration of Improved Grey Wolf Optimization (IGWO) techniques. The study utilizes mathematical modeling and statistical analysis to evaluate the performance of the proposed IGWO-based MPPT algorithm. This research, we first establish a comprehensive mathematical model of a solar PV energy system that accurately represents its non-linear operational characteristics, taking into account factors such as temperature variations, shading effects, and changing environmental conditions. Subsequently, we introduce the Improved Grey Wolf Optimization algorithm to optimize the MPPT process, aiming to enhance energy extraction efficiency by dynamically adapting to varying conditions. The statistical analysis includes the comparison of the IGWO-based MPPT algorithm with conventional MPPT methods, such as Perturb and Observe (P&O) and Incremental Conductance (IncCond), under various non-linear operational scenarios. Key performance metrics, including energy conversion efficiency, response time, and tracking accuracy, are thoroughly evaluated to assess the algorithm's effectiveness in real-world conditions. The results of this study demonstrate the superior performance of the IGWO-based MPPT algorithm in enhancing the energy harvesting capabilities of solar PV systems under non-linear operational conditions. The proposed approach not only improves the overall energy conversion efficiency but also reduces the adverse effects of environmental variables on the system's performance. In conclusion, the integration of Improved Grey Wolf Optimization into the MPPT process represents a promising advancement in the field of solar photovoltaic energy systems. The mathematical modeling and statistical analysis conducted in this research provide valuable insights into the practical benefits of this approach, paving the way for more efficient and reliable solar energy utilization in the future.

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Section: Introductionmentioning

confidence: 94%

Mathematical Modelling and Statistical Analysis of Improved Grey Wolf Optimized Maximum Tracking for Solar Photovoltaic Energy System Under Non Linear Operational Conditions

Sunil Kumar Gupta

2024

anvi

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“…Here, output has been directly connected to the grid so as to make it run like a solar generator on cloudy days. The simulation results from both the algorithms are compared under same conditions and the author concluded that the P & O is not very effective under varying atmospheric conditions while the later works accurately [23].…”

Section: Abul Kalam Azad Et Al (2016)mentioning

confidence: 99%

Mathematical Modelling Non Linearity Characteristic Analysis and Efficiency Enhancement Strategies for Hybrid Solar Photovoltaic Energy System

Naval Kishor Jain

2024

cana

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As the world continues to grapple with the challenges posed by climate change and the depletion of conventional energy sources, renewable energy systems such as solar photovoltaic (PV) technology have gained significant prominence. Hybrid solar PV systems, which combine multiple energy sources and storage solutions, offer a promising avenue to improve the reliability and efficiency of renewable energy generation. This research focuses on the mathematical modeling, non-linearity characteristic analysis, and the development of efficiency enhancement strategies for hybrid solar PV energy systems. The study begins with the development of comprehensive mathematical models that accurately represent the complex interactions within hybrid solar PV systems. These models consider various factors, including solar irradiance, temperature variations, load profiles, and the dynamic behavior of energy storage components. The incorporation of non-linear characteristics, often overlooked in conventional models, allows for a more realistic representation of system performance. To enhance the efficiency of hybrid solar PV systems, a range of strategies are proposed and evaluated. These strategies encompass advanced control algorithms, optimized sizing and placement of energy storage elements, and the integration of emerging technologies such as artificial intelligence and machine learning. The goal is to mitigate non-linear effects, maximize energy utilization, and improve system response to dynamic operating conditions. The findings from this research provide valuable insights into the design, operation, and performance optimization of hybrid solar PV energy systems. By addressing non-linearity characteristics and developing efficient strategies, this study contributes to the advancement of renewable energy technologies and fosters the transition towards a more sustainable and resilient energy future.

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“…The P&O method has a simple design and uses a very small number of sensors to measure voltage and current thus it can save costs [19]. Meanwhile, the Incremental Conductance method can find MPP accurately under rapidly changing radiation conditions [20]. The HC algorithm works by varying changes in output voltage or current and monitoring the resulting changes in power.…”

Section: Introductionmentioning

confidence: 99%

Development of Dc-Dc Buck Converter for Solar Panel Energy Storage Through Load Optimization and Feedback Circuit

Indrasari,

Rama,

Setiadi

et al. 2023

SPEKTRA

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Solar panels are crucial components in converting sun radiation into electrical energy through the photovoltaic effect. A solar panels cannot be connected directly to the load due to its low energy conversion efficiency and low output voltage. One of the methods used to control solar cells to operate efficiently at their maximum power point is MPPT. In this paper, we design a DC-DC converter by modifications of the Butterworth filter circuit and feedback circuit in the MPPT system for storing solar panel electrical using the Hill Climbing (HC) method. The device consists of a DC-DC buck converter circuit, two pieces of INA219 sensors, a DS18B20 temperature sensor, a MAX44009 light intensity sensor, a SD card module and a DS3231 RTC. The DC-DC buck converter circuit simulation is carried out to determine the optimal load. The load optimization was conducted by analyzing the AC simulation using Ltspice software. The magnitude of the output voltage ripple in nine different loads was observed. From the simulations performed, it was found that the 50 Ω load has an output voltage ripple of 8.96 mV and is smaller than the other loads. The main DC-DC buck converter circuit is designed using a butterworth low pass filter with a cut-off frequency of 1000 Hz (R = 50 Ω, L = 33.8 mH and C = 750 nF) and a feedback circuit with a cut-off frequency of 500 Hz is added. From the prototype measurement, it was found that the average output power was 3249,7 milliwatts and the average input power was 4779 milliwatts, thus the average efficiency was 68%. With these results, the DC-DC converter circuit configuration is suitable for use in electrical energy storage systems from solar panels that have high efficiency.

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Analysis of P&O and INC MPPT Techniques for PV Array Using MATLAB

Cited by 12 publications

References 11 publications

Mathematical Modelling and Statistical Analysis of Improved Grey Wolf Optimized Maximum Tracking for Solar Photovoltaic Energy System Under Non Linear Operational Conditions

Mathematical Modelling and Statistical Analysis of Improved Grey Wolf Optimized Maximum Tracking for Solar Photovoltaic Energy System Under Non Linear Operational Conditions

Mathematical Modelling Non Linearity Characteristic Analysis and Efficiency Enhancement Strategies for Hybrid Solar Photovoltaic Energy System

Development of Dc-Dc Buck Converter for Solar Panel Energy Storage Through Load Optimization and Feedback Circuit

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