Synergistic Optimization of Solar Panel Performance and Energy Supply Using Hybrid ANN-Jaya Algorithm Model for Maximum Power Point Tracking

Problem Definition

After reviewing the existing literature on maximum power point tracking (MPPT) techniques for solar panels, it is evident that there are several limitations and problems that need to be addressed. One of the main drawbacks of current systems is the presence of large oscillations, which can significantly impact the overall performance of the system. Additionally, many existing algorithms suffer from slow convergence rates and are prone to getting stuck in local minima when trying to find global solutions. This can hinder the efficiency and effectiveness of the MPPT process, ultimately leading to suboptimal energy production. Another issue highlighted in the literature is the inability of current models to provide power to loads during periods of low sunlight or weak wind conditions.

This limitation can have serious implications for off-grid systems or those located in areas with inconsistent renewable energy sources. With the increasing importance of renewable energy sources like solar power, it is clear that a new, robust MPPT strategy is needed to overcome these challenges and improve overall system performance.

Objective

The objective of the research is to address the limitations of existing Maximum Power Point Tracking (MPPT) systems for solar panels by proposing a hybrid approach that combines an Artificial Neural Network (NN) with the Jaya optimization algorithm. The goal is to enhance solar panel efficiency, reduce output oscillations, and ensure adequate power supply to loads, especially during periods of low sunlight or weak wind conditions. The proposed model aims to maximize the efficiency and stability of PV systems by optimizing the MPPT process and integrating additional energy sources like fuel cells for energy storage. The research emphasizes on improving overall system performance while overcoming the challenges faced by current MPPT strategies.

Proposed Work

This research aims to tackle the limitations of existing MPPT systems by proposing a hybrid approach that combines an Artificial Neural Network (NN) with the Jaya optimization algorithm. The NN is utilized to predict the optimal operating point of PV systems, while the Jaya algorithm fine-tunes the parameters for improved MPPT performance. By integrating these two techniques, the proposed model seeks to enhance solar panel efficiency, reduce output oscillations, and ensure adequate power supply to the loads. The Jaya algorithm is selected for its reputation for high convergence rates, ability to avoid local minima, and its parameter-free nature, making it an ideal choice for solving optimization problems. Additionally, the research encompasses two key phases: MPPT and energy sources integration.

In the MPPT phase, the focus is on maximizing output from the solar panel using the ANN-Jaya MPPT technique. The Jaya algorithm's capabilities complement the NN by optimizing the initial weights and hyperparameters for optimal performance. Furthermore, to address the issue of insufficient power supply in the absence of sunlight, the integration of additional energy sources such as a fuel cell is proposed. This integration not only enhances system performance but also enables energy storage during periods of low sunlight, ensuring a consistent power supply to the loads. Through the integration of advanced technologies and optimization techniques, the proposed approach aims to maximize the efficiency and stability of PV systems while overcoming the limitations of existing MPPT strategies.

Application Area for Industry

This project can be utilized in various industrial sectors such as renewable energy, power generation, and smart grid systems. The proposed MPPT approach addresses the challenges faced by industries in maximizing the efficiency and stability of solar panels. The use of Artificial Neural Network (NN) based techniques coupled with the Jaya algorithm enhances solar panel efficiency and reduces output oscillations, ensuring a more reliable power supply. Additionally, the integration of fuel cells as an alternative energy source during periods of low sunlight further enhances system performance and allows for energy storage. By combining advanced technologies and optimization strategies, industries can benefit from increased energy output and improved system stability, making this project highly applicable in sectors where renewable energy sources play a significant role.

Application Area for Academics

The proposed project presents a novel approach to maximizing power output in solar panels by integrating Artificial Neural Network (ANN) based MPPT technique and Jaya algorithm for optimization. This new method addresses the limitations of existing models by improving efficiency, reducing oscillations, and ensuring power supply to loads even in low sunlight conditions. By incorporating additional energy sources like fuel cells, the system's performance is enhanced, and energy storage capabilities are increased. This project has significant implications for academic research, education, and training in the field of renewable energy and power systems. Researchers can utilize the code and literature generated from this work to explore innovative research methods, simulations, and data analysis techniques within educational settings.

MTech students and PHD scholars focusing on solar panel optimization, neural networks, optimization algorithms, and energy storage systems can benefit from this project's methodology and findings. The relevance of this project extends to various technology and research domains such as renewable energy, power systems, artificial intelligence, and optimization. The integration of ANN and Jaya algorithm in the MPPT process offers a unique approach for maximizing solar panel efficiency, which can be applied in real-world systems. The inclusion of hybrid energy sources like fuel cells opens up avenues for exploring new ways to enhance energy storage and system stability. In conclusion, this project has the potential to enrich academic research by providing a comprehensive framework for optimizing solar panel performance and energy management.

The use of advanced algorithms and energy storage technologies makes it a valuable resource for researchers and students alike. Future scope of this work could involve further optimization of the ANN-Jaya model, exploring different energy storage options, and testing the proposed approach in practical applications to validate its effectiveness.

Algorithms Used

The research project utilizes an Artificial Neural Network (ANN) based technique in the MPPT phase to enhance solar panel efficiency and reduce output oscillations. The Jaya algorithm is incorporated to optimize the initial weights and hyperparameters of the ANN, maximizing or minimizing functions and avoiding local minima effectively. The Hybrid Energy Source model integrates additional energy sources such as fuel cells to ensure continuous power supply during low sunlight periods, enhancing overall system performance and stability. Through the combination of ANN, Jaya algorithm, and advanced energy storage technologies, the proposed approach aims to maximize solar panel output efficiency and stability.

Keywords

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SEO Tags

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