Optimizing Fault Detection in Photovoltaic Systems using Neural Networks and Pelican Optimization Algorithm

Problem Definition

The analysis of literature surrounding photovoltaic (PV) plants reveals a pressing issue of faults impacting their performance and efficiency. Accurate fault detection is crucial for maximizing energy generation in PV plants, with various machine learning (ML) algorithms, particularly Neural Networks, showing promise in this area. However, the effectiveness of Neural Networks is hindered by challenges such as initial weight values and hyperparameters, leading to a need for improved fault detection systems. These limitations highlight the necessity for developing more effective and accurate fault detection methods to optimize the performance of PV plants and ensure sustainable energy generation. Addressing these challenges will not only enhance the efficiency of PV plants but also contribute towards the advancement of renewable energy technologies.

Objective

The objective of this research is to develop an optimization-based neural network model to enhance fault detection in photovoltaic (PV) systems. By combining Neural Networks with the Pelican Optimization Algorithm (POA), the aim is to improve the accuracy and efficiency of fault detection in PV plants by addressing challenges related to weight values and hyperparameters. The goal is to optimize the neural network model to overcome limitations in traditional fault detection systems and contribute towards maximizing energy generation in PV plants. Ultimately, the research aims to advance fault detection technology in photovoltaic systems and promote sustainable energy generation.

Proposed Work

This work aims to address the problem of fault detection in photovoltaic (PV) systems by proposing an optimization-based neural network model. The existing literature highlights the importance of accurate fault detection in PV plants for optimizing energy generation. While Neural Networks have shown promising results in fault detection, their effectiveness is impacted by initial weight values and hyperparameters. By combining Neural Networks with the Pelican Optimization Algorithm (POA), this research seeks to enhance the accuracy and efficiency of fault detection in PV plants. The rationale behind the chosen approach lies in the proven effectiveness of Neural Networks and the ability of the POA to optimize weight values for improved performance.

The proposed work introduces a novel method that utilizes the strengths of Neural Networks and the POA to overcome limitations in traditional fault detection systems. The objective is to optimize the neural network model to enhance fault detection capabilities in PV systems by addressing challenges related to weight values and hyperparameters. By leveraging the optimization capabilities of the POA, the research aims to improve the accuracy of fault detection in PV systems. This approach is driven by the need to develop more effective and accurate fault detection systems that can optimize energy generation in PV plants. Ultimately, this research seeks to contribute to the advancement of fault detection technology in photovoltaic systems.

Application Area for Industry

This project can be utilized in various industrial sectors such as renewable energy, power generation, and electrical engineering. The proposed solutions in this project can address the specific challenges these industries face in optimizing energy generation and improving the efficiency of photovoltaic (PV) plants. By combining Neural Networks with the Pelican Optimization Algorithm (POA), the accuracy of fault detection in PV systems can be significantly enhanced, leading to improved performance and efficiency. This approach can benefit industries by providing more accurate fault detection systems that overcome the challenges associated with initial weight values and hyperparameters, ultimately leading to increased energy generation and cost savings. By implementing these solutions, industries can achieve optimized performance and efficiency in their PV plants, contributing to a more sustainable and reliable energy supply.

Application Area for Academics

The proposed project has the potential to enrich academic research, education, and training in the field of fault detection in photovoltaic (PV) systems. By combining Neural Networks and the Pelican Optimization Algorithm (POA), this research offers a novel approach to improving fault detection accuracy in PV plants. This innovation can benefit researchers, MTech students, and PhD scholars by providing them with a cutting-edge method to enhance the performance and efficiency of renewable energy systems. The relevance of this project lies in its potential applications for pursuing innovative research methods, simulations, and data analysis within educational settings. Specifically, the use of Neural Networks and optimization algorithms can enable researchers to develop more accurate fault detection systems for PV plants.

This opens up opportunities for exploring advanced machine learning techniques and optimization algorithms in the context of renewable energy systems. In terms of technology and research domains, the project covers the utilization of Artificial Neural Networks (ANN) and the Pelican Optimization Algorithm (POA) for fault detection in PV systems. Researchers in the field of renewable energy, machine learning, and optimization can leverage the code and literature from this project to enhance their own work. Similarly, MTech students and PhD scholars can use the proposed approach as a foundation for their research projects, contributing to the advancement of knowledge in the field. Looking ahead, the future scope of this research includes further refining the algorithm parameters, conducting extensive simulations, and testing the approach in real-world PV systems.

Additionally, exploring the integration of other optimization algorithms or machine learning models could offer new insights and opportunities for improving fault detection accuracy in renewable energy systems.

Algorithms Used

The Artificial Neural Network (ANN) is a machine learning model inspired by the structure and function of the human brain. In this project, the ANN is employed for fault detection in photovoltaic (PV) systems. ANN has been chosen for its proven effectiveness in modeling complex relationships and patterns in data. However, the accuracy of ANN can be influenced by initial weight values and hyperparameters. The Pelican Optimization Algorithm (POA) is introduced as an optimization algorithm to address the challenges related to tuning the weights of the ANN.

POA is a nature-inspired algorithm that mimics the behavior of pelicans in search of food. By optimizing the weight values of the neural network using POA, the accuracy of fault detection in PV systems can be improved. POA is used to enhance the performance of the ANN model and contribute to achieving the objective of improving fault detection capabilities in PV systems.

Keywords

PV systems, Photovoltaic systems, Fault detection, Neural network, Deep learning, Pelican Optimization Algorithm, Performance enhancement, Fault diagnosis, Fault classification, Anomaly detection, Renewable energy, Solar power, Fault analysis, Fault localization, Fault identification, Power electronics, Energy conversion, Artificial intelligence, Machine learning, Power system reliability

SEO Tags

PV systems, Photovoltaic systems, Fault detection, Neural network, Deep learning, Pelican Optimization Algorithm, Performance enhancement, Fault diagnosis, Fault classification, Anomaly detection, Renewable energy, Solar power, Fault analysis, Fault localization, Fault identification, Power electronics, Energy conversion, Artificial intelligence, Machine learning, Power system reliability, Neural Networks, Optimization algorithms, Fault detection systems, PV plants, Energy generation, Fault detection accuracy, ML algorithms, Weight values, Hyperparameters, Fault detection challenges, Fault detection research, Research methodology, Fault detection accuracy enhancement, Nature-inspired optimization algorithms, Research objectives.