Hybrid ANN-HBA Fault Detection: Enhancing Solar PV System Reliability

Problem Definition

From the literature review, it is evident that existing models for detecting faults in PV systems have certain limitations that hinder their performance. The majority of researchers have turned to machine learning (ML) algorithms for fault detection, but many do not employ specific techniques for optimizing or tuning parameters. Weight updates in ML algorithms are often done using features, leading to increased model complexity. Some authors have used optimizers for weight updates, yet increasing weights can also escalate model complexity, ultimately reducing system efficiency. Additionally, the absence of pre-processing techniques in previous works has further hampered the performance of current PV fault detection systems.

It is clear that there is a pressing need for an effective and efficient PV fault detection method that can address and overcome these identified limitations and challenges.

Objective

The objective of this project is to develop an enhanced fault detection method for PV systems by combining Artificial Neural Network (ANN) for defect identification and classification with the Honey Badger Algorithm (HBA) for optimizing the weights of the ANN. This approach aims to improve the accuracy and efficiency of fault detection in PV systems by addressing the limitations in existing models, such as complex weight updating in machine learning algorithms and the lack of pre-processing techniques. By utilizing ANN for classification and HBA for optimization, the project seeks to achieve more accurate fault detection results while reducing system complexity and increasing overall efficiency. The ultimate goal is to fill the research gap in optimizing ML algorithms for fault detection in PV systems and provide a more effective and efficient approach for detecting faults in these systems.

Proposed Work

In this project, the main problem identified is the limitations in existing PV fault detection systems due to the complexity and inefficiency in updating ML algorithms for fault detection in PV systems. To address this issue, a proposed PV fault detection method based on Artificial Neural Network (ANN) and Honey Badger Algorithm (HBA) is introduced. The primary objective of this proposed work is to enhance the accuracy of fault detection in PV systems by utilizing ANN for defect identification and classification and HBA for optimizing the weights of the ANN algorithm. The rationale behind choosing ANN is its effectiveness in fault detection as shown in previous literature, while HBA was selected for its ability to optimize the ANN weights without increasing the complexity of the model. By implementing data pre-processing techniques on the sample dataset obtained from GitHub, the input and target variables are separated, empty cells are filled, and redundant data is removed to enhance the efficiency and informativeness of the dataset prior to training and testing stages.

The proposed approach involves several stages including Data Collection, Data Pre-Processing, Data Separation, Training and Testing, and Classification using ANN. The use of HBA for optimization purposes provides a more effective and efficient way to update the weights of the ANN classifier, improving fault detection accuracy while reducing the complexity of the system. The selection of HBA as an optimization algorithm is attributed to its quick convergence and ability to avoid local minima, resulting in improved fault detection performance. This project aims to fill the research gap in optimizing ML algorithms for fault detection in PV systems by introducing a novel approach that combines ANN and HBA to achieve more accurate and efficient fault detection results.

Application Area for Industry

This project can be beneficially applied in various industrial sectors such as solar energy, renewable energy, and power generation industries. The proposed solutions in this project can address specific challenges faced by these sectors, such as accurately detecting faults in PV systems to ensure optimal performance and minimize downtime. By utilizing Artificial Neural Network (ANN) combined with the Honey Badger Algorithm (HBA), this project provides a more efficient and effective method for fault detection in PV systems. The implementation of the proposed model, which includes data pre-processing to improve database quality and HBA optimization to enhance the accuracy of fault detection, can lead to significant benefits for industries by increasing system efficacy, reducing complexity, and improving overall accuracy. This project's solutions can help industries optimize their PV systems, increase productivity, and minimize maintenance costs by detecting and addressing faults promptly and accurately.

Application Area for Academics

The proposed project can enrich academic research, education, and training in the field of fault detection in PV systems. By combining Artificial Neural Network (ANN) with the innovative optimization technique Honey Badger Algorithm (HBA), the project aims to improve the accuracy of fault detection while reducing the complexity of the system. This approach addresses the limitations of existing models by optimizing the weights of the ANN through HBA, thus enhancing the overall efficacy of fault detection in PV systems. This project has significant relevance and potential applications in pursuing innovative research methods, simulations, and data analysis within educational settings. Researchers, MTech students, and PhD scholars in the field of renewable energy and electrical engineering can utilize the code and literature of this project to improve their own work on fault detection in PV systems.

By implementing the proposed model, researchers can explore new avenues for enhancing the efficiency and accuracy of fault detection methods in renewable energy systems. The use of HBA as an optimizing method for ANN weights offers a novel approach to fault detection in PV systems, making this project a valuable resource for those looking to push the boundaries of traditional methods in the field. The research conducted in this project can serve as a foundation for further studies on fault detection techniques in renewable energy systems, offering a reference point for future research and development in the field. Overall, the proposed project has the potential to significantly impact academic research, education, and training by providing a cutting-edge approach to fault detection in PV systems. Through the integration of ANN and HBA, researchers and students can explore new possibilities for improving the accuracy and efficiency of fault detection methods in renewable energy systems, paving the way for future advancements in the field.

Algorithms Used

The proposed PV fault detection system utilizes the Artificial Neural Network (ANN) and Honey Badger Algorithm (HBA) to effectively identify defects in PV systems. The model goes through stages of data collection, pre-processing, separation, training, and classification using a dataset obtained from GitHub. The data pre-processing technique is implemented to clean and enhance the dataset for better accuracy. The ANN classifier is used for defect identification due to its effectiveness in fault detection. The HBA is applied to optimize the ANN weights, improving fault detection accuracy while reducing complexity.

The HBA's quick convergence and ability to avoid local minima make it an ideal optimization method for adjusting ANN weights.

Keywords

SEO-optimized keywords: PV fault detection, Photovoltaic system, Performance analysis, HBA-ANN model, Hybrid Bat Algorithm, Artificial Neural Network, Solar energy, Renewable energy, Energy conversion, Fault diagnosis, Fault classification, Data analysis, Data preprocessing, Machine learning, Solar panel monitoring, Solar power plant, Energy efficiency, Energy harvesting, Artificial intelligence

SEO Tags

PV fault detection, Photovoltaic system, Performance analysis, HBA-ANN model, Hybrid Bat Algorithm, Artificial Neural Network, Solar energy, Renewable energy, Energy conversion, Fault diagnosis, Fault classification, Data analysis, Data preprocessing, Machine learning, Solar panel monitoring, Solar power plant, Energy efficiency, Energy harvesting, Artificial intelligence