Optimizing photovoltaic system diagnostics: integrating machine learning and DBFLA for advanced fault detection and classification

Because of the exponential development in the number of photovoltaic (PV) power plant installations, standard inspection procedures have become ineffective. As a consequence of this, there is a requirement for more sophisticated approaches to the identification and categorisation of faults. In order to do this, the DBFLA method, which is a unique hybrid metaheuristic technique, will be presented in this paper. Based on the Dung Beetle Optimisation Algorithm and Fick's Law of Diffusion Algorithm, this technique is aimed to answer the issues that have been highlighted. It is a hybrid of the two algorithms. The DBFLA improves the efficiency of machine learning models such as ANN, SVM, and ensemble approaches by modifying their parameters in order to improve the precision of fault detection. This is accomplished through the process of adjusting the parameters. The identification of problems such as open circuits, short circuits, and module incompatibilities may be accomplished in a rapid and precise way. In accordance with the results of the study, DBFLA is able to construct a stacking classifier by making use of actual PV datasets. This classification strategy achieves an individual meta-learner accuracy of around 98.75%, which is a substantial improvement above the performance of standard machine learning approaches. The capacity of this technique to handle a bigger number of operating modes and a broader diversity of issue scenarios has resulted in the development of enhanced fault detection systems. This evolved as a subsequent consequence of the method's ability to accommodate these capabilities. The improvement in classification accuracy is the most important contribution that DBFLA brings to the table, in contrast to the optimisation procedures that have always been used before. The capability of the approach to properly strike a balance between exploration and exploitation is directly responsible for this advancement on the part of the method. This hybrid approach is provided in order to demonstrate how actual and simulated information may be merged to bring about PV defect detection algorithms that are both more accurate and more efficient. The goal of future research is to improve the operational efficiency and reliability of photovoltaic (PV) systems by incorporating these complicated models into real-time monitoring systems. This will be accomplished through the adoption of advanced modelling techniques.