Diagnosis of photovoltaic faults using digital twin and PSO-optimized shifted window transformer

This work proposes a new method for the detection, localization, and classification of grid-connected photovoltaic (PV) array faults. Line-to-line, open-module, shorted-module, open-string, and shorted-string faults as well as partial shading conditions are studied. The proposed method has two stages, which are (1) detection and localization of faults and (2) classification of faults. In the first stage, detection and localization are performed using a digital twin (DT) by analyzing the current ratio of each PV array. The measured DC (direct current) power after the operation of the DC/DC boost converter in the physical object is firstly converted into a 2-dimensional image using a recurrence plot (RP) and is then inputted to the second stage. A deep learning-based shifted windows (swin) transformer optimized by particle swarm optimization (PSO) is used in the classification stage, eliminating the need for model tuning by trial-and-error. A PV system of ten arrays with 49 kW is studied. The coefficients of determination (R2) between the results of the digital object (digital twin) and the physical object for different scenarios demonstrate the accuracy and success of the digital twin. R2 values of 0.99988 for varying irradiation with constant temperature and 0.97923 for constant irradiation with varying temperature indicate strong correlations between the digital and physical objects, further confirming the applicability of the digital twin in PV fault detection. The comparative evaluation of the PSO-optimized swin transformer against classical machine learning algorithms and state-of-the-art convolutional neural networks reveals the superior performance of the proposed method. It achieves an outstanding classification accuracy of 98.55%, demonstrating its ability to effectively classify various types of PV faults. The results of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, which measures the trade-off between true positive rate (TPR) and false positive rate (FPR), further illustrate the effectiveness of the proposed method for PV fault classification.