Abstract
Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification.
Highlights
The increasing energy demands of modern society raise significant environmental concerns.substantial research on renewable energy technologies is important for realizing the potential of cleaner energy resources [1]
The translation corresponds to movements along the time axis, and scaling refers to the spreading out of the wavelet. These two basic manipulations are used in the discrete wavelet transform (DWT) of the signal, which means that the DWT is implemented at several locations of the signal, and for several scales of the wavelet, with the purpose to capture features that are local in time and local in frequency
Euclidean kernel kernel cannot cannot be be adopted adopted for for training and testing of Further, a comparative analysis regarding the weights assigned for every training and testing of radial basis function networks (RBFNs)
Summary
The increasing energy demands of modern society raise significant environmental concerns. Algorithm, which creates a decision boundary to predict whether a certain fault has occurred or not They face a disadvantage in the selection of features, which, when done by trial and error method, may lead to the faulty output. In order to overcome the difficulty, a semi-supervised learning algorithm, which detects faults and identifies possible fault types, was introduced in [14] to speed up system recovery In this approach, two known parameters were chosen as a center for clustering and the unknown faults were grouped based on the vicinity to these clusters. The radial based functions used in the kernel method creates a decision boundary by importing data into a high dimensional feature space and converting them back into two-dimensional data This is achieved by calculating the Euclidean distance between landmarks and training data set and operating on parameter kernel radius for deciding the spread of the function. The developed classifier is introduced in a3 of 17 feedforward mode with the PV system to classify the increased number of faults efficiently
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