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This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in single phase grid-connected photovoltaic (PV) systems. To overcome the non-detection zone, excess and deficit power imbalance conditions are considered for different loading conditions. These imbalances are characterized by the voltage dip scenario and were subjected to feature extraction for training with the machine learning technique. This is experimentally realized by training the machine learning classifier with different events on a 5 &nbsp, kW grid-connected system. Using the concept of detection and false alarm rates, the performance of the trained classifier is tested for multiple faults and power imbalance conditions. The results showed the effective operation of the classifier with a detection rate of 99.2% and a false alarm rate of 0.2%.

A fault classification and identification system for prognosis of single-phase inverters was presented in this paper. This concept involves the resolution and analysis of different technical constraints associated with the operation and integration of the inverters. The work presented is centered on fault identification for single-phase inverters by developing a suitable technique utilizing signal processing and machine learning techniques. This task is approached by observing the behavior of inverters under various faults and the factors affecting them. Thereafter, the output voltage under both normal and faulted operation was subjected to signal processing techniques to observe the features. The obtained features are trained for classification and identification of faults using support vector machines. Simulation was carried out for operation of inverter under different events of a fault. By combining output voltages and their features with wavelet transforms and feature detection, the developed algorithms are capable of capturing the key features of inverters. The results depicted how to perform numerical fault analysis calculations using MATLAB classification learner by adapting the trained data for identifying the fault.

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.