A Novel Fault Classification Approach for Photovoltaic Systems

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.

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

“…The proposal from [42,45], on the other hand, works when the PV modules are normally operating. The first achieved 100% accuracy when detecting short circuit and open circuit faults while the second yielded overall accuracy of 97.52% for the same faults.…”

“…The system attained 98.1%, 97.9%, and 96.5% accuracy when tested in two plants, one with 2.2 kW and other with 4.16 kW when subject to normal operation, shadowing, and overcast conditions. Another Radial Bases Function Network was used in [45] to classify a 1 kW photovoltaic plant into one of 14 cases, including: Normal, short circuit, cell bypass, shading, ground fault, and nine converter/inverter's component faults. This system was only tested in simulations and achieved 97% test accuracy.…”

A Novel Fault Identification Method for Photovoltaic Array via Convolutional Neural Network and Residual Gated Recurrent Unit

“…The signal processing is a method to identify and locate faults by using the waveform signal decomposition, which is often used to solve the problems of line-to-line fault, dynamic shading, and arc fault in multi-series systems [9]- [13]. The line-to-line fault is also called as the mismatch fault, whose fault characteristics are very similar to the partial shading, and cannot be easily identified by the performance comparison.…”

“…It was also effective at the conditions of low irradiance and partial shading. Kurukuru et al [13] performed the wavelet decomposition of voltage and current signals to extract the energy, the entropy, the peak power spectral density, and the kurtosis as features, and then trained a radial-basis-function (RBF) neural network to identify 14 kinds of faults. Most of these online signal processing methods are characterized by the amount of signal mutation at the faulty time.…”

“…By this way, they can identify faults without shutting down the system operation and avoid human-made power loss. If the fault occurs at night or during the system shutdown period, no waveform data can be extracted such that these methods in [9]- [13] cannot work.…”