Fault Classification for Single Phase Photovoltaic Systems using Machine Learning Techniques

Ahmad, Sameen; Hasan, Nabeel; Kurukuru, Varaha Satya Bharath; Khan, Mohammed Ali; Haque, Ahteshamul

doi:10.1109/iicpe.2018.8709463

Cited by 17 publications

(18 citation statements)

References 25 publications

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“…The AC faults include gating and switching failures, open and short circuit switches [4], and filter failure-inducing harmonics in the circuits. Whereas DC faults include various module-based faults [5], failure of maximum power-point tracking (MPPT) algorithms, and faults associated with DC-DC converters [6,7]. Also, MPPT systems are responsible for injecting maximum power into the circuit.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Classification Approach for Photovoltaic Systems

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

A Novel Fault Classification Approach for Photovoltaic Systems

et al. 2020

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“…As argued before, classification performance relies on the feature selection process [19] and outcomes are highly dependant on the training process, that is, low performance scores in the training stage means that the features used are not good enough to separate classes. For example, to overcome the disadvantage of using features by trial and error, in [28], [29] the authors employ a matrix defined by eight features, some of them depending on statistical indices.…”

Section: Classification Approach For High Impedance Faults a Feature ...mentioning

confidence: 99%

Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults

et al. 2022

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This work proposes a new algorithm to classify high impedance faults (HIFs) in distribution systems. HIFs can be represented by small current magnitudes with non-linear variations, which complicate their detection in distribution grids. The proposed method uses the Hermite transform (HT) as a signal processing technique that offers several advantages, one of the more important being the ability to analyze the signal at multiple resolution levels and at different frequency bands thanks to their filter functions based on Gaussian derivatives. The Hermite coefficients are used to extract signal features that allow efficient identification of the transient behaviour related to HIFs and other typical faults. In this sense, a multichannel approach based on the Hermite transform is proposed to classify different types of faults and HIFs. This analysis is carried out in a distribution network considering photovoltaic (PV) systems considering three different classifiers, which are also used to compare our results with the discrete wavelet transform (DWT) as signal decomposition model; the comparison suggests that our proposal presents better performance discriminating HIFs from typical faults.

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“…As the faults considered fall under the transient fault category, each time represented signal is sampled into 100 samples, and four different features are extracted for each sample. Since the simulated outputs are non-deterministic, the energy, entropy, power spectral density and peak features are used to see how the signal is distributed over different time and frequency scales [56][57][58]. The extracted features for 8100 samples (81 outputs divided into 100 samples each) form a feature matrix of size 4 × 8100.…”

Section: Classifier Developmentmentioning

confidence: 99%

Model-Based Data Driven Approach for Fault Identification in Proton Exchange Membrane Fuel Cell

et al. 2020

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This paper develops a model-based data driven algorithm for fault classification in proton exchange membrane fuel cells (PEMFCs). The proposed approach overcomes the drawbacks of voltage and current density assumptions in conventional model-based fault identification methods and data limitations in existing data driven approaches. This is achieved by developing a 3D model of fuel cells (FC) based on semi empirical model, analytical representation of electrochemical model, thermal model, and impedance model. The developed model is simulated for membrane drying and flooding faults in PEMFC and their effects are identified for the action of varying temperature, pressure, and relative humidity. The ohmic, concentration, activation and cell voltage losses for the simulated faults are observed and processed with wavelet transforms for feature extraction. Furthermore, the support vector machine learning algorithm is adapted to develop the proposed fault classification approach. The performance of the developed classifier is tested for an unknown data and calibrated through classification accuracy. The results showed 95.5% training efficiency and 98.6% testing efficiency.

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Fault Classification for Single Phase Photovoltaic Systems using Machine Learning Techniques

Cited by 17 publications

References 25 publications

A Novel Fault Classification Approach for Photovoltaic Systems

A Novel Fault Classification Approach for Photovoltaic Systems

Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults

Model-Based Data Driven Approach for Fault Identification in Proton Exchange Membrane Fuel Cell

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