Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems

Hajji, Mansour; Harkat, Mohamed-Faouzi; Kouadri, Abdelmalek; Abodayeh, Kamaleldin; Mansouri, Majdi; Nounou, Hazem; Nounou, Mohamed

doi:10.1016/j.ejcon.2020.03.004

Cited by 77 publications

(39 citation statements)

References 20 publications

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“…Several effective features can be computed from the IKPCs. In the current work, the Hotelling's T 2 statistic, squared prediction error statistic (SPE), combined index ϕ, sampled mean M , variance D 2 , skewness S, and kurtosis K metrics are applied for feature selection [23], [32].…”

Section: B Feature Selectionmentioning

confidence: 99%

“…The faults were emulated at the common coupling point, inverter, sensors, and PV emulator [23], [32]. In the AC side, an open-circuit fault introduced on one inverter switch at the time is referred as an inverter fault F 1 , while the This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: A Pv System Data Collectionmentioning

confidence: 99%

“…Principal component analysis (PCA) [20], independent component analysis (ICA) [21] and partial least squares (PLS) [22] are the most commonly used feature extraction techniques. In [23], a fault classification method based on PCA technique and supervised machine learning was proposed for a grid-connected PV (GCPV) system. The PCA technique is also proposed for enhancing the diagnosis performance by extracting the most significant linear features from data.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation

et al. 2021

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This paper proposes a novel fault detection and diagnosis (FDD) technique for grid-tied PV systems. The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors,...) by using an interval-valued data representation, and with large-scale systems by using a dataset size-reduction framework. The failures encompassed in this study are the open-circuit/short-circuit, islanding, output current sensor, and partial shading faults. In the proposed FDD approach, named interval reduced kernel PCA (IRKPCA)-based Random Forest (IRKPCA-RF), the feature extraction and selection phase is performed using the IRKPCA models while the fault classification is ensured using the RF algorithm. The main contribution of the proposed approach is to provide a good trade-off between low computation time and high classification metrics. The performance of the proposed IRKPCA-RF approach is assessed using a set of emulated data of a grid-tied PV system operating under healthy and faulty conditions. The presented results show that the proposed IRKPCA-RF approach is characterized by enhanced diagnosis metrics, classification rate, and computation time compared to the classical techniques.

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Section: B Feature Selectionmentioning

confidence: 99%

Section: A Pv System Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation

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“…The remarked properties in (15) imply that the distance among intervals can be concluded to the Euclidean distance in the space…”

Section: ) Standardization Using the Dispersion Of Interval Center Amentioning

confidence: 99%

“…Data-based FDD methods can be divided into two principal phases: feature extraction and selection, and faults classification [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

et al. 2020

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The operation of heating, ventilation, and air conditioning (HVAC) systems is usually disturbed by many uncertainties such as measurement errors, noise, as well as temperature. Thus, this paper proposes a new multiscale interval principal component analysis (MSIPCA)-based machine learning (ML) technique for fault detection and diagnosis (FDD) of uncertain HVAC systems. The main goal of the developed MSIPCA-ML approach is to enhance the diagnosis performance, improve the indoor environment quality, and minimize the energy consumption in uncertain building systems. The model uncertainty is addressed by considering the interval-valued data representation. The performance of the proposed FDD is investigated using sets of synthetic and emulated data extracted under different operating conditions. The presented results confirm the high-efficiency of the developed technique in monitoring uncertain HVAC systems due to the high diagnosis capabilities of the interval feature-based support vector machines and k-nearest neighbors and their ability to distinguish between the different operating modes of the HVAC system.

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Machine Learning for PV System Operational Fault Analysis: Literature Review

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Communications in Computer and Information Science

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Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems

Cited by 77 publications

References 20 publications

A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation

A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation

Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

Machine Learning for PV System Operational Fault Analysis: Literature Review

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