Intelligent fault detection and diagnostics in solar plants

Coleman, Andon; Zalewski, Janusz

doi:10.1109/idaacs.2011.6072914

Cited by 37 publications

(18 citation statements)

References 14 publications

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“…Training the model with input-output data helps overcome the limitation of defining thresholds and aids in the detection and classification of faults. Some of the machine-learning techniques used so far are: modified ANN with the extension theory [29], evidence theory and Fuzzy mathematics [30], TSK-FRBS Fuzzy estimator [31], Bayesian belief networks [32], three-layered ANN [33], decision tree-based method [34], and graph-based semisupervised learning [35].…”

Section: Methods 4: Machine-learning Techniques (Mlts) By Learning mentioning

confidence: 99%

Modeling and Health Monitoring of DC Side of Photovoltaic Array

Akram

Lotfifard

2015

IEEE Trans. Sustain. Energy

141

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In this paper, a health monitoring method for photovoltaic (PV) systems based on probabilistic neural network (PNN) is proposed that detects and classifies short-and opencircuit faults in real time. To implement and validate the proposed method in computer programs, a new approach for modeling PV systems is proposed that only requires information from manufacturer's datasheet reported under normal-operating cell temperature (NOCT) conditions and standard-operating test conditions (STCs). The proposed model precisely represents characteristics of PV systems at different temperatures, as the temperature dependency of parameters such as ideality factor, series resistance, and thermal voltage is considered in the proposed model. Although this model can be applied to a variety of applications, it is specifically used to test and validate the performance of the proposed fault detection and classification method. Index Terms-Fault detection, monitoring systems, photovoltaic (PV) modeling, probabilistic neural network (PNN).

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Section: Methods 4: Machine-learning Techniques (Mlts) By Learning mentioning

confidence: 99%

Modeling and Health Monitoring of DC Side of Photovoltaic Array

Akram

Lotfifard

2015

IEEE Trans. Sustain. Energy

141

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“…Artificial neural networks (ANN) have been proposed for PV systems: working under partial shading conditions (Nguyen et al, 2009); for the monitoring and supervision of health status of a PV system in (Riley and Johnson, 2012); and for short-circuit fault detection of PV arrays in (Syafaruddin et al, 2011). In other works, Bayesian networks (Coleman and Zalewski, 2011), and fuzzy logic (AbdulHadi et al, 2004) have also been successful in estimating PV output or perform fault diagnoses. Data mining methods for fault detection and isolation in PV systems have also been proposed in the literature, such as decision-tree method (Zhao et al, 2012), K-nearest neighbor, support vector machine (SVM) (Yi and Etemadi, 2016), etc.…”

Section: Related Workmentioning

confidence: 99%

Deep Convolutional Neural Network for Automatic Detection of Damaged Photovoltaic Cells

Pierdicca

Malinverni

Piccinini

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

126

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The number of distributed Photovoltaic (PV) plants that produce electricity has been significantly increased, and issue of monitoring and maintaining a PV plant has become of great importance and involves many challenges as efficiency, reliability, safety, and stability. This paper presents the novel approach to estimate the PV cells degradations with DCNNs. While many studies have performed images classification, to the best of our knowledge, this is the first exploitation of data acquired with a drone equipped with a thermal infrared sensor. The experiments on “Photovoltaic images Dataset”, a collected dataset, are presented to show the degradation problem and comprehensively evaluate the method presented in this research. Results in terms of precision, recall and F1-score show the effectiveness and the suitability of the proposed approach.

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“…The solar power system failure causes are listed in Table 1, including long-term energy loss, sudden change in energy loss, and power off. Please refer to [5,6] for specific descriptions of failures. According to statistical data and published studies, the highest probability of the aforesaid failures is the high temperature failure caused by long-term covering.…”

Section: Solar Energy Fault Diagnosis Systemmentioning

confidence: 99%

Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

Chang

Lin

Kuo³

et al. 2014

Mathematical Problems in Engineering

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This study endeavors to develop a cloud monitoring system for solar plants. This system incorporates numerous subsystems, such as a geographic information system, an instantaneous power-consumption information system, a reporting system, and a failure diagnosis system. Visual C# was integrated with ASP.NET and SQL technologies for the proposed monitoring system. A user interface for database management system was developed to enable users to access solar power information and management systems. In addition, by using peer-to-peer (P2P) streaming technology and audio/video encoding/decoding technology, real-time video data can be transmitted to the client end, providing instantaneous and direct information. Regarding smart failure diagnosis, the proposed system employs the support vector machine (SVM) theory to train failure mathematical models. The solar power data are provided to the SVM for analysis in order to determine the failure types and subsequently eliminate failures at an early stage. The cloud energy-management platform developed in this study not only enhances the management and maintenance efficiency of solar power plants but also increases the market competitiveness of solar power generation and renewable energy.

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Intelligent fault detection and diagnostics in solar plants

Cited by 37 publications

References 14 publications

Modeling and Health Monitoring of DC Side of Photovoltaic Array

Modeling and Health Monitoring of DC Side of Photovoltaic Array

Deep Convolutional Neural Network for Automatic Detection of Damaged Photovoltaic Cells

Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

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