Research on Fault Detection of PV Array Based on Data Fusion and Fuzzy Mathematics

Cheng, Ze; Zhong, Dan; Li, Baolin; Li, Yanli

doi:10.1109/appeec.2011.5749018

Cited by 27 publications

(19 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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).

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The method on infrared images has low accuracy and needs expensive price. The method used in [7] needs large number of voltage sensors and current sensors, which increases the cost of the system. The new sensor-embedded method proposed in this study needs much fewer sensors, which decreases the cost of the whole system.…”

Section: Compared With Othermentioning

confidence: 99%

“…An electrical method proposed by Japanese scholars applied the high frequency reaction measurement with time domain analysis for the detection of failed modules [5,6], which had no real-time property and a low realistic possibility of operation. Despite these problems, most of the fault diagnosis methods based on voltage or current sensors can detect and locate certain kinds of faults [5,[7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Method of PV Array Faults Diagnosis in Smart Grid

Cheng

Wang

Cheng

2014

Journal of Applied Mathematics

Self Cite

View full text Add to dashboard Cite

A new fault diagnosis method is proposed for PV arrays with SP connection in this study, the advantages of which are that it would minimize the number of sensors needed and that the accuracy and anti-interference ability are improved with the introduction of fuzzy group decision-making theory. We considered five “decision makers” contributing to the diagnosis of PV array faults, including voltage, current, environmental temperature, panel temperature, and solar illumination. The accuracy and reliability of the proposed method were verified experimentally, and the possible factors contributing to diagnosis deviation were analyzed, based on which solutions were suggested to reduce or eliminate errors in aspects of hardware and software.

show abstract

“…[14], artificial neural network analysis was used. Cheng et al [15] employed fuzzy control theory in PV systems; it proposes a calculation criterion that detects the difference between measured module current and expected current to alarm for a fault. In Ref.…”

Section: Fault Analysis Of Pv Arraysmentioning

confidence: 99%

Research on Hybrid Renewable Energy Systems with Fault Detection Technology

Al-Sheikh¹,

Moubayed²

2013

JEPE

View full text Add to dashboard Cite

Early and accurate fault detection and diagnosis for renewable energy systems can increase their safety and ensure the continuity of their service. This paper presents a comprehensive review of different fault detection and diagnosis methods for hybrid renewable energy systems consisting of a wind turbine power generator, a PV (photovoltaic) array, a PEM (polymer electrolyte membrane) fuel cell and a battery storage system. The need of batteries to store the generated power from the solar panel, wind turbine or PEM fuel cell is also emphasized. Finally, an overview of the current methods used in the diagnosing of the lead-acid battery degradation is given.

show abstract

Research on Fault Detection of PV Array Based on Data Fusion and Fuzzy Mathematics

Cited by 27 publications

References 4 publications

Modeling and Health Monitoring of DC Side of Photovoltaic Array

Modeling and Health Monitoring of DC Side of Photovoltaic Array

A New Method of PV Array Faults Diagnosis in Smart Grid

Research on Hybrid Renewable Energy Systems with Fault Detection Technology

Contact Info

Product

Resources

About