Signal processing for fault detection in photovoltaic arrays

Braun, Henry; Buddha, S. T.; Krishnan, Venkatachalam; Spanias, Andreas; Tepedelenlioğlu, Cihan; Yeider, Ted; Takehara, Toru

doi:10.1109/icassp.2012.6288220

Cited by 48 publications

(24 citation statements)

References 9 publications

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“…A weather monitoring station nearby records environmental conditions for fusion with collected PV data. The facility is intended to enable experimental research with results obtained for various loading and shading conditions that will validate and extend various theoretical results reported in [1][2][3][4]. Other work that is based on a similar framework is reported in [2,8,9].…”

Section: Design Of the Solar Array Research Facilitymentioning

confidence: 87%

“…These are caused by shading, soiling, inverter faults and manufacturing mismatches [16]. Data acquired during faults tends to luster in the feature space consisting of current, voltage and temperature measurements [4]. Certain aspects of our algorithmic and experimental research using this facility will focus on modeling faults interns of clusters and using machine learning algorithm to form and track these clusters.…”

Section: Fault Detection In Pv Arraysmentioning

confidence: 99%

“…We have shown that fault detection can be performed using statistical outlier detection techniques [4]. However, performing diagnosis and localization of a fault is a much deeper problem.…”

Section: A Machine Learning In Fault Detectionmentioning

confidence: 99%

See 2 more Smart Citations

An 18 kW solar array research facility for fault detection experiments

Rao

Ramirez

Braun

et al. 2016

2016 18th Mediterranean Electrotechnical Conference (MELECON)

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Monitoring utility-scale solar arrays was shown to minimize cost of maintenance and help optimize the performance of the array under various conditions. In this paper, we describe the design of an 18 kW experimental facility that consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchanges via the use of wireless data sharing with servers, fusion and control centers, and mobile devices. Research planned at this stage includes developing machine learning methods for fault detection. Preliminary simulation results on fault detection using machine learning are given in this paper.

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Section: Design Of the Solar Array Research Facilitymentioning

confidence: 87%

Section: Fault Detection In Pv Arraysmentioning

confidence: 99%

See 1 more Smart Citation

An 18 kW solar array research facility for fault detection experiments

Rao

Ramirez

Braun

et al. 2016

2016 18th Mediterranean Electrotechnical Conference (MELECON)

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“…For example, in [22], an extended correlation function is used to identify faults between branches of the PV system. In [23], a statistical outlier detection method is employed. In [24], the expected output voltage value for different MPP is calculated and is used as a reference value for fault detection.…”

Section: A Methods 1: Direct Difference Measurement (Ddm) Between Expmentioning

confidence: 99%

“…SUN-inspector is used in [41]. MATLAB software is used in [27], [45], and [23]. LabVIEW is also used for simulation in [52].…”

Section: F Methods 6: External Injection Methods (Eim)mentioning

confidence: 99%

Modeling and Health Monitoring of DC Side of Photovoltaic Array

Akram

Lotfifard

2015

IEEE Trans. Sustain. Energy

138

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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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ICA ‐based solar photovoltaic fault diagnosis

Qureshi

Uddin

Satti

et al. 2020

Int Trans Electr Energ Syst

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Summary Faults are natural for any practical system including photovoltaic (PV) power generation systems that need to be diagnosed accordingly to maximize its efficiency. However, faults can be rectified only if they are diagnosed correctly. Therefore, a credible and reliable diagnosis is required for maintenance of the system. In this paper, independent component analysis (ICA)‐based wireless fault diagnosis technique is proposed for solar PV systems. ICA enables us to communicate without having any prior information about the signals and the wireless channel. The proposed technique can diagnose faults related to electrical failures, climatic impacts, variable irradiance, and irregular cell temperature. Effectiveness of the proposed technique is evaluated through extensive simulations utilizing practical data.

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Signal processing for fault detection in photovoltaic arrays

Cited by 48 publications

References 9 publications

An 18 kW solar array research facility for fault detection experiments

An 18 kW solar array research facility for fault detection experiments

Modeling and Health Monitoring of DC Side of Photovoltaic Array

ICA ‐based solar photovoltaic fault diagnosis

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