Fault detection and diagnosis of renewable energy systems: An overview

Al-Sheikh, Hiba; Moubayed, Nazih

doi:10.1109/redec.2012.6416687

Cited by 20 publications

(7 citation statements)

References 22 publications

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“…Given this challenge, the research in this stream specifically studies alternatives to improve/develop protective schemes for successful protection even if the complicated response exists. While faults in conventional power systems imply an increase in current magnitude, in IBMGs, it can be defined as the departure of an observed variable (voltage or current) from the acceptable range [12].…”

Section: Contributionmentioning

confidence: 99%

Trends in the protection of inverter‐based microgrids

Mahamedi

Fletcher

2019

IET Generation, Transmission & Distribution

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Section: Contributionmentioning

confidence: 99%

Trends in the protection of inverter‐based microgrids

Mahamedi

Fletcher

2019

IET Generation, Transmission & Distribution

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“…The most common approach for monitoring and locating defects in panels of PV systems is using thermal imaging cameras [9] or infrared imaging devices [10]. Once a fault occurs, the maintenance crew needs to manually monitor each module, take images, and perform further image processing to identify the type and location of the fault [11], [12]. Automated approaches for detection and classification of anomalies in the dc side of PV systems are thus needed.…”

Section: Introductionmentioning

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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“…Fault detection techniques are becoming crucial in renewable energy systems [1]. They offer prevention of components failure, reduction of maintenance cost, detection of degradation and thus increase the performance, efficiency, reliability and safety of these systems.…”

Section: Introductionmentioning

confidence: 99%

Research on Hybrid Renewable Energy Systems with Fault Detection Technology

Al-Sheikh¹,

Moubayed²

2013

JEPE

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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.

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Fault detection and diagnosis of renewable energy systems: An overview

Cited by 20 publications

References 22 publications

Trends in the protection of inverter‐based microgrids

Trends in the protection of inverter‐based microgrids

Modeling and Health Monitoring of DC Side of Photovoltaic Array

Research on Hybrid Renewable Energy Systems with Fault Detection Technology

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