Tree Search Fuzzy NARX Neural Network Fault Detection Technique for PV Systems with IoT Support

Natsheh, Emad; Samara, Sufyan

doi:10.3390/electronics9071087

Cited by 17 publications

(10 citation statements)

References 58 publications

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“…The recent works from [39,40] present a similar two-stage architecture when compared with this work. Additionally, they use auto regressive models to estimate the expected power output as a function of current environmental conditions.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 97%

“…The difference lies in the fault detection methods, which makes the works complimentary. While [39,40] uses fuzzy inference models yielding 98.2% accuracy with 16 combinations of shadowing, short circuit and open circuit, they cannot operate without disturbing the normal operation of the system, disconnecting the whole system to evaluate VxI curves or run the tree search algorithm.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…In a more recent approach from [39,40], another two stage architectures were used, but this time non-linear auto regressive models (NARX) were developed to estimate the generated power under different environmental conditions. Next, fuzzy inference models compared the estimated value to the sensed power in order to classify the system into one of a given set of fault configurations, which includes combinations of shadowing, short circuit and open circuit, yielding 98.2% accuracy, using 16 use cases.…”

Section: Fault Classificationmentioning

confidence: 99%

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A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Lazzaretti

Costa

Rodrigues

et al. 2020

Sensors

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Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 97%

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Section: Fault Classificationmentioning

confidence: 99%

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A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Lazzaretti

Costa

Rodrigues

et al. 2020

Sensors

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“…The detection of system underperformance occurs when the predefined difference values are reached. Other studies have proposed alternative methods based on hardware redundancy [8], as well as the combination of standard statistical models with artificial intelligence techniques [35,36], specifically machine learning algorithms [37] and neural network algorithms [38]. The developed fault detection algorithms depend on the variations of the voltage and the power of the PV systems; thus, they are capable of detecting faulty PV modules and different conditions.…”

Section: Related Workmentioning

confidence: 99%

Identification of Degrading Effects in the Operation of Neighboring Photovoltaic Systems in Urban Environments

Radovanović

Popović

2021

Electronics

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As photovoltaics technologies have emerged as one of the most promising renewable energy resources in urban environments, monitoring and maintaining of such systems have gained significance. In order to support reliable system operation during the projected in-field operation lifetime, effective strategies for identifying potential problems in photovoltaic systems operation are needed. In this paper, novel methods for the identification of degrading effects in the operation of neighboring photovoltaic systems are presented. The proposed methods are applicable for identifying panel aging properties, soiling effects, and the operation of photovoltaic systems under different shading scenarios. Since the proposed methods are based on the cross-correlation of the operation of neighboring systems, they are particularly suitable performance assessment in urban environments. The proposed identification methods are integrated according to the adopted fog computing model, providing a scalable solution capable of uniform integration into the distributed applications for monitoring and maintenance of photovoltaic systems in urban areas. The details regarding the implementation of the identification methods in the form of data processing services and service operation and dependencies are also provided in this paper. The identification methods, integration concept, and related service operation are verified through the presented case study.

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“…Other than that, there are also many difficulties in extending the application of solar photovoltaic systems as an electricity generation system, one of which is to timely detect the fault to preserve the efficiency and high productivity of the solar photovoltaic systems. In a newly presented research by (Natsheh & Samara, 2020), a new and efficient algorithm which is for fault diagnosis based on an artificial intelligent Nonlinear Autoregressive Exogenous (NARX) neural network and Sugeno fuzzy inference is proposed to isolate and identify the faults that may occur in a solar photovoltaic system. The fuzzy inference also includes the real sensed output power from the solar photovoltaic system as well as able to sense the surrounding condition.…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of Fault Notification Mechanism for IoT-based Photovoltaic Panel Monitoring and Analysis System

Fairuz

Sensor²,

Control³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

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The necessity of fault detection and notification has been exploited by the growth of renewable energy systems. The application of fault detection and notification is seen as an effective tool to monitor the performance of renewable energy systems. Looking at the importance of fault detection and notification integration into renewable energy systems based on the conducted literature review, this paper presents fault notification mechanism and implementation, which also consist of detection in an IoT-based photovoltaic panel monitoring and analysis system. The implemented fault notification detects the abnormalities of the voltage-current-temperature in the IoT-based photovoltaic panel monitoring and analysis system. The implemented fault notification in the IoT-based photovoltaic panel monitoring and analysis detects and notify any sort of abnormality of the voltage-current-temperature that is recorded at the photovoltaic panel. The implemented fault notification operates based on the threshold values of the voltage-current-temperature that has been programmed in the centralised controller. Hence, when any parameter input is below the preset threshold value, an email notification is sent to the centralised system to notify the maintenance team. Based on the implementation and obtained results, the implemented fault notification in the developed IoT-based photovoltaic panel monitoring and analysis system successfully performed and validated the purpose of the implementation.

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Tree Search Fuzzy NARX Neural Network Fault Detection Technique for PV Systems with IoT Support

Cited by 17 publications

References 58 publications

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Identification of Degrading Effects in the Operation of Neighboring Photovoltaic Systems in Urban Environments

Integration of Fault Notification Mechanism for IoT-based Photovoltaic Panel Monitoring and Analysis System

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