FPGA-Based Smart Sensor for Detection and Classification of Power Quality Disturbances Using Higher Order Statistics

Power quality (PQ) has received the attention of several research groups due to the impact of PQ disturbances (PQDs) and how they affect the operation of the electrical equipment connected to the grid, especially in industrial and healthcare facilities. The monitoring and analysis of PQD are general...

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Bibliographic Details
Published in:IEEE access 2017-01, Vol.5, p.14259-14274
Main Authors: de J Martinez-Figueroa, Gerardo, Morinigo-Sotelo, Daniel, Zorita-Lamadrid, Angel Luis, Morales-Velazquez, Luis, de J Romero-Troncoso, Rene
Format: Article
Language:English
Subjects:
Analyzers
Artificial neural networks
Classification
Disturbances
Electric equipment
Electrical installations
Experimentation
Field programmable gate arrays
Harmonic analysis
Health care facilities
higher order statistics
Hospital facilities
Instruments
Intelligent sensors
Monitoring
power quality
Sensor arrays
sensor systems and applications
Signal analysis
Signal monitoring
Signal processing
Smart sensors
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Summary:Power quality (PQ) has received the attention of several research groups due to the impact of PQ disturbances (PQDs) and how they affect the operation of the electrical equipment connected to the grid, especially in industrial and healthcare facilities. The monitoring and analysis of PQD are generally performed with specialized measuring equipment, such as power analyzers, based on the standards. However, this equipment is not suited to perform continuous monitoring and classification of PQD, and it cannot be configured to perform further analysis of the monitored signal. Smart sensors, on the other hand, can provide the functionality that the standard equipment cannot, by integrating several signal processing modules that can be reconfigured using a reconfigurable technology, such as field programmable gate arrays (FPGAs). This paper presents the development of an FPGA-based smart sensor that integrates the processing cores of higher order statistics (HOS) to provide a signal analysis aimed to detect and quantify PQD on electrical installations and an artificial neural network to classify the PQD. Experimentation is performed on the electrical installation in hospital facilities. Results from the HOS processing of electrical signals show that these processing methodologies are suitable for the quantification and classification of PQD on electrical installations.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2017.2732726