Abstract
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.
Highlights
Electric power is considered a primary resource due to the number of industrial activities that depend on it
The results show that the higher order statistics (HOS)-based smart sensor provides useful information for power quality (PQ) monitoring and detection and classification of PQ disturbances (PQDs)
The system can be categorized as a highperformance waveform analyzer, a useful PQ monitoring system, and a precise PQD classifier, with the ability to classify eight different types of single PQD and two combinations of PQD.The HOS cores make use of very lowFPGA resources, showing that this technology is well suited for the design and development of high-performance signal processing methods for smart sensors
Summary
Electric power is considered a primary resource due to the number of industrial activities that depend on it. The primary function of a power supply system is to feed loads economically and with appropriate levels of continuity and quality. The classification and identification of power quality disturbances (PQD) are defined in standards such as the IEEE 1159 [2], the IEC 61000-4-30 [3] and the EN 50160 [4]. There are two main approaches for PQ monitoring [5]: the development of indices to quantify the PQ, and the PQD detection and classification in harmonics, sags, swells, interruptions, transients, voltage fluctuations, notching, power frequency variations and so forth. Tools for the detection and classification of PQD are essential in industrial applications
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