Abstract
The study of power quality (PQ) has gained relevance over the years due to the increase in non-linear loads connected to the grid. Therefore, it is important to study the propagation of power quality disturbances (PQDs) to determine the propagation points in the grid, and their source of generation. Some papers in the state of the art perform the analysis of punctual measurements of a limited number of PQDs, some of them using high-cost commercial equipment. The proposed method is based upon a developed proprietary system, composed of a data logger FPGA with GPS, that allows the performance of synchronized measurements merged with the full parameterized PQD model, allowing the detection and tracking of disturbances propagating through the grid using wavelet transform (WT), fast Fourier transform (FFT), Hilbert–Huang transform (HHT), genetic algorithms (GAs), and particle swarm optimization (PSO). Measurements have been performed in an industrial installation, detecting the propagation of three PQDs: impulsive transients propagated at two locations in the grid, voltage fluctuation, and harmonic content propagated to all the locations. The results obtained show that the low-cost system and the developed methodology allow the detection of several PQDs, and track their propagation within a grid with 100% accuracy.
Highlights
Nowadays, power quality (PQ) is a combination of characteristics and conditions of the power supplied to the equipment to guarantee its continuous operation, and its studies are important for industrial processes to maintain the quality standards of the power grid and to avoid damage to equipment connected to the grid [1]
This paper describes a low-cost proprietary system which implements a methodology to track the propagation of power quality disturbances (PQDs) by performing synchronized measurements, using global positioning system (GPS), between different proprietary data loggers located at different points in the grid
This paper shows the hardware implementation of a full PQD parameterized model based on particle swarm optimization (PSO) and genetic algorithms (GAs) that allows the detection and classification of several PQDs
Summary
Power quality (PQ) is a combination of characteristics and conditions of the power supplied to the equipment to guarantee its continuous operation, and its studies are important for industrial processes to maintain the quality standards of the power grid and to avoid damage to equipment connected to the grid [1]. The authors in [20] developed an open architecture smart sensor network, based on field-programmable gate array (FPGA) technology, which is capable of monitoring PQ continuously in industrial facilities, public buildings, and residential buildings It is capable of estimating different PQ indices, as well as identifying disturbances and detecting connection and disconnection events, and it is capable of locating events in a synchronized way in different points of an electrical installation by using a real-time clock (RTC) that allows a synchronization of the measurements in different points of the grid. Transient impulsive, voltage fluctuation, and harmonic content disturbances have been found in measurements performed in industrial facilities, and are used to validate the system proposed in this work
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