Detection of Power Quality Disturbances in Real Time Based on FPGA

Detecting disturbances using digital signal processing methods and techniques that allow the correct extraction of their distinctive characteristics to make the classification more effective is necessary for Power Quality monitoring. But developing an automatic detection system to be applied in smart measurement devices is not a trivial task, especially in obtaining a low computational cost method that can be integrated into hardware, due to the need to coordinate the functions of data acquisition, preprocessing, detection, and data exchange in real-time. It has been demonstrated that FPGA is a sufficiently fast hardware platform that allows the detection of disturbances of transient nature. In this work, a methodology for detection and extraction of the distinctive features of seven simple power quality disturbances based on Discrete Wavelet Transform and methods of energy and RMS values extraction, implemented in real-time using the Artix-7 FPGA from Xilinx, is proposed. From implementing the proposed methodology on the hardware platform, the result obtained is an algorithm that allows extracting the distinctive features of the analyzed disturbances, making optimal use of memory and processing resources, which makes this procedure efficient for its implementation in real time.