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  3. Power Quality Disturbance Tracking Based On A Proprietary Fpga Sensor With Gps Synchronization.
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  3. Power Quality Disturbance Tracking Based On A Proprietary Fpga Sensor With Gps Synchronization.

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Power Quality Disturbance Tracking Based on a Proprietary FPGA Sensor with GPS Synchronization.

Oscar N Pardo-Zamora1, Rene de J Romero-Troncoso1, Jesus R Millan-Almaraz2

  • 1Faculty of Engineering, Autonomous University of Queretaro, San Juan del Rio 76806, Mexico.

Sensors (Basel, Switzerland)
|July 2, 2021

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a low-cost system for detecting and tracking power quality disturbances (PQDs) using synchronized measurements and advanced algorithms. The method accurately identifies the propagation of transients, voltage fluctuations, and harmonic content within an industrial grid.

Keywords:
field-programmable gate arraygenetic algorithmsglobal positioning systemindustrial facilitiesparticle swarm optimizationpower quality disturbancepropagation

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Area of Science:

  • Electrical Engineering
  • Power Systems Analysis
  • Signal Processing

Background:

  • Increasing non-linear loads necessitate robust power quality monitoring.
  • Existing methods for power quality disturbance (PQD) analysis are often costly and limited in scope.
  • Understanding PQD propagation is crucial for grid stability and fault identification.

Purpose of the Study:

  • To develop and validate a low-cost system for synchronized PQD measurements.
  • To detect and track the propagation of multiple PQDs within an electrical grid.
  • To assess the accuracy and effectiveness of the proposed methodology using advanced signal processing techniques.

Main Methods:

  • A proprietary system featuring an FPGA data logger with GPS for synchronized measurements.
  • Integration of a full parameterized PQD model with measurement data.
  • Application of Wavelet Transform (WT), Fast Fourier Transform (FFT), Hilbert-Huang Transform (HHT), Genetic Algorithms (GAs), and Particle Swarm Optimization (PSO) for analysis.
  • Field measurements conducted in an industrial installation.

    • Main Results:

    • Successfully detected and tracked the propagation of three types of PQDs: impulsive transients, voltage fluctuations, and harmonic content.
    • Impulsive transients were identified propagating to two distinct grid locations.
    • Voltage fluctuations and harmonic content were observed to propagate throughout the entire monitored grid.
    • The developed low-cost system and methodology achieved 100% accuracy in detecting and tracking PQDs.

    Conclusions:

    • The proposed low-cost system offers an effective solution for monitoring and analyzing power quality disturbances.
    • The integrated methodology accurately identifies the source and propagation paths of PQDs within complex grids.
    • This approach provides a valuable tool for enhancing power system reliability and management.