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A Hilbert transform-based smart sensor for detection, classification, and quantification of power quality

David Granados-Lieberman¹, Martin Valtierra-Rodriguez, Luis A Morales-Hernandez

¹HSPdigital-CA Mecatronica, Facultad de Ingenieria, Universidad Autonoma de Queretaro, Campus San Juan del Rio, Col. San Cayetano, San Juan del Rio, Qro. 76807, Mexico. dgranados@hspdigital.org

Sensors (Basel, Switzerland)

|May 24, 2013

Summary

This study introduces a smart sensor for detecting, classifying, and quantifying power quality disturbances (PQD). The system utilizes the Hilbert transform and a feedforward neural network for real-time monitoring and analysis of electrical grid health.

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Area of Science:

Electrical Engineering
Signal Processing
Artificial Intelligence

Background:

Power quality disturbances (PQD) pose a significant threat to electrical equipment due to increasing numbers of non-linear loads.
Effective detection, classification, and quantification of PQD are crucial for preventing equipment damage and assessing severity.
Existing monitoring systems may lack the real-time processing capabilities required for dynamic power systems.

Purpose of the Study:

To propose a novel smart sensor for the online detection, classification, and quantification of power quality disturbances.
To develop an integrated system capable of real-time PQD analysis using advanced signal processing and machine learning techniques.
To validate the performance of the proposed smart sensor under both simulated and real-world power system conditions.

Main Methods:

Utilized the Hilbert Transform (HT) for the initial detection of PQD.
Employed a Feedforward Neural Network (FFNN) for classifying the envelopes of detected PQD.
Quantified PQD using indices such as Vrms, Vpeak, CF, THD, and an instantaneous exponential time constant, calculated via HT and Parseval's theorem.
Implemented the methodology on a Field-Programmable Gate Array (FPGA) for online digital signal processing.

Main Results:

The proposed smart sensor successfully detected, classified, and quantified various PQD.
The FFNN demonstrated effective classification of PQD envelopes.
Real-time processing capabilities were achieved through FPGA implementation.
Validation with synthetic signals and testing under real operating conditions confirmed the system's performance.

Conclusions:

The developed smart sensor provides an effective solution for real-time PQD monitoring.
The integrated approach of HT, FFNN, and FPGA offers a robust platform for power quality analysis.
This technology can significantly contribute to maintaining the stability and reliability of power systems.