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Compressed Sensing Technique for the Localization of Harmonic Distortions in Electrical Power Systems
1Master of Electricity Program, Universidad Politécnica Salesiana, Quito 170525, Ecuador.
Sensors (Basel, Switzerland)
|September 9, 2022
Summary
Compressed sensing (CS) effectively detects harmonic frequencies in electrical systems by taking fewer random samples than traditional methods. This technique revolutionizes data compression for accurate power quality analysis.
Area of Science:
- Electrical Engineering
- Signal Processing
- Data Science
Background:
- Harmonic frequencies distort fundamental voltage and current waves in electrical systems.
- Traditional methods like the Shannon-Nyquist theorem require extensive sampling.
- Compressed sensing (CS) offers a novel approach to signal acquisition and reconstruction.
Purpose of the Study:
- To investigate the efficacy of compressed sensing (CS) for detecting harmonic frequencies in electrical systems.
- To compare the performance of CS with standard power quality analyzer equipment.
- To demonstrate the benefits of CS in reducing sample requirements for harmonic analysis.
Main Methods:
- Utilized compressed sensing (CS) algorithm for signal processing.
- Developed an electronic prototype for random, incoherent data acquisition from a nonlinear load.
- Employed Matlab software for CS algorithm implementation and analysis.
- Compared results with data from commercial power quality analyzer equipment.
Main Results:
- CS algorithm successfully identified harmonic distortions (THD-I) and the number of harmonics.
- The CS prototype required significantly fewer samples compared to standard equipment.
- Analysis included error calculation, sample count, processing time, and maximum sampling frequency.
Conclusions:
- Compressed sensing (CS) provides an efficient and accurate method for detecting harmonic distortions in electrical systems.
- CS significantly reduces the number of samples required for harmonic analysis, offering advantages over traditional methods.
- The study validates the potential of CS for revolutionizing data compression in electrical signal processing.
Keywords:
compressed sensingconvex optimizationdictionary matrixharmonic distortionsignal reconstructionsparse signalMore Related Videos
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