Induction Motor Impedance Estimation Based on the Compressed Sensing Approach

This paper presents a novel approach for induction motor impedance estimation using the Compressed Sensing (CS) technique. Traditional impedance measurement methods often require high sampling rates and specialized equipment, making them costly and time-consuming, particularly when measuring over a wide frequency range. In contrast, the CS approach leverages signal sparsity to reconstruct impedance information using significantly fewer measurements, reducing hardware requirements while maintaining accuracy. The paper outlines the theoretical background of frequency impedance estimation and demonstrates the effectiveness of the CS-based method through simulations conducted in MATLAB and Simulink. A pseudo-random binary sequence is used as the test signal to ensure incoherence, enabling effective impulse response estimation. The proposed method is validated through a series of simulations, evaluating its performance at different sampling frequencies. Additionally, the study analyzes the influence of different PRBS signals on reconstruction quality, revealing minimal impact on the final estimation.