Abstract
The frequency variating source, linear generator, and switching devices lead to dynamic characteristics of the low-frequency conducted emissions within maglev on-board distribution systems. To track the time-varying feature of these disturbances, a joint time–frequency representation combined adaptive optimal kernel with compressed sensing technique is proposed in this paper. The joint representation is based on Wigner–Ville distribution, and employs adaptive optimal kernel to remove undesirable cross terms. The compressed sensing technique is introduced to deal with the tradeoff between cross-component reduction and auto-component smearing faced by kernel-function-based bilinear time–frequency representation. The time–frequency aggregation and accuracy performance of joint time–frequency representation is quantified using Rényi entropy and l1-norm. To verify its performance in disturbance signature analysis for distribution systems and primarily characterize the low-frequency conducted emissions of maglev, a maglev on-board distribution system experimental platform is employed to extract the low-frequency disturbances which pose threats to the controlling system. Comparison with Wigner–Ville distribution demonstrates the joint time–frequency representation method outperforms in tracking time-varying and transient disturbances of maglev on-board distribution systems.
Highlights
The electromagnetic suspension (EMS) maglev is being rapidly developed as an efficient transportation tool featuring low noise levels, stable suspension and superior speed, the reliability and safety of an operating EMS maglev is being considered worldwide [1,2,3]
To build a compressed sensing system for time–frequency representation, we describe the discrete-time–frequency representation windowed by adaptive optimal kernel (AOK) with a 2-D Fourier transform matrix F 2D as: TFAOK = F2D {TFAOK }·A·Kopt
As a result of the effective cross-term suppression, the joint TF representation shows 72.85% reduction in l1 -norm distance compared to Wigner–Ville distribution (WVD), which is a significant improvement in accuracy
Summary
The electromagnetic suspension (EMS) maglev is being rapidly developed as an efficient transportation tool featuring low noise levels, stable suspension and superior speed, the reliability and safety of an operating EMS maglev is being considered worldwide [1,2,3]. As the maglev train is composed of various electrical and electronic devices, the electromagnetic emissions between the power supply system and the controlling system are unavoidable, which pose a threat to the stable operation of the maglev train [4,5]. The electromagnetic disturbance characterization and diagnosis form a crucial part of the manufacturing and security assessment procedures. Time–frequency representation has drawn extensive attention as a better solution for disturbance characterization and diagnosis of various distribution systems [11,12,13,14,15,16]
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