| Abstract: |
| Low-frequency oscillations (LFOs) in traction power supply systems (TPSSs) frequently arise when multiple electric trains simultaneously raise their pantographs under the same power supply arm. This phenomenon is characterized by low-frequency fluctuations (2–8 Hz) in the envelope waveforms of traction network voltage and current. It can lead to operational issues such as insufficient current acquisition and difficulties in depot entry or exit, thereby adversely affecting railway operations. Given the time-varying nature of LFOs, timely and accurate detection is critical for implementing effective mitigation strategies, with faster detection enabling improved outcomes. This paper proposes a real-time detection algorithm for LFOs in AC traction networks that integrates signal preprocessing, spectral analysis, and parameter optimization. First, the voltage signal is processed using a low-pass filter to suppress high-frequency noise. Then, a fast Fourier transform (FFT)-based spectral estimation method is applied to extract frequency-domain features. Oscillation parameter identification is triggered when the identified signal amplitude exceeds predefined thresholds in the 42–48 Hz and 52–58 Hz bands. Subsequently, during the identification stage, an LFO atom dictionary is constructed based on the FFT pre-analysis results. Finally, the matching pursuit algorithm is employed to achieve fast and accurate extraction of LFO parameters. The proposed method is validated using both simulated and real-world measurement data. Experimental results confirm its effectiveness in detecting LFOs under noisy conditions, demonstrating high accuracy and computational efficiency. The approach provides valuable insights for the threshold selection of protection devices, thereby enhancing the stability and reliability of TPSSs. |
| Key words: Traction power supply system, low-frequency voltage oscillation, atomic decomposition, signal processing. |
| DOI：10.23919/PCMP.2024.000415 |
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| Fund:This work is supported by the National Natural Science Foundation of China (No. 52472420) and the Fundamental Research Funds for the Central Universities (No. 2682024CX081). |
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