Abstract
The signal processing technique is one of the principal tools for diagnosing power quality (PQ) issues in electrical power systems. The Discrete Fourier Transform (DFT) is a frequency analysis technique used to process power system signals and identify PQ problems. However, the DFT algorithm may lead to spectral leakage and picket-fence effect problems for asynchronously sampled signals that contain harmonic, inter-harmonic, and flicker components. To resolve this shortcoming, a hybrid method for frequency estimation based on a second-level DFT approach and a frequency-domain interpolation algorithm to obtain the accurate fundamental frequency of a power system is proposed in this paper. This method uses a second-level DFT to compute the cosine and sine parts for the fundamental frequency components of the acquired signals. Then, a frequency-domain interpolation approach is adopted to determine the amplitude ratio for the cosine and sine parts of the system’s fundamental frequency. A set of mixed signals with harmonic, inter-harmonic, and flicker components with the fundamental frequency deviation is used. The evaluation results demonstrate the superiority of the new method over other approaches for assessing asynchronously sampled signals contaminated with noise, harmonic, inter-harmonic, and flicker components.
Highlights
Power system signal analysis is a key step in power quality (PQ) diagnosis
To verify the performance of the two-level Discrete Fourier Transform (DFT) and frequency domain interpolation hybrid method proposed in this paper, we performed numerical simulations based on the various voltage signals encountered by the actual power system in a MATLAB simulation environment
We used four frequency evaluation methods to compare all of the test results: the zero-crossing interpolation waveform reconstruction method (ZCIWR) [5], the frequencydomain interpolated (FDI) [8], the frequency-domain interpolated waveform reconstruction method (FDIWR) [9], and the method proposed in this paper
Summary
Power system signal analysis is a key step in PQ diagnosis. Effective extraction of power system signal features is helpful to understand the underlying physical nature of PQ’s phenomena, and to evaluate its health condition, thereby providing convincing evidences for diagnosis. In [5], using the waveform construction method to combine the Time Domain Frequency Estimation Algorithm with the Newton Interpolation method was proposed This approach can eliminate the leakage effect caused by the FFT (Fast Fourier Transform) calculation under asynchronous sampling conditions. In [6, 7], the authors proposed the digital filter and zero-crossing technique combination frequency evaluation method They broke down the original sample signals into two orthogonal component waveforms using cosine and sine filters.
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