With the increasing use of nonlinear loads in power systems, the harmonic pollution becomes more and more serious. It is well known that fast Fourier transform (FFT) is a powerful tool for power signal harmonic analysis, but leakage effect, picket fence effect, and aliasing effect make FFT suffer from specific restrictions. In this paper, we proposed a new method for power signal harmonic analysis. The major components of this method are a frequency and phasor estimating algorithm, a finite-impulse-response comb filter, and a correction factor. We also combine other methods to enhance our performance, such as discrete Fourier transform and least square error (LSE) method. To verify this method, we provided the comparisons of this method with FFT.

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A new method for power signal harmonic analysis

A physics-informed deep learning approach for bearing fault detection

A novel approach to harmonic analysis for industrial power systems based on windowed fast Fourier transform is presented. First, the desirable sidelobe window (DSW) is constructed through self-convolution of the maximum decay sidelobe window (MDW) in the time domain. Then, the power system signal parameters, i.e., frequency, phase, and amplitude, are calculated by the DSW-based discrete phase difference correction algorithm. It has been shown that the spectral leakage and harmonic interferences can be reduced considerably by weighting samples with the DSW because of its good sidelobe behaviors, i.e., the peak sidelobe level and the sidelobe rolloff rate are proportional to the order of the window (or the times of self-convolution). The DSW-based discrete phase difference correction algorithm can be easily implemented in embedded systems due to the low computation burden. Both simulative and experiment data tests have been performed to verify the effectiveness and practicability of the proposed method.

Spectral Correction Approach Based on Desirable Sidelobe Window for Harmonic Analysis of Industrial Power System

Weak harmonic components can easily be obscured by nearby strong harmonics due to the spectral leakage in the power system. To obtain a window suitable for solving the problem, the triangular self-convolution window (TSCW) is constructed, with the triangular window being the parent window to take advantages of its narrow major lobe and simple computation. A TSCW-based phase difference correction algorithm for calculating the power system signal parameters, such as frequency, phase, and amplitude, is presented in this paper. The TSCW has a low peak sidelobe level, a high sidelobe rolloff rate, and a simple spectral representation. Leakage errors and harmonic interferences are thus considerably reduced by weighting samples with the TSCW. The TSCW-based phase difference correction algorithm is free of solving high-order equations, and the overall method can easily be implemented in embedded systems. The effectiveness of the method proposed was analyzed by means of computer simulations and practical experiments for multifrequency signals without noise and with quantization noise.

Triangular Self-Convolution Window With Desirable Sidelobe Behaviors for Harmonic Analysis of Power System

Spectral correction-based method for interharmonics analysis of power signals with fundamental frequency deviation

FFT has been applied to technologies of spectrum analysis widely. However, since the signal sampling is at random, and FFT suffers specific restrictions, some defects inevitably occur, which means that harmonic parameters do not show on the spectrum exactly. These defects result from frequency scales not matching signal characteristics. This paper discusses the optimization of spectrum analysis for signal harmonics. By means of scale fine-tuning, the frequency scales will match the signal characteristics. The method eliminates both of the picket-fence effect and the leakage effect; it makes the harmonic parameters show on the spectrum more accurately. This paper takes reasonable and practical evaluations to verify the theory.

The optimization of spectral analysis for signal harmonics

This paper affords the theorem, procedures, and evaluation of ability of adjustable spectrum for signals. By mean of adjusting frequency resolution and frequency shift, we can adjust frequency scale. The paper evaluates every process of computation to verify accurate computation and fast response of adjustable spectrum. Since frequency scale of this spectrum is adjustable, it makes the spectrum analysis of signal reach optimization. The method eliminates the picket-fence effect and the leakage effect by fast Fourier transform (FFT). Since the procedures remain as the characteristic of fast computation that this method can be used for real-time analysis.

Theorem and application of adjustable spectrum

This paper provides a complete method to solve the defects of spectrum analysis for discrete signals. Many researches had issued to deal with the defects of fast Fourier transform (FFT). Those methods may cause the characteristics of original signal altered, or solve this problem in part only. The defects of spectrum analysis cannot be solved efficiently and completely by those methods. These defects result from that those frequency scales cannot match with signal characteristics. This paper is based on the concept to establish a complete solution, divided into three steps: First, this paper analyzes the signal characteristics to be the basis of spectrum adjustment. A simple and accurate algorithm is used to find the frequency and amplitude of each component. Next, this paper finds optimum spectrum parameters to make the new spectrum match with signal characteristics. Finally, this paper takes the parameters to reanalyze the original signal. The method will make spectrum analysis reach optimization. Every procedure in this paper compares with traditional ones to prove its benefits. Moreover, the authors verify the theory feasible by analyzing actual signals.

Ta-Peng Tsao

Papers

The optimization of spectral analysis for signal harmonics

Theorem and application of adjustable spectrum

The optimization of spectrum analysis for digital signals