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

Interharmonics analysis of power signals with fundamental frequency deviation using Kalman filtering

This paper deals with sensor-based motion planning method for a robot arm manipulator operating among unknown obstacles of arbitrary shape. It can be applied to online collision avoidance with no prior knowledge of the obstacles. Infrared sensors are used to build a description of the robot's surroundings. This approach is based on the configuration space but the construction of the C-obstacle surface is avoided. The point automation is confined on some planes with square grids in the C-space. A path-searching algorithm based on square grids is used to guide the automation maneuvering around the C-obstacles on the selected planes. To avoid the construction of the C-obstacle surface, the robot geometry model is expanded, and the static collision detection method is used. Hence, the computation time is reduced and the algorithm can work in real time. The effectiveness of the proposed method is verified by a series of experiments.

Real-time motion planning for robot manipulators in unknown environments using infrared sensors

Studies on the PLL (phase locked loop) based interharmonic measurements have been considerably increased recently. The method proposed in this study is a hybrid method based on the frequency analysis. The proposed method presents solutions for the leading problems about the interharmonic measurement, such as fundamental frequency shifting, spectral leakage, and picket-fence effect, using digital PLL and adaptive notch filter. In addition, a new approach is presented to solve the multizero crossing problem encountered in the analysis of the harmonic-interharmonic. Both simulative and field data tests have been performed to verify the effectiveness of the proposed method.

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PLL Based Digital Adaptive Filter for Detecting Interharmonics

This paper offers a new method to measure the operational condition of the induction motor. The acoustic signal is measured for analysis. It can not only still be detected normally when other signals can't, but it also has the function of motor fault monitoring when it combines with a monitoring system completely. It makes signal sampling more convenient and easier, and also expands the functions and items of monitoring. The way of this method includes the following steps: firstly, the acoustic signal must be transformed into a spectrum by fast Fourier transform; secondly, the first band of the spectrum must be analyzed, and the peak frequency of this band calculated; thirdly, peak frequency-speed relation must be established; fourthly, the unknown speed with Lagrange polynomial must be calculated. From these steps, the method has a high degree of accuracy, and could detect induction motor speed and power.

Detection of induction motor operation condition by acoustic signal

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.

Rong-Ching Wu

Papers

Theorem and application of adjustable spectrum

The optimization of spectrum analysis for digital signals