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Jingfang Wang
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Two regional power system PSO PID Control Research Jingfang Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2015
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this paper, we propose a PID parameter tuning of particle swarm optimization for multi-objective optimization characteristics of two regional power system PID controller design.  By defining a comprehensive consideration of system output overshoot, rise time and the fitness function term steady-state error indicators, such as the ITAE, and in accordance with the performance requirements of the actual control system, appropriate weighting of each index item. Use with base and improved particle swarm algorithm for multi-objective optimization PID. PSO optimization algorithm has good global search ability and high convergence rate. You can respond to the PID controller tuning parameters directly from the output of the PID control system easily balance between speed and stability control systems. After using PSO-PID control in two regional power system ，the amount of overshoot in the step total response downs from 65% to 5%. Adjust time  downs from 12 seconds to 3 seconds.  DOI: http://dx.doi.org/10.11591/telkomnika.v13i1.6835 
Robust Pitch Detection Based on Recurrence Analysis and Empirical Mode Decomposition Jingfang Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2015
Publisher : Institute of Advanced Engineering and Science

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Abstract

A new pitch detection method is designed by the recurrence analysis in this paper, which is combined of Empirical Mode Decomposition (EMD) and Elliptic Filter (EF). The Empirical Mode Decomposition (EMD) of Hilbert-Huang Transform (HHT) are utilized tosolve the problem, and a noisy voice is first filtered on the elliptic band filter. The two Intrinsic Mode Functions (IMF) are synthesized by EMD with maximum correlation of voice, and then the pitch be easily divided. The results show that the new method performance is better than the conventional autocorrelation algorithm and cepstrum method, especially in the part that the surd and the sonant are not evident, and get a high robustness in noisy environment. DOI:  http://dx.doi.org/10.11591/telkomnika.v14i1.7216 
Noisy Signal Processing Research based on Compressed Sensing Technology Guojun Qin; jingfang wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i3.pp489-495

Abstract

Compressed sensing (CS) is a kind of sampling method based on signal sparse property, it can effectively extract the signal which was contained in the message. In this study, a new noise speech enhancement method was proposed based on CS process.  Voice sparsity is used to this algorithm in the discrete fast Fourier transform (Fast Fourier transform, FFT),and observation matrix is  designed in complex domain,  and the noisy speech compression measurement and de-noising are made by soft threshold,  and the speech signal is sparsely reconstructed and restored by separable approximation (Sparse Reconstruction by Separable Approximation, SpaRSA) algorithm, speech enhancementis improved.  Experimental results show that the denoising compression reconstruction is made for the noisy signal in  the algorithm, SNR margin is improved greatly, and the background noise can be more effectively suppressed .
Co-Authors Guojun Qin