Pipe defect sizing with matching pursuit based on modified dynamic differential evolution algorithm to recognize guided wave signal

2011 ◽  
Author(s):  
Shen Chuanjun ◽  
Yuemin Wang ◽  
Zhou Fangjun ◽  
Sun Fengrui
Keyword(s):  
Differential Evolution ◽  
Matching Pursuit ◽  
Differential Evolution Algorithm ◽  
Guided Wave ◽  
Wave Signal ◽  
Evolution Algorithm

Application of Novel Subspace Pursuit on Guided Wave Signal Recognition Based on Modified Differential Evolution Algorithm

2012 ◽  
Vol 546-547 ◽  
pp. 708-713
Author(s):  
Chuan Jun Shen ◽  
Yue Min Wang ◽  
Yan Liu ◽  
Feng Rui Sun
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Guided Wave ◽  
Center Frequency ◽  
Measured Signal ◽  
Signal Recognition ◽  
Détection Signal ◽  
Useful Signal ◽  
Evolution Algorithm ◽  
Detection Signal

A kind of novel subspace pursuit method is proposed to reduce the complexity of subspace pursuit. The modified differential evolution algorithm (MDEA) is applied to the modified subspace pursuit (MSP) by choosing chirplet function as match atoms. A steel pipe with holes is detected by guided wave and the measured signal is decomposed and reconstructed by MSP. The matched result is compared to the process result from MP with DEA. The SNR of the processed signal is improved obviously, and the defect echo can be identified easily. The matched parameters get by MSP and MP are compared and analyzed. The Wigner-Ville distribution (WVD) of the detection signal and its matched result are computed and compared. The WVD of the detection signal is enhanced after processed by MSP. The defect locations and the center frequency of the excitation signal are more exactly get from MSP than from MP. The computation time by MSP is a little longer than by MP. Therefore, MSP is a useful signal recognition and defect location approach for pipes guided wave NDT.

Modified differential evolution algorithm for semi-supervised fuzzy clustering

2009 ◽  
Vol 29 (4) ◽  
pp. 1046-1047
Author(s):  
Song-shun ZHANG ◽  
Chao-feng LI ◽  
Xiao-jun WU ◽  
Cui-fang GAO
Keyword(s):  
Differential Evolution ◽  
Fuzzy Clustering ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm

Parameter Estimation By Using An Improved Bee Memory Differential Evolution Algorithm (Ibmde) To Simulate Biochemical Pathways

2013 ◽  
Vol 8 (999) ◽  
pp. 1-6
Author(s):  
Chuii Khim Chong ◽  
Mohd Saberi Mohamad ◽  
Safaai Deris ◽  
Mohd Shahir Shamsir ◽  
Lian En Chai ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Biochemical Pathways ◽  
Evolution Algorithm

The Economic Dispatch of Wind Integrated Power System based on an Improved Differential Evolution Algorithm

2020 ◽  
Vol 13 (3) ◽  
pp. 384-395
Author(s):  
Haiqing Liu ◽  
Jinmeng Qu ◽  
Yuancheng Li
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Differential Evolution Algorithm ◽  
Bee Colony ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Dynamic Economic Dispatch ◽  
Integrated Power System

Background: As more and more renewable energy such as wind energy is connected to the power grid, the static economic dispatch in the past cannot meet its needs, so the dynamic economic dispatch of the power grid is imperative. Methods: Hence, in this paper, we proposed an Improved Differential Evolution algorithm (IDE) based on Differential Evolution algorithm (DE) and Artificial Bee Colony algorithm (ABC). Firstly, establish the dynamic economic dispatch model of wind integrated power system, in which we consider the power balance constraints as well as the generation limits of thermal units and wind farm. The minimum power generation costs are taken as the objectives of the model and the wind speed is considered to obey the Weibull distribution. After sampling from the probability distribution, the wind speed sample is converted into wind power. Secondly, we proposed the IDE algorithm which adds the local search and global search thoughts of ABC algorithm. The algorithm provides more local search opportunities for individuals with better evolution performance according to the thought of artificial bee colony algorithm to reduce the population size and improve the search performance. Results: Finally, simulations are performed by the IEEE-30 bus example containing 6 generations. By comparing the IDE with the other optimization model like ABC, DE, Particle Swarm Optimization (PSO), the experimental results show that obtained optimal objective function value and power loss are smaller than the other algorithms while the time-consuming difference is minor. The validity of the proposed method and model is also demonstrated. Conclusion: The validity of the proposed method and the proposed dispatch model is also demonstrated. The paper also provides a reference for economic dispatch integrated with wind power at the same time.

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