scholarly journals Tuning of Proportional Derivative Control Parameters Base Particle Swarm Optimization for Automatic Brake System on Small Scale Wind Turbine Prototype

2015 ◽  
Vol 9 (2) ◽  
Author(s):  
Ali Musyafa ◽  
S. A. Pratama ◽  
Ronny D. Nuriyati
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Turbine ◽  
Particle Swarm ◽  
Brake System ◽  
Small Scale ◽  
Control Parameters ◽  
Swarm Optimization ◽  
Derivative Control

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

scholarly journals Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Chia-Hung Lin ◽  
Jian-Liung Chen ◽  
Zwe-Lee Gaing
Keyword(s):  
Particle Swarm Optimization ◽  
Probabilistic Neural Network ◽  
Particle Swarm ◽  
Fingerprint Recognition ◽  
Small Scale ◽  
Identification System ◽  
Swarm Optimization ◽  
Binary Images ◽  
Fractal Pattern ◽  
Fractal Patterns

This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO)-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP) and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD) from a two-dimensional (2D) image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN) as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Study on Identification of Damage to Wind Turbine Blade Based on Support Vector Machine and Particle Swarm Optimization

2015 ◽  
Vol 27 (3) ◽  
pp. 244-250 ◽  
Author(s):  
Guimei Gu ◽  
◽  
Rang Hu ◽  
Yuanyuan Li
Keyword(s):  
Support Vector Machine ◽  
Acoustic Emission ◽  
Particle Swarm Optimization ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Damage Identification ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization ◽  
Upper Computer

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270003/03.jpg"" width=""340"" />Classification results of SVM-PSO</div> In order to identify two failures of crack damage and edge damage to wind turbine blade, a damage identification system was designed by acoustic emission technique. This system took advantage of wireless technique for signal collection and transmission and upper computer for receiving and processing data. This system adopted acoustic emission sensor, NRF905 wireless transmission, upper computer designed by VB language, and the serial communication function of VB for data receiving. Data was firstly normalized after being received. Then, the energy features of data were abstracted by db wavelet. With the abstracted features, support vector machine model was established and verified, and the machine parameters were optimized by particle swarm optimization. Results show that the system is reliable in data collection and transmission, and the correctness of damage identification obviously increases by optimizing the support vector machine with particle swarm. The design provides method to monitor the status of rotating object, so this system can provide model base for subsequent studies.

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