scholarly journals Application of Probabilistic Neural Network in Fault Diagnosis of Wind Turbine Using FAST, TurbSim and Simulink

2015 ◽  
Vol 58 ◽  
pp. 186-193 ◽  
Author(s):  
Hasmat Malik ◽  
Sukumar Mishra
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Probabilistic Neural Network

Engine Fault Diagnosis using Probabilistic Neural Network

2021 ◽  
Author(s):  
Sheng Zhu ◽  
Min Keng Tan ◽  
Renee Ka Yin Chin ◽  
Bih Lii Chua ◽  
Xiaoxi Hao ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Probabilistic Neural Network

scholarly journals Research on Fan Operation Evaluation and Error State Judgment Relying on Improved Neural Network and Intelligent Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042005
Author(s):  
Xueyi Liu ◽  
Junhao Dong ◽  
Guangyu Tu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Performance Prediction ◽  
Optimization Method ◽  
Aerodynamic Performance ◽  
Evaluation Index ◽  
Genetic Optimization ◽  
Quantum Genetic Algorithm

Abstract Fan, as the most commonly used mechanical equipment, is widely used. In order to solve the problem of fan bearing fault diagnosis, this paper analyzes the main factors affecting fan spindle speed and power generation in operation. The input and output parameters of the performance prediction model are determined. The performance prediction model of wind turbine is established by using generalized regression neural network, and the smoothing factor of GRNN is optimized by comparing the prediction accuracy of the model. Based on this model, the sliding data window method is used to calculate the residual evaluation index of wind turbine speed and power in real time. When the evaluation index continuously exceeds the pre-set threshold, the abnormal state of wind turbine can be judged. In order to obtain wind turbine blades with better aerodynamic performance, a blade aerodynamic performance optimization method based on quantum heredity is proposed. The B é zier curve control point is used as the design variable to represent the continuous chord length and torsion angle distribution of the blade, the blade shape optimization model aiming at the maximum power is established, and the quantum genetic algorithm is used to optimize the chord length and torsion angle of the blade under different constraints. The optimization results of quantum genetic algorithm and classical genetic algorithm are compared and analyzed. Under the same parameters and boundary conditions, the proposed blade aerodynamic optimization method based on quantum genetic optimization is better than the classical genetic optimization method, and can obtain better blade aerodynamic shape and higher wind energy capture efficiency. This method makes up for the shortcomings of traditional fault diagnosis methods, improves the recognition rate of fault types and the accuracy of fault diagnosis, and the diagnosis effect is good.

scholarly journals Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

2020 ◽  
Vol 39 (6) ◽  
pp. 9027-9035
Author(s):  
Xi Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Rolling Bearing ◽  
Short Term ◽  
Swarm Optimization ◽  
Term Memory

During the COVID-19 pandemic, the maintenance of the wind turbine is unable to be processed due to the problem of personnel. This paper presents two neural network models: BP neural network and LSTM neural network combined with Particle Swarm Optimization (PSO) algorithm to realize obstacle maintenance detection for wind turbine. Aiming at the problem of gradient vanishing existing in the traditional regression neural network, a fault diagnosis model of wind turbine rolling bearing is proposed by using long-term and short-term memory neural network. Through the analysis of an example, it is verified that the diagnosis results of this method are consistent with the actual fault diagnosis results of wind turbine rolling bearing and the diagnosis accuracy is high. The results show that the proposed method can effectively diagnose the rolling bearing of wind turbine, and the long-term and short-term memory neural network still has good fault diagnosis performance when the difference of fault characteristics is not obvious, which shows the feasibility and effectiveness of the method.

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