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.

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

Optimization of Wind Turbine Airfoil With Good Stall Characteristics by Genetic Algorithm Using CFD and Neural Network

2013 ◽  
Author(s):  
Mohammad H. Djavareshkian ◽  
Amir Latifi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Turbine ◽  
Stokes Equations ◽  
Computational Cost ◽  
Numerical Procedure ◽  
Optimization Method ◽  
Navier Stokes ◽  
Design Variables ◽  
Wind Turbine Airfoil

In this research, optimization of a wind turbine airfoil is done by Genetic Algorithm (GA) as optimization method, coupled with CFD (Computational Fluid Dynamics) and Artificial Neural Network (ANN). A pressure-based implicit procedure is used to solve the Navier-Stokes equations on a nonorthogonal mesh with collocated finite volume formulation to calculate the aerodynamic coefficients. The boundedness criteria for the numerical procedure are determined from Normalized Variable Diagram (NVD) scheme and the k–ε eddy-viscosity turbulence model is utilized. ANN has been used as surrogate model to reduce computational cost and time. Single objective and multi objective optimization of wind turbine airfoil have been performed and the results of optimization are presented. To reduce the number of design variables and producing a smooth shaped airfoil, modified Hicks-Henne functions are used. In this process, the Eppler E387 airfoil has been applied as the base airfoil. Angle of attack varies from 0 to 20 degrees and Reynolds number of the flow is 460000.

scholarly journals International Trade Path with Multi-Polarization based on Multidirectional Mutation Genetic Algorithm Enabled Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qing Zhang ◽  
Choo Wei Chong ◽  
Abdul Rashid Abdullah ◽  
Mass Hareeza Ali
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
International Trade ◽  
Rapid Development ◽  
Optimization Method ◽  
Small Range ◽  
Genetic Optimization ◽  
Second Development ◽  
Neural Network Algorithm ◽  
Catch Up

At present, the development speed of international trade cannot catch up with the economic development speed, and the insufficient development speed of international trade will directly affect the rapid development of national economy. In order to solve the problem of international trade, the overall optimal scheduling of trade vehicles and the optimal planning of trade transportation path are very important to improve enterprise services, reduce enterprise costs, increase enterprise benefits, and enhance enterprise competitiveness. The second development of the program is based on the programming interface provided by Baidu map. This paper proposes a neural network algorithm for genetic optimization of multiple mutations, which overcomes the shortcoming of traditional genetic algorithm population “ten” character distribution by mixing multiple coding methods, and enhances the local search ability of genetic algorithm by introducing a new large-mutation small-range search population. The example application shows that the optimization method can realize the optimization of international trade path under real road conditions and greatly improve the work efficiency of actual trade.

scholarly journals Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6905
Author(s):  
Ling Zhou ◽  
Qiancheng Zhao ◽  
Xian Wang ◽  
Anfeng Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Search Algorithm ◽  
Principal Component ◽  
Comparison Results ◽  
Auto Associative Neural Network

When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).

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