Motor bearing fault is one of the common problems in motor equipment, and it is very important to accurately identify the type of fault to ensure the smooth operation of the equipment. However, the traditional fault diagnosis methods are often limited by noise interference and difficulty in feature extraction, which leads to low diagnostic accuracy. In order to improve the fault diagnosis rate of motor bearing, a method combining Bayesian optimization, PCA and BiLSTM is proposed to optimize the performance of the neural network model. First, the hyperparameters are optimized by Bayesian optimization, then the dimensionality is reduced by PCA algorithm to extract main features, filter noise and redundant information, and finally the fault features are classified and diagnosed by BiLSTM. Experimental results demonstrate that this method can effectively enhance the accuracy of motor bearing fault diagnosis and finally the accuracy of the test set reaches 99.07%, showing good robustness.
One of the essential technologies for wind energy utilization is adjusting the propeller's pitch. To address the challenges of PID-controlled variable pitch, ADRC is integrated into the variable pitch controller, resulting in the design of a speed-loop controlled variable pitch system. Beyond the rated wind speed, the system relies on speed as a feedback signal to maintain stable output power at the rated level. The parameters of the variable pitch controller are self-optimized using the Sparrow optimization algorithm. By utilizing MATLAB/Simulink simulation software, we conduct simulations on the variable pitch controller and its associated algorithm. The simulation results show that when the Sparrow algorithm is implemented in the controller, not only does optimization occur rapidly, but also the convergence speed is increased, further enhancing the system's anti-interference capabilities.
In order to reduce the cogging torque of IPMSM (Interior Permanent Magnet Synchronous Motor) symmetrical auxiliary slots are opened on the rotor surface. Firstly, the cogging torque is analyzed theoretically to find out the factors affecting the cogging torque of this motor. Conclusions are drawn from the finite element simulation analysis of the shape, position angle and radius of the auxiliary slot. Finally, the semi-circular auxiliary slot with position angle θ=38deg and radius r=0.3mm is used, and finally the waveforms of the cogging torque and no-load reverse potential of the motor are compared before and after cogging. The cogging torque can be weakened by 56.93% through the use of appropriate auxiliary slot parameters, as indicated by the simulation results, and the validity of the scheme is also confirmed.
The power of photovoltaic generation fluctuates greatly under the influence of weather conditions, so accurate prediction is beneficial to the management and dispatching of power grid. In this paper, pearson correlation coefficient method is used to analyze the factors affecting the photovoltaic power generation. the features with high correlation are selected as input variables. A hybrid kernel extreme learning machine model optimized by particle swarm optimization algorithm is proposed. The parameters in the model are optimized. Based on the model, the data was divided into four seasons. The prediction was carried out. The results showed that the PSO-HKELM model had a high power prediction accuracy for different seasons, so as to realize the effective prediction of photovoltaic power generation.
In this paper, the advantages and disadvantages of dual-motor synchronization control structure are analyzed. Combined with the application of wind power pitch control system, the cross-coupled dual-motor synchronization control structure and PID control strategy are selected. Based on the simulation platform of MATLAB, a wind power pitch control system based on dual-motor synchronization control is proposed under ideal conditions, and its feasibility is verified.
With the wide application of distributed generation technology, in order to meet the requirements of grid adaptability test of distributed system. A voltages control strategy based on Fuzzy-PI and quasi-proportional resonant (QPR) is proposed. Based on the traditional voltage outer loop PI control, fuzzy control is added to realize the real-time update when the system is disturbed and improve the stability of the system. It can improve the dynamic response and anti-interference ability of the system quickly when it is disturbed by external factors. MATLAB/Simulink was used for experimental simulation. According to the experimental results, the control strategy designed has a good tracking effect, which reduces the total harmonic distortion rate of the system and improves the steady-state performance of the system.
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