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.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.