As the core component of the power system, gas-insulated metal-enclosed switchgear (GIS) plays a crucial role in ensuring the safe and stable operation of the power system, so the fault diagnosis research of GIS has significant practical significance. At present, GIS fault diagnosis often adopts the insulation diagnosis method based on acousto-optic signals, but due to the complexity of the GIS operating environment, the ultra-high frequency signals in the environment will cause interference to the traditional partial discharge detection device. In order to solve the above problems, this paper builds a GIS fault diagnosis platform, effectively extracts the vibration signals under the fault condition through the vibration signal analysis method, and combines the short-time Fourier transform and convolutional neural network to diagnose the faults of the GIS by using the end-to-end fault pattern recognition model. The results show that the model has good accuracy and practicability for GIS fault diagnosis, and provides a new idea for the fault diagnosis of GIS equipment.
To solve the problem of wind-induced vibration of heliostat mirror in the solar thermal power generation system, it is necessary to build a wind-induced vibration monitoring and control system and use a vibration absorber to track and suppress wind-induced vibration. In this paper, the wind-induced vibration signal of the heliostat is collected, displayed, analyzed and extracted based on the LabVIEW platform. The Python deep reinforcement learning algorithm is used to build the vibration state and control state data sets, and the optimal control decisions are obtained through training. The excitation current is precisely controlled to control the stiffness of the magnetorheological elastomer in the vibration absorber, and then the vibration frequency of the dynamic vibration absorber is adjusted and controlled in real time, so that the time varying vibration signal of the mirror can be quickly tracked and suppressed.
Magnetorheological Elastomers (MREs) are a new type of intelligent magnetically controlled material consisting of a polymer matrix and magnetic particles. The modulus of elasticity of MREs varies with the external magnetic field strength due to the electromagnetic stress between the internal magnetic particle. However, the weak magnet-oenological effect of MREs limits their development. In order to improve the performance of the MREs, a two-dimensional model of MREs is developed based on the equivalent volume cell method, and the force-magnetic coupling analysis is carried out with COMSOL. In this paper, the effects of volume fraction, particle distribution, and magnetic field strength on the magnetostatic shear mechanical properties of MREs were investigated. The results show that: the stress distribution inside of MREs is mainly concentrated on the particles and the contact position between the particles and air. Increasing the magnetic field and the magnetic particle content can effectively improve the magneto-mechanical properties of MREs. Increasing the magnetic field from 0.5T to 1.7T, the magnetic shear modulus was increased by 8.81%. Increasing the particle volume fraction from 15% to 60%, the magnetic shear modulus was increased by 313.64%. Decreasing the particle distance in the chain contribute to the magneto-mechanical properties enhancement.
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