An Online Monitoring and Warning System for Power Tower Inclination Measurement based on Multi-algorithm Improved Grey Model

Electric poles and towers mainly play a supporting role in wind turbines and absorb vibrations. They are one of the main load-bearing components of wind turbines. Under the action of various forces and moments, they may tilt and settle. When the poles and towers are severely tilted or even collapsed, they will cause huge losses to the wind farm and even cause disasters. In this regard, this article conducts research on the online monitoring and early warning system for power tower inclination measurement based on a multi algorithm improved GM (Grey Model) model. The research results show that among the three algorithms, the trend of the prediction value of the improved GM model is more stable, and the average can be kept at about -0.398. However, the prediction value of wavelet denoising and Particle swarm optimization algorithm fluctuates greatly, and the average prediction value is −0.441 and −0.477, respectively. It can be seen that the algorithm in this article has a better effect in online monitoring and warning of power tower inclination. Through wireless communication, it is transmitted to the operation and maintenance staff and the backend service center, timely and effectively reminding the staff of the operation status of the tower. Through simulation experiments, its effectiveness, feasibility, and high accuracy have been verified, achieving real-time monitoring of the tilt status of ultra-high voltage transmission line towers.