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Related Experiment Video

Updated: Aug 9, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

A new method for axis adjustment of the hydro-generator unit using machine learning.

Jie Cao^1,2, Yang Li³, Zhaoyang Qu³

¹School of Computer Science, Northeast Electric Power University, Jilin, China. caojie@neepu.edu.cn.

Scientific Reports

|February 22, 2023

Summary

This study introduces an improved grey prediction model and swarm intelligence optimization for hydro-generator axis adjustment. The new method enhances accuracy and efficiency in fault elimination for stable hydropower station operation.

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Area of Science:

Engineering
Electrical Engineering
Mechanical Engineering

Background:

Hydro-generator unit stability is crucial for power quality and efficiency in hydropower stations.
Axis failure is a common issue that can disrupt operations and necessitate corrective measures.
Existing axis adjustment technologies may face limitations in accuracy and efficiency.

Purpose of the Study:

To propose a novel and effective method for hydro-generator unit axis adjustment.
To address the limitations of traditional grey prediction models and optimization algorithms.
To improve the accuracy and efficiency of axis adjustment for fault elimination.

Main Methods:

Pre-processing of oscillating axis swing sequences using sequence acceleration translation and mean value transformation.
Establishing an improved grey prediction model for axis net total swing using e1 and e2 factor transformation.
Employing the flamingo search algorithm for optimizing axis adjustment orientation by finding the maximum sine function value.

Main Results:

The improved grey prediction model overcomes the limitations of GM(1, 1) regarding monotone sequences.
The flamingo search algorithm avoids local optima, enhancing search efficiency.
Simulation examples demonstrate a significant improvement in the accuracy of axis adjustment.
The proposed method substantially increases the efficiency of azimuth search for axis adjustment.

Conclusions:

The developed method offers a more accurate and efficient approach to hydro-generator axis adjustment.
This technique effectively addresses axis failure issues, ensuring stable hydropower station operation.
The integration of improved grey prediction and swarm intelligence optimization presents a promising advancement in the field.