A vibration monitoring signal processing method for shunt reactor

Shunt reactor is a common type of power compensation equipment, which is used to improve the stability and efficiency of power systems. However, due to the complexity of the operating environment, its vibration measurement may be affected by a variety of factors, including equipment faults and external disturbances. Therefore, accurate processing and analysis of vibration data is required. In this paper, vibration data obtained from shunt reactor are processed by combining Isolated Forest (IF)、Inertia Weight Particle Swarm Optimization (IWPSO) and K-Means clustering algorithms. First, the IF algorithm is utilized for anomaly detection and elimination to improve the data quality. Next, feature selection and dimensionality reduction are performed using the IWPSO algorithm to improve clustering effectiveness and efficiency. Subsequently, the processed data were input into the K-Means clustering algorithm to cluster. Finally, the results of the three algorithms are combined to comprehensively analyze and process the vibration data for identifying potential failure modes or anomalies. The main objective of this study is to improve the accuracy and efficiency of shunt reactor vibration data processing and to provide are liable data base for fault diagnosis and prediction. It can be concluded that the combination of IF, IWPSO and K-Means clustering algorithms can process vibration data more comprehensively and improve the accuracy and efficiency of data processing. The significance of this study is to provide a new methodology and technical support for vibration data processing and fault diagnosis, which is expected to have important application value in the engineering field.