Model-Aided Approach for Intelligent Fault Detection System for SF6 High-Voltage Circuit Breaker With Spring Operating Mechanism

Supervised machine learning (ML) methods have been increasingly applied in the fault detection of components. However, they relied on the data set and data acquisition under faulty conditions, which is time-consuming with the risk of component damage. This paper presents a model to rectify this point through simulation of the spring drive operating mechanism as the main origin of major failures for a real case, i.e., a 72.5 kV, SF6, EDF high voltage circuit breaker (HVCB). The model is simulated using mechanical analysis software such as ADAMS and SOLIDWORKS to capture the travel curve (TC) as the primary signal for diagnosing HVCB mechanical faults. The model has been verified against the measured TC. Subsequently, a comprehensive database is generated consisting of various mechanical defects in addition to the healthy operation of HVCB. Afterward, different ML algorithms have been trained and compared. The results are indicated that support vector machines with a Gaussian kernel and decision tree are the most effective models for detecting operating mechanisms in trip/close operations. In addition to solving the data collection problem, intelligent classification methods were employed in this research to increase fault detection accuracy.

Automated summary

This paper presents a model that simulates a real case to solve the problem of data collection in fault detection and validates the effectiveness of the model. The results of the study indicate that support vector machines and decision trees are the most effective models for detecting operating mechanisms.