Mechanical fault diagnosis of high-voltage circuit breakers using multi-segment permutation entropy and a density-weighted one-class extreme learning machine

Abstract

Condition monitoring for high-voltage circuit breakers (HVCBs) is of great significance for the safety of power grids. Based on machine-learning methods, most relevant studies have contributed significantly to improving the classification accuracy of known states. However, these studies have neglected the detection of unknown faults. In this study, a new one-class classifier, called a density-weighted one-class extreme learning machine (DW-OCELM), was proposed to detect unknown faults of HVCBs. The DW-OCELM determines the classification boundary considering data distribution by introducing the notion of density weight, such that samples located in low-density regions are more likely to be separated, improving detection performance. On this basis, a multi-class classifier was developed based on the homogeneous combination of multiple DW-OCELMs to classify known states. In addition, the proposed classifiers were trained based on multi-segment permutation entropy calculated from vibration signals. Experiments on a 35 kV HVCB demonstrated that the proposed methods outperformed other state-of-the-art techniques.

Robust fault diagnosis of a high-voltage circuit breaker via an ensemble echo state network with evidence fusion

“…Feature extraction and machine learning algorithms are investigated in the fault diagnosis literature. For feature extraction, sound [7], contact travel curves [8,9], electromagnet coil currents [10,11], and vibrations [12][13][14][15] are typical signals used for fault diagnosis. Since abundant structural state-related information is contained in vibrations, most fault diagnosis studies are based on vibration analysis.…”

NOx emissions estimation of boiler based on mutual information feature reconstruction and optimization of extreme learning machine

“…The extreme learning machine (ELM) is a single hidden layer feedforward neural network [11]. This model effectively overcomes the limitation of training based on error backpropagation algorithm that is prone to falling into local extremum [12]. Its output weights are generated by solving the generalized inverse matrix, which has the advantages of fast learning speed and few adjustment parameters [13].…”

A Co-simulation Model for the Hydraulic Operating Mechanism of High-Voltage Circuit Breaker

“…Many scholars focused on mechanical fault diagnosis and health assessment based on all types of machine learning and online monitoring techniques [5][6][7], which signi cantly contributed to improving its service reliability. These studies improved the maintenance effectiveness of hydraulic OM but cannot reduce its machinery fault from design source.…”