This paper presents a state-of-the-art review on machine learning (ML) based intelligent
diagnostics that have been applied for partial discharge (PD) detection, localization, and …

Power cables, the most critical component of the power system, must be extremely reliable
in order to avoid revenue losses due to premature failure. The dielectric properties of cable …

In this article, a hypersensitive multispectral partial discharge (PD) optical sensor array was
developed, by which the optical pulses in seven independent bands can be acquired …

In this paper, the discrete wavelet transform (DWT) and a chaotic system were combined
with a convolutional neural network (CNN) and applied to the diagnosis of insulation faults …

In this study, an inter‐turn fault diagnosis method is proposed based on deep learning
algorithm. 12‐channel data is obtained in MATLAB/Simulink as the time‐domain monitoring …

The convolutional neural network (CNN) achieves excellent performance in pattern
recognition owing to its powerful automatic feature extraction capability and outstanding …

Recently, numerous data‐driven fault diagnosis methods have been developed, and the
tasks involving the same distribution of training and test data have been well solved …

In industrial environments, the partial discharge (PD) identification process can be limited by
the simultaneous presence of PD sources and electrical noise. Therefore, it is advisable to …

Partial discharge (PD) detection is crucial to diagnose the condition of the insulation system
of high-voltage equipment. Despite the existence of many different methods to monitor the …

In this paper, an algorithm for feature extraction and classification of high-pressure gas
insulation system defects based on statistical analysis of time-domain parameters of partial …