Dynamic Noise Reduction with Deep Residual Shrinkage Networks for Online Fault Classification.

Fault signals in high-voltage (HV) power plant assets are captured using the electromagnetic interference (EMI) technique. The extracted EMI signals are taken under different conditions, introducing varying noise levels to the signals. The aim of this work is to address the varying noise levels found in captured EMI fault signals, using a deep-residual-shrinkage-network (DRSN) that implements shrinkage methods with learned thresholds to carry out de-noising for classification, along with a time-frequency signal decomposition method for feature engineering of raw time-series signals.

Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder.

The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts the bushing at an unacceptable risk of failure. By monitoring relevant measurements we can trace any change that occurs and may indicate an anomaly in the equipment's condition.

Imaging Time Series for the Classification of EMI Discharge Sources.

In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques.

Entropy-Based Feature Extraction for Electromagnetic Discharges Classification in High-Voltage Power Generation.

This work exploits four entropy measures known as Sample, Permutation, Weighted Permutation, and Dispersion Entropy to extract relevant information from Electromagnetic Interference (EMI) discharge signals that are useful in fault diagnosis of High-Voltage (HV) equipment. Multi-class classification algorithms are used to classify or distinguish between various discharge sources such as Partial Discharges (PD), Exciter, Arcing, micro Sparking and Random Noise. The signals were measured and recorded on different sites followed by EMI expert's data analysis in order to identify and label the discharge source type contained within the signal.

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features.

Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features.

Impact of cryoablation catheter size on success rates in the treatment of atrioventricular nodal re-entry tachycardia in 160 patients with long-term follow-up.

Aims: To determine the efficacy and safety of cryoablation for the treatment of atrioventricular nodal re-entry tachycardia (AVNRT).

Methods And Results: We analysed the procedural characteristics, acute success, and recurrence rates of 160 consecutive patients treated with cryoablation for the AVNRT and followed up for 18 months. Mean procedural time was 119.