Transform Waveforms Into Signature Vectors for General-Purpose Incipient Fault Detection

Yang Weng,Qiushi Cui,Muhao Guo

doi:10.1109/tpwrd.2022.3151110

Yang Weng, Qiushi Cui + Show 1 more

Open Access

https://doi.org/10.1109/tpwrd.2022.3151110

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Abstract

Power system equipment presents special signatures at the incipient stage of faults. As more renewables are integrated into the systems, these signatures are harder to detect. If faults are detected at an early stage, economical losses and power outages can be avoided in modern power grids. Many researchers and power engineers have proposed a series of signature-specific methods for one type of equipment’s waveform abnormality. However, conventional methods are not designed to identify multiple types of incipient faults (IFs) signatures at the same time. Therefore, we develop a general-purpose IF detection method that detects waveform abnormality stemming from multiple types of devices. To avoid the computational burden of the general-purpose IF detection method, we embed the abnormality signatures into a vector and develop a pre-training model (PTM) for machine understanding. In the PTM, signal “words,” “sentences,” and “dictionaries” are designed and proposed. Through the comparison with a machine learning classifier and a simple probabilistic language model, the results show a superior detection performance and reveal that the training radius is highly related to the size of abnormal waveforms.

Highlights

F AULT analysis and prediction are with significant importance to distribution network operation and protection
If a general method that can detect all types of equipment anomalies is established, it will be helpful in understanding equipment anomalies coherently and comparably
This section first focuses on the demonstration of generalpurpose detection among multiple types of equipment

Summary

Introduction

F AULT analysis and prediction are with significant importance to distribution network operation and protection. Establishing effective fault analysis and prediction theory can help distribution network operators monitor distribution network systems and equipment health status. Many factors affect distribution network fault characteristics, such as equipment type, operating conditions, and noise, etc. The research on fault analysis and prediction methods [1]–[4] has been extensively studied. Distribution network fault analysis needs to detect anomalies in voltage and current waveforms [5]. If a general method that can detect all types of equipment anomalies is established, it will be helpful in understanding equipment anomalies coherently and comparably. Most of the present research proposes one algorithm for a specific device [6]–[9]. Various fault detection algorithms are developed for cables [6], [9], transformers [1], [10], [11], and lines [7]

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