Sig2Vec: Dictionary Design for Incipient Faults in Distribution Systems

There are extremely high demands on power equipment fault detection and diagnosis at the equipment level. At the system level, the proportion of renewable energy in the grid is increasing year by year. The morphological structure of distribution grids is also very different from the past. Meanwhile, the new power electronics-based generation equipment and loads have a great impact on the fault characteristics of power equipment, resulting to a significant challenge on power equipment’s incipient fault (IF) detection. Therefore, this paper designs a dictionary for an easy understanding of distribution systems waveforms and for achieving accurate IF detection. To reduce the IF identification complexity, the electric signal waveforms are first translated into vectors through the Sig2Vec technique and is then assembled into a waveform dictionary. We deploy a classical pre-training model to classify IFs and show this model is suitable for the proposed dictionary. It is learned that the types of IFs directly affect the high-dimensional characteristics clusters in the proposed general-purpose IF detection method. Furthermore, the proposed method is compared with a machine learning classifier and a probabilistic language model. The results demonstrate the proposed method can effectively detect incipient faults through waveform understanding.

I. Introduction

The development of the power grids and the progress of society have put forward higher requirements on the operation of the power grids. With the development of computer, communication, and network technology, etc., it is important to strengthen the diagnosis and treatment of power grid faults. Improving the performance of the fault diagnosis system has both research value and practical significance and can be adopted through advanced intelligent technology. In the past, the State-owned Assets Supervision and Administration Commission of the State Council (SASAC) issued the Notice on Accelerating the Digital Transformation of State-owned Enterprises, which provided the direction for the digital transformation of state-owned enterprises, including clear requirements related to the digital upgrade of power grids. Therefore, it aligns with the timely development to strengthen the intelligent analysis and prediction of power grid faults through digital equipment and methods.