Abstract
Recent development of artificial intelligence (AI) technology enquires the traditional power grid system involving additional information and connectivity of all devices for the smooth transit to the next generation of smart grid system. In an AI-enhanced power grid system, each device has its unique name, function, property, location, and many more. A large number of power grid devices can form a complex power grid knowledge graph through serial and parallel connection relationships. The scale of power grid equipment is usually extremely large, with thousands and millions of power devices. Finding the proper way of understanding and operating these devices is difficult. Furthermore, the collection, analysis, and management of power grid equipment become major problems in power grid management. With the development of AI technology, the combination of labeling technology and knowledge graph technology provides a new solution understanding the internal structure of a power grid. As a result, this study focuses on knowledge graph construction techniques for large scale power grid located in China. A semiautomatic knowledge graph construction technology is proposed and applied to the power grid equipment system. Through a series of experimental simulations, we show that the efficiency of daily operations, maintenance, and management of the power grid can be largely improved.
Highlights
In the era of big data and artificial intelligence (AI), a large number of data from various sources are constantly generated from different perspectives of human lives [1,2,3]
A labeling system refers to a summary of existing features of a specific group of objects, where in the current context it is referring to the grid equipment devices
In the power grid system, if you want to know whether a device failure will affect a certain key device, the traditional relational database searches for the relationship path between the two devices in advance, making the whole query process slow and difficult to edit
Summary
In the era of big data and artificial intelligence (AI), a large number of data from various sources are constantly generated from different perspectives of human lives [1,2,3]. Various AI powered service technologies are proposed utilizing the existing big data to facilitate the current sustainable smart city design, e.g., the development of smart grid [4], smart building [5], smart communication systems [6], and many more [7,8,9]. Such kinds of AI powered service technologies include Internet of ings (IoT) [10], cloud computing [11], edge computing [12], the fifth-generation (5G) mobile communication network [13], sensing networks [14], social networks [15, 16], big data recommendation systems [17], etc. According to the experimental results, the proposed search algorithm is two to three times faster than existing algorithms
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