Knowledge Graph Construction and Application of Power Grid Equipment

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.

Analysis and Design of the Project Risk Management System Based on the Fuzzy Clustering Algorithm

“…According to the Hamming distance method, the distance matrix can be obtained from the Hamming distance formula (2)…”

“…If a safety accident occurred during the construction process, it may lead to the extension of the project construction period or it may shutdown for rectication, so the research on the cost risk of the power grid project is becoming more and more important considering the new situations [1]. E ective and reasonable risk management can not only suppress the occurrence of risk events at the source but also prepare management strategies in advance for the risks that have not occurred; in order to achieve the purpose of reducing the probability of risk events, for the risk factors that have occurred, measures can be taken to act on the key factors of the risk event to achieve the purpose of reducing losses, as shown in Figure 1 [2].…”

Perspectives on Future Power System Control Centers for Energy Transition

“…AI applications deliver initial but promising results that include online security assessment in multiple renewable energy scenarios, fault location identification under different operating conditions, stability control, or others [59]- [61]. Additional applications support building advanced knowledge graphs [62] to provide overall an augmented understanding of the system and its operations. The enabling digital platform architecture in Fig.…”

Data switching method among heterogeneous power information system databases based on knowledge graph

“…If the probability is lower than the threshold, it is considered that there is a data collocation error in the data set. 13 Probability analysis is performed on the data in the data set, and the combination with a lower probability is regarded as an error. The error judgment mechanism is shown in Figure 4:…”