With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge‐driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision‐making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

Transformers are playing an increasingly significant part in energy conversion, transmission, and distribution, which link various resources, including conventional, renewable, and sustainable energy, from generation to consumption. Power transformers and their components are vulnerable to various operational factors during their entire life cycle, which may lead to catastrophic failures, irreversible revenue losses, and power outages. Hence, it is crucial to investigate transformer condition assessment to grasp the operating state accurately to reduce the failures and operating costs and enhance the reliability performance. In this context, comprehensive data mining and analysis based on intelligent algorithms are of great significance for promoting the comprehensiveness, efficiency, and accuracy of condition assessment. In this article, in an attempt to provide and reveal the current status and evolution of intelligent algorithms for transformer condition assessment and provide a better understanding of research perspectives, a unified framework of intelligent algorithms for transformer condition assessment and a survey of new findings in this rapidly-advancing field are presented. First, the failure statistics analysis is outlined, and the developing mechanism of the transformer internal latent fault is investigated. Then, in combination with intelligent demands of the tasks in each stage of transformer condition assessment under big data, we analyze the data source in-depth and redefine the concept and architecture of transformer condition assessment. Furthermore, the typical methods widely used in transformer condition assessment are mainly divided into rule, information fusion, and artificial intelligence. The new findings for intelligent algorithms are also elaborated, including differentiated evaluation, uncertainty methods, and big data analysis. Finally, future research directions are discussed.

Due to being completely exposed outdoors without backup, overhead contact lines (OCLs) are more vulnerable to external weather conditions. To perceive the weather-related failure risk and evaluate the associated potential risks, a multilayer Bayesian network (MLBN) approach is proposed for predictive probabilistic risk assessment for OCLs. More specifically, the weather-driven failure probabilities of OCLs are predicted using a first-layer Bayesian network (BN), including lightning strikes, windstorms, and fog-haze. Then, the weather-driven failure patterns of OCLs are investigated and captured, and furthermore, the second-layer BN is embedded to evaluate the risk propagation. Furthermore, the third-layer BN is implemented to calculate and quantify weather-driven risk consequences from perspectives of economic loss and social trust loss constrained by power outage time and train timetable. The three-layer BN models are built by connecting the nodes with causal relationships, avoiding complicated structure learning. The experiments on the actual OCL failure records and weather data demonstrate that the proposed MLBN approach can identify weather-involved risk activities and comprehensively analyze the weather-driven cumulative risks of OCLs from spatiotemporal perspectives. In addition, it is capable of giving reasonable guiding information for mitigating the operational risks of OCLs against external weather conditions.