Yachen Tang

Yachen Tang, Haiyun Han, Xianmao Yu et al.

The intelligent question answering (IQA) system can accurately capture users' search intention by understanding the natural language questions, searching relevant content efficiently from a massive knowledge-base, and returning the answer directly to the user. Since the IQA system can save inestimable time and workforce in data search and reasoning, it has received more and more attention in data science and artificial intelligence. This article introduced a domain knowledge graph using the graph database and graph computing technologies from massive heterogeneous data in electric power. It then proposed an IQA system based on the electrical power knowledge graph to extract the intent and constraints of natural interrogation based on the natural language processing (NLP) method, to construct graph data query statements via knowledge reasoning, and to complete the accurate knowledge search and analysis to provide users with an intuitive visualization. This method thoroughly combined knowledge graph and graph computing characteristics, realized high-speed multi-hop knowledge correlation reasoning analysis in tremendous knowledge. The proposed work can also provide a basis for the context-aware intelligent question and answer.

Yachen Tang, Chee-Wooi Ten, Kevin P. Schneider

Tampering of metering infrastructure of an electrical distribution system can significantly cause customers' billing discrepancy. The large-scale deployment of smart meters may potentially be tampered by malware by propagating their agents to other IP-based meters. Such a possibility is to pivot through the physical perimeters of a smart meter. While this framework may help utilities to accurately energy consumption information on the regular basis, it is challenging to identify malicious meters when there is a large number of users that are exploited to vulnerability and kWh information being altered. This paper presents a reconfiguration switching scheme based on graph theory incorporating the concept of distributed generators to accelerate the anomaly localization process within an electrical distribution network. First, a data form transformation from a visualized grid topology to a graph with vertices and edges is presented. A conversion from the graph representation to machine recognized matrix representation is then performed. The connection of the grid topology is illustrated as an adjacency or incidence matrix for the following analysis. A switching procedure to change elements in the topological matrix is used to detect and localize the tampered node or cluster. The procedure has to meet the electrical and the temporary closed-loop operational constraints. The customer-level anomaly detection is then performed in accordance with probability derived from smart meter anomalies.

Yachen Tang, Tingting Liu, Guangyi Liu et al.

Accurate retrieval of the power equipment information plays an important role in guiding the full-lifecycle management of power system assets. Because of data duplication, database decentralization, weak data relations, and sluggish data updates, the power asset management system eager to adopt a new strategy to avoid the information losses, bias, and improve the data storage efficiency and extraction process. Knowledge graph has been widely developed in large part owing to its schema-less nature. It enables the knowledge graph to grow seamlessly and allows new relations addition and entities insertion when needed. This study proposes an approach for constructing power equipment knowledge graph by merging existing multi-source heterogeneous power equipment related data. A graph-search method to illustrate exhaustive results to the desired information based on the constructed knowledge graph is proposed. A case of a 500 kV station example is then demonstrated to show relevant search results and to explain that the knowledge graph can improve the efficiency of power equipment management.