Evaluation on model-driven knowledge graph and platform for grid operation and maintenance

Abstract

With the continuous expansion of the power grid, the forms of faults are becoming increasingly complex, with a wide range of impacts and long maintenance cycles, posing increasingly severe challenges for power grid operators. In the current power system (PS), due to system limitations, there is a large amount of data, and the current computing system is limited by hardware and computing power, making it difficult to satisfy the requirements of real-time computing, comprehensive analysis, and expansion. This paper analyzed the technical basis of the knowledge graph (KG) of power grid operation and maintenance (O&M), and clarified the key role of model driving in the current large-scale smart grid (SG), which can effectively improve the efficiency and reliability of data processing in the SG. This paper also analyzed the model-driven PG O&M KG platform, and analyzed the role of particle swarm optimization (PSO) algorithm in PG operation and fault maintenance. After applying the PSO algorithm in this article, in the experimental results section, the fault rate of Transformer 5 was 2.1% lower than that of Transformer 4. The knowledge mapping model of grid O&M and the particle swarm algorithm in this paper can significantly reduce the failure rate of the grid, which has wide extension value.