Power system transmission line tripping analysis using a big data platform with 3D visualization

Big Data technology has been introduced into power industry and seen breakthroughs in various aspects including system stability analysis, equipment fault detection, risk evaluation and etc. In order to improve the safety and stability of a city (Guangzhou, China) level power grid, a Big Data platform with 3D visualization is proposed in this study. Firstly, the lightning activities in Guangzhou area in recent years is analysed, and the operational status of power transmission lines and tripping records caused by lightning and storms are summarized. On this basis, the transient effects on voltage and current of the tripped transmission lines can be directly presented using 3D simulation system, thus the tripping events and the affected substations can be visualized. Moreover, Big Data mining technologies are applied to analyse the correlated factors of tripping events, such as the lightning and storms and other weather conditions, seeking for any potential links among the factors. Eventually, the proposed system can establish a correlative information database of lightning, tripping and other related factors for Guangdong power grid, and provide effective technical support for making protection principles of grid operation, carry out daily maintenance of lightning protection, and improve the operation level of the grid.