With the integration of large-scale renewable energy and the implementation of demand response

the power system operation scenarios have become increasingly complicated and variable

leading to new requirements and challenges in power system security operation. Therefore

based on artificial intelligences

a hybrid model and data driven platform for power system security feature selection and knowledge discovery is established in the Guangdong Power Grid in China

to keep power system in secure

stable and economic conditions. Firstly

the definitions of power system security feature and knowledge are put forward

the concepts for the hybrid model and data driven platform are described

and the methods for reducing error are analyzed. Secondly

parallel techniques for the platform are discussed. Thirdly

the software and hardware architecture of the platform is designed. Finally

the application results in the Guangdong Power Grid are demonstrated

which show that: (1)from the perspective of rule-making

the platform transfers the pattern that conservative operation rules should be made by expert offline to the pattern that specific operation rules can be discovered by artificial intelligences online; (2)from the perspective of rule-application

the platform transfers the pattern that operation rules used online should be determined by operators artificially to the pattern that the operation rules can be determined by artificial intelligences automatically.