Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightene...Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.展开更多
As electronic devices become increasingly complex, traditional fault diagnosis methods face significant challenges. Machine learning technologies offer new opportunities and solutions for electronic device fault diagn...As electronic devices become increasingly complex, traditional fault diagnosis methods face significant challenges. Machine learning technologies offer new opportunities and solutions for electronic device fault diagnosis. This paper explores the application of machine learning in electronic device fault diagnosis, focusing on common machine learning algorithms, data preprocessing techniques, and diagnostic model construction methods. Case study analysis elucidates the advantages of machine learning in improving diagnostic accuracy, reducing diagnosis time, and implementing predictive maintenance. Research indicates that machine learning techniques can effectively enhance the efficiency and precision of electronic device fault diagnosis, providing robust support for device reliability and maintenance strategy optimization. In the future, as artificial intelligence technology further develops, machine learning will play an increasingly important role in the field of electronic device fault diagnosis.展开更多
基金The financial support from the Program for Science and Technology of Henan Province of China(Grant No.242102210148)Henan Center for Outstanding Overseas Scientists(Grant No.GZS2022011)Songshan Laboratory Pre-Research Project(Grant No.YYJC032022022).
文摘Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.
文摘As electronic devices become increasingly complex, traditional fault diagnosis methods face significant challenges. Machine learning technologies offer new opportunities and solutions for electronic device fault diagnosis. This paper explores the application of machine learning in electronic device fault diagnosis, focusing on common machine learning algorithms, data preprocessing techniques, and diagnostic model construction methods. Case study analysis elucidates the advantages of machine learning in improving diagnostic accuracy, reducing diagnosis time, and implementing predictive maintenance. Research indicates that machine learning techniques can effectively enhance the efficiency and precision of electronic device fault diagnosis, providing robust support for device reliability and maintenance strategy optimization. In the future, as artificial intelligence technology further develops, machine learning will play an increasingly important role in the field of electronic device fault diagnosis.
