A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks

Cyber-attacks can tamper with measurement data from physical systems via communication networks of smart grids, which could potentially lead circuit breakers to trip creating a false fault in the absence of any faulty section. Accordingly, a fault diagnosis method should first determine whether a fault is actually present; however, current diagnosis methods of power systems struggle to achieve this goal. This paper proposes a novel method for fault diagnosis based on memory spiking neural P systems, which can distinguish false faults caused by measurement tampering attacks. The proposed method consists of three modules with the functions of suspicious fault section detection, measurement tamper attack identification and fault diagnosis, respectively. The suspicious fault section detection module is used to find candidate sections to reduce the fault diagnosis scope. The attack identification module is designed to identify whether a possibly faulty section is under the measurement tampering attack or not. The fault diagnosis module is devised to diagnose true faults, detecting both the fault sections and their corresponding fault types. To achieve the above goals, inspired by the memory recall mechanism of human brains, a memory spiking neural P system and a corresponding general matrix reasoning algorithm are proposed, which can synthetically utilize the remote measurements and remote signals via a new modeling mechanism. Finally, case studies based on the IEEE 14 and IEEE 118 bus systems verify the feasibility and effectiveness of the proposed method.This research was partially funded by grants from the National Natural Science Foundation of China (61703345, 51907097), the Chunhui Project Foundation of the Education Department of China (Z201980), the Open Research Subject of Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education (szjj2019-27) and the Young Scholars Reserve Talents Support Project of Xihua University. The participation of Luis Valencia was also supported in Spain by FEDER/Ministerio de Ciencia e Innovación C Agencia Estatal de Investigación/Project TIN2017-89842-P