A secure forecasting-aided state estimation framework for power distribution systems against false data injection attacks

As a critical session of cyber-energy system operation, the state estimation (SE) is becoming more vulnerable to various malicious cyber-attacks like false data injection attacks (FDIAs), which can circumvent the detection mechanism and tamper with the data transmission to cause abnormal state deviation. Driven by this motivation, this paper tries to address the specific issue and propose a novel secure forecasting-aided SE (FASE) framework for the power distribution system with high penetration of renewable energy. The unbalanced distribution system FASE model is constructed first, and then the modeling process of FDIAs with incomplete system infor-mation considering limited attacking costs is illustrated. Afterward, a modified noise statistics estimator is combined with an unscented Kalman filter (UKF) to perform FASE and establish a historical state database be-forehand. Moreover, a novel security enhancement strategy for FASE against FDIAs based on sliding time win-dow (STW) theory and optimal state forecasting is innovatively proposed to correct the compromised measurements introduced by potential attacks. Comprehensive case studies are conducted on two modified distribution systems and the results demonstrate the effectiveness and credibility of the proposed secure FASE framework.

Automated summary

This paper proposes a novel secure forecasting-aided state estimation framework for power distribution systems with high penetration of renewable energy, addressing the impact of false data injection attacks on state estimation. By constructing a FASE model and combining the sliding time window theory and optimal state forecasting strategy, the compromised measurements introduced by potential attacks can be corrected effectively.