Data Integrity Attack in Dynamic State Estimation of Smart Grid: Attack Model and Countermeasures

A smart grid integrates advanced sensors, efficient measurement methods, progressive control technologies, and other techniques and devices to achieve safe, efficient and economical operation of the grid system. However, the diversified and open environment of a smart grid makes energy and information of the smart grid vulnerable to malicious attacks. As a representative cyber-physical attack, the data integrity attack has an extremely severe impact on the grid operation for it can bypass the traditional detection mechanisms by adjusting the attack vector. In this paper, we first present the attack strategy against dynamic state estimation of power grid in the perspective of adversary and formulate the data integrity attack detection problem that has the characteristic of sequential decision making as a partially observable Markov decision process. Then, a deep reinforcement learning-based approach is proposed to detect against data integrity attacks, which utilizes the Long Short-Term Memory layer to extract the state features of previous time steps in determining whether the system is currently under attack. Moreover, the noisy networks are employed to ensure effective agent exploration, which prevents the agent from sticking to the non-optimal policy. The principle of a multi-step learning is adopted to increase the estimation accuracy of Q value. To address the sparse rewards problem, the prioritized experience replay is proposed to increase training efficiency. Simulation results demonstrated that the proposed detection approach surpasses the benchmarks in the comparison metrics: delay error rate and false rate.

Automated summary

A deep reinforcement learning-based approach is proposed in this paper to detect data integrity attacks in a smart grid. The approach utilizes the state features of previous time steps to determine whether the system is under attack, and employs noisy networks and prioritized experience replay to improve detection performance.