Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 12, Issue 4, Pages 3613-3623Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2021.3062700
Keywords
Perturbation methods; Power systems; Topology; Optimization; Network topology; Data models; Load flow; Power system optimization applications; deep reinforcement learning; vulnerability assessment; false data injection attack
Categories
Funding
- Singapore National Research Foundation, through the National Cybersecurity Research and Development Program [NRF2018NCR-NCR005-0001]
- Singapore National Research Foundation [NCR NRF-2018NCR-NSOE003-0001]
- NRF Investigatorship [NRF-I06-2020-0022, NRFNRFI06-2020-0001]
- Ministry of Education, Republic of Singapore [2017-T1-001-228 (RG92/17)]
- Nanyang Assistant Professorship from Nanyang Technological University, Singapore
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This paper investigates the vulnerability of deep reinforcement learning models for power system topology optimization under data perturbations and cyber-attacks. It proposes a vulnerability assessment method and indices to identify perturbations that may cause malfunction of DRL models, showing significant performance degradation under cyber-attacks and data perturbations.
This paper studies the vulnerability of deep reinforcement learning (DRL) models for power systems topology optimization under data perturbations and cyber-attack. DRL has recently solved many complex power system optimization problems. However, it has been practically proven that small perturbations of input data can lead to drastically different control decisions and induce danger. To evaluate and mitigate the security risks of DRL models in power systems, we propose a vulnerability assessment method for such DRL models under noisy data and cyber-attack. In specific, we assess the vulnerability of a DRL model in a way that perturbations are constructed to minimize the model's performance. Besides, several vulnerability indices are proposed to identify the characteristics of perturbations that may cause malfunction of DRL. Simulations on the 14-bus system and the IEEE 118-bus system for topology optimization are carried out to validate the effectiveness of the proposed vulnerability assessment method. The results show that the performance of DRL models for power systems can be significantly degraded under cyber-attack and data perturbations, especially when a proposed vulnerability index has abnormal values.
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