Impact analysis of false data injection attacks on distribution system state estimation

Practical applicability of distribution system state estimation (DSSE) is limited by different factors, of which data quality may be considered as the most critical one. The increasing number of applied smart metering devices may lead to cybersecurity problems, since experienced hackers are able to exploit their vulnerabilities and may falsify data used as input parameters by the estimation. This paper aims to demonstrate how data manipulation may affect the performance of the state estimation (SE) by presenting the theoretical background of DSSE and bad data detection, and by analyzing the effects of corrupted data on the estimation results. Throughout PandaPower-based simulations, the detectability and the impact of false data injection attacks were measured both with and without the presence of photovoltaic generation. The results showed that when solar power generation was assumed, the attacks were detected with a lower probability, and unified attacks were found to have a bigger impact on the estimation results.