Multi-Area State Estimation in Adversarial Environment

Electric power systems have changed rapidly these years with the integration of smart grid technologies as well as the development of control and computing techniques. After the deregulation of modern power systems, operation and control over a large-scale power system are distributed to regional transmission organizations (RTOs). However, under the cyber-physical environment, power systems undertake a lot of challenges and may also be vulnerable to malicious cyber attacks. These changes and challenges in the wide-area monitoring systems (WAMSs) suggest the need for the development on distributed multi-area monitoring, control and computing algorithms. Power system state estimation (SE) is a data processing algorithm that converts meter readings and other information into an estimate of a static state. SE serves as central function of Energy Management System (EMS) which communicates with Supervisory Control and Data Acquisition (SCADA) front end after data is obtained from remote terminal units (RTUs) and intelligent electronic devices (IEDs). The performance of downstream applications such as contingency analysis and economic dispatch will heavily depend on SE. In terms of system monitoring, we propose a class of false analog data injection attack that can misguide the system as if topology errors had occurred. Since calculating measurements given the state value is an underdetermined problem, an optimization method is proposed to conduct a reverse estimation based on the target topology and state to achieve the topology attack. Then we investigate the bad data detection algorithm in a multi-area environment and a detection algorithm based on area sensitivity is proposed to help locate bad data and possible false date injection attacks. In order to improve the computing efficiency of SE so that the computing time can catch up with SCADA rate, we propose a graph-based parallel computing algorithm for static SE. The proposed algorithm can help control center to achieve SCADA-rate SE and further help with the computing time of downstream applications