Wide area monitoring system operations in modern power grids: A median regression function-based state estimation approach towards cyber attacks

• Published in: Sustainable Energy, Grids and Networks Grids and Networks, 2023-06, Vol.34, p.101009, Article 101009
• Main Authors: Khalid, Haris M., Flitti, Farid, Mahmoud, Magdi S., Hamdan, Mutaz M., Muyeen, S.M., Dong, Zhao Yang
• Format: Article
• Language: English
• Subjects: Bad-data injection attacks; Cyber security; Cyber–physical systems; Dynamic vulnerability assessments (DVA); Exogenous; Generation mix; Median filter; Model prediction; Phasor measurement unit (PMU); Regression; Renewable energy integration (REI); Situational awareness; State estimation; Synchrophasor; Variable renewable energy resources (VRES); Wide area monitoring system (WAMS)
• ISSN: 2352-4677; 2352-4677
• DOI: 10.1016/j.segan.2023.101009

Modern power grid is a generation mix of conventional generation facilities and variable renewable energy resources (VRES). The complexity of such a power grid with generation mix has routed the utilization of infrastructures involving phasor measurement units (PMUs). This is to have access to real-time grid information. However, the traffic of digital information and communication is potentially vulnerable to data-injection and cyber attacks. To address this issue, a median regression function (MRF)-based state estimation is presented in this paper. The algorithm was stationed at each monitoring node using interacting multiple model (IMM)-based fusion architecture. An exogenous variable-driven representation of the state is considered for the system. A mapping function-based initial regression analysis is made to depict the margins of state estimate in the presence of data-injection. A median regression function is built on top of it while generating and evaluating the residuals. The tests were conducted on a revisited New England 39-Bus system with large scale photovoltaic (PV) power plant. The system was affected with multiple system disturbances and severe data-injection attacks. The results show the effectiveness of the proposed MRF method against the mainstream and regression methods. The proposed scheme can accurately estimate the states and evaluate the contaminated measurements while improving the situation awareness of wide area monitoring systems (WAMS) operations in modern power grids [Display omitted] •Modern Power Grids – Complex interconnected network with increased demand of power and interface to renewable energy integration.•PMUs deployed to have access to real-time grid information to enhance situational awareness.•Traffic of digital information and communication - Vulnerable to data-injection and cyber-attacks.•Cyber-security of network-based digital infrastructure of WAMS applications.•A median regression function-based state estimation using interactive multiple model-based fusion architecture.•An exogenous variable-driven representation of the state to depict the margins of state estimate in the presence of data-injection.