Growing system size and complexity along with the large amount of data provided by phasor measurement units (PMUs) are the drivers for accurate state estimation algorithms for online monitoring and operation of power grids. State estimation is the process of estimating unknown state variables in a power grid, which are then used for energy management system (EMS) functions in the control centre. Even with modern computing power, the large volume of computation in the state estimation process is highly time and memory intensive, and a serious bottleneck for online operation and control of the grid.

Furthermore, the deployment of new smart grid technologies in communication and smart metering technologies brings new challenges to the state estimation problem. In addition to failure of physical infrastructure, the smart grid is also sensitive to cyber-attacks on its communication layer. Intelligent cyber-attacks can be designed to be unobservable by the traditional bad data detector. Such attacks can significantly impact the decision making process in control centres and can result in potentially catastrophic outcomes.

The focus of this research is to design parallel computational algorithms to accelerate dynamic state estimation for both static and dynamic states in large-scale power networks. Moreover, a stochastic model is proposed to guard the system against intelligent cyberattacks. Utilizing the massively parallel architecture of graphic processors and using detailed models of power system components, the proposed research achieved the required accuracy as well as the computational speed-up in the results.