CEIES FUNDED PROJECTS

Power System State Estimation

State estimation is an essential part of modern power systems. State estimation assists in ensuring proper performance and operation, monitoring, control and optimization, contingency analysis, security assessment, bad data detection, and real time modelling of the system. The project deals with the static and dynamic state estimation of power systems in general. Linear and nonlinear adaptive filtering algorithms are being developed and implemented to address the problem of power system state estimation. The aim of the project is to implement and analyze existing state space based algorithms e.g. Kalman filter, extended Kalman filter, Fractional Kalman filter, State Space Least Mean Square etc.

In this project, state space model based linear and nonlinear estimation algorithms are being developed and analyzed for the state estimation tasks. More specifically, the group has developed state space least mean fourth (SSLMF) algorithm and a generalized family of state space least mean algorithms (SSLM). These developed algorithms are investigated in the state estimation of various linear and nonlinear systems. One of the aim of the project is to carry out the convergence and stability analysis of the developed algorithms along with the investigation of their computational complexity.

In this project, a novel variant of the LMS algorithm is developed by employing the concept of Fractional calculus. The aim of the work is to explore the impact of fractional derivative on the stochastic gradient based adaptive design. This project also investigated the development of time varying fractional exponent to deal with the adaptive scenarios. The developed algorithms are analyzed in their convergence and consequently stability bounds are derived.