Power system stabilizer tuning using swarm based optimization in ETAP

Power system stabilizer (PSS) can play an important role for stability of power systems. However, in many cases a PSS is not properly used and some cases it is kept out-of-service because of the proper knowledge of its applications and parameter values. Therefore, the author from ETAP develops Dynamic Parameter Estimation and Tuning (DPET) algorithm that perfectly tunes parameters of dynamic models including PSS. DPET can tune parameters of a PSS model where the deviation between the recorded data and the calculated output of the PSS model using the tuned parameters is minimum. It is assumed that transfer function model structures of PSS, typical values of that model parameters and field recorded data using smart sensor devices like PMUs are known for DPET. It is a complex constraint optimization problem because the above mentioned dynamic systems are highly non-linear with limiters (saturations) and are highly sensitive to parameters; they have multiple inputs/outputs (multi-objective) and there exists multiple solutions. In ETAP, the tuning response is accomplished by using an intelligent swarm based optimization approach which automatically adjusts the tunable settings/parameters in the model to make the controller response match that of field recorded data.