A new SPARLS algorithm for tuning power system stabilizer

Generators have to meet the change in real and reactive power demand of practical power system. The real power variations in the system are met out by the rescheduling process of the generators. But there is a huge trust to meet out the reactive power load demand. The excitation loop of the corresponding generator is adjusted with its electric limits to activate the reactive power of the network. To expedite the reactive power delivery, power system stabilizer (PSS) is connected in the exciter loop of the generator for various system conditions. In this paper, a new Sparse Recursive Least Square (SPARLS) algorithm is demonstrated to tune the power system stabilizer parameters to meet the vulnerable conditions. The proposed SPARLS algorithm makes use of expectation maximization (EM) updation to tune the PSS. A comparative study between the proposed SPARLS and RLS algorithm has been performed on single machine infinite bus system (SMIB). The simulation results obtained will validate the effectiveness of the proposed algorithm and the impact of stability studies of the power system operation under disturbances. The SPARLS algorithm is also used to tune the parameters of PSS to achieve quicker settling time for the system parameter such as load angle, field voltage and speed deviation. It is found that the SPARLS is a better algorithm for the determination of optimum stabilizer parameter.