A.K. AL-Othman and K.M. EL-Nagger (Kuwait)
Particle Swarm Optimization (PSO), Estimation, least error square (LS), Stability, Damping & Synchronizing Coefficient.
This paper presents an efficient digital methodology for estimating synchronizing and damping torque coefficients of a synchronous machine. These coefficients are used as a measurement of power system stability. The proposed algorithm is based on Particle Swarm optimization (PSO) technique that uses digital samples of the machine time responses to perform the estimation process. The problem is formulated as a dynamic estimation problem. The goal is to minimize the error square of the estimated coefficients. The method is tested using simulated case study. Results are reported and compared with those obtained using genetic algorithms (GA) and least error square (LS) estimation technique. The comparison shows that the proposed method can successfully estimate the required coefficients even in very critical stable cases where other methods may fail. It is also shown that the presence of bad data has no effect on the estimated results. The method can be considered as a very reliable and accurate tool for estimating the damping and synchronizing torque coefficient for power system stability assessment.
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