A Genetic Algorithm Solution to the Governor-Turbine Dynamic Model Identification in Multi-Machine Power Systems

Speed governors are key elements in the dynamic performance of electric power systems. Therefore, accurate governor models are of great importance in simulating and investigating the power system transient phenomena. Model parameters of such devices are, however, usually unavailable or inaccurate, especially when old generators are involved. Most methods for speed governor parameter estimation are based on measurements of frequency and active power variations during transient operation. This paper proposes a genetic algorithm based optimization technique for parameter estimation, which makes use of such measurements. The proposed methodology uses a real-coded genetic algorithm. The paper estimates the parameters of all system generators simultaneously, instead of every machine independently, which is fully in line with the interest to treat the electric power system as a whole and study its comprehensive behaviour. Moreover, the methodology is not model-dependent and, therefore, it is readily applicable to a variety of model types and for many different test procedures. The proposed methodology is applied to the electric power system of Crete and the results demonstrate the feasibility and practicality of this approach.