Abstract
In this paper, the problem of tuning lead-lag power system stabilizer parameters of a multi machine power system is considered. This problem is formulated as an optimization problem, which is solved using Genetic Algorithms (GA). Simulation results show the effectiveness of the proposed Genetic Algorithms-based lead-lag power system stabilizer to damp the oscillation of multimachine system and work effectively under variable loading and fault conditions. The proposed GA-PSS is evaluated on the IEEE 14 bus test system as a multi machine electric power system.
Highlights
Power systems are inherently nonlinear and undergo an adaptive power system stabilizer (PSS) which considers the a wide range of transient conditions, which results in nonlinear nature of the plant and adapts to the changes in under damped low frequency speed as well as power the environment is required for the power system
In this paper, the problem of tuning lead-lag power system stabilizer parameters of a multi machine power system is considered. This problem is formulated as an optimization problem, which is solved using Genetic Algorithms (GA)
The proposed GA-PSS is evaluated on the IEEE 14 bus test system as a multi machine electric power system
Summary
In large interconnected power focus of the search to the areas of the systems, the damping torque of observed best performance. In order to obtain the damping torque to the generator generation Genetic operators rotor oscillations by controlling its manipulate the characters (genes) excitation using auxiliary that constitute the chromosomes stabilizing signal. Where, ∆ω is the speed deviation in p.u. mutation (Randy & Sue, 2004) This type of PSS consists of a washout filter, a dynamic Design methodology compensator. The dynamic order to optimization, the performance index is compensator is made up to two lead–lag stages with time considered as (3). The major point in the PSS design is to find the optimal values of KDC and T1–T4. An optimization method is used to find the best values of the proposed parameters. The optimum values of KDC and the time constants of T1–T4 are obtained by using GA
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