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Modern power systems suffer from a well‐known problem of low‐frequency oscillations. Flexible AC transmission systems devices are used to overcome this problem. The aim of this paper is to develop a particle swarm optimization (PSO) based supplementary damping control system design for thyristor control series compensator (TCSC).

The problem is formulated as an optimization problem with an eigenvalue‐based multi‐objective function. PSO is then used to find optimal set of controller parameters by minimizing the objective function.

The performance and robustness of the proposed approach is validated through small signal and large signal for different loading conditions of a multi‐machine power system.

The paper presents a novel PSO‐based control system design that exhibits robustness and excellent damping performance.

The aim of the paper is to develop a novel genetic algorithm (GA)‐based supplementary NeuroFuzzy damping control system for the unified power flow controller (UPFC).

The designed scheme employs a micro‐GA (μ‐GA) to avoid being trapped in a local minimum as opposed to the use of the classical back‐propagation technique. The scheme also uses the “Grand‐Parenting” technique for seeding the initial population to hasten the GA convergence speed. To further speed up the GA for solving the optimization problem, a parallel μ‐GA scheme is also used.

It has been discovered that a parallel μ‐GA scheme with three computers setup is approximately three times faster than the μ‐GA with a single computer node. Also when μ‐GA is integrated with the “Grand‐Parenting” technique for seeding the initial population, it would hasten the convergence speed. The control scheme exhibits strong robustness and excellent damping performance when tested on a multi‐machine power system.

Presentation of a novel NeuroFuzzy‐based UPFC that exhibits strong robustness and excellent damping performance.