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Abstract--In order to achieve the optimal design based on some specific criteria by applying conventional techniques, sequence of design, selected location of PSSs are critical involved factors. This paper presents a method to simultaneously tune PSSs in multimachine power system using hierarchical genetic algorithm (HGA) and parallel micro genetic algorithm (parallel micro-GA) based on multiobjective function comprising the damping ratio, damping factor and number of PSSs. First, the problem of selecting proper PSS parameters is converted to a simple multiobjective optimization problem. Then, the problem will be solved by a parallel micro GA based on HGA. The stabilizers are tuned to simultaneously shift the lightly damped and undamped oscillation modes to a specific stable zone in the splane and to self identify the appropriate choice of PSS locations by using eigenvalue-based multiobjective function. Many scenarios with different operating conditions have been included in the process of simultaneous tuning so as to guarantee the robustness and their performance. A 68-bus and 16-generator power system has been employed to validate the effectiveness of the proposed tuning method.Index Terms-Hierarchical genetic algorithm, multiobjective design, parallel micro genetic algorithm, power system stabilizer tuning.

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Abstracl-This paper proposes an enhanced tabu search (ETS) algorithm for solving ramp rate constrained economic dispatch (ED) problems with linear decreasing and decreasing staircase incremental cost (IC) functions. To determine the global optimal solution, ETS uses a new hinary coding design representing the power outputs of generating units at either the highest or lowest possible power outputs except the reference unit output which is used to satisfy the power balance constraint. ETS is tested and compared to the normalized binary coding tabu search (NBTS), micro genetic algorithm (MGA), merit order loading methods (MOLs), MGA based on migration and MOL solutions (MGAM-MOL), simulated annealing (SA), and combined genetic algorithm and SA (CGSA). The result of the proposed ETS is shown to be viable to the online implementation of the constrained ED due to substantial generator fuel cost savings and fast computational times. Index Terms-Economic dispatch (ED), Kuhn-'hcker theorem (KT), tahn search (TS), genetic algorithm (GA), simulated annealing (SA), merit order loading (MOL).