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International Conference on Power System Technology (POWERCON 2004)

作者： Hongesombut, K Mitani, Y Dechanupaprittha, S Ngamroo, I Pasupa, K Tippayachai, J Kyushu Inst Technol Dept Elect Engn Kitakyushu Fukuoka 8048550 Japan

ISBN: (纸本)0780386108

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 s-plane 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.

关键词： hierarchical genetic algorithm multiobjective design parallel micro genetic algorithm power system stabilizer tuning

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parallel micro genetic algorithm for constrained economic dispatch

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IEEE TRANSACTIONS ON POWER SYSTEMS 2002年第3期17卷 790-797页

作者： Tippayachai, J Ongsakul, W Ngamroo, I Thammasat Univ Elect Power Program Sirindhorn Int Inst Technol Pathum Thani 12121 Thailand Asian Inst Technol Inst Energy Problems Chem Phys Energy Program Pathum Thani 12120 Thailand

This paper proposes a parallel micro genetic algorithm (PMGA) for solving ramp rate constrained economic dispatch (ED) problems for generating units with nonmonotonically and monotonically increasing incremental cost (IC) functions. The developed PMGA algorithm is implemented on the thirty-two-processor Beowulf cluster with ethernet switches network on the systems with the number of generating units ranging from 10 to 80 over the entire dispatch periods. The PMGA algorithm carefully schedules its processors, computational loads, and synchronization overhead for the best performance. The speedup upper bounds and the synchronization overheads on the Beowulf cluster are shown on different system sizes and different migration frequencies. The proposed PMGA is shown to be viable to the online implementation of the constrained ED due to substantial generator fuel cost savings and high speedup upper bounds.

关键词： Beowulf cluster economic dispatch parallel micro genetic algorithm

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Implementation of advanced genetic algorithm to modern power system stabilization control

Implementation of advanced genetic algorithm to modern power...

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IEEE PES Power Systems Conference and Exposition

作者： Hongesombut, K Mitani, Y Kyushu Inst Technol Dept Elect Engn Kitakyushu Fukuoka 8048550 Japan

ISBN: (纸本)078038718X

This paper focuses on the use of advanced techniques in genetic algorithm for solving power system stabilization control problems. At the outset, the proposed hierarchical genetic algorithm (HGA) and parallel micro genetic algorithm (parallel micro-GA) are proposed and then they win be extended to solve two example problems. In the first example, these techniques are applied to simultaneously tune power system stabilizers (PSSs). The PSSs are optimally tuned to simultaneously shift the lightly damped and undamped oscillation modes to a stable zone in the s-plane and to self identify the appropriate PSS locations. In second example, parallel micro-GA is used to design the fuzzy logic controller (FLC) of STATCOM. The applied technique for designing a FLC helps us save time and does not require experts for designing. From these results, we can see that these advanced techniques provide enhanced versatility for solving problems in power system stabilization control.

关键词： power system stabilization advanced genetic algorithm hierarchical genetic algorithm parallel micro genetic algorithm

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