Adaptive hydrogenerator governor tuning with a genetic algorithm

Lansberry, J. E.; Wozniak, L.; Goldberg, D.E.

doi:10.1109/60.282490

Cited by 64 publications

(9 citation statements)

References 3 publications

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“…Another approach is to adapt the PID gains to counteract changes in plant parameters. Lansberry and Wozniak [9] investigated the use of a genetic algorithm as an adaptive mechanism to auto-tune the controller as changes occurred in the conduit time of the plant constant and the stiffness of the power grid. Bonnet and Wozniak [10] developed an adaptive speed controller for a hydro-generator based on recursive least squares identification to identify an open-loop transfer function for the plant, followed by root locus design of controllers that provided uniform closed loop response over a wide range of operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of gain scheduled predictive control in a nonlinear MIMO model of a hydropower station

Muñóz-Hernández

Graciós-Marin

Jones³

et al. 2015

International Journal of Electrical Power & Energy Systems

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Section: Introductionmentioning

confidence: 99%

Evaluation of gain scheduled predictive control in a nonlinear MIMO model of a hydropower station

Muñóz-Hernández

Graciós-Marin

Jones³

et al. 2015

International Journal of Electrical Power & Energy Systems

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“…Follow-on work since that time has shown its usefulness for optimization problems requiring the search of large and complex problem spaces from engineering design to combinatorial optimization to control [7] [8] [9].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

A hardware genetic algorithm for the traveling salesman problem on Splash 2

Graham

Nelson

1995

Field-Programmable Logic and Applications

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Abstract. With the introduction of Splash, Splash 2, PAM, and other reconfigurable computers, a wide variety of algorithms can now be feasibly constructed in hardware. In this paper, we describe the Splash 2 Parallel Genetic.Algorithm (SPGA), which is a parallel genetic algorithm for optimizing symmetric traveling salesman problems (TSPs) using Splash 2. Each processor in SPGA consists of four Field Programmable Gate Arrays (FPGAs) and associated memories and was found to perform 6.8 to 10.6 times the speed of equivalent software on a state-of-the-art workstation. Multiple processor SPGA systems, which use up to eight processors, find good TSP solutions much more quickly than single processor and software-based implementations of the genetic algorithm. The four-processor island-parallel SPGA implementation out performed all other SPGA configurations tested. We conclude noting that the described parallel genetic algorithm appears to be a good match for reconfigurable computing machines and that Splash 2's various interconnect resources and support for linear systolic and MIMD computing models was important for the implementation of SPGA.

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“…ANN and FLS have been successfully applied to various power system problems including PSS design [l1-16J. On the other hand, GA have been applied to various power system problems with promising results [17][18][19][20][21][22]. This paper introduces GA into the design of a power system stabilizer for single machine infinite bus system.…”

Section: Introductionmentioning

confidence: 99%

A Genetic-Based Power System Stabilizer

Abido

Abdel-Magid

1998

Electric Machines & Power Systems

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A Genetic-based Power System Stabilizer (GPSS) is presented in this paper to improve power system dynamic stability. The proposed GPSS parameters are optimized using Genetic Algorithms (GA). The main advantage of the proposed GPSS is that far less information than other design techniques is required without the need for linearization process. Time domain simulations of a synchronous machine subject to major disturbances are investigated. The performance of the proposed GPSS is compared with that of conventional lead-lag power system stabilizer (CPSS) to demonstrate the superiority of the proposed GPSS. The effect of parameter changes on the proposed stabilizer performance is also examined. The results show the robustness of the proposed GPSS and its capability to enhance the system damping over a wide range of operating conditions and system parameter variations.

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Adaptive hydrogenerator governor tuning with a genetic algorithm

Cited by 64 publications

References 3 publications

Evaluation of gain scheduled predictive control in a nonlinear MIMO model of a hydropower station

Evaluation of gain scheduled predictive control in a nonlinear MIMO model of a hydropower station

A hardware genetic algorithm for the traveling salesman problem on Splash 2

A Genetic-Based Power System Stabilizer

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