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[1, 2006] 

Journal of Electrical Engineering, Vol 57, 1 (2006) 3-11

ARTIFICIAL NEURAL NETWORK BASED TURBINE FAST VALVING FOR ENHANCEMENT OF POWER SYSTEM TRANSIENT STABILITY

Ramnarayan Patel - Krishnan V. Pagalthivarthi

   Fast valving is one of the effective and economic means of improving the stability of a power system under large and sudden disturbances. Conventional schemes of fast valving generate a fixed valve stroke sequence for the control of turbine valves under transient conditions. A simple fixed valve control sequence cannot give optimum result for different fault conditions and loading levels, due to its poor adaptability. This paper presents an artificial-neural- network (ANN) based controller to govern the operation of the turbine control valves and intercept valves under different fault conditions. A quasi-optimal scheme based on the generator speed deviation and accelerating power generates the valve control sequences under different fault cases. These sequences are used to train the ANN-based controller for known fault cases. The controller then decides the valve timings for any unknown fault case based on the controller inputs. The simulation results show that an ANN-based controller has very good generalization capability. The results are compared with the conventional schemes of fast valving control. The trained ANN controller gives satisfactory stability performance for a variety of conditions.

Keywords: fast valving, transient stability, artificial neural networks, back propagation


[full-paper]


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