Synthesis of a novel adaptive wavelet optimized neural cascaded steam blow-off control system for a nuclear power plant. | [Proceedings of Pakistan Academy of Sciences • 2013]

Author(s):

1. Arshad H Malik: Department of Nuclear Instrumentation System, Pakistan Atomic Energy Commission, A-104, Block-B, Kazimabad, Model Colony, Karachi, Pakistan

2. Aftab A Memon: Department of Telecommunication Engineering, Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan

3. Feroza Arshad: Department of Management Information System, Pakistan Atomic Energy Commission, B-63, Block-B, Kazimabad, Model Colony, Karachi, Pakistan

Abstract:

In this paper, a new Multi-Input Multi-Output Adaptive Wavelet Neural Network based Steam Blow-Off System Controller (MIMO AWNN-SBOSC) is designed based on real time dynamic parametric plant data of steam blow-off system with conventional Single-Input Multi-Output Proportional plus Integral plus Derivative Controller (SIMO PIDC). The proposed MIMO AWANN-SBOSC is designed using three Multi-Input Single-Output Adaptive Wavelet Neural Network based Steam Blow-Off System Controllers (MISO AWNN-SBOSC). The hidden layer of each MISO AWNN-SBOSC is formulated using Mother Wavelet Transforms (MWT). Using nonlinear dynamic neural data of designed MIMO AWNN-SBOSC, a Multi-Input Multi-Output Adaptive Wavelet Neural Network based Steam Blow-Off System Model (MIMO AWNN-SBOSM) is developed in cascaded mode. MIMO AWNN-SBOSM is designed using two MISO AWNN-SBOSM. All training, testing and validation of MIMO AWNN-SBOSC and MIMO AWNN-SBOSM are carried out in MATLAB while all simulation experiments are performed in Visual C. The results of the new design is evaluated against conventional controller based measured data and found robust, fast and much better in performance.

Page(s): 177-188

DOI: DOI not available

Published: Journal: Proceedings of Pakistan Academy of Sciences, Volume: 50, Issue: 3, Year: 2013

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