With the increasing need to reduce maintenance costs, improve efficiency and increase generation from ageing power plants, it is essential to study the operating behaviour of equipment and design aspects of the power plant. The decision for refurbishment and uprating of complex systems like hydro generating stations is a difficult task, which involves a lot of financial implications. The inaccurate estimation for replacing existing equipment [17] may sometimes lead to significant and unnecessary expenditure.

This paper presents the methodology for decision-making in refurbishment and uprating of power plants using neural networks.

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