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Hydropower Generation Optimization and Forecasting Using PSO

  • D. Kiruthiga
  • T. AmudhaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)

Abstract

Deriving optimal operation rules for maximizing the hydropower generation in a multi-purpose reservoir is relatively challenging among the various other purposes such as irrigation and flood control. This paper addresses the optimal functioning of a multi-purpose reservoir for improving hydropower generation. Efficient bio-inspired optimization techniques were proposed for hydropower optimization and hydrological variables forecasting. A particle swarm optimization (PSO)-based methodology is proposed for maximal hydropower generation through optimal reservoir release policies of Aliyar reservoir, located in Coimbatore district of TamilNadu state in India. The reservoir release is also optimized by Global Solver LINGO and compared with PSO, and it is explored that PSO-based model is powerful in hydropower maximization. To handle the uncertain behavior of hydrologic variables, artificial neural networks model is also applied for forecasting reservoir inflow and hydropower generation. The results obtained through the optimal reservoir release patterns suggested in this work have shown that the Aliyar Mini Hydel Power Station has a huge potential in generating considerably more hydropower than the actual generation observed from the power plant over the past years.

Keywords

Hydropower optimization Bio-inspired methods Optimal release policies Hydropower forecasting Artificial neural network 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ApplicationsBharathiar UniversityCoimbatoreIndia

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