Performance optimization of hydroelectric power-plants using computational intelligence techniques

Due to the increasing complexity of industrial systems, transportation systems etc. traditional optimization techniques fail to provide a global solution of performance related issues. Most of the governments also tried to establish smart cities for betterment of human life. In such contexts, computational intelligence can be proved beneficial to predict the optimum solution and estimate the parameters. For this purpose, a novel stochastic model is developed for a hydroelectric power-plant with all constant failure and repair rates of all sub-systems to highlight the importance of computational techniques. In this study, availability evaluation of a hydroelectric power plant is made by considering various nature-inspired algorithms under constant failure and repair rates. The importance of computational intelligence techniques is observed in availability optimization of hydroelectric power plants due to their wide applicability. Availability optimization and parameter estimation of hydroelectric power plant is performed by using different swarm-based computational intelligence techniques. Grey Wolf Optimization, Dragonfly Optimization, Grasshopper Optimization and Whale Optimization Algorithms are used optimization at different iterations and population sizes. The numerical result of the system may be utilized for designing the hydroelectric power plants and planning the maintenance strategies.