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J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks (ICNN’95), Perth, Australia, 27 November-1 December 1995, Vol. 5, pp. 2753-2756.

has been cited by the following article:

• TITLE: Optimal Scheduling of Cascaded Hydrothermal Systems Using a New Improved Particle Swarm Optimization Technique

  AUTHORS: Kamal K. Mandal, Niladri Chakraborty

  KEYWORDS: Hydrothermal Systems, Cascaded Reservoirs, Self-Organizing Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients (SOHPSO_TVAC)

  JOURNAL NAME: Smart Grid and Renewable Energy, Vol.2 No.3, August 4, 2011

  ABSTRACT: Optimum scheduling of hydrothermal plants generation is of great importance to electric utilities. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for solving short-term economic generation scheduling of hydrothermal systems to avoid premature convergence. A multi-reservoir cascaded hydrothermal system with nonlinear relationship between water discharge rate, power generation and net head is considered here. The performance of the proposed method is demonstrated on two test systems comprising of hydro and thermal units. The results obtained by the proposed methods are compared with other methods. The results show that the proposed technique is capable of producing better results.