A Crazy Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch
Leena Daniel1, Krishna Teerth Chaturvedi2
1Leena Daniel*, Electrical Engineering, UIT, RGPV, Bhopal, India.
2Dr. Krishna Teerth Chaturvedi, Electrical Engineering, UIT, RGPV, Bhopal, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1205-1213 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3614129219/2020©BEIESP | DOI: 10.35940/ijeat.B3614.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In power generating plants, the expenses on combustible fuel is extremely costly and the concept of ELD (Economic Load Dispatch) make possible to save the considerable portion of profits. Practically generators have economic dispatch problems in terms of non-convexity. These kinds of problem cannot be resolved by conventional optimization techniques because the complication escalates due to manifold constrained that require to be fulfilled in all operating conditions. Recently a Particle Swarm Optimization (PSO) algorithm stimulated by collective conduct of swarm can be applied effectively to translate the ELD problems. The classical PSO bears the difficulty of early convergence mainly when the space of search is asymmetrical. To overcome the trouble “Crazy PSO with TVAC (Time Varying Acceleration Coefficients)” is launched which improve the search ability of the PSO by rebooting the vector of velocity whenever diffusion or saturation locate inside and to employ a scheme of parameter automation to maintain correct equilibrium between global hunt and local hunt and also circumvent the congestion. This arrangement is developed crazy PSO with TVAC and also demonstrated on two different model experimental structures of three generation units and six generation units. The result acquired from proposed method is evaluate with classical PSO and Real coded genetic algorithm (RGA) and it is found to be superior. This method is mathematically simple, gives fast convergence and robustness to resolve the rigid optimization inconvenience.
Keywords: Particle swarm optimization, Time varying acceleration coefficient, Ramp rate limit.