Euclidean distance-based multi-objective particle swarm optimization for optimal power plant set points

Autor: Hanan Kamal, Mohamed Sayed, Sawsan Morkos Gharghory
Rok vydání: 2015
Předmět:
Zdroj: Energy Systems. 7:569-583
ISSN: 1868-3975
1868-3967
Popis: Generating the power required for load demand in power plants under different operating conditions is an important issue. In addition, satisfying the multiple conflicting objectives such as the optimal load tracking, life extension of major equipment, reduction of fuel consumption and environmental impact from pollutant emission needs the optimal mapping of power-pressure set points to be realized. Unfortunately, the fixed nonlinear mapping used for optimal set points generation does not satisfy the multi conflicting goals of power plant under different operating conditions. This paper proposes Pareto solutions based on Euclidean distance for multi-objective particle swarm optimization that is named EMOPSO. The proposed technique is based on selecting non dominated global best and local best for each particle in the swarm with minimum Euclidean distance in the search space. The ability of the proposed approach to achieve the optimal power pressure set points and to capture the true Pareto front is investigated through its applicability for boiler turbine power plant. The simulation results prove the superiority of the proposed algorithm in achieving the optimal tradeoff between the essential requirements and the conflicting objectives of the power plant and demonstrate the great impact of the algorithm on the convergence and diversity of the Pareto front optimality.
Databáze: OpenAIRE
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