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Solving Optimal Power Flow Problem via Improved Constrained Adaptive Differential Evolution
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The optimal power flow problem is one of the most widely used problems in power system optimizations, which are multi-modal, non-linear, and constrained optimization problems. Effective constrained optimization methods can be considered in tackling the optimal power flow problems. In this paper, an ϵ-constrained method-based adaptive differential evolution is proposed to solve the optimal power flow problems. The ϵ-constrained method is improved to tackle the constraints, and a p-best selection method based on the constraint violation is implemented in the adaptive differential evolution. The single and multi-objective optimal power flow problems on the IEEE 30-bus test system are used to verify the effectiveness of the proposed and improved εadaptive differential evolution algorithm. The comparison between state-of-the-art algorithms illustrate the effectiveness of the proposed and improved εadaptive differential evolution algorithm. The proposed algorithm demonstrates improvements in nine out of ten cases.
Title: Solving Optimal Power Flow Problem via Improved Constrained Adaptive Differential Evolution
Description:
The optimal power flow problem is one of the most widely used problems in power system optimizations, which are multi-modal, non-linear, and constrained optimization problems.
Effective constrained optimization methods can be considered in tackling the optimal power flow problems.
In this paper, an ϵ-constrained method-based adaptive differential evolution is proposed to solve the optimal power flow problems.
The ϵ-constrained method is improved to tackle the constraints, and a p-best selection method based on the constraint violation is implemented in the adaptive differential evolution.
The single and multi-objective optimal power flow problems on the IEEE 30-bus test system are used to verify the effectiveness of the proposed and improved εadaptive differential evolution algorithm.
The comparison between state-of-the-art algorithms illustrate the effectiveness of the proposed and improved εadaptive differential evolution algorithm.
The proposed algorithm demonstrates improvements in nine out of ten cases.
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