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Resolvendo problemas de fluxo de potência ótimo DC através de uma rede de hopfield modificada

机译:通过改进的Hopfield网络解决最佳直流潮流问题

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Problems involving DC Optimal Power Flow have been solved by several neural approaches proposed in the literature. The approaches based on Hopfield models are those more used for mapping these types of problems. However, most of these models ignore the transmission system, which leave of taking into account important active power constraints. This fact can lead to solutions obtained by the networks to improper dispatch policies. Moreover, problems involving convergence speed and difficulty of guaranteeing feasible solutions are also found in some approaches. This paper presents a modified Hopfield network to solve DC Optimal Power Flow in an efficient way. The transmission system in this model is represented by linear load flow equations and constraints on active power flows. The internal parameters of the modified Hopfield network are computed using the valid-subspace technique, which guarantees the obtainment of feasible solutions (equilibrium points) by the developed network. Simulation results and a sensitive analisys involving IEEE test systems are presented to illustrate the efficiency of the proposed approach.
机译:文献中提出的几种神经方法已经解决了涉及直流最优潮流的问题。基于Hopfield模型的方法更多地用于映射这些类型的问题。但是,大多数模型都忽略了传输系统,而忽略了重要的有功功率限制。这个事实可能导致网络获得不正确的分发策略的解决方案。此外,在某些方法中还发现了涉及收敛速度和难以保证可行解决方案的问题。本文提出了一种改进的Hopfield网络,以有效地解决直流最优潮流问题。该模型中的传输系统由线性潮流方程和对有功潮流的约束表示。修改后的Hopfield网络的内部参数是使用有效子空间技术计算的,这保证了发达网络获得可行解(平衡点)的能力。给出了仿真结果和涉及IEEE测试系统的敏感analisys,以说明所提出方法的效率。

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