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首页> 外文会议>2014 IEEE Innovative Smart Grid Technologies - Asia >Intelligent power management strategy of hybrid distributed generation system using artificial neural networks
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Intelligent power management strategy of hybrid distributed generation system using artificial neural networks

机译：基于人工神经网络的混合分布式发电系统智能电源管理策略

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摘要

This paper presents the application of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural network for managing active and reactive powers of distributed generation (DG) units in distribution systems. A two-stage intelligent technique is proposed using an iterative interior-point algorithm optimization procedure for collecting the optimal power settings of several DG units in the first stage. In the second-stage, the optimal data obtained from the optimization process are then used for training the MLP and RBF neural networks which will then predict the next time step of active and reactive power references of each DG unit for online application. The results show that the MLP network has the ability in predicting the optimal power reference of the DG units with small errors compared to the RBF network. However, the RBF network converges faster compared to the MLP network.
机译：本文介绍了多层感知器（MLP）和径向基函数（RBF）神经网络在配电系统中管理分布式发电（DG）单元的有功功率和无功功率的应用。提出了一种采用迭代内点算法优化程序的两阶段智能技术，用于收集第一阶段中多个DG机组的最佳功率设置。在第二阶段，将从优化过程中获得的最佳数据用于训练MLP和RBF神经网络，然后将预测每个DG单元有功和无功功率参考的下一个时间步，以进行在线应用。结果表明，与RBF网络相比，MLP网络能够以较小的误差预测DG单元的最佳功率参考。但是，与MLP网络相比，RBF网络的收敛速度更快。

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