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has been cited by the following article:

• TITLE: Multi-Deployment of Dispersed Power Sources Using RBF Neural Network

  AUTHORS: Yaser Soliman Qudaih, Takashi Hiyama

  KEYWORDS: Dispersed Power Sources Deployment, RBF Neural Network, Power Losses Reduction

  JOURNAL NAME: Energy and Power Engineering, Vol.2 No.4, November 26, 2010

  ABSTRACT: Multi-deployment of dispersed power sources became an important need with the rapid increase of the Distributed generation (DG) technology and smart grid applications. This paper proposes a computational tool to assess the optimal DG size and deployment for more than one unit, taking the minimum losses and voltage profile as objective functions. A technique called radial basis function (RBF) neural network has been utilized for such target. The method is only depending on the training process; so it is simple in terms of algorithm and structure and it has fast computational speed and high accuracy; therefore it is flexible and reliable to be tested in different target scenarios. The proposed method is designed to find the best solution of multi- DG sizing and deployment in 33-bus IEEE distribution system and create the suitable topology of the system in the presence of DG. Some important results for DG deployment and discussion are involved to show the effectiveness of our proposed method.