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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jiang Hui-lan An Min Liu Jie Xu Jian-qiang |
| Copyright Year | 2005 |
| Description | Author affiliation: Tianjin Univ. (Jiang Hui-lan; An Min; Liu Jie) |
| Abstract | This paper presents a method of calculating energy losses in distribution systems based on RBF network. For representational samples of reflecting the relation between energy losses and characteristic parameters of distribution net, RBF network with strong regression characteristic is adopted to map complex non-linearly relation between energy losses and characteristic parameters of distribution net, the trend of energy losses varying with distribution net structure and operation parameters is memorized accurately. Meanwhile this paper proposes an optimal clustering criterion of being used to determine the number of nodes of hidden layer, and therefore the use efficiency of the RBF network is improved. In addition, there are some special samples which are difficult to be attracted strongly by network, so the computational error of energy losses for these lines is larger than others. This paper proposes a mechanism of reconstructing network to enhance the precision for samples with larger error by using the fuzzy processing. The distribution net with 68 lines is used as an example, the simulation results have verified that the way presented in this paper has the advantages of simple model, speedy learning and high precision |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 332642 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780391144 |
| DOI | 10.1109/TDC.2005.1546858 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-08-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Computational modeling Artificial neural networks Distribution systems RBF network Regression analysis Equations Petroleum Self-adaptive clustering algorithm Energy loss Neural networks Clustering algorithms Radial basis function networks Energy losses Computer networks |
| Content Type | Text |
| Resource Type | Article |
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