@article{MAKHILLIJSC201712121397,
    title = {An Application of Fast Learning Radial Basis Function Networks for an
Accurate Estimation of Fault Location in Electrical Distribution Networks},
    journal = {International Journal of Soft Computing},
    volume = {12},
    number = {1},
    pages = {72-78},
    year = {2017},
    issn = {1816-9503},
    doi = {ijscomp.2017.72.78},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2017.72.78},
    author = {Surender Kumar,Purnachandra Rao and},
    keywords = {Fault location,fault classification,distribution networks,artificial intelligence,radial basis function networks,neural network},
    abstract = {Fault location is one of the important tasks of automated distribution systems. In this research, fast
learning Radial Basis Function Neural networks (RBFNs) were employed to automatically locate the faults in
distribution networks. The radial basis networks are simpler in structure, faster and more efficient than the
conventional multilayer feed forward networks. These are functionally considered equivalent to more successful
fuzzy connectionist hybrid models. An IEEE test distribution system was used for analyzing the potential and
accuracy ofthese networks in the estimation of fault location information. The distribution network
wassimulated and tested in MATLAB/SIMULINK. Three fundamental tasks of this RBFN Models, fault type
classification, faulted line section detection and pin pointing of fault location on the faulted line were executed
by multiple RBFN Models which were designed in MATLAB environment. All the required fault data for
training and testing of the models was generated by triggering various fault scenarios on the simulated
distribution network. The test results obtained demonstrate good degree of accuracy. This vital fault location
information supplied by RBFNs can greatly support the search efforts of distribution substation repair crew
in quickly pin pointing the faulty spot and restoring the power to the affected customers. This reduces the
customer service interruption duration and thus contributes in enhancing the power system reliability and
quality.}
    }