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Short Circuit Faults Identification and Localization in IEEE 34 Nodes Distribution Feeder Based on the Theory of Wavelets | ||
| Iraqi Journal for Electrical And Electronic Engineering | ||
| Article 1, Volume 14, Issue 1, 2018, Pages 65-79 PDF (0 K) | ||
| DOI: 10.33762/eeej.2018.144342 | ||
| Author | ||
| Sara J. Authafa | ||
| Abstract | ||
| In this paper a radial distribution feeder protection scheme against short circuit faults is introduced. It is based on utilizing the substation measured current signals in detecting faults and obtaining useful information about their types and locations. In order to facilitate important measurement signals features extraction such that better diagnosis of faults can be achieved, the discrete wavelet transform is exploited. The captured features are then utilized in detecting, identifying the faulted phases (fault type), and fault location. In case of a fault occurrence, the detection scheme will make a decision to trip out a circuit breaker residing at the feeder mains. This decision is made based on a criteria that is set to distinguish between the various system states in a reliable and accurate manner. After that, the fault type and location are predicted making use of the cascade forward neural networks learning and generalization capabilities. Useful information about the fault location can be obtained provided that the fault distance from source, as well as whether it resides on the main feeder or on one of the laterals can be predicted. By testing the functionality of the proposed scheme, it is found that the detection of faults is done fastly and reliably from the view point of power system protection relaying requirements. It also proves to overcome the complexities provided by the feeder structure to the accuracy of the identification process of fault types and locations. All the simulations and analysis are performed utilizing MATLAB R2016b version software package. | ||
| Keywords | ||
| distribution feeder; wavelets; fault detection; fault classification; fault location; cascade forward neural networks; IEEE | ||
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