High impedance fault detection: Discrete wavelet transform and fuzzy function approximation

This paper presents a method including a combination of the wavelet transform and fuzzy function approximation (FFA) for high impedance fault (HIF) detection in distribution electricity network. Discrete wavelet transform (DWT) is used in this paper as a tool for a signal analysis and after studying different types of mother signals, detailed types and feeder signal, the best case is selected. In the next step, the DWT is used to extract the features. The extracted features are used as the FFA Systems inputs. The FFA system uses the input-output pairs to create a function approximation of the features. The FFA system is able to classify the new features. The combined model is used to model the HIF. This combined model has the high ability to model different types of HIF. In the proposed method, different kind of loads including nonlinear and asymmetric loads and HIF types are studied. The results show that the proposed method has high capability to distinguish between no fault and HIF states accurately.