دانلود رایگان مقاله تشخیص شبکه توزیع بخش و محل خطا با آنتروپی موجک و شبکه عصبی

ABSTRACT

Fault location in power system distribution networks is especially difficult because of the existence of several laterals/tap-offs in distribution networks. This implies that the calculated fault point can be wrongly estimated to be in any of the laterals. This paper proposes a new hybrid method combining Discrete Wavelet Transform (DWT) and artificial neural network (ANN) for fault section identification (FSI) and fault location (FL) in power system distribution networks. DWT was used in the analysis and extraction of the characteristic features from fault transient signals of the three phase line current measurements obtained at a single substation relaying point, rather than the double-ended approach used in the existing literature. Entropy Per Unit (EPU) indices are afterwards computed from the DWT decomposition, and are used as input to multi-layer ANN models serving as FSI classifiers and FL predictors respectively. The proposed hybrid method is tested using a benchmark IEEE 34-node test feeder. Comparisons, veri- fication, and analysis made using the experimental results obtained from the application of the method showed very good performance for different fault types, fault locations, fault inception angles, and fault resistances. The proposed hybrid method is unique because of the pre-processing stage done with the DWT-EPU indices, the use of only line current measurements from a single relaying point, and the division of the FSI and FL tasks into sub-problems with respective ANN models.

5. Conclusion

This paper has developed a hybrid 2-stage method for distribution network fault section identification (FSI) and faultlocation (FL) based on the coefficients from level-5 detail coefficients obtained from DWT decomposition using db4 mother wavelet. Wavelet Energy Spectrum Entropy (WEE) and Entropy Per Unit(EPU)indices are computed from the DWT detail coefficients. These indices are used in training artificial neural network models for the FSI and FL tasks respectively. Comparison of ANN models trained using the EPU and WEE indices is carried out in terms of the prediction accuracy, computation time, processor usage, and memory usage. In order to validate the proposed hybrid method, it is applied to the IEEE 34-node benchmark test feeder. The proposed method can easily be implemented practically using actual data obtained from Digital Fault Recorders (DFR) or Intelligent Electronic Devices (IEDs). Although, data obtained from DFRs/IEDs are usually noisy, the noise would be filtered out as a result of the DWT decomposition. The ANN models trained using EPU indices were shown to require less computer memory, less processor usage, and gave faster computation speed.