Ming-Ta Yang

Detection of high impedance fault in distribution feeder using wavelet transform and artificial neural networks

This work presents a novel analysis method that can simulate the potential effect of high impedance fault (HIF). The proposed method offers a new scheme for protecting the overhead distribution feeder. The wavelet transform (WT) method was successfully applied in many fields. The characteristics of scaling and translation of WT can be used to identify stable and transient signals. Discrete wavelet transforms (DWT) are initially used to extract distinctive features of the voltage and current signals, and are transformed into a series of detailed and approximated wavelet components. The coefficients of variation of the wavelet components are then calculated. This information is introduced into the training artificial neural networks (ANN) to determine an HIF from the operations of the switches. The simulated results clearly reveal that the proposed method can accurately identify the HIF in the distribution feeder.

Detecting High Impedance Faults Utilizing Combined Phase Voltages with Neutral Line Current

This study aims to present a new approach to detecting high impedance faults (HIFs) in the distribution feeder. Discrete wavelet transformations (DWT) and neural networks (NN) have been widely applied in power system research. Consequently, this study developed a novel technique to discriminate effectively between the HIFs and the switch operations by combining DWT with NN. The proposed approach has three distinct features. First, the input signal of this algorithm is neutral line current, rather than the conventional currents based on three individual phases. Second, HIFs identification uses the details at levels 3, 4 and 5 and the approximations at level 5 of the neutral line current are utilized for. Third, the input signals of the three-phase voltages classify the faulty and healthy phases. The results of simulation and field staged fault clearly show that the proposed technique can accurately identify the HIFs in the distribution feeder.

Evaluation of algorithms for high impedance faults identification based on staged fault tests

The main objective of this study is to develop an intelligent relay which is capable of protecting aerial lines from high impedance faults (HIFs). This investigation successfully develops a novel intelligent HIF detector that applies neutral line current to solve HIF problems. A self-turning scheme based on the chi-square distribution and 95 % confidence interval is first applied to set the threshold level automatically for the neutral line current variances examined. The feature extraction system based on wavelet transform and the pattern recognition technique found on neural networks are then applied to discriminate effectively between the HIFs and the switch operations. Two staged fault tests were undertaken to examine the feasibility of the proposed algorithm and measure its performance. The performance other un-intelligent relaying algorithms found in the literature was also compared with proposed intelligent HIF detector based on the staged fault records. Experimental results demonstrate that the proposed intelligent relay is feasible performance well

Detection of downed conductor in distribution system

The aim of this paper is to present an analysis and simulation methodology to enhance the detection robustness of high impedance fault (HIF) in the distribution feeder. The techniques of discrete wavelet transformations (DWT) and neural networks (NN) have been widely applied in power system research. Consequently, this study developed a novel technique to effectively discriminate between the HIF and the switch operations by combining DWT with NN. The simulated results clearly show that the proposed technique can accurately identify the HIF.

A novel intelligent protection scheme for high impedance fault detection in distribution feeder

This paper presents an analysis and simulation methodology to discuss the possible impacts of high impedance fault (HIF) in distribution feeder. Its object is to establish protection recommendations for both insulated and un-insulated overhead distribution feeder. As ground impedance and arc are nonlinear in nature, the feeder fallen on the ground with high impedance will cause a nonlinear fault current. However, the conventional ground fault protection schemes and algorithms have difficulty in HIF recognition. The wavelet transform (WT) technique has been successfully applied in many fields. The properties of scaling and translation of WT can be used to identify the low frequency components in stable signal and high frequency components in transient signal. Hence, this paper proposed a novel technique to effectively discriminate between the HIF and the switch operations in distribution feeder by combining a preprocessing module based on discrete wavelet transform (DWTs) with a neural networks (NN). The DWTs is firstly applied to extract of distinctive features of the voltage and current signals at the supply side of feeder and transform into a series of detailed and approximated wavelet components. Then, the coefficients of variation of the wavelet components are calculated. This information is introduced to training NN for identifying an HIF from the switches operations. The simulated results clearly show that the proposed technique can accurately identify the HIF in insulated and un-insulated overhead distribution feeder.

Detection of High Impedance Faults in Distribution System

This investigation seeks to present a new method of identifying high impedance faults (HIFs) in the distribution feeder. Discrete wavelet transformations (DWT) and neural networks (NN) have been widely used in power system research. Consequently, this work developed a novel method to distinguish effectively the HIFs by integrating DWT with NN. The proposed scheme has two distinct features. First, the input signal of this algorithm is neutral line current, rather than the traditional currents based on three individual phases. Second, HIFs identification applies the details at levels 2, 3 and 4 and the approximations at level 4 of the neutral line current are employed for. The results of staged fault clearly indicate that the proposed can accurately find the HIFs in the distribution feeder

Real-time Measurement Approach for Accurate Estimating the AV10 Value of EAF

This study investigates ac EAFs by making field measurements; with those results indicate that the estimated DeltaV10 value obtained using the conventional method is significantly lower than the surveyed value. This paper found the DeltaQmax value of the original design is too small to respond the actual variation of reactive power. Meanwhile, the difference of DeltaQmax between design value and actual value is quite big. However, this investigation suggests that the DeltaQmax estimate calculation must adopt a stricter standard. Therefore, we refer to the operation experience of manufacturers, namely that the biggest value of Costhetasr equals 1.0 for ac EAFs. Furthermore, surveyed results reveal that the revised method can yield more accurate DeltaV10 estimates than traditional method

Novel method to be applied for voltage flicker prediction caused by EAF

This investigation develops an enhanced method for estimating the voltage flicker of the electric arc furnace (EAF). The method not only considers the reactive power variation but also the active power variation in calculating the estimated /spl Delta/V/sub 10/ value of EAF. This study also considers field measurements of ac and dc EAFs. The results reveal that the estimated /spl Delta/V/sub 10/ value is significantly smaller than the observed value. The conventional way of estimating /spl Delta/V/sub 10/ is ineffective. The survey results demonstrate that variations of active power and reactive power of EAFs are strongly alike. Meanwhile, an /spl Delta/V/sub 10/ estimate must account for the effect of active power variation. However, this study proposes a maximum complex apparent power fluctuation method (MCAPFM) that can yield a more accurate /spl Delta/V/sub 10/ estimation than the conventional method.