A method to detect High Impedance Faults in Distribution Feeders Sanujit Sahoo Student Member, IEEE Mesut Baran Fellow, IEEE High Impedance Fault Detection Vegetation in high proximity to overhead lines Hi-Z Fault Current Levels Problem Statement High Impedance Fault Data from Substation • Current waveforms • Voltage waveforms High Impedance Fault Detection Method - Possibility of Tree touching overhead lines Predictive Maintenance by Utility HIF Detection Method Data Processing • Current waveforms obtained at the substation • Normalization Feature Extraction • Decomposition using DWT • Breaking components into 2 parts each • Max and Energy of each part Classification • Support Vector Machines Step 1: Data Generation & Processing Discrete Model for HIF in Simulink Challenge: Difficult to obtain current waveforms related to HIFs from utilities HIF model had the following components in series: • Arc component (Mayr’s Arc Model) • High Impedance Mayr’s Equation Continuous version Discrete version 1100 mples Step 1: Data Generation & Processing HIF current waveforms HIF current waveform in one of the references Fault current(A) 20 1200 10 HIF current waveform obtained from our model 0 -10 -20 1000 1100 1200 Time samples 1300 Step 1: Data Generation & Processing Test Circuit • HIFs and the other switching events were simulated at different line segments • 55 waveforms for HIF and 129 waveforms for other events were obtained Step 2: Feature Extraction MRA using Discrete Wavelet Transform Original Signal Frequency Range: 0-f Hz D1 A1 (f/2-f Hz) (0-f/2 Hz) D2 A2 (f/4-f/2 Hz) (0-f/4 Hz) D3 A3 (f/4-f/8 Hz) (0-f/8 Hz) D4 (f/8f/16 Hz) A4 (0f/16 Hz) Step 2: Feature Extraction Analysis of waveforms Original Neutral Current Signal Normalized Original Neutral Current Signal Normalized Original Neutral Current Signal Normalized 2 2 2 0 0 0 -2 0 100 200 300 A5(0-60Hz) 400 500 600 -2 -2 0 100 200 300 A5(0-60Hz) 400 500 600 2 2 2 0 0 0 -2 0 100 200 300 D5(60-120Hz) 400 500 600 -2 -2 0 100 200 300 D5(60-120Hz) 400 500 600 2 2 0 0 0 -2 -2 -2 0 100 200 300 D4(120-240Hz) 400 500 600 0.2 -0.2 0 100 200 300 D3(240-480Hz) 400 500 600 0.1 0 -0.1 0 100 200 300 D2(480-960Hz) 400 500 600 0.05 0 -0.05 5 0 -3 x 10 100 200 300 400 D1(960-1920Hz) 500 600 100 200 300 D4(120-240Hz) 400 500 600 0 -0.2 0 100 200 300 Normal 400 500 600 0 100 200 300 D3(240-480Hz) 400 500 600 0.1 0.1 0 0 -0.1 0 100 200 300 D2(480-960Hz) 400 500 600 0.05 0.05 0 0 -0.05 200 300 A5(0-60Hz) 400 500 600 0 100 200 300 D5(60-120Hz) 400 500 600 0 100 200 300 D4(120-240Hz) 400 500 600 0 100 200 300 D3(240-480Hz) 400 500 600 0 100 200 300 D2(480-960Hz) 400 500 600 0 -3 x 10 100 200 300 400 D1(960-1920Hz) 500 600 0 100 200 500 600 0.2 0 5 0 -5 0 -0.2 -0.1 100 2 0.2 0 0 -0.05 0 -3 x 10 100 200 300 400 D1(960-1920Hz) 500 600 5 0 0 -5 -5 0 100 200 300 HIF 400 500 600 300 HIF 400 Step 2: Feature Extraction Analysis of waveforms Original Neutral Current Signal Normalized Original Neutral Current Signal Normalized 2 2 0 0 -2 -2 0 100 200 300 A5(0-60Hz) 400 500 600 0 100 200 300 A5(0-60Hz) 400 500 600 0 100 200 300 D5(60-120Hz) 400 500 600 0 100 200 300 D4(120-240Hz) 400 500 600 0 100 200 300 D3(240-480Hz) 400 500 600 0 100 200 300 D2(480-960Hz) 400 500 600 0 -3 x 10 100 200 300 400 D1(960-1920Hz) 500 600 0 100 200 500 600 2 2 0 0 -2 -2 0 100 200 300 D5(60-120Hz) 400 500 600 2 2 0 0 -2 -2 0 100 200 300 D4(120-240Hz) 400 500 600 0.5 0.2 0 0 -0.5 -0.2 0 100 200 300 D3(240-480Hz) 400 500 600 0.1 0.1 0 0 -0.1 -0.1 0 100 200 300 D2(480-960Hz) 400 500 600 0.05 0.05 0 0 -0.05 -0.05 0 -3 x 10 100 200 300 400 D1(960-1920Hz) 500 600 5 1 0 -1 0 0 100 200 300 400 Load Switching 500 600 -5 300 400 Cap Bank Switching Step 2: Feature Extraction Extracted Features • • • • • • • • • • • • Max_D1_Part1: Maximum value of the first part of the D1 component Max_D2_Part1: Maximum value of the first part of the D2 component Max_D3_Part1: Maximum value of the first part of the D3 component Energy_D1_Part1: Energy of the first part of the D1 component Energy_D2_Part1: Energy of the first part of the D2 component Energy_D3_Part1: Energy of the first part of the D3 component Max_D1_Part2: Maximum value of the second part of the D1 component Max_D2_Part2: Maximum value of the second part of the D1 component Max_D3_Part2: Maximum value of the second part of the D1 component Energy_D1_Part2: Energy of the second part of the D1 component Energy_D2_Part2: Energy of the second part of the D2 component Energy_D3_Part2: Energy of the second part of the D3 component Step 3: Classification Support Vector Machines • Constructs a decision surface such that the margin of separation between the two data sets (labeled +1 and -1) is maximized • In case of non linear data, the original feature space can always be mapped to some higher-dimensional feature space where the training set is separable. The “Kernel Trick” is used Step 3: Classification Performance Measures 𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 = # 𝒐𝒇 𝒔𝒂𝒎𝒑𝒍𝒆𝒔 𝒄𝒐𝒓𝒓𝒆𝒄𝒕𝒍𝒚 𝒄𝒍𝒂𝒔𝒔𝒊𝒇𝒊𝒆𝒅 𝒕𝒐𝒕𝒂𝒍 # 𝒐𝒇 𝒔𝒂𝒎𝒑𝒍𝒆𝒔 Confusion Matrix Predicted Positive Class Predicted Negative Class Actual Positive Class True Positive (TP) False Negative(FN) Actual Negative Class False Positive(FP) True Negative(TN) Test Results Parameter selection for SVM 100 90 80 log10(C) = 1 70 log10(C) = 2 Accuracy 60 log10(C) = 3 50 log10(C) = 4 40 log10(C) = 5 log10(C) = 6 30 log10(C) = 7 20 10 0 -5 -4 -3 -2 -1 log10(Gamma value) 0 1 Variation of accuracy of SVM with change in the value of C and gamma Test Results Average Correct Classification 94.86% Average Incorrect Classification Classified as HIF Classified as Non HIF 5.14% HIF 26.81% 2.92% Sensitivity (Ratio of HIF classified correctly) Specificity (Ratio of non HIF classified correctly) g-mean Precision of HIF prediction Precision of non – HIF prediction Non-HIF 2.22% 68.05% 0.9018 0.9684 0.9345 0.923 5 0.958 8 Test Results Sensitivity Analysis Conclusions • The performance of the SVM classifier is very good. Since only the neutral current is being used, one classifier can detect HIFs in all three phases making it a cost effective device. • The data used in this work was obtained from simulations. Although, the modeling of HIF was done accurately, the performance of the classifiers will take a dip once they are trained and tested with real data.