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.