# High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

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## Abstract

**:**

## 1. Introduction

## 2. System Modelling

#### Background of Wavelet Analysis

- CWT requires a large number of scales to show the signal components, which makes it useless for online application.
- CWT is highly redundant transform as its wavelet coefficients contain more information than necessary.
- CWT provides the region where the fault occurs, but DWT localize the fault more efficient.
- DWT preserve all the information of the function with minimum number of wavelet coefficients.
- Computational time is faster for DWT analysis.
- Construction of CWT inverse is more difficult.

## 3. Proposed HIF Detection Methodology

**Step 1—Pre-processing:**The fault current is obtained by simulating the MV distribution network with various faults in the power system.

**Step 2—Processing:**The original fault current signal is extracted from noise by decomposing the signal using DWT at various levels.

**Step 3—Feature Extraction:**The standard deviation (SD) for location of fault is extracted using 5-level DWT.

**Step 4—Traning:**The extracted SD values for various cases were used for training ANFIS system.

**Step 5—Classification:**Trained ANFIS based classifier algorithm identify the type of fault that occurs in the system.

#### 3.1. Discrete Wavelet Transform

#### 3.1.1. Choice of Mother Wavelet

#### 3.1.2. Feature Extraction

## 4. Intelligence-Based Classifier

#### 4.1. Fuzzy Logic System

**Step****1:**- Define the problem and classify the data i.e., SD values
**Step****2:**- Define the input and output fuzzy sets with variable name.
**Step****3:**- Define the type of member function for each variable.
**Step****4:**- Frame the rules.
**Step****5:**- Built and test the system.
**Step****6:**- Tune and validate the system.

- If (S1 is normal) and (S2 is normal) and (S3 is normal) then (trip output is Normal)
- If (S1 is fault) and (S2 is fault) and (S3 is fault) then (trip output is ABC fault)
- If (S1 is ground) and (S2 is ground) and (S3 is normal) then (trip output is ABG fault)
- If (S1 is normal) and (S2 is ground) and (S3 is ground) then (trip output is BCG fault)
- If (S1 is ground) and (S2 is normal) and (S3 is ground) then (trip output is ACG fault)
- If (S1 is ground) and (S2 is normal) and (S3 is normal) then (trip output is AG fault)
- If (S1 is normal) and (S2 is ground) and (S3 is normal) then (trip output is BG fault)
- If (S1 is normal) and (S2 is normal) and (S3 is ground) then (trip output is CG fault)
- If (S1 is fault) and (S2 is fault) and (S3 is normal) then (trip output is AB fault)
- If (S1 is normal) and (S2 is fault) and (S3 is fault) then (trip output is BC fault)
- If (S1 is fault) and (S2 is normal) and (S3 is fault) then (trip output is AC fault)
- If (S1 is HIF) and (S2 is normal) and (S3 is normal) then (trip output is HIF fault at Phase A)
- If (S1 is normal) and (S2 is HIF) and (S3 is normal) then (trip output is HIF fault at PhaseB)
- If (S1 is normal) and (S2 is normal) and (S3 is HIF) then (trip output is HIF fault at Phase C)

#### 4.2. Adaptive Neuro Fuzzy Inference System

- It is capable of handling complex and nonlinear problems even if the targets are not given.
- The learning duration of ANFIS is very short than Neural Network (NN) which implies that ANFIS reaches the target faster than neural network.
- Reduces the complexity of the problem, in case of system with huge amount of data.
- In training of the data, ANFIS gives result with minimum total error compared to other type of NN.

- IF x
_{1}is A_{1} - AND x
_{2}is A_{2} - AND x
_{m}is A_{m} - THEN y = f (x
_{1}, x_{2}, …, x_{m})

_{1}, x

_{2}, …, x

_{m}are input variables; A

_{1}, A

_{2}, …, A

_{m}are fuzzy sets. When y is a constant, a zero-order sugeno fuzzy model is obtained in which the subsequent of a rule is specified by a singleton. When y is a first-order polynomial equation, (i.e.,) y = k

_{0}+ k

_{1}*x

_{1}+k

_{2}*x

_{2}+ … + k

_{m}*x

_{m}, a first-order sugeno fuzzy model is obtained. The following Figure 6 illustrates the ANFIS structure with 6 layers.

## 5. Results and Discussion

#### 5.1. Matlab Simulation Results for Different Cases

#### 5.2. DWT Analysis

_{f}= 0.01 ohm in this section. The current signal of all phases under Normal operation of the system and also the current signal of faulty phases of different faults is shown from Figure 16, Figure 17, Figure 18, Figure 19, Figure 20, Figure 21, Figure 22, Figure 23, Figure 24, Figure 25, Figure 26 and Figure 27 respectively for better understanding. It is seen the magnitude of noise presents in the level d1 to d3 is high for all cases of fault and the transients are completely eradicated in the level d4 and d5. A5 is the approximation signal of level d5 and the feature extraction (SD values) obtained using Equation (7) presented in Section 3.1.2 is given in Table 3.

_{f}= 0.01 ohm, presented in Table 4. The rules are framed using the SD values of current signal for different value of fault resistance to train FLS and ANFIS system.

#### 5.3. Comparative Analysis

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviation

Variables | Explanation |

D1 to D5 | Detailed coefficients of level 1 to 5 |

A5 | Approximate coefficients of level 5 |

LG | Line to ground fault |

LL | Line to Line fault |

LLG | Double line to ground fault |

LLLG | Three phase fault |

HIF | High impedance fault |

SD | Standard Deviation |

Db9 | Daubichies’s mother wavelet |

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Detailed Coefficient Levels | Frequency Band kHz |
---|---|

D1 | 5 to 2.5 |

D2 | 2.5 to 1.25 |

D3 | 1.25 to 0.625 |

D4 | 0.625 to 0.3125 |

D5 | 0.3125 to 0.15625 |

S.No | Fault Type | Assigned Output |
---|---|---|

1 | No fault | 0 |

2 | HIF in phase C | 0.2 |

3 | HIF in phase B | 0.3 |

4 | HIF in phase A | 0.4 |

5 | LLL-G | 0.5 |

6 | LG (AG) | 0.6 |

7 | LG (BG) | 0.7 |

8 | LG (CG) | 0.8 |

9 | LL (AB) | 0.9 |

10 | LL (BC) | 1.0 |

11 | LL (AC) | 1.1 |

12 | LLG (ABG) | 1.2 |

13 | LLG (BCG) | 1.3 |

14 | LLG (ACG) | 1.4 |

Cases | Power System Distribution | SD Values of I_{f} |
---|---|---|

1 | Normal Case | |

Phase A | 21 | |

Phase B | 20.3 | |

Phase C | 19.0 | |

2 | HIF | |

Phase A | 20.33 | |

Phase B | 19.0 | |

Phase C | 0.264 | |

3 | Three Phase Fault | |

Phase A | 0.3467 | |

Phase B | 0.3477 | |

Phase C | 0.341 | |

4 | LL Fault | |

Phase A | 0.0113 | |

Phase B | 0.0132 | |

Phase C | 0.0105 | |

5 | LG Fault | |

Phase A | 0.0127 | |

Phase B | 0.02 | |

Phase C | 0.0227 | |

6 | LLG Fault | |

Phase A | 0.0115 | |

Phase B | 0.0143 | |

Phase C | 0.0255 |

State | Fault with Various R_{f} | S1 | S2 | S3 | FUZZY Output | Remarks | ANFIS Output | Remarks |
---|---|---|---|---|---|---|---|---|

Normal | Normal | 20.33 | 21.22 | 23 | Normal | ✓ | Normal | ✓ |

3 Phase Fault | ABC/20 Ohm | 40.33 | 41.54 | 46 | ABC | ✓ | ABC | ✓ |

ABC/40 Ohm | 31.38 | 33 | 35.98 | ABC | ✓ | ABC | ✓ | |

ABC/60 Ohm | 28 | 27.74 | 27 | ABC | ✓ | ABC | ✓ | |

LLG Fault | ABG/20 Ohm | 30 | 34.5 | 23 | ABG | ✓ | ABG | ✓ |

ABG/40 Ohm | 29 | 30 | 22.45 | ABG | ✓ | ABG | ✓ | |

ABG/60 Ohm | 28.42 | 28.88 | 21 | ABG | ✓ | ABG | ✓ | |

BCG/20 Ohm | 20.03 | 34.76 | 34 | BCG | ✓ | BCG | ✓ | |

BCG/40 Ohm | 20 | 32 | 31 | BCG | ✓ | BCG | ✓ | |

BCG/60 Ohm | 19.55 | 27 | 29 | BCG | ✓ | BCG | ✓ | |

ACG/20 Ohm | 34.45 | 23.33 | 35.1 | ACG | ✓ | ACG | ✓ | |

ACG/40 Ohm | 32 | 22.3 | 31 | ACG | ✓ | ACG | ✓ | |

ACG/60 Ohm | 29 | 20 | 28 | ACG | ✓ | ACG | ✓ | |

LG fault | AG/20 Ohm | 40.33 | 23 | 22.64 | AG | ✓ | AG | ✓ |

AG/40 Ohm | 35 | 21 | 20.06 | AG | ✓ | AG | ✓ | |

AG/60 Ohm | 29.98 | 19 | 20 | AG | ✓ | AG | ✓ | |

BG/20 Ohm | 21 | 47 | 20.06 | BG | ✓ | BG | ✓ | |

BG/40 OHMS | 18 | 37 | 18.63 | BG | ✓ | BG | ✓ | |

BG/60 Ohm | 19.73 | 30 | 22 | BG | ✓ | BG | ✓ | |

CG/20 Ohm | 18.6 | 23 | 47 | CG | ✓ | CG | ✓ | |

CG/40 Ohm | 19.18 | 22 | 34.98 | CG | ✓ | CG | ✓ | |

CG/60 Ohm | 21 | 20.87 | 29.61 | CG | ✓ | CG | ✓ | |

LL Fault | AB/20 Ohm | 45.55 | 46.7 | 21 | AB | ✓ | AB | ✓ |

AB/40 Ohm | 40 | 37 | 20.1 | AB | ✓ | AB | ✓ | |

AB/60 Ohm | 34 | 32 | 23 | ABG | ✕ | AB | ✓ | |

BC/20 Ohm | 21 | 45 | 44 | BC | ✓ | BC | ✓ | |

BC/40 Ohm | 20.45 | 36 | 37 | BC | ✓ | BC | ✓ | |

BC/60 Ohm | 24 | 32 | 29.24 | BCG | ✕ | BC | ✓ | |

AC/20 Ohm | 45 | 23 | 46.9 | AC | ✓ | AC | ✓ | |

AC/40 Ohm | 35.55 | 22.1 | 36 | AC | ✓ | AC | ✓ | |

AC/60 Ohm | 32 | 21 | 29 | ACG | ✕ | AC | ✓ | |

HIF Fault | HIF A/75 Ohm | 8 | 21 | 22.2 | HIF A | ✓ | HIF A | ✓ |

HIF A/50 Ohm | 11 | 20.09 | 23.4 | HIF A | ✓ | HIF A | ✓ | |

HIF A/40 ohm | 14.5 | 19 | 24 | NORMAL | ✕ | HIF A | ✓ | |

HIF B/75 Ohm | 21 | 9 | 20.01 | HIF B | ✓ | HIF B | ✓ | |

HIF B/50 Ohm | 20.09 | 12.4 | 23.05 | HIF B | ✓ | HIF B | ✓ | |

HIF B/ 40 Ohm | 19 | 14 | 22 | Normal | ✕ | HIF B | ✓ | |

HIF C/75 Ohm | 18.76 | 21 | 8.13 | HIF C | ✓ | HIF C | ✓ | |

HIF C/50 Ohm | 19.61 | 20.19 | 12.09 | HIF C | ✓ | HIF C | ✓ | |

HIF C/40 Ohm | 20.08 | 19.89 | 15.5 | Normal | ✕ | HIF C | ✓ |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Veerasamy, V.; Abdul Wahab, N.I.; Ramachandran, R.; Mansoor, M.; Thirumeni, M.; Lutfi Othman, M.
High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System. *Energies* **2018**, *11*, 3330.
https://doi.org/10.3390/en11123330

**AMA Style**

Veerasamy V, Abdul Wahab NI, Ramachandran R, Mansoor M, Thirumeni M, Lutfi Othman M.
High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System. *Energies*. 2018; 11(12):3330.
https://doi.org/10.3390/en11123330

**Chicago/Turabian Style**

Veerasamy, Veerapandiyan, Noor Izzri Abdul Wahab, Rajeswari Ramachandran, Muhammad Mansoor, Mariammal Thirumeni, and Mohammad Lutfi Othman.
2018. "High Impedance Fault Detection in Medium Voltage Distribution Network Using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System" *Energies* 11, no. 12: 3330.
https://doi.org/10.3390/en11123330