# A Review of Fault Diagnosing Methods in Power Transmission Systems

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## Abstract

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## 1. Introduction

## 2. Disclose the Valuable Information

#### 2.1. Transformations

#### 2.1.1. Wavelet Transform (WT)

#### 2.1.2. Fourier Transform (FT)

#### 2.1.3. S-Transform (ST)

#### 2.2. Dimensionality Reduction

#### 2.3. Modal Transformation

## 3. Fault Detection (FD)

## 4. Fault-Types Classification (FC)

#### 4.1. Artificial Neural Network (ANN) Based FC

#### 4.1.1. Feedforward Neural Network (FNN)

#### 4.1.2. Radial basis Function Network (RBFN)

#### 4.1.3. Probabilistic Neural Network (PNN)

#### 4.1.4. Chebyshev Neural Network (ChNN)

#### 4.2. FC Based on Fuzzy Interface Systems (FIS)

#### 4.3. FC Based on Decision Tree (DT) Technique

#### 4.4. FC Based on Support Vector Machine (SVM)

#### 4.5. FC Based on Logic Flow (LF)

## 5. Comparison of Fault-Type Classification Methods

## 6. Future Trends in Fault-Type Classification

## 7. Fault Location Finding Methods

#### 7.1. Wide-Area FL Approach

#### 7.2. Fault Location Finding Algorithm for Series Compensated TLs

#### 7.3. FL Methods for Hybrid TLs

#### 7.4. ANN-based Algorithm for FL

#### 7.5. FIS based Algorithm for FL

#### 7.6. Support Vector Regression-Based Approach for FL

## 8. Comparison of Fault Location Methods

## 9. Future Trends in Fault Location Estimation

## 10. Weaknesses and Strengths of Different Emerging Computational Intelligence Methods

## 11. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 9.**Wide-area fault location method: the local site is where the application request originates and is responsible for coordinating the remote replicas.

No. | Algorithm | Input | Test System | Feature | Complexity | Result | Ref. |
---|---|---|---|---|---|---|---|

1 | Fuzzy-neuro method | Fault current and voltage samples | 220 kV, 177.4 km, 50 Hz | Back-propagation and fuzzy controllers are employed. | Medium | FD is realized in less than 10 ms | [53] |

PSCAD/EMTDC used for simulations. | |||||||

High harmonic components removed via FFT. | |||||||

2 | DWT and ANNs | Current and voltage signals | 60 Hz, 230 kV, 188 km | The sampling frequency is 1.2 kHz. | Complex | FD accuracy is 100% with 99.83% FC accuracy | [54] |

Normalization voltage and current signals are from 1 to −1. | |||||||

Db4 is mother wavelet. | |||||||

720 fault cases are considered. | |||||||

3 | WT and self-organized artificial neural network | current and voltage waveforms | 50 Hz, 500 kV, 200 miles | 200 kHz is the sampling rate. | Complex | FD accuracy is 99.7% for single line and 92% for parallel lines. FC accuracy is 99.65% | [55] |

Db5 mother wavelet is used to decompose the signal up to 3 levels. | |||||||

3960 fault cases are considered. | |||||||

An adaptive resonance theory-based neural network is used. | |||||||

4 | Linear discriminant analysis (LDA) and WT | Current signals | 100 km, 400 kV, 50 Hz single-circuitTL | WT is used to process the current samples up to 3 levels. | Medium | Both FD and FC is 100% | [56] |

Reach of the relay set-up to 90% of the TL. | |||||||

5 | Superimposed sequence components based integrated impedance (SSCII) | Current and voltage profiles at both ends of TL | 300 km, 400 kV, 50 Hz with a static VAR compensator (SVC) | The sampling frequency is 1 kHz. | Complex | FD time is less than 20 ms | [57] |

Reliable for low and high resistance faults. | |||||||

The pilot relaying scheme is suitable for high-speed communication channels. | |||||||

6 | Bayesian classifier and adaptive wavelet | Current signals | 500 kV, 864 km, 50 Hz | 500 kHz is the sampling frequency. | Complex | Both FD and FC accuracies are 100%, | [58] |

Db4 is selected as a mother wavelet, used to decompose current signals up to 3 stages. | |||||||

Directional zone protection is obtained. | |||||||

5328 fault cases are analyzed. |

No. | Algorithm | Input | Test System | Feature | Complexity | Result | Ref. |
---|---|---|---|---|---|---|---|

1 | FNN | Voltage and current samples | Double circuit TL of 100 km length. Operated at 380 kV | The sampling frequency is 1.1 kHz. | Simple | 7 ms is the fault-type classification time | [100] |

30 input nodes, two hidden layers, and 11 output layer nodes. | |||||||

Training patterns are 45000. | |||||||

2 | Back-propagation network classifier | Voltage and current samples | Double circuit 128 km long TL with 35 GVA and five GVA generations, respectively | 800 Hz is the sampling frequency and results obtained via three sample data windows. | Simple | Misclassification rate is less than 1% | [101] |

The number of Kohonen neurons is greatly dependent on the number of training sets. BP network classifier is employed as a front end to the output layer with supervised learning. | |||||||

3 | Fuzzy logic and WT- based method | Current signals | 50 Hz, 300 km long TL. operate at 400 kV | 4.5 kHz is selected as sampling rate and Db8 mother wavelet is used. | Complex | FC time is less than 10 ms with 99% accuracy | [102] |

Wavelet is dissolved into four levels. | |||||||

Online FC is done. | |||||||

Fast, robust and accurate FC is obtained. | |||||||

4 | Fuzzy logic | Current signals | 50 Hz, 300 km long TL and operate at 400kV | Digital distance protection is implemented. | Medium | FC accuracy is more than 97% | [103] |

FC time is 10 ms and studied cases were 2400. | |||||||

5 | ST and PNN | Current signals | 50 Hz, 300 km long TL. Operate at 230 kV with Thyristor-controlled series capacitor (TCSC) | Scalable Gaussian window is used for ST with a sampling rate of 1 kHz. | Complex | FC accuracy is 98.62% and faulty section identification accuracy is 99.86% | [104] |

Standard deviation and energy are the features. | |||||||

200 dataset used for testing, 300 for training out of 500 datasets. | |||||||

6 | SVM | Post-fault current and oltage signals | 50 Hz, 300 km long TL. operate at 400 kV | 1 kHz is the sampling frequency. | Simple | Classification of faulty phase and ground detection is done with an error of less than 2% | [105] |

SVM-1 and SVM-2 are trained and tested with 300 datasets for ground detection and phase selection, respectively. | |||||||

Gaussian and polynomial based SVMs are used. | |||||||

7 | Field-programmable gate array (FPGA) with WT. | Current signals | 50 Hz, 300 km long TL and operate at 400kV | 2 kHz is the sampling frequency. | Complex | FC time is 6 ms with 100% accuracy | [106] |

Db6 mother wavelet is employed. | |||||||

3520 test cases created. | |||||||

Karrenbauer’s transformation is used to avoid the need for multipliers. | |||||||

8 | ANFIS | 50 Hz, TL is 20 km and operate at 500 kV | 128 rule system with seven inputs and two membership functions. | Medium | FC accuracy is more than 99.92% | [107] | |

The sampling frequency is 30.24 kHz. | |||||||

2660 fault cases considered for training. | |||||||

9 | Bayesian classifier with adaptive wavelet algorithm | Current signals | 50 Hz, 390 km long TL and operate at 500 kV | 500 kHz is the sampling rate. | Complex | Results with 100% accuracy | [108] |

Db4 is the mother wavelet. | |||||||

Fault cases considered for training: 546. | |||||||

10 | Polynomial-based ChNN and discrete wavelet packet transform (DWPT) | Current signals | 300 km long TL which operates at 400 kV. TCSC is installed at the midpoint | PSCAD/EMTDC is used to study fault patterns. | Medium | 99.93% accurate | [109] |

4 kHz is the sampling frequency. | |||||||

11 | CART algorithm | Positive sequence voltages | 345 kV, 300 km, 50 Hz | CART is a non-parametric DT learning technique that is in the form of if-else statements. | Medium | Results are 99.98% accurate | [110] |

2880 fault cases considered. | |||||||

12 | Dyadic WT and SVM | Current samples | 330 km, 230 kV, 50 Hz | 160 kHz is the sampling frequency and signals are decomposed into 5 levels. | Medium | FC is 100% accurate | [111] |

Fault cases: 1500. | |||||||

SVM trained via 800 faults and remaining 700 used for testing. | |||||||

Random noise is removed via wavelet transform |

No. | Algorithm | Input | Test System | Feature | Complexity | Result | Ref. |
---|---|---|---|---|---|---|---|

1 | ANN | Pre-fault current and voltage samples | La Lomba–Herrera 380 kV, 189.3 km long TL, Spanish power system (50 Hz) | FALNEUR software is used to train network data. | Medium | The maximum error noted is 0.7% while 0.12% is the minimum error in locating fault distance. | [146] |

Training time varies from 5 s to 2.5 min to accomplish the mentioned error level. | |||||||

BP based on Levenberg–Marquardt optimization technique is selected. | |||||||

The ‘ansig’ is selected as a transfer function for the hidden layer, and the linear function for the output layer. | |||||||

2 | Least error square | Current and voltage magnitudes | Length of TL is 100 km, 400 kV, 50 Hz | The sampling frequency is 6400 Hz. | Simple | 0.0099% is the relative error | [147] |

20 ms is the duration of the data window. | |||||||

3 | Impedance-based Algorithm (IBA) | Voltage profile | 500 kV, 200 miles TL, 50 Hz | Shunt capacitance is neglected of the TL which is desirable for online applications. | Simple | 1% error is recorded for IBA | [148] |

Data synchronization is not required | |||||||

4 | Neuro-fuzzy systems and WT | Current and voltage profiles | Hybrid transmission system: 6.06 km cable and 14 km TL with 154 kV operating voltage | DC offset is removed via FIR. | Medium | -- | [149] |

Db4 mother wavelet is used and decomposed into three levels. | |||||||

Back-propagation is used for learning and 228 various faults created for analysis. | |||||||

Post-fault time is a half-cycle. | |||||||

5 | WT | Current samples | 50 Hz, 60 km, 400 kV | Db5 mother wavelet is used and decomposed into three Levels. | Simple | -- | [150] |

The sampling frequency is 3840 Hz with 64 samples/cycle. | |||||||

The fault is located within 1 cycle via A3 components. | |||||||

6 | ANN and wavelet packet transform (WPT) | Current and voltage samples | 360 km, 380 kV and 50 Hz | Db4 mother wavelet is employed and dissolved up to three levels by WPT. | Complex | Minimum and maximum errors in finding fault location are 0.06% and 1.67%, respectively | [151] |

The 10 kHz is the sampling frequency. | |||||||

The computation burden is reduced as it is a reduction technique. | |||||||

Pre and post-fault is a half-cycle. | |||||||

7 | RBF-based SVM and scaled conjugate gradient (SCALCG)-based NN approach | Positive sequence voltage and line currents | 150 km double circuit TL, 400 kV is operating voltage | The 5 kHz is selected as the sampling rate. | Complex | Maximum fault error observed is 1.852 km while 7.874e-003 km is minimum | [152] |

The 2e-004s is time to locate the fault. | |||||||

RBF kernel is used to extract principal eigenvectors of the feature space and to remove noise from the signal. | |||||||

8 | Nelder–Mead simplex | Post-fault Voltage phasors | 320 km, 500 kV, 50 Hz | 960 Hz is the sampling rate. | Complex | 2.7% error is expected with ±5% error in post-fault voltages | [153] |

Current transformer (CT) errors are avoided by not using post-fault current. | |||||||

9 | ANN and WPT | Current samples | 360 km, 380 kV, 50 Hz | Wavelet entropy and energy features are extracted from the decomposed signal. | Complex | FL finding error is Less than 2.05 % | [154] |

Db4 is the mother wavelet. | |||||||

10 kHz is the sampling rate. | |||||||

10 | ANFIS | Zero and fundamental components of three-phase currents | Hybrid transmission system: 10 km cable and 90 km TL. Operating voltage is 220 kV | ANFIS is trained for 2132 patterns. Where 1520 patterns are for TL and rest for cable. | Medium | The maximum error in finding FL is expected below than 0.07% | [155] |

During training, the maximum percentage error of 0.031% and 0.0109% is observed for TL and cable, respectively. | |||||||

During the testing process, the maximum % error of 0.0277% and 0.039% are observed for TL and cable, respectively. | |||||||

11 | ANNs with FPGA | Pre-fault current and voltage samples from one end | L 380 kV, 189.3 km long TL, Spanish power system (50 Hz) | SARENEUR tool is used to run ANN. | Complex | Error in finding fault location is 0.03% | [156] |

Hardware is also implemented. | |||||||

FPGA is designed for 60 MHz and consumes less power | |||||||

12 | FFT with traveling-wave theory | Current samples measured from one end | 50 Hz, 240 km and 400 kV | The selected sampling frequency is 25.6 kHz and 512 samples are collected. | Simple | Fault location error is 0.12% | [157] |

To reduce FFT leakage Hanning window is employed | |||||||

13 | Impedance based method | Current and voltage samples | 300 km, 380 kV, TL with series capacitor | DIgSILENT is used to simulate the test system. | Simple | Achieved FL error is less than 1% | [158] |

10 kHz is the sampling frequency with simulation time 0.2 s. |

Technique | Strength | Weakness |
---|---|---|

ANN Technique | ANN is pretty good in determining the exact fault-type and its implementation is easy. | The training process is quite complex for high-dimension problems. |

Its use is easy, with the adjustment of only a few parameters. | A local optimum solution is provided by the gradient-based back-propagation technique for non-linear separable pattern classification problem. | |

It has a lot of applications in real-life problems. | ANN offers slow convergence in the BP algorithm. | |

ANN learns and no need for reprogramming. | Convergence is dependent on the selection of the initial value of weight constraints connected to the network. | |

PNN Technique | The learning process is not required. | It requires high processing time for large networks. |

Determination of initial weights of the network is not needed. | ||

No correlation of the recalling process and learning process. | ||

Convergence in Bayesian classifier is certain. | Not easy to determine how many layers and neurons are required. | |

PNN show fast learning time. | Large memory space is required to save the model | |

Fuzzy Methods | Simple ‘if-then’ relation is used to solve uncertainty problems. | No robustness is observed. |

Experts are mandatory in order to determine membership function and fuzzy rules, for large training data. | ||

ANFIS Technique | Parameters are tuned properly by the hybrid learning rule. | ANFIS is highly complex in computation. |

It offers a faster convergence. | ||

The search space dimension is reduced. | ||

ANFIS is smooth and adaptable | ||

SVM Technique | SVM is a highly accurate approach. | Demands for more size and speed for the testing and training |

SVM works quite well even for non-linearly separable data in base feature space. | ||

The probability of misclassification is very low. | ||

To reduce error bound, it maximizes the margin. | ||

Upper bound error does not affect the space dimension | Complexity is high in classification and thus large memory is required | |

Decision Tree | Easy interpretation and understanding | When high uncertainty or a number of outcomes are involved, calculations become very complex. |

Compatible with other available decision methods. | DT may suffer from over-fitting | |

Rules can be set easily | Information gain in DTs is biased in favor of those features which have more levels. | |

Wide-area Fault Location | It performs both control and monitoring operations. | PMU placement is a tough task in power systems |

Modal Transform | It is not dependent on electrical values and frequency | Modal parameters are required |

The single transformation matrix is for the three-phase system (identical for current and voltages) | ||

Transposition and non-transposition of electrical values are done by simple multiplication of matrices. No convolution methods are required. | Not reliable for complex structures | |

Deep Learning | Best-in-class performance on problems that significantly outperforms other solutions in multiple domains. This is not by a little bit, but by a significant amount. | A large amount of data is required |

DL reduces the need for feature engineering, one of the most time-consuming parts of machine learning practice. | DL is computationally expensive to train and takes weeks to train via hundreds of machines equipped with expensive graphical processing units (GPUs) | |

It is an architecture that can be adapted to new problems relatively with ease e.g., time series, languages, etc., are using techniques like convolutional neural networks, recurrent neural networks, long short-term memory, etc | Determining the topology/training method for DL is a black art with no theory |

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**MDPI and ACS Style**

Raza, A.; Benrabah, A.; Alquthami, T.; Akmal, M.
A Review of Fault Diagnosing Methods in Power Transmission Systems. *Appl. Sci.* **2020**, *10*, 1312.
https://doi.org/10.3390/app10041312

**AMA Style**

Raza A, Benrabah A, Alquthami T, Akmal M.
A Review of Fault Diagnosing Methods in Power Transmission Systems. *Applied Sciences*. 2020; 10(4):1312.
https://doi.org/10.3390/app10041312

**Chicago/Turabian Style**

Raza, Ali, Abdeldjabar Benrabah, Thamer Alquthami, and Muhammad Akmal.
2020. "A Review of Fault Diagnosing Methods in Power Transmission Systems" *Applied Sciences* 10, no. 4: 1312.
https://doi.org/10.3390/app10041312