A Review and Taxonomy on Fault Analysis in Transmission Power Systems
Abstract
:1. Introduction
1.1. Background
1.2. Contribution of the Paper
- Unlike other surveys, this study provides a deeper insight into the comprehensive and most recent state-of-the-art techniques for academic and industrial research communities;
- We highlight the key challenges presented in the recent literature and summarize the related research work in terms of their strengths, weaknesses, and gaps;
- We provide a novel classification scheme to classify relevant fault-analysis techniques according to the method used and the target task (i.e., detection, classification, or localization).
1.3. Review Methodology
1.4. The Paper Outline
2. Fault Classification and Monitoring
3. Fault Analysis Techniques
3.1. Control Theory
3.1.1. Fuzzy Logic
3.1.2. Boolean Logic
3.2. Artificial Intelligence
3.2.1. Probabilistic Methods for Uncertain Reasoning
3.2.2. Artificial Neural Networks
Feature Extraction
3.2.3. Evolutionary Computation
- (1)
- An initialized population of potential solutions is randomly generated;
- (2)
- The fitness function is evaluated;
- (3)
- If it satisfies the optimization/termination criteria or constraints, the best output is generated; otherwise the process is terminated;
- (4)
- The population is subjected to natural selection in order to select the best-fitting parents for breeding;
- (5)
- To create a new generation, genetic operators such as crossover and mutation are used;
- (6)
- Go to step number (2).
3.2.4. Statistical Learning Methods
3.3. Communication Networks
3.3.1. Global System for Mobile Communications (GSM)
3.3.2. Wireless Sensor Networks (WSNs)
4. Summary of The Analysis Techniques
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Fault Analysis Method | Work | Detection | Classification | Localization | General Remarks |
---|---|---|---|---|---|
Conventional | [21] | ✓ | 1-terminal; the assumption of the angles’ equality may lead to erroneous locations | ||
[84] | ✓ | 2-terminal; phasor measurement units (PMUs); tolerant to time-synch signal and PMU losses | |||
[85] | ✓ | 1-terminal; EHV transmission lines; has low efficiency—Better to synch data from both ends | |||
[86] | ✓ | ✓ | Voltage measurements and network bus admittance matrix; transient stability assessment problem | ||
[87,88,89] | ✓ | 2-terminal pre-fault and post-fault of current and voltage; using a wide-area measurement system to handle a large-scale of data for accurate fault location | |||
Wavelet | [90] | ✓ | 1-terminal; detected using conventional discrete wavelet transform; more prominent than other various types of mother wavelets | ||
[91] | ✓ | 1-end measurements; using both voltage and current signals; tested on a 380 kV prototype power system; find the fault locations in a short time | |||
[13] | ✓ | 2-terminal transmission line; a wavelet entropy-based method; accurate with a fair degree of noise tolerance | |||
[92] | ✓ | ✓ | ✓ | 2-terminal transmission line; GPS for synch; efficient approach with a fair degree of accuracy | |
Fuzzy Logic | [28] | ✓ | ✓ | ✓ | Overhead or underground; localization by section; evaluated under different fault locations, inception angles and several fault resistances; accurate but requires many ANFIS networks to work |
[27] | ✓ | Fault locations caused by lightning strike; gather current values from SCADA system; requires 216 sets of rules to create an expert database | |||
Hidden Markov process | [33,35] | ✓ | Phasor measurements; detect faults in a short time | ||
Bayesian Networks | [37] | ✓ | Single-phase grounding accidents; “Storm”, “Aging”, and “Icing” are the most critical hazards of single-phase grounding, respectively; however, sensitivity analysis requires further validation | ||
[39] | ✓ | Fault section localization; feasible and efficient; however, the lack of prior knowledge of the domain experts impacts location accuracy | |||
Kalman Filter | [43] | ✓ | Estimate the pre- and post-fault state of voltages and currents | ||
[44] | ✓ | 2-terminal with extended KF; robustness and accuracy of localization are subject to sampling frequency; time synch is required between both terminals | |||
ANN | [46,47] | ✓ | ✓ | 2-terminal; high detection and classification accuracy | |
[52] | ✓ | 1-terminal; small localization error (up to 5%) | |||
[93] | ✓ | ✓ | 2-terminal; a single hidden layer is sufficient to learn and classify and localize faults | ||
[94] | ✓ | ✓ | Utilize samples of currents and voltages | ||
[95] | ✓ | 1-terminal; the classifier is suited for classifying faults in double-circuit lines | |||
[96] | ✓ | ✓ | 1-terminal pre-fault and post-fault data; feasible and tested against different types of faults at several operating conditions | ||
[97] | ✓ | 2-terminal; maximum localization error less than 2%; error could be further reduced if longer training time is applied (>2.5 min) | |||
[98] | ✓ | ✓ | LG, LL, and LLG; reliable and attractive approach for protecting relaying system in power transmission systems | ||
[49] | ✓ | ✓ | Deep learning neural network: use stacked auto-encoders (SAE) to train the deep model; increase the reliability and stability of power systems and; the accuracy is parameter-dependent—proper selection of the numbers of hidden layers and epochs is vital for an accuracy of around 80% | ||
Genetic Algorithm (GA) | [59] | ✓ | Pre-fault for maintenance purposes; efficient method to solve complex non-linear optimization problems | ||
[65] | ✓ | 2-terminal transmission line; significant maintenance savings of power systems | |||
[99] | ✓ | Measurement of short-circuit current at sending terminal; high degree of accuracy | |||
Decision Tree | [66] | ✓ | Detect and identify lines that lead to cascading failure | ||
[68] | ✓ | ✓ | 1-terminal; robust and accurate | ||
[100] | ✓ | Current signals sampled at 1920 Hz; high impedance fault | |||
Support Vector Machine | [69] | ✓ | 2-terminal; combined with GA; the system uses voltage and current phasor from PMU to identify fault types; uncertain phasors pose diagnosis inaccuracy | ||
[76] | ✓ | 1-terminal; high accuracy with average localization of less than 100 m (in 200 km transmission line, i.e., 0.05%) and the maximum error is below 2 km (i.e., 1%) | |||
[101] | ✓ | 1-terminal; 340 km long 3-phase transmission line | |||
[102] | ✓ | ✓ | ✓ | Post-fault current samples and ground detection | |
GSM | [77] | ✓ | Combined with the Internet of Things (IoT) | ||
[78] | ✓ | 1-terminal pre-fault and post-fault of current and voltage | |||
[79] | ✓ | ✓ | Sensed signals are forwarded to a microcontroller for detection and classification of faults; less accurate in extreme weather conditions | ||
WSN | [19] | ✓ | Use IoT device NODE MCU (Esp8266) to detect voltage change considering weather conditions; works in a real-time manner; reliable, and can be used to locate faulty lines; bounded by Wi-Fi communication range | ||
[82] | ✓ | Monitoring power lines with no optical cables or GPRS; Wi-Fi through TCP/IP; high overhead handling and processing of aggregated data from multiple sensors on operation center; electromagnetic compatibility is a challenge | |||
[83] | ✓ | Zigbee and GPRS; good for remote areas; the system has low power consumption |
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Al Mtawa, Y.; Haque, A.; Halabi, T. A Review and Taxonomy on Fault Analysis in Transmission Power Systems. Computation 2022, 10, 144. https://doi.org/10.3390/computation10090144
Al Mtawa Y, Haque A, Halabi T. A Review and Taxonomy on Fault Analysis in Transmission Power Systems. Computation. 2022; 10(9):144. https://doi.org/10.3390/computation10090144
Chicago/Turabian StyleAl Mtawa, Yaser, Anwar Haque, and Talal Halabi. 2022. "A Review and Taxonomy on Fault Analysis in Transmission Power Systems" Computation 10, no. 9: 144. https://doi.org/10.3390/computation10090144
APA StyleAl Mtawa, Y., Haque, A., & Halabi, T. (2022). A Review and Taxonomy on Fault Analysis in Transmission Power Systems. Computation, 10(9), 144. https://doi.org/10.3390/computation10090144