Electrical power transmission systems suffer from unexpected failures due to various random causes. Un-predicted faults that occur in power systems are required to prevent from propagation to other area in the protective system. The functions of the protective systems are to detect, then classify and finally determine the location of the faulty. This paper presents some techniques that helps to find, determine and diagnosing faults in transmission line. Artificial neural networks, impedance measurement based methods, fuzzy expert method, wavelet transform and so on have been used to achieve fault identification and classification.This paper will review the type of fault that possibly occurs in an electric power system, the type of fault detection and location technique that are available together with the protection device that can be utilized in the power system to protect the equipment from electric fault.
Table of Contents
I. INTODUCTION
II. OBJECTIVE OF THE REVIEW
III. MOTIVATION OF THE REVIEW
IV. OVERVIEW OF TRANSMISSION LINE FAULTS
A. Causes transmission line faults
B. Types of transmission line Faults
V. FAULT DETECTION TECHNIQUES OF TRANSMISSION LINE
VI. FAULT PROTECTION DEVICES IN TRANSMISSION SYSTEM
VII. OBSERVATIONS
VIII. CONCLUSION AND RECOMMENDATION
A. Conclusion
B. Recommendations
IX. REFERENCES
Research Objectives and Topics
The primary objective of this review is to consolidate existing research on fault detection, identification, and location techniques for electrical power transmission lines, while identifying current research gaps and comparing various methodological approaches.
- Causes and classification of transmission line faults
- Application of Artificial Neural Networks (ANN) in fault diagnosis
- Use of Fuzzy Logic and Expert Systems in protection schemes
- Performance of Wavelet Transform in transient analysis
- Integration of PMU and SVM technologies for system reliability
Excerpt from the Book
IV. OVERVIEW OF TRANSMISSION LINE FAULTS
Transmission line fault could not be avoided in an electrical power system, some protection devices are needed to protect the expensive equipment in electric power systems before the fault occurrence. But after the fault occurs once on the system we must determine fault locations and types for maintaining the system. Because of those reason, we will design smart devices and techniques in our system [28].
A. Causes transmission line faults
The most common causes of faults in overhead lines are [78]:
Aircraft and cars hitting lines and structures
Birds and animals
Contaminated insulators
Ice and snow loading
Lighting
Partial discharges (corona) not controlled
Punctured or broken insulators
Trees
Wind and so on.
Summary of Chapters
I. INTODUCTION: Provides an overview of the vulnerability of transmission lines and reviews previous research efforts regarding fault detection algorithms.
II. OBJECTIVE OF THE REVIEW: States the paper's goal of compiling and comparing various identification and detection methods to identify research gaps.
III. MOTIVATION OF THE REVIEW: Highlights the necessity of efficient fault diagnosis as an ongoing and critical research challenge in power systems.
IV. OVERVIEW OF TRANSMISSION LINE FAULTS: Details the primary environmental causes of faults and categorizes them into series and shunt fault types.
V. FAULT DETECTION TECHNIQUES OF TRANSMISSION LINE: Explains the classification of techniques into fault type determination and distance calculation, including SVM and PMU methods.
VI. FAULT PROTECTION DEVICES IN TRANSMISSION SYSTEM: Discusses various protective hardware components used to identify abnormal signals in the system.
VII. OBSERVATIONS: Evaluates the effectiveness of different approaches, such as ANN, feed-forward networks, and wavelet-based techniques, for specific fault scenarios.
VIII. CONCLUSION AND RECOMMENDATION: Summarizes the effectiveness of machine learning methods and suggests future improvements regarding data augmentation and fault dictionaries.
Keywords
Artificial neural networks, Fault identification and classification, Fuzzy expert method, Impedance measurement based methods, Transmission systems, Wavelet transform, Protection device, SVM, PMU, Power systems, Fault diagnosis, Machine learning, Deep learning, Electrical failures.
Frequently Asked Questions
What is the core focus of this research paper?
This paper provides a comprehensive review of various techniques and technologies currently used to detect, classify, and locate faults in electrical power transmission lines.
What are the primary themes discussed in the paper?
The paper covers the causes of transmission faults, the categorization of fault types (series vs. shunt), and a variety of detection techniques including AI-based and mathematical modeling approaches.
What is the main goal of the authors?
The goal is to gather studies under a single reference framework to compare different methodologies and highlight areas where further research is needed.
Which scientific methods are analyzed?
The authors analyze several methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Wavelet Transform, Fuzzy Logic, and Phasor Measurement Unit (PMU) techniques.
What is covered in the main part of the paper?
The main sections cover the environmental causes of faults, the classification of faults, detailed descriptions of specific diagnostic techniques, and a review of hardware protection devices.
Which keywords best characterize this research?
Key terms include artificial neural networks, fault identification, transmission systems, wavelet transform, protection devices, and fault classification.
How do series and shunt faults differ according to the text?
Series faults involve open conductors and unbalanced impedance, whereas shunt faults represent short circuits, which can be further classified as symmetrical or unsymmetrical.
What role do PMUs play in fault location?
Phasor Measurement Units allow for synchronized measurements across different terminal points, which is critical for accurate fault location in multi-section or composite transmission lines.
What do the authors recommend for future research?
The authors recommend expanding the size of existing fault dictionaries and further investigating the impact of data augmentation to improve the accuracy of artificial neural networks.
- Quote paper
- Seada Hussen Adem (Author), 2020, Fault Detection, Protection and Location on Transmission Line. A Review, Munich, GRIN Verlag, https://www.grin.com/document/936637
