Distribution systems are continuously exposed to fault occurrences due to various reasons, such as lightning strike, failure of power system components due to aging of equipment and human errors. These phenomena affect the system reliability and results in expensive repairs, lost of productivity and power loss to customers. Since fault is unpredictable, a fast fault location and isolation is required to minimize the impact of fault in distribution systems. Therefore, many methods have been developed since the past to locate and detect faults in distribution systems with distributed generation. The methods can be divided into two categories, conventional and artificial intelligence techniques. Conventional techniques include travelling wave method and impedance based method while artificial intelligence techniques include Artificial Neural Network (ANN), Support Vector Machine (SVM), Fuzzy Logic, Genetic Algorithm (GA) and matching approach. However, fault location using intelligent methods are challenging since they require training data for processing and are time consuming. In this paper, most of the techniques that have been developed since the past and commonly used to locate and detect faults in distribution systems with distributed generation are reviewed. Research works in fault location area, the working principles, advantages and disadvantages of past works related to each fault location technique are highlighted in this paper. Hence, from this review, the opportunities in fault location research area in power distribution system can be explored further.

Fault location and detection techniques in power distribution systems with distributed generation: A review

https://hal.archives-ouvertes.fr/hal-03100614/document

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

Dynamic protection of power systems with high penetration of renewables: A review of the traveling wave based fault location techniques

Due to the difficulty in locating high-resistance grounding faults, this paper proposes a novel fault location method for HVdc transmission lines by considering double-end unsynchronized using Hilbert–Huang transform and one-dimensional convolutional neural network (1D-CNN). After the fault signal is collected at both ends, the proposed method can achieve high-precision fault location, requiring only the two ends data transmission without time synchronization. After Empirical Mode Decomposition (EMD), the high-frequency components of the double-terminal fault signals are connected in series to make a characteristic waveform. This waveform contains characteristics of different fault types and distances, which can be learned by CNN. The trained CNN can then be used to achieve fault location effectively. As a comparison, two fault location methods based on traditional traveling wave and machine learning are introduced. Electromagnetic transient simulation software PSCAD/EMTDC has been used to carry out various types of fault simulation on the ± 500 kV HVdc transmission system. The results show that the proposed method can reliably and accurately locate line faults under fault resistance up to 5200 Ω.

A Novel HVDC Double-Terminal Non-Synchronous Fault Location Method Based on Convolutional Neural Network

Transmission lines are one of the most widely distributed engineering systems meant for transmitting bulk amount of power from one corner of a country to the farthest most in the other directions. The expansion of the lines over different terrains and geographic locations makes these most vulnerable to different kinds of atmospheric calamities which more often develops faults in line. It is imperative to remove the faulty line at the earliest to restrict undue outflow of bulk power through the faulted point as well as restore system stability earliest to resume normal power flow operation. Here lays the importance of having a robust fault identification, classification and localization algorithm which would be successfully able to drive as well as actuate the digital relaying system. Researchers have worked out several methodologies in developing improved power system protection algorithms which would be able to serve to eliminate faults immediately on occurrence of the same. A brief yet exhaustive review has been presented in this article including the several methodologies adopted by numerous researchers for developing effective fault diagnosis schemes, mentioning about the highlights as well as the shortcoming of each of the methods. This compact and effective survey of literature works would help researchers to take up appropriate techniques for different purposes of transmission line fault analysis.

Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods

A Method of Line Fault Location Based on Traveling Wave Theory

Electricity stealing not only makes great economic losses, but also threatens the order and security of power supply seriously. This paper starts from electricity theft, and puts forward an anti-electricity theft design based on electricity comparative method. According to programming realization and simulation test by MATLAB, we verify the feasibility and effectiveness of the design. The anti-electricity theft design can be applied to demotic users and middle/small-sized enterprises to reduce loss from electricity theft.

/pdf/study-on-anti-electricity-theft-based-on-electricity-3daodzqq9s.pdf

Study on Anti-electricity Theft Based on Electricity Comparative Method

Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR) will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

/pdf/a-model-of-intelligent-fault-diagnosis-of-power-equipment-43ptdsf4a9.pdf

A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Gang Ma

Author Tools

Chat about Author

Papers

A Method of Line Fault Location Based on Traveling Wave Theory

Study on Anti-electricity Theft Based on Electricity Comparative Method

A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR