A fault protection and location method for a dc bus microgrid system is presented in this paper. Unlike traditional ac systems, dc bus systems cannot survive or sustain high-magnitude fault currents. And if a fault causes the dc bus to de-energize completely, it makes locating faults very difficult. The main goal of the proposed scheme is to detect and isolate faults in the dc bus without de-energizing the entire system and identifying the fault location. In order to achieve this, a ring-type bus was used in this paper. The bus was segmented into overlapping nodes and links with circuit breakers (CBs) to isolate the segment in the event of a fault. Backup protection is implemented for circuit breaker failures to improve system reliability. A noniterative fault-location technique using a probe power is also presented in this paper. This probe power can also be used for a pilot test before main CB reclosing to avoid system issues that can be expected when the reclosing fails due to a permanent fault. The proposed algorithm can be implemented and executed by an intelligent electrical device for individual node. The proposed concepts have been verified with computer simulations and hardware experiments.

DC Ring-Bus Microgrid Fault Protection and Identification of Fault Location

This paper proposes a fault detection and classification method for medium voltage DC (MVDC) shipboard power systems (SPSs) by integrating wavelet transform (WT) multiresolution analysis (MRA) technique with artificial neural networks (ANNs). The MVDC system under consideration for future all-electric ships presents a range of new challenges, in particular the fault detection and classification issues addressed in this paper. The WT-MRA and Parseval's theorem are employed in this paper to extract the features of different faults. The energy variation of the fault signals at different resolution levels are chosen as the feature vectors. As a result of analysis and comparisons, the Daubechies 10 (db10) wavelet and scale 9 are the chosen wavelet function and decomposition level. Then, ANN is adopted to automatically classify the fault types according to the extracted features. Different fault types, such as short circuit faults on both dc bus and ac side, as well as ground fault, are analyzed and tested to verify the effectiveness of the proposed method. These faults are simulated in real time with a digital simulator and the data are then initially analyzed with MATLAB. The case study is a notional MVDC SPS model, and promising classification accuracy can be obtained according to simulation results. Finally, the proposed fault detection algorithm is implemented and tested on a real-time platform, which enables it for future practical use.

Fault Detection and Classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks

The integration of distributed generation (DG) units into distribution networks has challenged the operating principles of traditional AC distribution systems, and also motivated the development of emerging DC systems. Of particular concern are the challenges associated with the operation of conventional protection schemes and/or devices. This paper first analyses the fault current characteristics in AC and DC distribution systems; it then presents a comprehensive review of the latest protection methods proposed for distribution systems embedding DGs. In addition, the advantages and disadvantages of each method are outlined and compared. The differences between the protection algorithms employed in/proposed for AC and DC systems are also discussed. Finally, this study identifies the future trends and provides recommendation for researches in the field of protections of DC distribution networks.

Protection of AC and DC distribution systems. Embedding distributed energy resources: a comparative review and analysis

Protection techniques for DC microgrid- A review

A fault protection and location method for ungrounded dc traction power systems is presented in this paper. Many dc traction power systems have an ungrounded power circuit to increase the leakage path resistance. Although ungrounded systems can continue operating with a single ground contact, unlike solid- or low-resistance grounded systems, because of the very low fault current, a second ground fault in another pole will result in a line-to-line fault that could cause significant system damage. However, it is difficult to detect the first ground contact due to the low ground current and even harder to locate because it can be seen by many detectors in the network. The proposed scheme in this paper uses a probe unit to detect and locate the first single ground fault in ungrounded traction power systems. The probe unit applies probe voltage to detect the fault and, once the fault is detected, analyzes the response to dc or swept-frequency ac probe voltage to locate the fault. The proposed concepts have been verified with computer simulations and hardware experiments and demonstrated a successful performance.

Ground Fault Detection and Location for Ungrounded DC Traction Power Systems

A method is proposed for the real-time detection of DC-link short-circuit faults in DC transit systems. The discrete wavelet transform is implemented to detect any surges in the DC third-rail current waveform. In the event of a surge the wavelet transform extracts a feature vector from the current waveform and feeds it to a self-organising neural network. The neural network determines whether the feature vector belongs to a normal or a fault current surge.

Real-time detection using wavelet transform and neural network of short-circuit faults within a train in DC transit systems

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.

B. Liu

Author Tools

Chat about Author

Papers

Real-time detection using wavelet transform and neural network of short-circuit faults within a train in DC transit systems