Distribution Network Power Quality Insights With Optimally Placed Micro-PMUs Incorporating Synthetic and Real Field Data
Author:
Affiliation:
1. School of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC, Australia
Funder
Royal Melbourne Institute of Technology (RMIT) University RTP Stipend Scholarship
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10292636.pdf?arnumber=10292636
Reference107 articles.
1. Symmetrical Components-Based Modified Technique for Power-Quality Disturbances Detection and Classification
2. Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree
3. Recognition of power quality disturbances using S-transform based ruled decision tree and fuzzy C-means clustering classifiers
4. EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments
5. Hybrid Wavelet and Hilbert Transform With Frequency-Shifting Decomposition for Power Quality Analysis
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1. Synchrophasor Technology Applications and Optimal Placement of Micro-Phasor Measurement Unit (μPMU): Part I;2024 28th International Electrical Power Distribution Conference (EPDC);2024-04-23
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