Power Quality Analysis Based on Time Series Similarity and Extreme Learning Machine

2018 ◽  
pp. 157-170
Author(s):  
Meimei Li ◽  
Shouqing Jia ◽  
Shuqing Zhang ◽  
Zheng Gong ◽  
Guaifu Li
Keyword(s):  
Time Series ◽  
Power Quality ◽  
Extreme Learning Machine ◽  
Quality Analysis ◽  
Power Quality Analysis ◽  
Learning Machine

scholarly journals Bundle Extreme Learning Machine for Power Quality Analysis in Transmission Networks

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1449 ◽  
Author(s):  
Ferhat Ucar ◽  
Jose Cordova ◽  
Omer F. Alcin ◽  
Besir Dandil ◽  
Fikret Ata ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Extreme Learning Machine ◽  
Quality Analysis ◽  
Quality Data ◽  
Real Field ◽  
Support Vector ◽  
Transmission Networks ◽  
Power Quality Analysis ◽  
Learning Machine

This paper presents a novel method for online power quality data analysis in transmission networks using a machine learning-based classifier. The proposed classifier has a bundle structure based on the enhanced version of the Extreme Learning Machine (ELM). Due to its fast response and easy-to-build architecture, the ELM is an appropriate machine learning model for power quality analysis. The sparse Bayesian ELM and weighted ELM have been embedded into the proposed bundle learning machine. The case study includes real field signals obtained from the Turkish electricity transmission system. Most actual events like voltage sag, voltage swell, interruption, and harmonics have been detected using the proposed algorithm. For validation purposes, the ELM algorithm is compared with state-of-the-art methods such as artificial neural network and least squares support vector machine.

Simulation and power quality analysis of a Loose-Coupled bipolar DC microgrid in an office building

2021 ◽  
Vol 303 ◽  
pp. 117606
Author(s):  
Ruiting Wang ◽  
Wei Feng ◽  
Huijie Xue ◽  
Daniel Gerber ◽  
Yutong Li ◽  
...  
Keyword(s):  
Power Quality ◽  
Office Building ◽  
Quality Analysis ◽  
Dc Microgrid ◽  
Power Quality Analysis

Design of Power Quality Management Software Based on Virtual Instrument and Oracle

2013 ◽  
Vol 427-429 ◽  
pp. 2441-2444
Author(s):  
Wei Chen ◽  
Long Chen ◽  
Ming Li
Keyword(s):  
Power Quality ◽  
Software Design ◽  
Virtual Instrument ◽  
Quality Analysis ◽  
Quality Data ◽  
Data Handling ◽  
Data Display ◽  
Report Generation ◽  
Management Capacity ◽  
Power Quality Analysis

This paper presents a software design useful for power quality analysis and data management. The software was programmed in LabVIEW and Oracle, running on Windows in a regular PC. LabVIEW acquires data continuously from the lower machine via TCP/IP. Using its database connection toolkit, LabVIEW accesses to Oracle to stores and retrieve the power quality data according to different indicators. A friendly GUI was built for data display and user operation, taking advantage of the powerful data-handling capacity of LabVIEW and its rich controls. Moreover, Excel reports can be exported using report generation toolkit in LabVIEW. The software greatly improves the data analysis and management capacity.

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