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Real-Time Reduction of Micro Phasor Measurement Units and Noise Detection: California

Fitzgerald, Barry (2019) Real-Time Reduction of Micro Phasor Measurement Units and Noise Detection: California. Masters thesis, Dublin, National College of Ireland.

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Abstract

The electrical grid at present is going through one of its most fundamental changes where there is a high concentration of distributed renewable energy generation such as Wind Farms, Tidal Waves and Solar Power creating enormous intermittent flows of energy that can be fed back onto the power grid. Digital technology is being used to monitor the actual flow and consumption of electricity in real-time. To actively monitor the distribution network Micro-Phasor Measurement Units (μPMUs) are starting to be rolled out that create time-stamped Global Positioning System (GPS) synchrophasor data that actively monitors the state of the network in real-time. To actively monitor, and store this information this report provides a solution that addresses the dimensionality of synchrophasor data using Cassandra and Incremental Principal Components Analysis (IPCA) on a distributed system using Spark processing that reduces the dimensionality of the synchrophasor data from 15 dimensions to 1 principal component capturing over 98.9% of the variance for current and 2 components capturing over 96.5% for voltage. This approach captures over 96.5% of the energy without too much loss of information using five real-time μPMUs from the Lawrence Berkeley National Laboratory – Berkeley Lab1 in the US. The resultant principal components are then clustered using DBSCAN to detect noise which can have a detrimental effect on dimensionality reduction. The resultant information can then be used to actively create a Wide Area Monitoring (WAM) system for the smart grid.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electricity Supply
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 27 Nov 2019 12:22
Last Modified: 27 Nov 2019 12:22
URI: http://norma.ncirl.ie/id/eprint/4107

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