NDLI: Combined analysis of distribution-level PMU data with transmission-level PMU for early detection of long-term voltage instability

Content Provider	IET Digital Library
Author	Zanjani, Mohsen Ghalei Monfared Mazlumi, Kazem Kamwa, Innocent
Abstract	In the present study, a new method is proposed for early voltage instability detection based on the statistical analysis of the data obtained from PMUs and micro-PMUs. Although, the use of high sampling rate measurements helps operators to assess the dynamic behavior of the systems, but it may lead to processing a large volume of data, which is a main challenge in this regard. Here, K-Medoid partitioning method is used for clustering and reducing the computational burden. Clustering is done based on the analysis of the voltage magnitude variance in unstable scenarios. Based on the critical slowing down phenomenon, the voltage magnitude variance in the critical transmission-level busbars and in the power plant busbars are used as instability detection indices. The data measured by PMUs give information about severity of the events, and micro-PMUs data provide information on operating status of the over-excitation limiters as well as the resiliency of network to keep voltage. In different conditions of the Nordic test system, all contingencies are considered for data training. Efficiency of the proposed method for early detection of instability in online operation is evaluated by AdaBoost algorithm, and the obtained results are compared by those of other classifiers.
Starting Page	3634
Ending Page	3641
Page Count	8
ISSN	17518687
Volume Number	13
e-ISSN	17518695
Issue Number	Issue 16, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/13/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.0311
Journal	IET Generation, Transmission & Distribution
Publisher Date	2019-07-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Busbars Combined Analysis Computational Burden Critical Transmission-level Busbars Data Training Distribution Level Distribution-level PMU Data Dynamic Behaviour Early Voltage Instability Detection High Sampling Rate Measurement Instability Detection Indices K-Medoid Partitioning Method Long-term Voltage Instability MicroPMUs Data Nordic Test System Online Operation Operating Status Pattern Clustering Phasor Measurement PMUs Power Grid Power Plant Busbars Power System Measurement Power System Measurement And Metering Power System Reliability Reliability Statistical Analysis Statistics Transmission Level Transmission-level PMU Unstable Scenario Voltage Magnitude Variance
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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