scholarly journals Frequency domain analysis of low-frequency oscillations in large electric power systems. Interim report. Applications of a small-scale computer program

1978 ◽  
Author(s):  
R. T. Byerly ◽  
D. E. Sherman ◽  
R. J. Bennon
Keyword(s):  
Power Systems ◽  
Computer Program ◽  
Electric Power ◽  
Frequency Domain ◽  
Low Frequency ◽  
Electric Power Systems ◽  
Frequency Domain Analysis ◽  
Small Scale ◽  
Interim Report ◽  
Low Frequency Oscillations

On-line estimation of low frequency modes in electric power systems

1988 ◽  
Vol 14 (2) ◽  
pp. 149-153
Author(s):  
M.A. Magdy ◽  
K. Korzeniowski ◽  
H.M. Jones
Keyword(s):  
Power Systems ◽  
Electric Power ◽  
Low Frequency ◽  
Electric Power Systems ◽  
On Line

scholarly journals Active Power Oscillation Property Classification of Electric Power Systems Based on SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ju Liu ◽  
Wei Yao ◽  
Jinyu Wen ◽  
Haibo He ◽  
Xueyang Zheng
Keyword(s):  
Power Systems ◽  
Power System ◽  
Electric Power ◽  
Large Scale ◽  
Control Strategies ◽  
Damping Ratio ◽  
Low Frequency ◽  
Electric Power Systems ◽  
Active Power ◽  
Power Oscillation

Nowadays, low frequency oscillation has become a major problem threatening the security of large-scale interconnected power systems. According to generation mechanism, active power oscillation of electric power systems can be classified into two categories: free oscillation and forced oscillation. The former results from poor or negative damping ratio of power system and external periodic disturbance may lead to the latter. Thus control strategies to suppress the oscillations are totally different. Distinction from each other of those two different kinds of power oscillations becomes a precondition for suppressing the oscillations with proper measures. This paper proposes a practical approach for power oscillation classification by identifying real-time power oscillation curves. Hilbert transform is employed to obtain envelope curves of the power oscillation curves. Twenty sampling points of the envelope curve are selected as the feature matrices to train and test the supporting vector machine (SVM). The tests on the 16-machine 68-bus benchmark power system and a real power system in China indicate that the proposed oscillation classification method is of high precision.

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