Data-Driven Low Frequency Oscillation Mode Identification and Preventive Control Strategy Based on Gradient Descent

2020 ◽  
Vol 189 ◽  
pp. 106544
Author(s):  
Yiwei Fu ◽  
Lei Chen ◽  
Zhe Yu ◽  
Yishen Wang ◽  
Di Shi
Keyword(s):  
Control Strategy ◽  
Gradient Descent ◽  
Oscillation Mode ◽  
Low Frequency ◽  
Data Driven ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Preventive Control ◽  
Mode Identification

Smoothness Prior Approach to Removing Nonlinear Trends from Signals in Identification of Low Frequency Oscillation Mode

2014 ◽  
Vol 672-674 ◽  
pp. 1070-1074
Author(s):  
Yu Jiang Wang ◽  
Jian Li ◽  
Hui Xu ◽  
Hai Ou Yan ◽  
Jia Yuan ◽  
...  
Keyword(s):  
Regularization Parameter ◽  
Oscillation Mode ◽  
Low Frequency ◽  
Filter Method ◽  
Measured Signal ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Mode Identification ◽  
Nonlinear Trend ◽  
Nonlinear Trends

The online identification of low frequency oscillation mode based on measured signal from PMU is the base of damping control. Removing nonlinear trend from the signal effectively can ensure the precision of mode identification. A method named smoothness prior approach (SPA) is proposed to remove the nonlinear trend from measured signal. In order to meet the demand of removing nonlinear trend for identification of low frequency oscillation based on analyzing the basic principle of smoothness prior approach, it determines regularization parameter of smoothness prior approach according to its characteristic of frequency response. It is used to analyze the simulation signals from IEEE-39 bus power system and the measured signals in some power grid, and compared with empirical mode decomposition and digital filter method. The results demonstrate that this proposed method can successfully remove nonlinear trend from the signal and improve the speed of computation, as well as the precision of mode identification, which has a relatively high practical value.

The New Identification Method for Low Frequency Oscillation Mode in Power System Based on Prony Algorithm and Neural Network

2013 ◽  
Vol 321-324 ◽  
pp. 1400-1404 ◽  
Author(s):  
Ying Jie Tang ◽  
Ying Jun Tang ◽  
Xin Liang Xie
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Power System ◽  
High Reliability ◽  
Oscillation Mode ◽  
Low Frequency ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Mode Identification ◽  
Prony Algorithm

This paper presented a new improved Prony algorithm based on neural network to train weights.The algorithm solved some problems that difficulty and low precision during matrix inversion in Prony method. According to real-time transform characteristics of low frequency oscillation in power system, the algorithm used limited data windows in on-line parameter estimation and pattern recognition, and improved pattern recognition precision. The simulation results proved that this proposal algorithm has some features of directly ,effective, high reliability, less calculation amount and minor error when it be used to analysis oscillation characteristics and mode identification. So it is suitable for identification of low frequency oscillation mode in power system.

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