Distinction of the Property of Low Frequency Oscillation Based on ARMA Mode Identification

2013 ◽  
Vol 336-338 ◽  
pp. 1086-1091 ◽  
Author(s):  
Lian Tu ◽  
Di Chen Liu ◽  
Qing Fen Liao ◽  
Fei Fei Dong ◽  
Xing Pei Ji ◽  
...  
Keyword(s):  
Damping Ratio ◽  
Low Frequency ◽  
Arma Model ◽  
Recursive Least Squares ◽  
Mechanism Analysis ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Mode Identification ◽  
Negative Damping ◽  
The Difference

Aiming at how to solve the question of quantitatively distinguish negative damping low frequency oscillation and forced power oscillation, the difference in frequency and damping ratio between the two kinds of oscillations have been discovered through mechanism analysis, and a new quantitative distinction criterion for the property of the low frequency is proposed. The oscillation data is windowing identified by ARMA model based on weighted recursive least squares algorithm dynamically, and low frequency type can be distinguished according to the changes of oscillation frequency and damping ratio during the oscillation. The simulative results have shown that the method is feasible and effective.

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.

scholarly journals Inertia Control Strategy of DFIG-Based Wind Turbines Considering Low-Frequency Oscillation Suppression

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 29
Author(s):  
Haoming Liu ◽  
Suxiang Yang ◽  
Xiaoling Yuan
Keyword(s):  
Wind Turbines ◽  
Control Strategy ◽  
Low Frequency ◽  
Frequency Regulation ◽  
Analysis Method ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
The Difference ◽  
Inertia Control ◽  
The Impact

It has become a basic requirement for wind turbines (WTs) to provide frequency regulation and inertia support. The influence of WTs on the low-frequency oscillation (LFO) of the system will change after adopting inertia control methods. This paper intends to investigate and compare in detail the IC effects on LFO characteristics in two systems with different structures. First, the mechanism of inertia control of doubly fed induction generator (DFIG)-based WTs is analyzed. Then, the small-signal analysis method and modal analysis method are used to study the influence of the inertia control on the LFO characteristics based on the two-machine infinite-bus system and the four-machine two-area system, respectively. The difference in impact rules of IC on LFO is compared in detail. Finally, considering that the inertia control might worsen the LFO in some systems, an improved inertia control strategy of DFIG-based WTs is proposed to suppress the LFO. The simulation results demonstrate that, in systems with different structures, the impact rules of the inertia control parameters on LFO are different. With the improved inertia control strategy, DFIG-based WTs can suppress the LFO of the system and provide inertia support for the system.

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.

Analysis and Control on Oscillation Characteristics of Smart Grid with Large-Scale Wind Power Integration

2013 ◽  
Vol 732-733 ◽  
pp. 1342-1347
Author(s):  
Jia Chen Zhong ◽  
Wen Ying Liu ◽  
Wei Zheng
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Damping Ratio ◽  
Low Frequency ◽  
Power Network ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Wind Power Integration ◽  
And Control ◽  
Oscillation Characteristics

To deal with the problem of large-scale wind power integration and its influence on low frequency oscillation characteristics of Gansu power network, this paper built the low frequency oscillation simulation model with large amount of wind power integration, and proposed an index, namely grid structural weakness degree, based on the damping ratio index, to investigate low frequency oscillation characteristics. The simulation shows that the damping ratio decreases as the wind turbine output increases; and when the damping ratio is lower than 3%, or weakness degree lower than 4, it is more likely to cause low frequency oscillation in Gansu power network, and early-warning should be taken. The analysis provides a reference for low frequency oscillation early-warning and control.

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