Wide frequency oscillation mode identification based on improved Prony algorithm

2020 ◽  
Author(s):  
Chen Yang ◽  
Wenfei Yi ◽  
Chen Fan ◽  
Yiping Yu ◽  
Renhui Dou
Keyword(s):  
Oscillation Mode ◽  
Frequency Oscillation ◽  
Mode Identification ◽  
Prony Algorithm

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.

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 Householder-based Prony Method for Identification of Low-frequency Oscillation in Power System

2019 ◽  
Vol 118 ◽  
pp. 02073
Author(s):  
Simin Zhang ◽  
Guangye Liu ◽  
Yaxin Li ◽  
Jiangang Yao
Keyword(s):  
Power System ◽  
Oscillation Mode ◽  
Low Frequency ◽  
Large Error ◽  
Analysis Tool ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
Prony Method ◽  
Prony Algorithm ◽  
Actual Operation

In this paper, the Prony analysis tool with the software used in the actual operation has a large error. The improved Prony method is used to identify the low-frequency oscillation mode of the power system. The traditional Prony algorithm has the problem of insensitivity to parameter error estimation in low frequency oscillation, which affects the accuracy and accuracy of the least squares calculation. Therefore, this paper proposes a Prony method power system low frequency oscillation method using Householder transform to make the calculation more Accurate and proven to be more efficient and reliable.

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