Sensitivity analysis and principal component analysis in free energy calculations

1991 ◽  
Vol 95 (24) ◽  
pp. 9628-9630 ◽  
Author(s):  
C. F. Wong ◽  
H. Rabitz
Keyword(s):  
Principal Component Analysis ◽  
Sensitivity Analysis ◽  
Free Energy ◽  
Principal Component ◽  
Component Analysis ◽  
Free Energy Calculations ◽  
Energy Calculations

scholarly journals Online Monitoring and Early Warning of Subsynchronous Oscillation Using Levenberg–Marquardt and Backpropagation Algorithm Combined with Sensitivity Analysis and Principal Component Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lingjie Wu ◽  
Ming Zhou ◽  
Yanwen Wang ◽  
Le Wang ◽  
Xu Tian
Keyword(s):  
Principal Component Analysis ◽  
Sensitivity Analysis ◽  
Early Warning ◽  
Online Monitoring ◽  
Attenuation Factor ◽  
Principal Component ◽  
Component Analysis ◽  
Assessment Model ◽  
Subsynchronous Oscillation ◽  
Major Factors

Over the past few years, with the access of large-scale new energy sources, the problem of subsynchronous oscillation (SSO) in power systems has presented a novel multisource and multitransformation form, which may be significantly threatening. Conventional control and protection methods primarily give rise to device protection actions in the presence of severe oscillation. On the whole, online monitoring only identifies the frequency and amplitude, whereas it cannot identify the attenuation factor. Moreover, the determination of the warning threshold is more dependent on human experience, so the reliability and rapidity of the early warning cannot be ensured. This study conducts an in-depth investigation of the wind-thermal power bundling and extreme high-voltage alternating current- (AC-) direct current (DC) hybrid transmission system. The major factors of SSO using this system are unclear, which brings difficulties to effective monitoring. Given the mentioned problems, a method combining Levenberg–Marquardt- (LM-) Backpropagation (BP) machine learning and Sensitivity Analysis (SA) and principal component analysis (PCA) is developed. First, the sensitivity analysis of each factor in the system is conducted to identify the major factors of SSO. Subsequently, the historical sample data are reduced with the principal component analysis to reduce the redundancy, which is adopted to train the regression model to determine the attenuation factor and frequency and then send them to the classifier for classification to complete the task of the assessment model. When a novel data signal is uploaded, the assessment model identifies the attenuation factor and frequency and subsequently determines the presence of SSO. Accordingly, an early warning is conducted. The system's refined simulation model and machine learning model verify the effectiveness of the method.

Construction of the free energy landscape of biomolecules via dihedral angle principal component analysis

2008 ◽  
Vol 128 (24) ◽  
pp. 245102 ◽  
Author(s):  
Alexandros Altis ◽  
Moritz Otten ◽  
Phuong H. Nguyen ◽  
Rainer Hegger ◽  
Gerhard Stock
Keyword(s):  
Principal Component Analysis ◽  
Free Energy ◽  
Dihedral Angle ◽  
Energy Landscape ◽  
Principal Component ◽  
Component Analysis ◽  
Free Energy Landscape
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