Online monitoring of batch processes using multi-phase principal component analysis

2006 ◽  
Vol 16 (10) ◽  
pp. 1021-1035 ◽  
Author(s):  
José Camacho ◽  
Jesús Picó
Keyword(s):  
Principal Component Analysis ◽  
Online Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Batch Processes ◽  
Multi Phase

scholarly journals Online Monitoring of Water-Quality Anomaly in Water Distribution Systems Based on Probabilistic Principal Component Analysis by UV-Vis Absorption Spectroscopy

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Dibo Hou ◽  
Shu Liu ◽  
Jian Zhang ◽  
Fang Chen ◽  
Pingjie Huang ◽  
...  
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Contamination ◽  
Distribution Systems ◽  
Water Distribution ◽  
Online Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Probabilistic Principal Component Analysis ◽  
Contamination Events

This study proposes a probabilistic principal component analysis- (PPCA-) based method for online monitoring of water-quality contaminant events by UV-Vis (ultraviolet-visible) spectroscopy. The purpose of this method is to achieve fast and sound protection against accidental and intentional contaminate injection into the water distribution system. The method is achieved first by properly imposing a sliding window onto simultaneously updated online monitoring data collected by the automated spectrometer. The PPCA algorithm is then executed to simplify the large amount of spectrum data while maintaining the necessary spectral information to the largest extent. Finally, a monitoring chart extensively employed in fault diagnosis field methods is used here to search for potential anomaly events and to determine whether the current water-quality is normal or abnormal. A small-scale water-pipe distribution network is tested to detect water contamination events. The tests demonstrate that the PPCA-based online monitoring model can achieve satisfactory results under the ROC curve, which denotes a low false alarm rate and high probability of detecting water contamination events.

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.

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