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.

scholarly journals Early warning of tsunami from seismo-ionospheric fluctuation after Japan’s March 11, 2011, M=9.0 Tohoku earthquake using two-dimensional principal component analysis

2015 ◽  
Vol 58 (4) ◽  
Author(s):  
Jyh-Woei Lin
Keyword(s):  
Principal Component Analysis ◽  
Shock Waves ◽  
Early Warning ◽  
Principal Component ◽  
Tohoku Earthquake ◽  
Component Analysis ◽  
Satellite System ◽  
Electron Content ◽  
Two Dimensional ◽  
Total Electron

<p>Two-dimensional principal component analysis (2DPCA) is implemented to analyze the total electron content (TEC) anomalies after Japan’s Tohoku earthquake that occurred at 05:46 on March 11, 2011 (UTC) (M<sub>w</sub>=9). 2DPCA and TEC data processing were conducted just after this devastating earthquake. Analysis results show an earthquake-associated TEC anomaly near the epicenter that began at 05:47. This may represent an extension of the precursor of the earthquake, to the precursor of China’s Wenchuan earthquake on May 12, 2008, detected by the study of Lin [2012], for which the data were obtained at a height of 150-200 km by the FORMOSAT-3 satellite system. It is impossible that such precursor caused by the acoustic shock waves. Another TEC anomaly near the epicenter occurred at 05:53, and this initiated the propagation of the tsunami effect related to the ionosphere through the acoustic shock waves from the epicenter. However, the TEC anomalies did not appear to be affected by a contemporaneous geomagnetic storm and other non-earthquake effects. The propagation of anomalous fluctuation could be an early warning of the tsunami for the regions far from the epicenter as it began to propagate with the higher speed of 3960-4950 km/h than the tsunami speed (720-800 km/h).</p><div> </div>

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.

The Establishment of Principal Component Analysis Assessment Model for Drinking Water Quality of City Resource

2014 ◽  
Vol 675-677 ◽  
pp. 960-963
Author(s):  
Li Feng Sun ◽  
Qing Jie Qi ◽  
Xiao Liang Zhao ◽  
Rui Feng Li
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Water Resources ◽  
Principal Component ◽  
Water Source ◽  
Component Analysis ◽  
Assessment Model ◽  
Affecting Factors

In order to effectively control pollution of sources of drinking water, improve the environmental quality of drinking water and guarantee the sanitation of drinking water, it is very important to assess water source quality. Main factors of drinking water were identified. Then principal component analysis was used to establish assessment model of drinking water, which could ensure that under the condition that the primitive data information was in the smallest loss, a small number of variables were used to replace the integrated multi-dimensional variables to simplify the data structure. The weightings of principal component were determinated as theirs pollution ratios. This paper was based on the theoretical study of principal component analysis, used the monitoring data on water quality of the main water resources in 2013 to evaluate and analyze the water quality of water resources. Analysis content included the main affecting factors, cause of pollution and the degree of pollution.The resulted showed that: the main affecting factors on water quality of Fo Si water source was CODMn, TP, fluoride.

Evaluation of Knowledge Resource Service Ability Based on Robust Projection Pursuit - Principal Component Analysis under Cloud Manufacturing Environment

2013 ◽  
Vol 397-400 ◽  
pp. 42-46
Author(s):  
Nan Zhao ◽  
Hong Yu Shao
Keyword(s):  
Principal Component Analysis ◽  
Projection Pursuit ◽  
Principal Component ◽  
Component Analysis ◽  
Cloud Manufacturing ◽  
Assessment Model ◽  
Design Knowledge ◽  
Manufacturing Environment ◽  
Knowledge Resource ◽  
Resource Service

According to the current situations of the unorganized and disorderly design knowledge as well as the weak innovation capability for SMEs under cloud manufacturing environment, and aiming at combining the design knowledge into ordered knowledge resource series, the service ability assessment model of knowledge resource was eventually proposed, and moreover, the Projection Pursuit-Principal Component Analysis (PP-PCA) algorithm for service ability assessment was further designed. The study in this paper would contribute to the realization of the effectiveness and accuracy of the knowledge push service, which exhibited a significant importance for improving the reuse efficiency of knowledge resources and knowledge service satisfaction under the cloud manufacturing environment.

Structural performance assessment considering both observed and latent environmental and operational conditions: A Gaussian process and probability principal component analysis method

2022 ◽  
pp. 147592172110620
Author(s):  
Yi-Chen Zhu ◽  
Wen Xiong ◽  
Xiao-Dong Song
Keyword(s):  
Principal Component Analysis ◽  
Gaussian Process ◽  
Performance Assessment ◽  
Structural Changes ◽  
Principal Component ◽  
Component Analysis ◽  
Assessment Model ◽  
Monitoring Data ◽  
Model Parameters ◽  
Structural Responses

Structural faults like damage and degradations will cause changes in structure response data. Performance assessment can be conducted by investigating such changes. In real implementations however, structural responses are affected by environmental and operational variations (EOVs) as well. Such variation should be well captured by the assessment model when detecting structural changes. It should be noted that not all EOVs can be measured by the monitoring system. When both observed and latent EOVs have significant effects on the monitored structural responses, these two effects should be considered properly. Furthermore, uncertainties can be significant for the monitoring data since loads and EOVs cannot be directly controlled under working conditions. To address these problems, this work proposes a performance assessment method considering both observed and latent EOVs. A Gaussian process is used to model the functional behaviour between structural response and observed EOVs whilst principal component analysis is used to eliminate the effect of latent EOVs. These two methods are combined using a Bayesian formulation and the effect of both observed and latent EOVs are modelled. The associated model parameters are inferred through probability density functions to account for the uncertainties. A synthetic data example is presented to validate the proposed method. It is also applied to the monitoring data of a long-span cable-stayed bridge with different damage scenarios considered, illustrating its capability of real implementations in structural health monitoring.

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