scholarly journals A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Neng Zhang ◽  
Huimin Qian ◽  
Yuchao He ◽  
Lirong Li ◽  
Chaoyun Sun
Keyword(s):  
Power System ◽  
Knowledge Discovery ◽  
Data Driven ◽  
Instability Mode ◽  
Mode Identification ◽  
Transient Instability

Data-driven fault diagnosis of satellite power system using fuzzy Bayes risk and SVM

2019 ◽  
Vol 84 ◽  
pp. 1092-1105 ◽  
Author(s):  
Mingliang Suo ◽  
Baolong Zhu ◽  
Ruoming An ◽  
Huimin Sun ◽  
Shengzhong Xu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Data Driven ◽  
Bayes Risk

scholarly journals Data-Driven Power System Operations

2006 ◽  
pp. 448-455 ◽  
Author(s):  
E. H. Abed ◽  
N. S. Namachchivaya ◽  
T. J. Overbye ◽  
M. A. Pai ◽  
P. W. Sauer ◽  
...  
Keyword(s):  
Power System ◽  
Data Driven ◽  
Power System Operations

Effective Statistical Methods for Big Data Analytics

2017 ◽  
pp. 280-299 ◽  
Author(s):  
Cheng Meng ◽  
Ye Wang ◽  
Xinlian Zhang ◽  
Abhyuday Mandal ◽  
Wenxuan Zhong ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Knowledge Discovery ◽  
Statistical Methods ◽  
Large Scale ◽  
Big Data Analytics ◽  
Divide And Conquer ◽  
Data Driven ◽  
The Past ◽  
Large Scale Dataset

With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.

Knowledge Discovery Process Models

2012 ◽  
pp. 72-100 ◽  
Author(s):  
Mouhib Alnoukari ◽  
Asim El Sheikh
Keyword(s):  
Life Cycle ◽  
Knowledge Discovery ◽  
Process Model ◽  
Common Factor ◽  
Final Outcome ◽  
Process Models ◽  
Data Driven ◽  
Discovery Process ◽  
The Common ◽  
Discovery Process Models

Knowledge Discovery (KD) process model was first discussed in 1989. Different models were suggested starting with Fayyad’s et al (1996) process model. The common factor of all data-driven discovery process is that knowledge is the final outcome of this process. In this chapter, the authors will analyze most of the KD process models suggested in the literature. The chapter will have a detailed discussion on the KD process models that have innovative life cycle steps. It will propose a categorization of the existing KD models. The chapter deeply analyzes the strengths and weaknesses of the leading KD process models, with the supported commercial systems and reported applications, and their matrix characteristics.

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