A Comparative Analysis of Decision Trees, Support Vector Machines and Artificial Neural Networks for On-line Transient Stability Assessment

被引:0
作者
Gregory Baltas, Nicholas [1 ]
Mazidi, Peyman [1 ]
Ma, Jin [2 ]
de Asis Fernandez, Francisco [3 ]
Rodriguez, Pedro [4 ]
机构
[1] Loyola Andalucia Univ, Seville, Spain
[2] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW, Australia
[3] Loyola Andalucia Univ, Cordoba, Spain
[4] Tech Univ Catalonia, Loyola Andalucia Univ, Seville, Spain
来源
2018 INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND TECHNOLOGIES (SEST) | 2018年
基金
欧盟地平线“2020”;
关键词
Transient Stability Assessment; Decision Trees; Support Vector Machines; Artificial Neural Networks; Imbalanced Datasets; Machine Learning; PREDICTION;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Transient instability is considered the most severe form of instability in power systems with grave socioeconomic repercussions if not prevented. Conventional methods, such as time domain simulations and direct methods impose limitations to fast on-line transient stability assessment in modern power systems. The development of phasor measurement units paved the way for transient stability assessment by means of artificial intelligence for pattern recognition and classification. Many classification algorithms have been reported in the literature for assessing transient stability. This paper aims to provide insights regarding which algorithm is more suitable for a given dataset for power system stability assessment. For this purpose, decision trees, support vector machines and artificial neural networks are investigated for their ability to address the binary stability classification problem in a comparative analysis for two datasets. The two datasets differ in terms of class distribution so that the impact of imbalanced datasets on classification accuracy could also be studied. The above datasets are created using MATLAB with two extension packages of MATPOWER and MATDYN to simulate different contingency scenarios in IEEE-9 bus test system.
引用
收藏
页数:6
相关论文
共 28 条
[1]   Transient stability prediction by a hybrid intelligent system [J].
Amjady, Nima ;
Majedi, Seyed Farough .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2007, 22 (03) :1275-1283
[2]   Transient Instability Prediction Using Decision Tree Technique [J].
Amraee, Turaj ;
Ranjbar, Soheil .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2013, 28 (03) :3028-3037
[3]   Real-Time Prediction and Control of Transient Stability Using Transient Energy Function [J].
Bhui, Pratyasa ;
Senroy, Nilanjan .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2017, 32 (02) :923-934
[4]   A survey on feature selection methods [J].
Chandrashekar, Girish ;
Sahin, Ferat .
COMPUTERS & ELECTRICAL ENGINEERING, 2014, 40 (01) :16-28
[5]  
Chawla NV, 2010, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, SECOND EDITION, P875, DOI 10.1007/978-0-387-09823-4_45
[6]  
Chiang H., 2010, Direct Methods for Stability Analysis of Electric Power Systems: Theoretical Foundation, BCU Methodologies, and Applications
[7]   Stability issues and new techniques for on-line transient stability analysis in Asia [J].
Chung, TS ;
Dazhong, F .
ELECTRIC POWER SYSTEMS RESEARCH, 1998, 44 (01) :45-52
[8]  
De Silva Jayasekara KYB, 2007, DETERMINATION TRANSI
[9]   Robust Relief-Feature Weighting, Margin Maximization, and Fuzzy Optimization [J].
Deng, Zhaohong ;
Chung, Fu-Lai ;
Wang, Shitong .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2010, 18 (04) :726-744
[10]   EEM 2017 Forecast Competition: Wind power generation prediction using autoregressive models [J].
Dimoulkas, Ilias ;
Mazidi, Peyman ;
Herre, Lars .
2017 14TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM 17), 2017,