DEFECT DIAGNOSTICS OF POWER PLANT GAS TURBINE USING HYBRID SVM-ANN METHOD

被引：0

作者：

Lee, Sangmyeong ^{[1
]}

Lee, Sanghun ^{[1
]}

Lim, Juchang ^{[1
]}

Lee, Sangbin ^{[1
]}

机构：

[1] POSCO ENERGY, Inchon, South Korea

来源：

PROCEEDINGS OF THE ASME GAS TURBINE INDIA CONFERENCE 2012 | 2012年

关键词：

ARTIFICIAL NEURAL-NETWORKS; SUPPORT VECTOR MACHINES; FAULT-DIAGNOSIS; ALGORITHM;

D O I：

暂无

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

A hybrid method of an artificial neural network (ANN) combined with a support vector machine (SVM) has been developed for the defect diagnostic system applied to the power plant gas turbine. This method has been suggested to overcome the demerits of the general ANN with the local minima problem and low classification accuracy in case of many nonlinear data. This hybrid approach takes advantage of the reduction of learning data and converging time without any loss of estimation accuracy therefore it has been applied for the power plant monitoring system in order to detect fails and status of compressors and turbines in detail. The results have shown the suggested defect diagnostic algorithm has reliable and suitable efficiency estimation accuracy.

引用

收藏

页码：725 / 732

页数：8

相关论文

共 50 条

[41] Event diagnosis method for a nuclear power plant using meta-learning [J].

Lee, Hee-Jae ;

Lee, Daeil ;

Kim, Jonghyun .

NUCLEAR ENGINEERING AND TECHNOLOGY, 2024, 56 (06) :1989-2001

[42] Enhanced Feature Selection Method Based on ANN and GA for Coal Boiler Plants Using Real Time Plant Data [J].

Haider, Zeeshan ;

Yin, Cheng ;

Zhang, Weidong ;

Zhang, Lizong ;

Yousaf, Mohammad ;

Ali, Nasir .

PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016, 2016, :7115-7119

[43] Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis [J].

Nanfak, Arnaud ;

Eke, Samuel ;

Meghnefi, Fethi ;

Fofana, Issouf ;

Ngaleu, Gildas Martial ;

Kom, Charles Hubert .

IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION, 2023, 30 (05) :2421-2428

[44] A hybrid method for power system state estimation using Cellular Computational Network [J].

Rahman, Md. Ashfaqur ;

Venayagamoorthy, Ganesh Kumar .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2017, 64 :140-151

[45] Improved frequency dynamic in isolated hybrid power system using an intelligent method [J].

Modirkhazeni, Amirhossein ;

Almasi, Omid Naghash ;

Khooban, Mohammad Hassan .

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2016, 78 :225-238

[46] A novel hybrid metaheuristic optimization method to estimate medium-term output power for horizontal axis wind turbine [J].

Ekinci, Firat ;

Demirdelen, Tugce ;

Aksu, Inayet Ozge ;

Aygul, Kemal ;

Esenboga, Burak ;

Bilgili, Mehmet .

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY, 2019, 233 (05) :646-658

[47] Fault detection and diagnostics of complex dynamic systems using Gaussian Process Models - nuclear power plant case study [J].

Puchalski, Bartosz .

2023 27TH INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN AUTOMATION AND ROBOTICS, MMAR, 2023, :286-291

[48] NUCLEAR-POWER-PLANT TRANSIENT DIAGNOSTICS USING ARTIFICIAL NEURAL NETWORKS THAT ALLOW DONT-KNOW CLASSIFICATIONS [J].

BARTAL, Y ;

LIN, J ;

UHRIG, RE .

NUCLEAR TECHNOLOGY, 1995, 110 (03) :436-449

[49] Condition monitoring of gas-turbine power units using the Derivative-free nonlinear Kalman Filter [J].

Rigatos, G. ;

Zervos, N. ;

Serpanos, D. ;

Siadimas, V. ;

Siano, Pierluigi ;

Abbaszadeh, Masoud .

2018 INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND TECHNOLOGIES (SEST), 2018,

[50] Bio-Inspired PHM Model for Diagnostics of Faults in Power Transformers Using Dissolved Gas-in-Oil Data [J].

Dong, Huanyu ;

Yang, Xiaohui ;

Li, Anyi ;

Xie, Zihao ;

Zuo, Yuanlong .

SENSORS, 2019, 19 (04)

← 1 2 3 4 5 →