Assistant Professor Chanarong Banmongkol, D.Eng.
ผศ. ดร.ชาญณรงค์ บาลมงคล
Education
- D.Eng. (Electrical Engineering),Nagoya University,Japan.
- M.Eng. (Electrical Engineering),Nagoya University,Japan.
- B.Eng. Electrical Engineering Chulalongkorn University, Thailand.
Email: channarong.b@chula.ac.th
Research Interest
- Condition assessment of high-voltage equipment
- Power system transient analysis
- Modern power system protection
Research Cluster
Link to
1.
Abdullah, A; Banmongkol, C; Hoonchareon, N; Hidaka, K
Fault identification usingcombined adaptive neuro-fuzzy inference system and Gustafson-Kessel algorithm Journal Article
In: Journal of Engineering Research (Kuwait), vol. 6, no. 1, 2018, ISSN: 23071885, (cited By 1).
@article{Abdullah2018,
title = {Fault identification usingcombined adaptive neuro-fuzzy inference system and Gustafson-Kessel algorithm},
author = {A Abdullah and C Banmongkol and N Hoonchareon and K Hidaka},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074912603&partnerID=40&md5=d249ac0cb81434e9bc76d0b8f4dea265},
issn = {23071885},
year = {2018},
date = {2018-01-01},
journal = {Journal of Engineering Research (Kuwait)},
volume = {6},
number = {1},
publisher = {University of Kuwait},
abstract = {Issueson detecting the occurrence of a fault, justifying the type, and estimating the exact location of the faultshould be resolved to eliminate faults promptly and restore power supply with minimum interruption. Conventional approaches have contributed toassisting power utility in overcomingthese issues. However, these approaches rely on line parameters and involve a few complex mathematical equations. In this paper, a new method for fault identification pertinent to classification and location is proposed by utilizing the combined adaptive neuro-fuzzy inference system (ANFIS) and Gustafson-Kessel (GK) clustering algorithm. The effectiveness and practicability of this method aredemonstrated by the simulation results. This method uses the GK fuzzy clustering algorithm to decide on the premise configuration and its parameter and identifies itssucceeding parameter usingthe orthogonal least square. The proposed method is independent of the line parameter information and obtains high accuracy on estimation of fault locations. © 2018 University of Kuwait. All rights reserved.},
note = {cited By 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Issueson detecting the occurrence of a fault, justifying the type, and estimating the exact location of the faultshould be resolved to eliminate faults promptly and restore power supply with minimum interruption. Conventional approaches have contributed toassisting power utility in overcomingthese issues. However, these approaches rely on line parameters and involve a few complex mathematical equations. In this paper, a new method for fault identification pertinent to classification and location is proposed by utilizing the combined adaptive neuro-fuzzy inference system (ANFIS) and Gustafson-Kessel (GK) clustering algorithm. The effectiveness and practicability of this method aredemonstrated by the simulation results. This method uses the GK fuzzy clustering algorithm to decide on the premise configuration and its parameter and identifies itssucceeding parameter usingthe orthogonal least square. The proposed method is independent of the line parameter information and obtains high accuracy on estimation of fault locations. © 2018 University of Kuwait. All rights reserved.
2.
Abdullah, A; Banmongkol, C; Hoonchareon, N; Hidaka, K
A study on the gustafson-kessel clustering algorithm in power system fault identification Journal Article
In: Journal of Electrical Engineering and Technology, vol. 12, no. 5, pp. 1798-1804, 2017, ISSN: 19750102, (cited By 0).
@article{Abdullah2017,
title = {A study on the gustafson-kessel clustering algorithm in power system fault identification},
author = {A Abdullah and C Banmongkol and N Hoonchareon and K Hidaka},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027586677&doi=10.5370%2fJEET.2017.12.5.1798&partnerID=40&md5=7692e6f9d32c5878c2d43fe976dc90e5},
doi = {10.5370/JEET.2017.12.5.1798},
issn = {19750102},
year = {2017},
date = {2017-01-01},
journal = {Journal of Electrical Engineering and Technology},
volume = {12},
number = {5},
pages = {1798-1804},
publisher = {Korean Institute of Electrical Engineers},
abstract = {This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm’s performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM. © The Korean Institute of Electrical Engineers.},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents an approach of the Gustafson-Kessel (GK) clustering algorithm’s performance in fault identification on power transmission lines. The clustering algorithm is incorporated in a scheme that uses hybrid intelligent technique to combine artificial neural network and a fuzzy inference system, known as adaptive neuro-fuzzy inference system (ANFIS). The scheme is used to identify the type of fault that occurs on a power transmission line, either single line to ground, double line, double line to ground or three phase. The scheme is also capable an analyzing the fault location without information on line parameters. The range of error estimation is within 0.10 to 0.85 relative to five values of fault resistances. This paper also presents the performance of the GK clustering algorithm compared to fuzzy clustering means (FCM), which is particularly implemented in structuring a data. Results show that the GK algorithm may be implemented in fault identification on power system transmission and performs better than FCM. © The Korean Institute of Electrical Engineers.
3.
Abdullah, A; Banmongkol, C; Hoonchareon, N; Hidaka, K
Implementation of adaptive neuro-fuzzy inference system in fault location estimation Journal Article
In: Lecture Notes in Electrical Engineering, vol. 398, pp. 737-748, 2017, ISSN: 18761100, (cited By 0; Conference of 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, RoViSP 2016 ; Conference Date: 2 February 2016 Through 3 February 2016; Conference Code:184869).
@article{Abdullah2017a,
title = {Implementation of adaptive neuro-fuzzy inference system in fault location estimation},
author = {A Abdullah and C Banmongkol and N Hoonchareon and K Hidaka},
editor = {Teoh Mustaffa S S M T Ibrahim H. Iqbal S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992754538&doi=10.1007%2f978-981-10-1721-6_80&partnerID=40&md5=2296ffc86e63869ddb0884cb96c1fa36},
doi = {10.1007/978-981-10-1721-6_80},
issn = {18761100},
year = {2017},
date = {2017-01-01},
journal = {Lecture Notes in Electrical Engineering},
volume = {398},
pages = {737-748},
publisher = {Springer Verlag},
abstract = {This paper proposed a new scheme using hybrid intelligent technique that combines artificial neural network and fuzzy inference system. This technique, known as Adaptive Neuro-Fuzzy Inference System (ANFIS) has associated with the advantage of wavelet transform as a pattern recognition method. The algorithm used to identify the type of fault either single line to ground, double line, double line to ground or three phase occur on a power transmission line. Other than that, this scheme is capable to analyze the fault location without the knowledge of line parameters. A power clustering algorithm called Gustafson Kessel is implemented for better performance. Alternative Transient Program/Electromagnetic Transient Program (ATP/EMTP) is used for simulation purposes and Matlab for further analysis. Outcomes indicated that the scheme is efficient and has a high percentage of accuracy. © Springer Science+Business Media Singapore 2017.},
note = {cited By 0; Conference of 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, RoViSP 2016 ; Conference Date: 2 February 2016 Through 3 February 2016; Conference Code:184869},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper proposed a new scheme using hybrid intelligent technique that combines artificial neural network and fuzzy inference system. This technique, known as Adaptive Neuro-Fuzzy Inference System (ANFIS) has associated with the advantage of wavelet transform as a pattern recognition method. The algorithm used to identify the type of fault either single line to ground, double line, double line to ground or three phase occur on a power transmission line. Other than that, this scheme is capable to analyze the fault location without the knowledge of line parameters. A power clustering algorithm called Gustafson Kessel is implemented for better performance. Alternative Transient Program/Electromagnetic Transient Program (ATP/EMTP) is used for simulation purposes and Matlab for further analysis. Outcomes indicated that the scheme is efficient and has a high percentage of accuracy. © Springer Science+Business Media Singapore 2017.