Login Register Cart 0
Generic placeholder image

Recent Patents on Engineering

Editor-in-Chief

ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Reliability Assessment of Power Generation System Using an Optimized State Enumeration Method

Author(s): Zhiyan Zhang, Kaixuan Wang*, Guangxi Tian, Gang Xu and Hongfei Zhao

Volume 16, Issue 3, 2022

Published on: 26 August, 2021

Article ID: e260821195856 Pages: 8

DOI: 10.2174/1872212115666210826162958

Abstract

Background: The single state enumeration method cannot meet the requirement of accuracy and high efficiency in the reliability assessment of complex power systems because of many uncertain factors and the large scale of the power grid.

Methods: A new method of generating system reliability assessment based on Self-Organizing Map (SOM) neural network and state enumeration is presented. First, the input parameters of the state enumeration method are optimized by using the feature of the SOM neural network algorithm that can automatically, quickly, and accurately classify the sample parameters in this method. Second, combining with Markov Model, the optimized system state samples are divided into fault state and normal state, and then the reliability indexes are enumerated. Finally, this method is used to calculate the reliability indexes of IEEE-RTS single-stage power units under different operation conditions.

Results: The results show that this method is superior to the single state enumeration method in calculating time; it can be used to evaluate the reliability of modern complex power systems.

Conclusion: The optimized state enumeration method is more suitable to evaluate the reliability of the system with a large network scale, and its reliability index is more accurate; while retaining the higher calculation accuracy of the state enumeration method, it can promote the safe, reliable, and economical operation of the power system.

Keywords: Power unit, reliability assessment, SOM neural network, state enumeration method, single-stage power unit, power grid.

Graphical Abstract

[1]
X.U. Pengcheng, L.I.U. Wenxia, and C.H.E.N. Qi, "Reliability evaluation of large power system based on combination of important sampling and extreme learning machine", Elec. Power Automat. Equip., vol. 39, no. 2, pp. 204-210, 2019.
[2]
X.I.A.O. Li, L.I.U. Zhengyang, and S.H.I. Youyi, "Reliability evaluation of secondary system of smart substation based on markov model", Elec. Measur. Instrum., vol. 55, no. 7, pp. 36-40, 2018.
[3]
Z. Libo, C. Haoran, and Z. Pingliang, "Reliability evaluation of generation system using johnson system", J. Shanghai Jiaotong Univ., vol. 51, no. 5, pp. 555-562, 2017.
[4]
W. Min, M. Xintong, and W. Hemei, "Reliability analysis of generation systems with photovoltaic stations considering correlations based on copula theory", Kexue Jishu Yu Gongcheng, vol. 16, no. 4, pp. 54-59, 2016.
[5]
L. Huishi, C. Lin, and L. Sige, "Monte carlo simulation based reliability evaluation of distribution system containing microgrids", Power Sys. Technol., vol. 35, no. 10, pp. 76-81, 2011.
[6]
H. Yukun, W. Lixiu, and L. Jie, "A quasi-monte carlo method based power system reliability evaluation", Power Sys. Technol., vol. 39, no. 3, pp. 744-750, 2015.
[7]
Z. Yuan, Z. Niancheng, and X. Kaigui, "Research on technique of system state selection for bulk power system reliability assessment", Elec. Power, vol. 39, no. 6, pp. 5-9, 2006.
[8]
Z. Zhiyan, D. Kailang, and P. Xiaochen, "Research on the EV charging load estimation and mode optimization methods", Archives Elec. Eng., vol. 68, no. 4, pp. 831-842, 2019.
[9]
C. Borges, and J. Dias, "A model to represent correlated time series in reliability evaluation by non-sequential monte carlo simulation", IEEE Trans. Power Syst., vol. 7, pp. 1511-1519, 2017.
[10]
Z. Weifeng, C. Yanbo, and L. Yangsheng, "Improved latin hypercube sampling method for reliability evaluation of power systems", Dianli Xitong Zidonghua, vol. 39, no. 4, pp. 52-57, 2015.
[11]
P. Gautam, P. Piya, and R. Karki, "Development and integration of momentary event models in active distribution system reliability assessment", IEEE Trans. Power Syst., vol. 35, no. 4, pp. 3236-3246, 2020.
[http://dx.doi.org/10.1109/TPWRS.2019.2962177]
[12]
Z. Li, "Student member, wenchuan wu, boming zhang. analytical reliability assessment method for complex distribution networks considering post-fault network reconfiguration", IEEE Trans. Power Syst., vol. 35, no. 2, pp. 1457-1467, 2020.
[http://dx.doi.org/10.1109/TPWRS.2019.2936543]
[13]
A. Tabares, G. Munoz-Delgado, and J.F. Franco, "An enhanced algebraic approach for the analytical reliability assessment of distribution systems", IEEE Trans. Power Syst., vol. 34, no. 4, pp. 2870-2879, 2019.
[http://dx.doi.org/10.1109/TPWRS.2019.2892507]
[14]
Q. Hou, N. Zhang, and D.S. Kirschen, "Sparse oblique decision tree for power system security rules extraction and embedding", IEEE Trans. Power Syst., pp. 1-12, 2020.
[15]
S. Adhikari, R. Karki, and P. Piya, "Recovery risk mitigation of wind integrated bulk power system with flywheel energy storage", IEEE Trans. Power Syst., vol. 34, no. 5, pp. 3484-3493, 2019.
[http://dx.doi.org/10.1109/TPWRS.2019.2905782]
[16]
L. Qu, C. Han, and Y. Li, "Recent advances on reliability evaluation and optimization of linear multistate consecutively connected systems", Rec. Pat. Eng., vol. 15. 2021, no. 3, pp. 314-325.
[http://dx.doi.org/10.2174/1872212114999200517123155]
[17]
Y. Zhang, P. Agarwal, V. Bhatnagar, S. Balochian, and J. Yan, "Swarm intelligence and its applications", Sci. World J., vol. 2013, p. 528069, 2013.
[http://dx.doi.org/10.1155/2013/528069] [PMID: 24222739]
[18]
R. Sergo, P. Arsen, and A. Praveen, "Application of the fractional oscillator model to describe damped vibrations", Turk. J. Phys., vol. 43, pp. 236-242, 2019.
[http://dx.doi.org/10.3906/fiz-1811-16]
[19]
P. Agarwal, M. Abd-Allah, and H. Zakarya, "Well-posedness of stochastic modified Kawahara equation", Adv. Differ. Equ., vol. 1, pp. 1-10, 2020.
[20]
N.A. Shah, P. Agarwal, and J.D. Chung, "Analysis of optical solitons for nonlinear schrödinger equation with detuning term by iterative transform method", Symmetry (Basel), vol. 12, no. 11, p. 1850, 2020.
[http://dx.doi.org/10.3390/sym12111850]
[21]
S. Thongsuwana, S. Jaiyena, and A. Padcharoen, "ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost", Nucl. Eng. Technol., 2020.
[22]
A.M. Mohamed, A. Elbrekib, and M. Shaaban, "Revitalizing operational reliability of the electrical energy system in Libya: Feasibility analysis of solar generation in local communities", J. Clean. Prod., vol. 279. 2021, p. 123647.
[23]
A. Escalera, E.D. Castronuovo, and M. Prodanovic, "Reliability assessment of distribution networks with optimal coordination of distributed generation, energy storage and demand management", Energies, vol. 12, no. 16, p. 12, 2019.
[http://dx.doi.org/10.3390/en12163202]
[24]
X. Yujian, L. Min, and X. Tiantang, "Fault diagnosis of transformer winding and core based on som", J. Elec. Power Sci. Technol., vol. 33, no. 2, pp. 129-134, 2018.
[25]
G. Lijin, and L. Fengpei, "Temperature prediction of granary based on som clustering algorithm and grey improved neural network", Grain & Fat, vol. 32, no. 11, pp. 97-100, 2019.
[26]
L.R. Rodriguez, B.I. Miramond, and B.T.R. Granado, "Tow-ard a sparse self-organizing map for neuromorphic architectures", ACM J. Emerg. Technol. Comput. Syst., vol. 33, no. 4, pp. 1-25, 2019.
[27]
Available at: http://shumo.neepu.edu.cn/index.php/Home/zxdt/news/id/70.htmlaccessed May 2019.

Rights & Permissions Print Cite
Article Metrics
13
1
© 2024 Bentham Science Publishers | Privacy Policy