Load flow analysis using intelligence-based hopfield neural network for voltage stability assessment

Veerapandiyan Veerasamy; Noor Izzri Abdul Wahab; Rajeswari Ramachandran; Mohammad Lutfi Othman; Hashim Hizam; Mohammad Zohrul Islam; Mohamad Nasrun Mohd Nasir; Andrew Xavier Raj Irudayaraj

doi:10.1109/SPIES48661.2020.9242541

Load flow analysis using intelligence-based hopfield neural network for voltage stability assessment

Veerapandiyan Veerasamy, Noor Izzri Abdul Wahab, Rajeswari Ramachandran, Mohammad Lutfi Othman, Hashim Hizam, Mohammad Zohrul Islam, Mohamad Nasrun Mohd Nasir, Andrew Xavier Raj Irudayaraj

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

This paper presents a novel intelligence-based recurrent hopfield neural network (HNN) for solving the non-linear power flow equations. The proffered method is an energy function-based approach formulated using power residuals of the system. The dynamics associated with the neural networks are minimized by intelligence-based technique to determine the unknown parameters such as voltage magnitude (V) and phase angle (d) of the system. A hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) has been used to minimize the dynamics of HNN and its stability is proved in Lyapunov sense of notion. The effectiveness of the method is tested on IEEE 14-bus system and the results obtained are compared to the conventional newton raphson method. Moreover, the stability indices such as voltage stability load index, line stability index, fast voltage stability index and line stability factor pertaining to the assessment of stability under the contingency case of N-1-1-1 was evaluated using the presented load flow analysis technique to study the stability of the system.

Original language	English
Title of host publication	2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES 2020)
Editors	Mohd. Hasan Ali, Rakibuzzaman Shah
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	21-26
Number of pages	6
ISBN (Electronic)	9781728166117
ISBN (Print)	9781728166124
DOIs	https://doi.org/10.1109/SPIES48661.2020.9242541
Publication status	Published - 2020
Externally published	Yes
Event	International Conference on Smart Power and Internet Energy Systems 2020 - Bangkok, Thailand Duration: 15 Sept 2020 → 18 Sept 2020 Conference number: 2nd https://ieeexplore.ieee.org/xpl/conhome/9242535/proceeding (Proceedings) https://www.icspies.org/spies2020/#:~:text=September%2015%2D18%2C%202020%20%7C%20Bangkok%2C%20Thailand&text=The%20event%20will%20be%20entirely,September%2015%2D18%2C%202020. (Website)

Conference

Conference	International Conference on Smart Power and Internet Energy Systems 2020
Abbreviated title	SPIES 2020
Country/Territory	Thailand
City	Bangkok
Period	15/09/20 → 18/09/20
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9242535/proceeding (Proceedings) https://www.icspies.org/spies2020/#:~:text=September%2015%2D18%2C%202020%20%7C%20Bangkok%2C%20Thailand&text=The%20event%20will%20be%20entirely,September%2015%2D18%2C%202020. (Website)

Keywords

Hopfield neural network
Load flow analysis
Newton raphson and voltage stability analysis
Particle swarm optimization - gravitational search algorithm

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10.1109/SPIES48661.2020.9242541

Cite this

Veerasamy, V., Wahab, N. I. A., Ramachandran, R., Othman, M. L., Hizam, H., Islam, M. Z., Nasir, M. N. M., & Irudayaraj, A. X. R. (2020). Load flow analysis using intelligence-based hopfield neural network for voltage stability assessment. In M. Hasan Ali, & R. Shah (Eds.), 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES 2020) (pp. 21-26). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SPIES48661.2020.9242541

Veerasamy, Veerapandiyan ; Wahab, Noor Izzri Abdul ; Ramachandran, Rajeswari et al. / Load flow analysis using intelligence-based hopfield neural network for voltage stability assessment. 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES 2020). editor / Mohd. Hasan Ali ; Rakibuzzaman Shah. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2020. pp. 21-26

@inproceedings{fed47d8de3d44af2aa5486f42842bfab,

title = "Load flow analysis using intelligence-based hopfield neural network for voltage stability assessment",

abstract = "This paper presents a novel intelligence-based recurrent hopfield neural network (HNN) for solving the non-linear power flow equations. The proffered method is an energy function-based approach formulated using power residuals of the system. The dynamics associated with the neural networks are minimized by intelligence-based technique to determine the unknown parameters such as voltage magnitude (V) and phase angle (d) of the system. A hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) has been used to minimize the dynamics of HNN and its stability is proved in Lyapunov sense of notion. The effectiveness of the method is tested on IEEE 14-bus system and the results obtained are compared to the conventional newton raphson method. Moreover, the stability indices such as voltage stability load index, line stability index, fast voltage stability index and line stability factor pertaining to the assessment of stability under the contingency case of N-1-1-1 was evaluated using the presented load flow analysis technique to study the stability of the system.",

keywords = "Hopfield neural network, Load flow analysis, Newton raphson and voltage stability analysis, Particle swarm optimization - gravitational search algorithm",

author = "Veerapandiyan Veerasamy and Wahab, {Noor Izzri Abdul} and Rajeswari Ramachandran and Othman, {Mohammad Lutfi} and Hashim Hizam and Islam, {Mohammad Zohrul} and Nasir, {Mohamad Nasrun Mohd} and Irudayaraj, {Andrew Xavier Raj}",

note = "Funding Information: This work is supported by Universiti Putra Malaysia (UPM) under Putra Grant (9630000). Publisher Copyright: {\textcopyright} 2020 IEEE.; International Conference on Smart Power and Internet Energy Systems 2020, SPIES 2020 ; Conference date: 15-09-2020 Through 18-09-2020",

year = "2020",

doi = "10.1109/SPIES48661.2020.9242541",

language = "English",

isbn = "9781728166124",

pages = "21--26",

editor = "{Hasan Ali}, Mohd. and Rakibuzzaman Shah",

booktitle = "2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES 2020)",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

url = "https://ieeexplore.ieee.org/xpl/conhome/9242535/proceeding, https://www.icspies.org/spies2020/#:~:text=September%2015%2D18%2C%202020%20%7C%20Bangkok%2C%20Thailand&text=The%20event%20will%20be%20entirely,September%2015%2D18%2C%202020.",

}

Veerasamy, V, Wahab, NIA, Ramachandran, R, Othman, ML, Hizam, H, Islam, MZ, Nasir, MNM & Irudayaraj, AXR 2020, Load flow analysis using intelligence-based hopfield neural network for voltage stability assessment. in M Hasan Ali & R Shah (eds), 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES 2020). IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 21-26, International Conference on Smart Power and Internet Energy Systems 2020, Bangkok, Thailand, 15/09/20. https://doi.org/10.1109/SPIES48661.2020.9242541

Load flow analysis using intelligence-based hopfield neural network for voltage stability assessment. / Veerasamy, Veerapandiyan; Wahab, Noor Izzri Abdul; Ramachandran, Rajeswari et al.
2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES 2020). ed. / Mohd. Hasan Ali; Rakibuzzaman Shah. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2020. p. 21-26.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Load flow analysis using intelligence-based hopfield neural network for voltage stability assessment

AU - Veerasamy, Veerapandiyan

AU - Wahab, Noor Izzri Abdul

AU - Ramachandran, Rajeswari

AU - Othman, Mohammad Lutfi

AU - Hizam, Hashim

AU - Islam, Mohammad Zohrul

AU - Nasir, Mohamad Nasrun Mohd

AU - Irudayaraj, Andrew Xavier Raj

N1 - Conference code: 2nd

PY - 2020

Y1 - 2020

N2 - This paper presents a novel intelligence-based recurrent hopfield neural network (HNN) for solving the non-linear power flow equations. The proffered method is an energy function-based approach formulated using power residuals of the system. The dynamics associated with the neural networks are minimized by intelligence-based technique to determine the unknown parameters such as voltage magnitude (V) and phase angle (d) of the system. A hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) has been used to minimize the dynamics of HNN and its stability is proved in Lyapunov sense of notion. The effectiveness of the method is tested on IEEE 14-bus system and the results obtained are compared to the conventional newton raphson method. Moreover, the stability indices such as voltage stability load index, line stability index, fast voltage stability index and line stability factor pertaining to the assessment of stability under the contingency case of N-1-1-1 was evaluated using the presented load flow analysis technique to study the stability of the system.

AB - This paper presents a novel intelligence-based recurrent hopfield neural network (HNN) for solving the non-linear power flow equations. The proffered method is an energy function-based approach formulated using power residuals of the system. The dynamics associated with the neural networks are minimized by intelligence-based technique to determine the unknown parameters such as voltage magnitude (V) and phase angle (d) of the system. A hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) has been used to minimize the dynamics of HNN and its stability is proved in Lyapunov sense of notion. The effectiveness of the method is tested on IEEE 14-bus system and the results obtained are compared to the conventional newton raphson method. Moreover, the stability indices such as voltage stability load index, line stability index, fast voltage stability index and line stability factor pertaining to the assessment of stability under the contingency case of N-1-1-1 was evaluated using the presented load flow analysis technique to study the stability of the system.

KW - Hopfield neural network

KW - Load flow analysis

KW - Newton raphson and voltage stability analysis

KW - Particle swarm optimization - gravitational search algorithm

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U2 - 10.1109/SPIES48661.2020.9242541

DO - 10.1109/SPIES48661.2020.9242541

M3 - Conference Paper

AN - SCOPUS:85096939551

SN - 9781728166124

SP - 21

EP - 26

BT - 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES 2020)

A2 - Hasan Ali, Mohd.

A2 - Shah, Rakibuzzaman

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - International Conference on Smart Power and Internet Energy Systems 2020

Y2 - 15 September 2020 through 18 September 2020

ER -

Veerasamy V, Wahab NIA, Ramachandran R, Othman ML, Hizam H, Islam MZ et al. Load flow analysis using intelligence-based hopfield neural network for voltage stability assessment. In Hasan Ali M, Shah R, editors, 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES 2020). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2020. p. 21-26 doi: 10.1109/SPIES48661.2020.9242541

Load flow analysis using intelligence-based hopfield neural network for voltage stability assessment

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