Nothing Special   »   [go: up one dir, main page]

Skip to main content

Advertisement

Log in

Presenting a hybrid method for fault tolerance based on neural fuzzy logic in distribution networks using phasor measurement units

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

In this paper, a hybrid approach to increase the performance reliability of power distribution networks using phasor measurement units (PMU) is presented. Electricity distribution networks are very important as a vital part of electricity transmission systems in providing energy to users. Considering the ever-increasing complexity and the huge amount of existing demand, maintaining the optimal performance and reliability of these networks is vital. One of the main challenges in this industry is identifying and finding problems. In this regard, improvements in phasor measurement technology using phasor measurement units (PMU) allow engineers in this industry to more accurately evaluate data, diagnose and locate network faults. In this research, one of the non-linear methods for finding ground faults in power distribution networks using voltage phasor measurement in several network stations using phasor measurement units (D-PMU) has been demonstrated. In the first approach, genetic optimization algorithms and bullet optimization algorithm (PSO) have been used for nonlinear modeling of fault position estimation based on different types of 1-phase, 2-phase and 3-phase faults. The second method uses fuzzy network training to provide details about phasor voltages and fault types. By simulating a 9-station system using MATLAB software, the usefulness of the proposed methods has been shown. In modeling, 1-phase, 2-phase and 3-phase faults along with different line lengths and line characteristics at different stations have been investigated. Also, the findings are presented and the location of the defect is identified immediately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The authors do not have permission to share data.

References

  • Alharthi, A. M., Kadir, D. H., Al-Fakih, A. M., Algamal, Z. Y., Al-Thanoon, N. A., & Qasim, M. K. (2024). Improving golden jackel optimization algorithm: An application of chemical data classification. Chemometrics and Intelligent Laboratory Systems, 250, 105149.

  • Ameen, A. K., Kadir, D. H., Abdullah, D. A., Maolood, I. Y., & Khidir, H. A. (2024). Assessing E-government effectiveness. Aro-The Scientific Journal of Koya University, 12(2), 52-60.

  • Arefifar SA, Alam MS, Hamadi A (2023) A review on self-healing in modern power distribution systems. J Modern Power Syst Clean Energy 11(6):1719–1733

    Article  Google Scholar 

  • Arsoniadis CG, Apostolopoulos CA, Georgilakis PS, Nikolaidis VC (2021) A voltage-based fault location algorithm for medium voltage active distribution systems. Electric Power Syst Res 196:107236

    Article  Google Scholar 

  • Barman S, Roy BKS (2018) Detection and location of faults in large transmission networks using minimum number of phasor measurement units. IET Gener Transm Distrib 12(8):1941–1950

    Article  Google Scholar 

  • Belagoune S, Bali N, Bakdi A, Baadji B, Atif K (2021) Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems. Measurement 177:109330

    Article  Google Scholar 

  • Bramareswara Rao SNV, Kumar YV, Amir M, Muyeen SM (2024) Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks. Electr Eng. https://doi.org/10.1007/s00202-024-02329-4

    Article  Google Scholar 

  • Butt AS, Nul Huda, Amin AA (2023) Design of fault-tolerant control system for distributed energy resources based power network using phasor measurement units. Meas Control 56(1–2):269–286

    Article  Google Scholar 

  • Chavez JJ, Kumar NV, Azizi S, Guardado JL, Rueda J, Palensky P, Terzija V, Popov M (2021) PMU-voltage drop based fault locator for transmission backup protection. Electric Power Syst Res 196:107188

  • Ding X, Yao R, Khezri E (2023) An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints. Wirel Netw 1–18

  • Elsisi M, Tran M-Q, Mahmoud K, Lehtonen M, Darwish MMF (2021) Robust design of ANFIS-based blade pitch controller for wind energy conversion systems against wind speed fluctuations. IEEE Access 9:37894–37904

  • Fakhri PS, Asghari O, Sarspy S, Marand MB, Moshaver P, Trik M (2023) A fuzzy decision-making system for video tracking with multiple objects in non-stationary conditions. Heliyon 9(11)

  • Fei C, Qi G, Li C (2018) Fault location on high voltage transmission line by applying support vector regression with fault signal amplitudes. Electric Power Syst Res, 160:173–179

  • HassanVandi B, Kurdi R, Trik M (2021) Applying a modified triple modular redundancy mechanism to enhance the reliability in software-defined network. Mapta J Electric Comput Eng (MJECE) 3(1):10–16

  • Hosseini, E., Al-Ghaili, A. M., Kadir, D. H., Gunasekaran, S. S., Ahmed, A. N., Jamil, N.,... & Razali, R. A. (2024). Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018–2023). Energy Strategy Reviews, 53, 101409.

  • Jahangiri G, Nabavian SR, Davoodi MR, Neya BN, Mostafavian S (2020) Effect of noise on output-only modal identification of beams. arXiv preprint arXiv:2008.10416

  • Kadir, D. H., Abdullah, K., Jafaar, A. M., Salih, R. H., Smail, S. W., Rahman, G. Q.,... & Khudhur, Z. O. (2024). Statistical Analysis of Lipid Profiles Associated with Coronary Heart Disease in Erbil City-Iraq. Polytechnic Journal, 13(1).

  • Karabulut E, Gholizadeh F, Akhavan-Tabatabaei R (2022) The value of adaptive menu sizes in peer-to-peer platforms. Trans Res Part C: Emerg Technol 145:103948

  • Khalafi M, Boob D (2023) Accelerated primal-dual methods for convex-strongly-concave saddle point problems. In: International conference on machine learning. pp 16250–16270

  • Khezri E, Zeinali E, Sargolzaey H (2023) SGHRP: secure Greedy highway routing protocol with authentication and increased privacy in vehicular ad hoc networks. PLoS ONE, 18(4):e0282031

  • Khezri E, Yahya RO, Hassanzadeh H, Mohaidat M, Ahmadi S, Trik M (2024) DLJSF: data-locality aware job scheduling IoT tasks in fog-cloud computing environments. Res Eng 21:101780

  • Li, J., Jasim, D. J., Kadir, D. H., Maleki, H., Esfahani, N. N., Shamsborhan, M., & Toghraie, D. (2024). Multi-objective optimization of a laterally perforated-finned heat sink with computational fluid dynamics method and statistical modeling using response surface methodology. Engineering Applications of Artificial Intelligence, 130, 107674.

  • Li Y, Wang H, Trik M (2024) Design and simulation of a new current mirror circuit with low power consumption and high performance and output impedance. Analog Integr Circ Sign Process 1–13

  • Majidpour, J., Khezri, E., Hassanzade, H., & Mohammed, K. S. (2015, May). Interactive tool to improve the automatic image annotation using MPEG-7 and multi-class SVM. In 2015 7th Conference on Information and Knowledge Technology (IKT) (pp. 1-7). IEEE

    Google Scholar 

  • Mayo-Maldonado JC, Valdez-Resendiz JE, Guillen D, Bariya M, von Meier A, Salas-Esquivel EA, Ostfeld A (2020) Data-driven framework to model identification, event detection, and topology change location using D-PMUs. IEEE Trans Instrum Meas 69(9):6921–6933

  • Meng C, Motevalli H (2024) Link prediction in social networks using hyper-motif representation on hypergraph. Multimedia Syst 30(3):123

    Article  Google Scholar 

  • Mohaidat M, Grantner JL, Shebrain SA, Abdel-Qader I (2022) Multi-class detection and tracking of intracorporeal suturing instruments in an FLS laparoscopic box trainer using scaled-YOLOv4. In: International symposium on visual computing. Springer, Cham, pp. 211–221

  • Morteza A, Yahyaeian AA, Mirzaeibonehkhater M, Sadeghi S, Mohaimeni A, Taheri S (2023) Deep learning hyperparameter optimization: application to electricity and heat demand prediction for buildings. Energy Build 289:113036

  • Mouco A, Abur A (2020) Improving the wide-area PMU-based fault location method using ordinary least squares estimation. Electric Power Syst Res 189:106620

  • Najmi M, Ayari MA, Sadeghsalehi H, Vaferi B, Khandakar A, Chowdhury ME, Jawhar ZH (2022) Estimating the dissolution of anticancer drugs in supercritical carbon dioxide with a stacked machine learning model. Pharmaceutics 14(8):1632

  • Pignati M, Zanni L, Romano P, Cherkaoui R, Paolone M (2016) Fault detection and faulted line identification in active distribution networks using synchrophasors-based real-time state estimation. IEEE Trans Power Delivery 32(1):381–392

  • Saidabad MY, Hassanzadeh H, Ebrahimi SHS, Khezri E, Rahimi MR, Trik M (2024) An efficient approach for multi-label classification based on advanced Kernel-based learning system. Intell Syst Appl 21:200332

  • Samiei M, Hassani A, Sarspy S, Komari IE, Trik M, Hassanpour F (2023) Classification of skin cancer stages using a AHP fuzzy technique within the context of big data healthcare. J Cancer Res Clin Oncol 149(11):8743–8757

  • Shafiee A, Banerjee S, Chakrabarty K, Pasricha S, Nikdast M (2022) LoCI: an analysis of the impact of optical loss and crosstalk noise in integrated silicon-photonic neural networks. In: Proceedings of the great lakes symposium on VLSI 2022. pp 351–355

  • Singh P, Huang YP (2023a) A four-valued ambiguous logic: application in designing ambiguous inference system for control systems. Int J Fuzzy Syst 25(8):2921–2938

  • Singh P, Huang YP (2023b) Membership functions, set-theoretic operations, distance measurement methods based on ambiguous set theory: a solution to a decision-making problem in selecting the appropriate colleges. Int J Fuzzy Syst 25(4):1311–1326

  • Sun J, Zhang Y, Trik M (2022) PBPHS: a profile-based predictive handover strategy for 5G networks. Cybern Syst 1–22

  • Sun T, Wong P-K, Wang X (2024) Back propagation neural network-based fault diagnosis and fault tolerant control of distributed drive electric vehicles based on sliding mode control-based direct Yaw moment control. Vehicles 6(1):93–119

  • Trick M, Boukani B (2014) Placement algorithms and logic on logic (LOL) 3D integration. J Math Comput Sci 8(2):128–136

  • Trik M, Mozaffari SP, Bidgoli AM (2021a) Providing an adaptive routing along with a hybrid selection strategy to increase efficiency in NoC-based neuromorphic systems. Comput Intell Neurosci 2021

  • Trik M, Mozafari P, S., Bidgoli AM (2021b) An adaptive routing strategy to reduce energy consumption in network on chip. J Adv Comput Res 12(3):13–26

  • Trik M, Molk AMNG, Ghasemi F, Pouryeganeh P (2022) A hybrid selection strategy based on traffic analysis for improving performance in networks on chip. J Sens 2022

  • Trik M, Akhavan H, Bidgoli AM, Molk AMNG, Vashani H, Mozaffari SP (2023) A new adaptive selection strategy for reducing latency in networks on chip. Integration 89:9–24

  • Wang H, Huang C, Yu H, Zhang J, Wei F (2021) Method for fault location in a low-resistance grounded distribution network based on multi-source information fusion. Int J Electric Power Energy Syst 125:106384

  • Wang Z, Jin Z, Yang Z, Zhao W, Trik M (2023a) Increasing efficiency for routing in internet of things using binary gray wolf optimization and fuzzy logic. J King Saud Univ-Comput Inform Sci 35(9):101732

  • Wang G, Wu J, Trik M (2023b) A novel approach to reduce video traffic based on understanding user demand and D2D communication in 5G networks. IETE J Res 1–17

  • Xiao L, Cao Y, Gai Y, Khezri E, Liu J, Yang M (2023) Recognizing sports activities from video frames using deformable convolution and adaptive multiscale features. J Cloud Comput 12(1):167

  • Zhang L, Hu S, Trik M, Liang S, Li D (2024) M2M communication performance for a noisy channel based on latency-aware source-based LTE network measurements. Alexandria Eng J 99:47–63

  • Zhu J, Hu C, Khezri E, Ghazali MMM (2024) Edge intelligence-assisted animation design with large models: a survey. J Cloud Comput 13(1):48

  • Zoraghchian AA, Asghari A, Trik M (2014) Thermal control methods for reducing heat in 3D ICs-TSV. Through-Silicon-Via

Download references

Acknowledgements

The research was supported by the National Natural Science Foundation of China (No. 62261023), the project is entitled: “Research on data security of intelligent state monitoring at the edge of power grid”, and science and technology research project of Jiangxi Provincial Department of Education (GJJ191324), the project is entitled: “Research on privacy data protection of smart meters based on reconfiguration and data security aggregation”, and science and technology project of Jiangxi Vocational and Technical College of Communication, 2020KY09, the project is entitled: “Research on security of power supply monitoring data of high speed railway catenary based on blockchain and SMS4”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pengwei Zhang.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 18.6 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, P., Tu, Y., Zeng, Y. et al. Presenting a hybrid method for fault tolerance based on neural fuzzy logic in distribution networks using phasor measurement units. J Ambient Intell Human Comput 15, 4009–4021 (2024). https://doi.org/10.1007/s12652-024-04876-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-024-04876-x

Keywords

Navigation