Open Access Open Access  Restricted Access Subscription or Fee Access

Predicting Load Current of Electrical Distribution System Using Artificial Intelligence Techniques


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/iree.v17i3.21643

Abstract


Artificial Intelligence (AI) is currently used by people working in different fields of science, also the need for Smart Grids in electrical networks requires the use of intelligent technologies. Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods are the creation of AI. For the first time, AI methods will be used to determine the Load Current (LC) of distribution network’s buses. The objective is to test and compare ANN and ANFIS methods in order to predict LC in every bus of the I3E 33 bus system and to evaluate the possibility of using them in systems where iterative methods do not converge and also to obtain the predicted values in real time. For this comparison, two discrepancies indicators have been used to test the reliability of the two methods. Generally, the obtained results from the study have been good enough to recommend the employment of AI techniques to solve difficult problems that lack data or have large and complicated systems. In addition, ANFIS method is more precise and requires less time for training and predicting compared with ANN method, indicating that combining two or more intelligent methods can be beneficial.
Copyright © 2022 Praise Worthy Prize - All rights reserved.

Keywords


Artificial Intelligence; Smart Grid; Artificial Neural Network; Adaptive Neuro-Fuzzy Inference System; Load Current

Full Text:

PDF


References


N. Ketfi, Contribution to the management of distribution networks in the presence of dispersed energy generation, PhD diss, University of Batna, 2014.

B. Li, B. Hou, W. Yu, et al, Applications of artificial intelligence in intelligent manufacturing: a review, Frontiers Inf Technol Electronic Eng 18, 86-96 (2017).
https://doi.org/10.1631/FITEE.1601885

J.D. Rudie, A.M. Rauschecker, R.N. Bryan, Ch. Davatzikos, S. Mohan, Emerging Applications of Artificial Intelligence in Neuro-Oncology, Radiology 2019; 290:607-618.
https://doi.org/10.1148/radiol.2018181928

A. Rusul, H. Dia, S. Liyanage, S. A. Bagloee, 2019. Applications of Artificial Intelligence in Transport: An Overview, Sustainability 11, no. 1: 189.
https://doi.org/10.3390/su11010189

Akhikpemelo, A., Evbogbai, M., Okundamiya, M., Fault Detection on a 132kV Transmission Line Using Artificial Neural Network, (2019) International Review of Electrical Engineering (IREE), 14 (3), pp. 220-225.
https://doi.org/10.15866/iree.v14i3.17054

Matar, M., Mohamed, O., Fault Classification on a Power Transmission Line Using Discrete Wavelet Transform and Artificial Neural Networks, (2019) International Review of Electrical Engineering (IREE), 14 (5), pp. 349-357.
https://doi.org/10.15866/iree.v14i5.17017

X.P. Wang, Y.Sh. Huang, Predicting risks of capital flow using artificial neural network and Levenberg Marquardt algorithm, 2008 International Conference on Machine Learning and Cybernetics, Kunming, China, 2008, pp. 1353-1357.

A.K. Ali, N. H. El-Amary, A.M. Ibrahim and S.F. Mekhamer, Voltage instability detector based on Phasor Measurement Units using artificial neural network, 2015 Workshop on Engineering Applications - International Congress on Engineering (WEA), Bogota, Colombia, 2015, pp. 1-6.
https://doi.org/10.1109/WEA.2015.7370151

E. Aparaschivei, O. Ivanov and M. Gavrilaş, Load flow estimaton in electrical systems using artificial neural networks, 2012 International Conference and Exposition on Electrical and Power Engineering, Iasi, Romania, 2012, pp. 276-279.
https://doi.org/10.1109/ICEPE.2012.6463917

H. Kashef, K. Mahmoud, M. Abdel-Nasser, Power loss estimation in smart grids using a neural network model, 2018 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, Egypt, 2018, pp. 258-263.
https://doi.org/10.1109/ITCE.2018.8316635

M. Şimşir, R. Bayır, Y. Uyaroğlu, Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network, Computational Intelligence and Neuroscience, vol. 2016, Article ID 7129376, 13 pages, 2016.
https://doi.org/10.1155/2016/7129376

P.E. Srokosz, M. Bagińska, Application of adaptive neuro-fuzzy inference system for numerical interpretation of soil torsional shear test results, Advances in Engineering Software, Volume 143, 2020, ISSN 0965-9978.
https://doi.org/10.1016/j.advengsoft.2020.102793

B. Savkovic, P. Kovac, B. Dudic, D. Rodic, M. Taric, M. Gregus, Application of an Adaptive "Neuro-Fuzzy" Inference System in Modeling Cutting Temperature during Hard Turning, Appl. Sci. 2019, 9, 3739.
https://doi.org/10.3390/app9183739

A. Al-Hmouz, J. Shen, R. Al-Hmouz and J. Yan, Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning, in IEEE Transactions on Learning Technologies, vol. 5, no. 3, pp. 226-237, July-Sept. 2012.
https://doi.org/10.1109/TLT.2011.36

M. Fikri, B. Cheddadi and O. Sabri, Predicting Moroccan Real Network's Power Flow Employing the Artificial Neural Networks, 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco, 2019, pp. 1-6.
https://doi.org/10.1109/ICDS47004.2019.8942331

M. Fikri, O. Sabri, B. Cheddadi, Calculating voltage magnitudes and voltage phase angles of real electrical networks using artificial intelligence techniques, IJECE, Vol. 10, No. 6, December 2020, pp. 5749~5757, ISSN: 2088-8708.
https://doi.org/10.11591/ijece.v10i6.pp5749-5757

G. Weng, Y. Zhang, Y. Hu, Application of Artificial Intelligence Technique in Distributed Generation System, In: Yu W., He H., Zhang N. (eds) Advances in Neural Networks - ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg.
https://doi.org/10.1007/978-3-642-01510-6_19

M. Pertl, K. Heussen, O. Gehrke, M. Rezkalla, Voltage estimation in active distribution grids using neural networks, 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 2016, pp. 1-5.
https://doi.org/10.1109/PESGM.2016.7741758

N. Pramanik, R.K. Panda, (2009), Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction, Hydrological Sciences Journal, 54:2, 247-260.
https://doi.org/10.1623/hysj.54.2.247

N. Pradhan, G. Rani, V.S. Dhaka, R.Ch. Poonia, Diabetes prediction using artificial neural network, 2020, Pages 327-339, ISBN 9780128190616.
https://doi.org/10.1016/B978-0-12-819061-6.00014-8

D. Graupe, Principles of Artificial Neural Networks Advanced Series in Circuits and Systems, vol. 7, 2013. ISBN 978-981-4522-73-1.

V.B. Rao, C++ Neural Networks and Fuzzy Logic MTBooks, IDG Books Worldwide, Inc., 1995. ISBN: 1558515526.

A. Shenfield, D. Day, A. Ayesh, Intelligent intrusion detection systems using artificial neural networks, ICT Express, Volume 4, Issue 2, 2018, Pages 95-99, ISSN 2405-9595.
https://doi.org/10.1016/j.icte.2018.04.003

M. Jamil, , S.K. Sharma, R. Singh, Fault detection and classification in electrical power transmission system using artificial neural network, SpringerPlus 4, 334 (2015).
https://doi.org/10.1186/s40064-015-1080-x

A.E. Djokhrab, Trajectory Planning and Optimization of a 6-DOF Robot Manipulator by Neuro-Fuzzy Techniques. PhD diss, Université Mohamed Khider-Biskra, 2015.

E. Czogala, J. Leski, Fuzzy and Neuro-Fuzzy Intelligent Systems, Heidelberg; New York: Physica-Verl., 2000, ISBN 978-3-662-00389-3.
https://doi.org/10.1007/978-3-7908-1853-6

M.F. Azeem. Fuzzy Inference System -Theory and Applications ISBN 978-953-51-0525-1, April. Croatia, 2012.

A.A. Sallam, O.P. Malik, Electric Distribution Systems, 2nd Edition 2019 IEEE, ISBN: 978-1-119-50931-8.

W.H. Kersting, Distribution System Modeling and Analysis, (2012) CRC Press; 3rd edition, ISBN-13: 978-1-4398-5647-5.

R. Smeets, L.V.D. Sluis, M. Kapetanovi, D. Peelo, A. Janssen, Switching In Electrical Transmission And Distribution Systems 2015 John Wiley & Sons, Ltd, ISBN: 978-1-118-38135-9.


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize