nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Exploring the Machine Learning Algorithms for Load Forecasting and Fault Detection in Smart Grids

verfasst von : Vikram Koti Mourya Vangara, Sandeep Vuddanti, Bhaskar Kakani

Erschienen in: Control Applications in Modern Power Systems

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Modern Power Systems tend to get more complex along with their constant growth. This is due to unpredictable rise in the loads and new power sources like windmills, hydropower plants etc. entering the system every year. This impetuous behaviour in the grid leads to confusions in the power generation and might cause an imbalance in the generation and consumption sides. Traditional machine learning algorithms lack the ability to help with this problem of the modern power system. More sophisticated algorithms are needed to help in solving such problem and successfully operating the power grid. This paper reviews the applications of Machine Learning in the two main aspects of the grid, i.e. Load Forecasting and Fault Detection. The drawbacks of implementing the same are discussed at the end of the article.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Energy Management System for Stand-Alone Microgrid with Renewable Energy Resource

Nächstes Kapitel An ANN-Based MPPT Technique for Partial Shading Photo Voltaic Distribution Generation

Samuel AL (2000) Some studies in machine learning using the game of checkers. IBM J Res Dev 44(1.2):206–226

Hastie T, Tibshirani R, Friedman J (2001) The Elements of statistical learning. Springer, New YorkCrossRef

Agneeswaran VS, Tonpay P, Tiwary J (2013) Paradigms for realizing machine learning algorithms. Big Data 1(4):207–214CrossRef

Online Document. Growth of electricity sector in India from 1947–2019 (PDF). Central Electricity Authority. http://www.cea.nic.in/reports/others/planning/pdm/growth_2019.pdf

Chen B-J, Chang M-W et al (2004) Load forecasting using support vector machines: a study on EUNITE competition 2001. IEEE Trans Power Syst 19(4):1821–1830CrossRef

Pai P-F, Hong W-C (2005) Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers Manage 46(17):2669–2688CrossRef

Zhu Z, Sun Y, Li H (2007) Hybrid of EMD and SVMS for short-term load forecasting. In: IEEE international conference on control and automation, 2007. ICCA 2007. IEEE, 2007, pp 1044–1047

Baric ECVCA (2013) A strategy for short-term load forecasting by support vector regression machines. IEEE Trans Power Syst 28:4356–4364CrossRef

Hossen T, Plathotam SJ, Angamuthu RK et al (2017) Short-term load forecasting using deep neural networks (DNN). In: Presented at 2017 North American power symposium (NAPS).

10.

Hosein S, Hosein P (2017) Load forecasting using deep neural networks. In: Presented at 2017 IEEE power & energy society innovative smart grid technologies conference (ISGT)

11.

Dedinec A, Filiposka S, Dedinec A, Kocarev L (2016) Deep belief network-based electricity load forecasting: an analysis of the Macedonian case. Energy 115(part 3):1688–1700

12.

Qiu X, Zhang L, Ren Y, Suganthan PN, Amaratunga Q (2014) Ensemble deep learning for regression and time series forecasting. In: 2014 IEEE symposium on computational intelligence in ensemble learning (CIEL), pp 1–6

13.

He YS, Deng JH, Li HJ (2017) Short-term power load forecasting with deep belief network and copula models. In: 2017 9th International conference on intelligent human-machine systems and cybernetics, Hangzhou, China, 26–27 Aug

14.

Mnih V, Larochelle H, Hinton G (2011) Conditional restricted Boltzmann machines for structured output prediction. In: Proceedings of the international conference on uncertainty in artificial intelligence, Barcelona, Spain, 14–17 Jul

15.

Taylor GW, Hinton GE, Roweis ST (2011) Two distributed-state models for generating high-dimensional time series. J Mach Learn Res 12:1025–1068MathSciNetMATH

16.

Amarasinghe K, Marino DL, Manic M (2017) Deep neural networks for energy load forecasting. In: The 26th IEEE international symposium on industrial electronics (ISIE), Edinburgh, Scotland, UK, 19–21 Jun

17.

Dong XS, Qian LJ, Huang L (2017) Short-term load forecasting in smart grid: a combined CNN and K-means clustering approach. In: IEEE international conference on big data and smart computing (BigComp), Jeju, Korea, 13–16 Feb

18.

Sogabe T, Ichikawa H et al. (2016) Optimization of decentralized renewable energy system by weather forecasting and deep machine learning techniques. In: The 2016 IEEE innovative smart grid technologies—Asia (ISGT-Asia), Melbourne, Australia, Nov 28–Dec 1

19.

Marino DL, Amarasinghe K, Manic M (2016) Building energy load forecasting using deep neural networks. In: IECON 2016—42nd Annual conference of the IEEE industrial electronics society, Yokohama, Japan, 23–26 Oct

20.

Ryu S, Noh J, Kim H (2017) Deep neural network-based demand side short term load forecasting. Energies 10(1):3CrossRef

21.

Park DC, El-Sharkawi M, Marks R, Atlas L, Damborg M (1991) Electric load forecasting using an artificial neural network. IEEE Trans Power Syst 6(2):442–449CrossRef

22.

Coelho IM, Coelho VN, Luz EJdaS, Ochi LS, Guimarães FG, Rios E (2017) A GPU deep learning metaheuristic-based model for time series forecasting. Appl Energy 201:412418

23.

Shi H, Xu M, Li R (2017) Deep learning for household load forecasting–a novel pooling deep RNN. IEEE Transactions on Smart Grid

24.

Vermaak J, Botha E (1998) Recurrent neural networks for short-term load forecasting. IEEE Trans Power Syst 13:126–132CrossRef

25.

Gensler A, Henze J, Sick B, Raabe N (2016) Deep learning for solar power forecasting? an approach using autoencoder and LSTM neural networks. In: 2016 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 002 858–002 865

26.

Kong W, Dong ZY, Hill DJ, Luo F, Xu Y (2017) Short-term residential load forecasting based on resident behaviour learning. IEEE Trans Power Syst

27.

Kandil N, Wamkeue R, Saad M, Georges S (2016) An efficient approach for short term load forecasting using artificial neural networks. Int J Electr Power Energy Syst 28(8):525–530CrossRef

28.

Dahua G, Yi W, Shuo Y, Chongqing K (2018) Embedding based quantile regression neural network for probabilistic load forecasting. J Mod Power Syst Clean Energy 6:244–254CrossRef

29.

Hu R, Wen S, Zeng Z, Huang T (2017) A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221:24–31. ISSN 0925-2312

30.

Gan D, Wang Y, Zhang N, Zhu W (2017) Enhancing short-term probabilistic residential load forecasting with quantile long–short-term memory. J Eng 2017:2622–2627CrossRef

31.

Hsiao YH (2015) Household electricity demand forecast based on context information and user daily schedule analysis from meter data. IEEE Trans Ind Inf 11:33–43CrossRef

32.

Gajowniczek K, Ząbkowski T (2015) Electricity forecasting on the individual household level enhanced based on activity patterns. PLOS ONE 12:e0174098

33.

Hamid MBA, Rahman TKA (2010) Short term load forecasting using an artificial neural network trained by artificial immune system learning algorithm. In: 2010 12th International conference on computer modelling and simulation, Cambridge, pp 408–413

34.

Dudek G (2008) Artificial immune system for short-term electric load forecasting. In: Rutkowski L, Tadeusiewicz R, Zadeh LA, Zurada JM (eds) Artificial intelligence and soft computing—ICAISC 2008. ICAISC 2008. Lecture notes in computer science, vol 5097. Springer, Berlin, Heidelberg

35.

Mordjaoui M, Haddad S, Medoued A, Laouafi A (2017) Electric load forecasting by using dynamic neural network. Int J Hydrogen Energy 42(28). ISSN 0360-3199

36.

Cui M, Wang J, Yue M (2019) Machine learning-based anomaly detection for load forecasting under cyberattacks. IEEE Trans Smart Grid 10(5):5724–5734

37.

Zhao ZB, Fan XQ, Xu GZ et al (2016) Multi-patch deep features for power line insulator status classification from aerial images. In: IEEE International Joint Conference on Neural Networks (IJCNN), New Delhi, India

38.

Shi X, Zhu YL (2015) The application of deep neural network in power transformer fault diagnosis. Electric Power Constr 36(12):116–122

39.

Peng X, Pan FL, Liang YC et al (2017) Fault detection algorithm for power distribution network based on sparse self-encoding neural network. In: 2017 International conference on smart grid and electrical automation (ICSGEA), Changsha, China

40.

Tokel HA, Halaseh RA, Alirezaei G, Mathar R (2018) A new approach for machine learning-based fault detection and classification in power systems. In: 2018 IEEE power & energy society innovative smart grid technologies conference (ISGT), Washington, DC, pp 1–5

41.

Jamil M, Sharma SK, Singh R (2015) Fault detection and classification in electrical power transmission system using artificial neural network. Springerplus 4:334CrossRef

42.

Chen K, Huang C, He J (2016) Fault detection, classification and location for transmission lines and distribution systems: a review on the methods. High Voltage 1(1):25–33CrossRef

43.

Wang F (2011) Fault diagnosis for power systems based on neural networks. In: 2011 IEEE 2nd international conference on software engineering and service science, Beijing, pp 352–355

44.

Lala H, Karmakar S, Ganguly S (2017) Fault diagnosis in distribution power systems using stationary wavelet transform and artificial neural network. In: 2017 7th International conference on power systems (ICPS), Pune, pp 121–126

45.

Silva S, Costa P, Gouvea M, Lacerda A, Alves F, Leite D (2018) High impedance fault detection in power distribution systems using wavelet transform and evolving neural network. Electric Power Systems Res 154:474–483. ISSN 0378-7796

46.

Samantaray SR, Tripathy LN, Dash PK (2009) Combined EKF and SVM based high Impedance Fault detection in power distribution feeders. In: 2009 International conference on power systems, Kharagpur, 2009, pp 1–6

47.

Shahid N, Aleem SA, Naqvi IH, Zaffar (2012) Support vector machine based fault detection & classification in smart grids. In: 2012 IEEE Globecom workshops, Anaheim, CA, 2012, pp 1526–1531

48.

Mair AJ, Davidson EM, McArthur SDFJ, Srivastava SK, Schoder K, Cartes DA (2009) Machine learning techniques for diagnosing and locating faults through the automated monitoring of power electronic components in shipboard power systems. In: 2009 IEEE electric ship technologies symposium, Baltimore, MD, 2009, pp 469–476

49.

Cordova J, Faruque MO (2015) Fault location identification in smart distribution networks with distributed generation. In: 2015 North American power symposium (NAPS), Charlotte, NC, 2015, pp 1–7

50.

Butler KL, Momoh JA (1993) Detection and classification of line faults on power distribution systems using neural networks. In: Proceedings of 36th Midwest Symposium on Circuits and Systems, Detroit, MI, USA, 1993, pp 368–371

51.

Abdelgayed TS, Morsi WG, Sidhu TS (2018) Fault detection and classification based on co-training of semisupervised machine learning. IEEE Trans Industr Electron 65(2):1595–1605CrossRef

52.

Mori H, Aoyama H, Yamanaka T, Urano S (2002) A hybrid intelligent system for fault detection in power systems. In: Proceedings of the 2002 international joint conference on neural networks. IJCNN'02 (Cat. No.02CH37290), Honolulu, HI, USA, 2002, vol3, pp 2138–2143

53.

Jiang H, Zhang JJ, Gao W, Wu Z (2014) Fault Detection, identification, and location in smart grid based on data-driven computational methods. IEEE Trans Smart Grid 5(6):2947–2956CrossRef

54.

Laouti N, Sheibat-Othman N, Othman S (2011) Support vector machines for fault detection in wind turbines. In: IFAC Proc Vol 44(1):7067–7072. ISSN 1474-6670

55.

Helbing G, Ritter M (2018) Deep learning for fault detection in wind turbines. Renew Sustain Energy Rev 98: 189–198. ISSN 1364-0321

56.

Ramesh Babu N, Jagan Mohan B (2017) Fault classification in power systems using EMD and SVM. Ain Shams Eng J 8(2):103–111. ISSN 2090-4479

57.

Zhang Y, Zhang J, Ma J, Wang Z (2009) Fault detection based on discriminant analysis theory in electric power system. In: 2009 International conference on sustainable power generation and supply, Nanjing, 2009, pp 1–5

58.

Zhang Z, Huang W-H, Xiao D-M, Liu Y-L (2018) Fault detection of power transformers using genetic programming method. In: Proceedings of 2004 international conference on machine learning and cybernetics, Shanghai, China, 2004, pp 3018–3022

Titel: Exploring the Machine Learning Algorithms for Load Forecasting and Fault Detection in Smart Grids
verfasst von: Vikram Koti Mourya Vangara
Sandeep Vuddanti
Bhaskar Kakani
Verlag: Springer Nature Singapore
Buch: Control Applications in Modern Power Systems
Print ISBN: 978-981-19-0192-8

Electronic ISBN: 978-981-19-0193-5

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-981-19-0193-5_31