Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 + 1062 word(s) 1062 2022-06-20 10:28:42 |
2 layout -33 word(s) 1029 2022-06-22 02:39:20 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Andresen, C.A.;  Hoffmann, V.;  Torsæter, B.N.;  Rosenlund, G. Data-Driven Methods in Power Grids. Encyclopedia. Available online: (accessed on 21 June 2024).
Andresen CA,  Hoffmann V,  Torsæter BN,  Rosenlund G. Data-Driven Methods in Power Grids. Encyclopedia. Available at: Accessed June 21, 2024.
Andresen, Christian Andre, Volker Hoffmann, Bendik Nybakk Torsæter, Gjert Rosenlund. "Data-Driven Methods in Power Grids" Encyclopedia, (accessed June 21, 2024).
Andresen, C.A.,  Hoffmann, V.,  Torsæter, B.N., & Rosenlund, G. (2022, June 21). Data-Driven Methods in Power Grids. In Encyclopedia.
Andresen, Christian Andre, et al. "Data-Driven Methods in Power Grids." Encyclopedia. Web. 21 June, 2022.
Data-Driven Methods in Power Grids

Applications of data-driven methods in power grids are motivated by the need to predict and mitigate intermittency in a (future) grid that is expected to lean heavily on renewables.

machine learning power systems harmonic distortion

1. Background

The introduction of ever-increasing amounts of intermittent renewable generation, coupled with the increasing electrification of societies, leads to an increased strain on the power grid and its operation [1][2][3]. In order to maintain high security of supply, it is paramount to evolve the tools used for power systems operations [4]. One such tool would be the ability to predict undesired events with sufficient prediction horizon to facilitate mitigating actions [5][6][7].
The development of such tools is encouraged by recent advancements in data-driven techniques, machine learning (ML), available data volumes, and computational resources [8][9][10]. These algorithms can derive insights from data without being explicitly told what to look for in the vast data steams [11][12], which is particularly beneficial in the domain of power system fault prediction. An explicit detailed modeling of the power system is cumbersome and would not encapsulate conditions the modeler does not know about, that could lead to faults, such as icing on transmission lines, faults in critical components or reoccurring abnormalities.
Data driven methods are only as good as the data they rely on, and only have the capability to predict situations that the model have been trained on [13][14][15]. In a best case scenario, the models are trained on a complete and large dataset, and it can rely on the automatic tuning of model parameters [16][17]. This is, however, often not the case in real word applications. In the case of fault prediction in the power system, the number of faults occurring are very small compared to normal operating conditions [18].
To achieve high performance with data driven methods, the analyst must therefore pre-process the data—essentially guiding the algorithms in selecting their focus. This type of pre-processing includes dimensionality reduction, feature selection, feature engineering, and rescaling of features and prediction targets [19][20]. While there are aspects of an art (or, more precisely, intuition based on experience and domain knowledge) to these activities, they depend on an understanding of the behaviour of the underlying power system.

2. Data-Driven Methods in Power Grids

Applications of data-driven methods in power grids are motivated by the need to predict and mitigate intermittency in a grid that leans heavily on renewables [21][22]. Works tend to focus on: (i) equipment degradation; (ii) forecasting (and control) of demand and production; or (iii) grid-scale power quality (PQ) and continuity of supply. For equipment degradation, focus is either on individual assets (usually with the aim of predictive maintenance) or their interaction with the grid at large. The most relevant assets are wind turbines, hydroelectric power plants, photovoltaic power plants, and distribution transformers.
Focusing on key assets (and their subcomponents), refs. [23][24] used event and state logs from wind-turbine control systems to train supervised learning algorithms (neural networks, boosted trees, and support vector machines). They report successful prediction of fault states with lead times in the order of five minutes to an hour. In a similar vein, refs. [25][26] monitoring data from sub-components (e.g., compressors, generators, turbines) are used to detect and predict anomalous behaviour in hydro power stations. They demonstrate implementations of self-organizing maps and neural networks within the control loops, but unfortunately do not report on model performance. For photovoltaic systems, forecasting of faults appears to be less advanced and the literature focuses on fault detection and characterization. For example, ref. [27] integrates system data (currents, voltages, temperature) and uses neural networks to detect and classify abnormal operating conditions. Based on multispectral drone imagery, ref. [28] deploys convolutional neural networks (CNNs) to detect various types of panel damage. Overall, there is significant potential in machine learning approaches to predicting the condition of photovoltaic system due to the large amount of non-correlated data sources (weather, system data, and imagery), see also [29][30]. Finally, multiple works attempt to predict failure of distribution transformers by combining event logs and data from outgassing of insulating oil. While [31] deploys a fairly complicated scheme involving agents, neural networks, and evolutionary methods, ref. [32] uses gradient boosted trees and claims a superior performance compared to their reviewed literature. The state-of-the-art in the use of machine learning to predict transformer failures is reviewed in [33].
On the production side, data-driven forecasting methods for wind and photovoltaic systems are mainly concerned with: (i) using (and improving upon) numerical weather prediction models; and (ii) relating the weather conditions to actual power output. For example, ref. [34] uses neural networks to accelerate wind-field computation for a complicated topography while [35] uses model ensembles (k-nearest neighbours, support vector regression, and decision trees) to relate local wind-speed measurements to turbine power output. For solar forecasting, ref. [36] compare 68 machine learning-based forecasting models and find that (a) tree-based methods perform best but (b) that there is significant variation between the performance of different models in space and time. See also [37][38] for reviews. Hydro power forecasting, on the other hand, is more often cast as a scheduling problem. For example, ref. [39] feeds climate data, expected demand curves, and market conditions into a reinforcement learning system for optimal (most profitable) long-term scheduling. See also [40] for a recent review. Research on demand forecasting, on the other hand, is frequently coupled to control schemes for residential and commercial smart buildings [41][42] or vehicle-to-grid technologies [43][44]. In addition, there is a sprawling literature on customer segmentation [45][46], building performance assessments [47], and residential level demand forecasting [48][49].
With a focus on components and their impact on the remainder of the grid, ref. [50] uses the recurrent incidence of minor events to predict major outages, ref. [51] couple event logs from distribution transformers to meteorological data, and [52] connects meteorological data to component states to predict the impact of extreme weather. Focusing on power quality alone, refs. [53][54] detect and identify PQ anomalies using either neural networks and decision trees, extensive feature engineering, or semi-supervised learning approaches, respectively. Finally, ref. [55] include anomaly prediction and—by using random forests—obtains inherently explainable models. Similarly, researchers' own recent works have also focused on predicting PQ disturbances using a variety of data sources, methods, and features [56][57][58][59][60][61]. Unfortunately, most works (including researchers' own) omit describing the underlying data, and instead jump straight to feature engineering and machine learning.


  1. Kumar, G.V.B.; Sarojini, R.K.; Palanisamy, K.; Padmanaban, S.; Holm-Nielsen, J.B. Large Scale Renewable Energy Integration: Issues and Solutions. Energies 2019, 12, 1996.
  2. Muljadi, E.; McKenna, H. Power quality issues in a hybrid power system. IEEE Trans. Ind. Appl. 2002, 38, 803–809.
  3. Rönnberg, S.; Bollen, M. Power quality issues in the electric power system of the future. Electr. J. 2016, 29, 49–61.
  4. Balasubramaniam, P.M.; Prabha, S.U. Power Quality Issues, Solutions and Standards: A Technology Review. J. Appl. Sci. Eng. 2015, 18, 371–380.
  5. Sallam, A.A.; Malik, O.P. Electric Distribution Systems; Wiley-Blackwell: Hoboken, NJ, USA, 2018; pp. 1–604.
  6. Bashir, A.K.; Khan, S.; Prabadevi, B.; Deepa, N.; Alnumay, W.S.; Gadekallu, T.R.; Maddikunta, P.K.R. Comparative analysis of machine learning algorithms for prediction of smart grid stability. Int. Trans. Electr. Energy Syst. 2021, 31, e12706.
  7. Azad, S.; Sabrina, F.; Wasimi, S. Transformation of smart grid using machine learning. In Proceedings of the 29th Australasian Universities Power Engineering Conference (AUPEC), Nadi, Fiji, 26–29 November 2019; pp. 1–6.
  8. Rangel-Martinez, D.; Nigam, K.; Ricardez-Sandoval, L.A. Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage. Chem. Eng. Res. Des. 2021, 174, 414–441.
  9. Hossain, E.; Khan, I.; Un-Noor, F.; Sikander, S.S.; Sunny, M.S.H. Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review. IEEE Access 2019, 7, 13960–13988.
  10. Ibrahim, M.S.; Dong, W.; Yang, Q. Machine learning driven smart electric power systems: Current trends and new perspectives. Appl. Energy 2020, 272, 115237.
  11. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
  12. Shrestha, A.; Mahmood, A. Review of deep learning algorithms and architectures. IEEE Access 2019, 7, 53040–53065.
  13. Hastie, T.; Tibshirani, R.; Friedman, J.H.; Friedman, J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Springer: Berlin/Heidelberg, Germany, 2009; Volume 2.
  14. James, G.; Witten, D.; Hastie, T.; Tibshirani, R. An Introduction to Statistical Learning; Springer: Berlin/Heidelberg, Germany, 2013; Volume 112.
  15. Raschka, S. Model evaluation, model selection, and algorithm selection in machine learning. arXiv 2018, arXiv:1811.12808.
  16. Yu, T.; Zhu, H. Hyper-parameter optimization: A review of algorithms and applications. arXiv 2020, arXiv:2003.05689.
  17. Probst, P.; Wright, M.N.; Boulesteix, A.L. Hyperparameters and tuning strategies for random forest. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2019, 9, e1301.
  18. CEER. 6th CEER Benchmarking Report on the Quality of Electricity and Gas Supply; CEER: Brussels, Belgium, 2016.
  19. García, S.; Ramírez-Gallego, S.; Luengo, J.; Benítez, J.M.; Herrera, F. Big data preprocessing: Methods and prospects. Big Data Anal. 2016, 1, 1–22.
  20. Heaton, J. An empirical analysis of feature engineering for predictive modeling. In Proceedings of the SoutheastCon, Norfolk, VA, USA, 30 March–3 April 2016; pp. 1–6.
  21. Al-Sheikh, H.; Moubayed, N. Fault detection and diagnosis of renewable energy systems: An overview. In Proceedings of the 2012 International Conference on Renewable Energies for Developing Countries (REDEC), Beirut, Lebanon, 28–29 November 2012; pp. 1–7.
  22. Pérez-Ortiz, M.; Jiménez-Fernández, S.; Gutiérrez, P.A.; Alexandre, E.; Hervás-Martínez, C.; Salcedo-Sanz, S. A Review of Classification Problems and Algorithms in Renewable Energy Applications. Energies 2016, 9, 607.
  23. Kusiak, A.; Li, W. The prediction and diagnosis of wind turbine faults. Renew. Energy 2011, 36, 16–23.
  24. Kusiak, A.; Verma, A. Analyzing bearing faults in wind turbines: A data-mining approach. Renew. Energy 2012, 48, 110–116.
  25. Betti, A.; Crisostomi, E.; Paolinelli, G.; Piazzi, A.; Ruffini, F.; Tucci, M. Condition monitoring and predictive maintenance methodologies for hydropower plants equipment. Renew. Energy 2021, 171, 246–253.
  26. Fu, C.; Ye, L.; Liu, Y.; Yu, R.; Iung, B.; Cheng, Y.; Zeng, Y. Predictive maintenance in intelligent-control-maintenance-management system for hydroelectric generating unit. IEEE Trans. Energy Convers. 2004, 19, 179–186.
  27. Garoudja, E.; Chouder, A.; Kara, K.; Silvestre, S. An enhanced machine learning based approach for failures detection and diagnosis of PV systems. Energy Convers. Manag. 2017, 151, 496–513.
  28. Li, X.; Li, W.; Yang, Q.; Yan, W.; Zomaya, A.Y. An unmanned inspection system for multiple defects detection in photovoltaic plants. IEEE J. Photovolt. 2019, 10, 568–576.
  29. Berghout, T.; Benbouzid, M.; Ma, X.; Djurović, S.; Mouss, L.H. Machine Learning for Photovoltaic Systems Condition Monitoring: A Review. In Proccedings of the IECON 2021—47th Annual Conference of the IEEE Industrial Electronics Society, Toronto, ON, Canada, 13–16 October 2021; pp. 1–5.
  30. Bosman, L.B.; Leon-Salas, W.D.; Hutzel, W.; Soto, E.A. PV System Predictive Maintenance: Challenges, Current Approaches, and Opportunities. Energies 2020, 13, 1398.
  31. Sica, F.C.; Guimarães, F.G.; de Oliveira Duarte, R.; Reis, A.J. A cognitive system for fault prognosis in power transformers. Electr. Power Syst. Res. 2015, 127, 109–117.
  32. Kabir, F.; Foggo, B.; Yu, N. Data Driven Predictive Maintenance of Distribution Transformers. In Proccedings of the 2018 China International Conference on Electricity Distribution (CICED), Tianjin, China, 17–19 September 2018; pp. 312–316.
  33. Mirowski, P.; LeCun, Y. Statistical Machine Learning and Dissolved Gas Analysis: A Review. IEEE Trans. Power Deliv. 2012, 27, 1791–1799.
  34. Donadio, L.; Fang, J.; Porté-Agel, F. Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast. Energies 2021, 14, 338.
  35. Heinermann, J.; Kramer, O. Machine learning ensembles for wind power prediction. Renew. Energy 2016, 89, 671–679.
  36. Yagli, G.M.; Yang, D.; Srinivasan, D. Automatic hourly solar forecasting using machine learning models. Renew. Sustain. Energy Rev. 2019, 105, 487–498.
  37. Voyant, C.; Notton, G.; Kalogirou, S.; Nivet, M.L.; Paoli, C.; Motte, F.; Fouilloy, A. Machine learning methods for solar radiation forecasting: A review. Renew. Energy 2017, 105, 569–582.
  38. Foley, A.M.; Leahy, P.G.; Marvuglia, A.; McKeogh, E.J. Current methods and advances in forecasting of wind power generation. Renew. Energy 2012, 37, 1–8.
  39. Riemer-Sørensen, S.; Rosenlund, G.H. Deep Reinforcement Learning for Long Term Hydropower Production Scheduling. In Proceedings of the 2020 International Conference on Smart Energy Systems and Technologies (SEST), Istanbul, Turkey, 7–9 September 2020; pp. 1–6.
  40. Bordin, C.; Skjelbred, H.I.; Kong, J.; Yang, Z. Machine Learning for Hydropower Scheduling: State of the Art and Future Research Directions. Procedia Comput. Sci. 2020, 176, 1659–1668.
  41. Fotopoulou, M.C.; Drosatos, P.; Petridis, S.; Rakopoulos, D.; Stergiopoulos, F.; Nikolopoulos, N. Model Predictive Control for the Energy Management in a District of Buildings Equipped with Building Integrated Photovoltaic Systems and Batteries. Energies 2021, 14, 3369.
  42. Wu, X.; Hu, X.; Moura, S.; Yin, X.; Pickert, V. Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array. J. Power Sources 2016, 333, 203–212.
  43. Mouli, G.R.C.; Kefayati, M.; Baldick, R.; Bauer, P. Integrated PV charging of EV fleet based on energy prices, V2G, and offer of reserves. IEEE Trans. Smart Grid 2017, 10, 1313–1325.
  44. Wang, X.; Nie, Y.; Cheng, K.W.E. Distribution system planning considering stochastic EV penetration and V2G behavior. IEEE Trans. Intell. Transp. Syst. 2019, 21, 149–158.
  45. McLoughlin, F.; Duffy, A.; Conlon, M. A clustering approach to domestic electricity load profile characterisation using smart metering data. Appl. Energy 2015, 141, 190–199.
  46. Haben, S.; Singleton, C.; Grindrod, P. Analysis and clustering of residential customers energy behavioral demand using smart meter data. IEEE Trans. Smart Grid 2015, 7, 136–144.
  47. Seyedzadeh, S.; Rahimian, F.P.; Glesk, I.; Roper, M. Machine learning for estimation of building energy consumption and performance: A review. Vis. Eng. 2018, 6, 1–20.
  48. Chou, J.S.; Tran, D.S. Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy 2018, 165, 709–726.
  49. Gonzalez-Briones, A.; Hernandez, G.; Corchado, J.M.; Omatu, S.; Mohamad, M.S. Machine learning models for electricity consumption forecasting: A review. In Proceedings of the 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia, 1–3 May 2019; pp. 1–6.
  50. Manivinnan, K.; Benner, C.L.; Don Russell, B.; Wischkaemper, J.A. Automatic identification, clustering and reporting of recurrent faults in electric distribution feeders. In Proceedings of the 19th International Conference on Intelligent System Application to Power Systems, San Antonio, TX, USA, 17–20 September 2017.
  51. Viegas, J.L.; Vieira, S.M.; Melicio, R.; Matos, H.A.; Sousa, J.M. Prediction of events in the smart grid: Interruptions in distribution transformers. In Proceedings of the 2016 IEEE International Power Electronics and Motion Control Conference, Varna, Bulgaria, 25–28 September 2016.
  52. Eskandarpour, R.; Khodaei, A. Machine Learning Based Power Grid Outage Prediction in Response to Extreme Events. IEEE Trans. Power Syst. 2017, 32.
  53. Kumar, R.; Singh, B.; Shahani, D.T.; Chandra, A.; Al-Haddad, K. Recognition of Power-Quality Disturbances Using S-Transform-Based ANN Classifier and Rule-Based Decision Tree. IEEE Trans. Ind. Appl. 2015, 51.
  54. Zyabkina, O.; Domagk, M.; Meyer, J.; Schegner, P. A feature-based method for automatic anomaly identification in power quality measurements. In Proceedings of the 2018 International Conference on Probabilistic Methods Applied to Power Systems, Boise, ID, USA, 24–28 June 2018.
  55. Vantuch, T.; Misak, S.; Jezowicz, T.; Burianek, T.; Snasel, V. The Power Quality Forecasting Model for Off-Grid System Supported by Multiobjective Optimization. IEEE Trans. Ind. Electron. 2017, 64, 9507–9516.
  56. Hoffmann, V.; Michałowska, K.; Andresen, C.; Torsæter, B.N. Incipient Fault Prediction in Power Quality Monitoring. In Proceedings of the 25th International Conference on Electricity Distribution (CIRED), Madrid, Spain, 3–6 June 2019.
  57. Andresen, C.A.; Torsæter, B.N.; Haugdal, H.; Uhlen, K. Fault Detection and Prediction in Smart Grids. In Proceedings of the 9th International Workshop on Applied Measurements for Power Systems, Bologna, Italy, 26–28 September 2018.
  58. Hoiem, K.W.; Santi, V.; Torsater, B.N.; Langseth, H.; Andresen, C.A.; Rosenlund, G.H. Comparative Study of Event Prediction in Power Grids using Supervised Machine Learning Methods. In Proceedings of the 2020 International Conference on Smart Energy Systems and Technologies (SEST), Istanbul, Turkey, 7–9 September 2020.
  59. Rosenlund, G.H.; Hoiem, K.W.; Torsater, B.N.; Andresen, C.A. Clustering and Dimensionality-reduction Techniques Applied on Power Quality Measurement Data. In Proceedings of the 2020 International Conference on Smart Energy Systems and Technologies (SEST), Istanbul, Turkey, 7–9 September 2020.
  60. Tyvold, T.S.; Nybakk Torsater, B.; Andresen, C.A.; Hoffmann, V. Impact of the Temporal Distribution of Faults on Prediction of Voltage Anomalies in the Power Grid. In Proceedings of the 2020 International Conference on Smart Energy Systems and Technologies (SEST), Istanbul, Turkey, 7–9 September 2020.
  61. Michalowska, K.; Hoffmann, V.; Andresen, C. Impact of seasonal weather on forecasting of power quality disturbances in distribution grids. In Proceedings of the 2020 International Conference on Smart Energy Systems and Technologies (SEST), Istanbul, Turkey, 7–9 September 2020.
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , , ,
View Times: 513
Revisions: 2 times (View History)
Update Date: 22 Jun 2022
Video Production Service