Review of Big Data Analytics for Smart Electrical Energy Systems
Abstract
:1. Introduction
2. Data in Electrical Energy Systems
2.1. V’s of Big Data
2.1.1. Volume
2.1.2. Velocity
2.1.3. Variety
2.1.4. Veracity
2.1.5. Value
2.2. Data Types
2.2.1. Domain Data
2.2.2. Off-Domain Data
2.3. Data Structures
2.3.1. Structured
2.3.2. Semistructured
2.3.3. Unstructured
2.4. Data Preprocessing
2.4.1. Data Acquisition
2.4.2. Data Integration
3. Big Data Analytics in a Power System Context
3.1. Big Data Analytics
3.1.1. Artificial Neural Network (ANN)
3.1.2. Deep Learning Techniques
3.2. Approaches to Integrate BDA in a Power System Context
4. BDA Applications in Smart Power/Energy Systems
4.1. Load Forecasting
4.2. Fault or Outage Detection and Diagnosis
4.3. Voltage Sag Estimation
4.4. BDA Applications in Power System Management
4.5. Future Implementation Opportunities and Barriers
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Nomenclature
AI | Artificial Intelligence |
ANN | Artificial Neural Network |
BDA | Big Data Analytics |
CNN | Convolutional Neural Network |
DNN | Deep Neural Networks |
DSM | Demand Side Management |
DT | Decision Tree |
EV | Electric Vehicle |
FC | Fully Connected |
IT | Information Technology |
KNN | K-Nearest Neighbors |
LCT | Low Carbon Technology |
LSTM | Long Short-Term Memory |
MLP | Multilayer Perceptrons |
MRA | Monitor Reach Area |
PMU | Phasor Measurement Unit |
RF | Random Forest |
RNN | Recurrent Neural Network |
SAFRI | System Average RMS Variation Frequency Index |
SCADA | Supervisory Control and Data Acquisition |
SVM | Support Vector Machine |
V2G | Vehicle-To-Grid |
VSE | Voltage Sag Estimation |
References
- Yan, Y.; Sheng, G.; Qiu, R.C.; Jiang, X. Big Data Modeling and Analysis for Power Transmission Equipment: A Novel Random Matrix Theoretical Approach. IEEE Access 2017, 6, 7148–7156. [Google Scholar] [CrossRef]
- De Mauro, A.; Greco, M.; Grimaldi, M. A formal definition of Big Data based on its essential features. Libr. Rev. 2016, 65, 122–135. [Google Scholar] [CrossRef]
- Zhang, Y.; Huang, T.; Bompard, E.F. Big data analytics in smart grids: A review. Energy Inform. 2018, 1, 8. [Google Scholar] [CrossRef]
- Akhavan-Hejazi, H.; Mohsenian-Rad, H. Power systems big data analytics: An assessment of paradigm shift barriers and prospects. Energy Rep. 2018, 4, 91–100. [Google Scholar] [CrossRef]
- Gurney, K. An Introduction to Neural Networks; Taylor & Francis, Inc.: London, UK, 1997. [Google Scholar]
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Commun. ACM 2017, 60, 84–90. [Google Scholar] [CrossRef]
- BEIS; Ofgem. Transitioning to a Net Zero Energy System: Smart Systems and Flexibility Plan 2021. 2021. Available online: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1003778/smart-systems-and-flexibility-plan-2021.pdf (accessed on 16 March 2023).
- EPRI. Enhancements to ANNSTLF, EPRI’s Short Term Load Forecaster; Pattern Recognition Technologies, Inc.: Dallas, TX, USA, 1997. [Google Scholar]
- Khotanzad, A.; Afkhami-Rohani, R.; Maratukulam, D. ANNSTLF-Artificial Neural Network Short-Term Load Forecaster-generation three. IEEE Trans. Power Syst. 1998, 13, 1413–1422. [Google Scholar] [CrossRef]
- Liao, H.; Anani, N. Fault Identification-based Voltage Sag State Estimation Using Artificial Neural Network. Energy Procedia 2017, 134, 40–47. [Google Scholar] [CrossRef]
- Angadi, R.V.; Venkataramu, P.S.; Daram, S.B. Role of Big Data Analytics in Power System Application. E3S Web Conf. 2020, 184, 01017. [Google Scholar] [CrossRef]
- Zhu, T.; Xiao, S.; Li, Y.; Yi, P.; Gu, Y.; Zhang, Q. Emergent Technologies in Big Data Sensing: A Survey. Int. J. Distrib. Sens. Netw. 2015, 11, 902982. [Google Scholar] [CrossRef]
- Kerai, M. Information about the Smart Meters Statistics in Great Britain, Quarterly Report to End September 2022; Department for Business, Energy and Industrial Strategy (BEIS): London, UK, 2022. Available online: https://www.gov.uk/government/collections/smart-meters-statistics (accessed on 16 March 2023).
- Huang, Z.; Luo, H.; Skoda, D.; Zhu, T.; Gu, Y. E-Sketch: Gathering large-scale energy consumption data based on consumption patterns. In Proceedings of the 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 27–30 October 2014. [Google Scholar]
- Li, N.; Xu, M.; Cao, W.; Gao, P. Researches on data processing and data preventing technologies in the environment of big data in power system. In Proceedings of the 2015 5th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), Changsha, China, 26–29 November 2015; pp. 2491–2494. [Google Scholar]
- Sagiroglu, S.; Terzi, R.; Canbay, Y.; Colak, I. Big data issues in smart grid systems. In Proceedings of the 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA), Birmingham, UK, 20–23 November 2016. [Google Scholar]
- Russom, P. Big Data Analytics. In TDWI Best Practices Report; The Data Warehousing Institute (TDWI), 1105 Media Inc.: Renton, WA, USA, 2011. [Google Scholar]
- Arghandeh, R.; Zhou, Y. Big Data Application in Power Systems; Elsevier: Amsterdam, The Netherlands, 2011. [Google Scholar]
- Qiu, Y.; Feng, Y.; Sun, J.; Zhang, W.; Infield, D. Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data. IET Renew. Power Gener. 2016, 10, 661–668. [Google Scholar] [CrossRef]
- Huang, Y.; Warnier, M.; Brazier, F.M.T.; Miorandi, D. Social Networking for Smart Grid Users: A Preliminary Modeling and Simulation Study. In Proceedings of the 2015 IEEE 12th International Conference on Networking, Sensing and Control, Taipei, Taiwan, 9–11 April 2015. [Google Scholar]
- Moreno-Munoz, A.; Bellido-Outeirino, F.; Siano, P.; Gómez-Nieto, M. Mobile social media for smart grids customer engagement: Emerging trends and challenges. Renew. Sustain. Energy Rev. 2016, 53, 1611–1616. [Google Scholar] [CrossRef]
- Alghamdi, T.A.; Javaid, N. A Survey of Preprocessing Methods Used for Analysis of Big Data Originated from Smart Grids. IEEE Access 2022, 10, 29149–29171. [Google Scholar] [CrossRef]
- Wang, H.; Yemeni, Z.; Ismael, W.M.; Hawbani, A.; Alsamhi, S.H. A reliable and energy efficient dual prediction data reduction approach for WSNs based on Kalman filter. IET Commun. 2021, 15, 2285–2299. [Google Scholar] [CrossRef]
- Koprinska, I.; Rana, M.; Agelidis, V.G. Correlation and instance based feature selection for electricity load forecasting. Knowl. Based Syst. 2015, 82, 29–40. [Google Scholar] [CrossRef]
- Saleh, A.I.; Rabie, A.H.; Abo-Al-Ez, K.M. A data mining based load forecasting strategy for smart electrical grids. Adv. Eng. Inform. 2016, 30, 422–448. [Google Scholar] [CrossRef]
- Li, Z.; Liu, J.; Lin, Y.; Wang, F. Grid-Constrained Data Cleansing Method for Enhanced Bus Load Forecasting. IEEE Trans. Instrum. Meas. 2021, 70, 1–10. [Google Scholar] [CrossRef]
- Haque, M.T.; Kashtiban, A.M. Application of Neural Networks in Power Systems; A Review. Int. J. Energy Power Eng. 2007, 1, 897–901. [Google Scholar]
- Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 1998, 86, 2278–2324. [Google Scholar] [CrossRef]
- Brahma, P.P.; Wu, D.; She, Y. Why Deep Learning Works: A Manifold Disentanglement Perspective. IEEE Trans. Neural Netw. Learn. Syst. 2015, 27, 1997–2008. [Google Scholar] [CrossRef]
- Nielson, M.A. Neural Networks and Deep Learning; Determination Press, 2015; Available online: http://neuralnetworksanddeeplearning.com/ (accessed on 16 March 2023).
- Varga, E.D.; Beretka, S.F.; Noce, C.; Sapienza, G. Robust Real-Time Load Profile Encoding and Classification Framework for Efficient Power Systems Operation. IEEE Trans. Power Syst. 2014, 30, 1897–1904. [Google Scholar] [CrossRef]
- Claessens, B.J.; Vrancx, P.; Ruelens, F. Convolutional neural networks for automatic state-time feature extraction in reinforcement learning applied to residential load Control. IEEE Trans. Smart Grid 2016, 9, 3259–3269. [Google Scholar] [CrossRef]
- Liao, H.; Milanović, J.V.; Rodrigues, M.; Shenfield, A. Voltage Sag Estimation in Sparsely Monitored Power Systems Based on Deep Learning and System Area Mapping. IEEE Trans. Power Deliv. 2018, 33, 3162–3172. [Google Scholar] [CrossRef]
- Mazhar, T.; Asif, R.N.; Malik, M.A.; Nadeem, M.A.; Haq, I.; Iqbal, M.; Kamran, M.; Ashraf, S. Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods. Sustainability 2023, 15, 2603. [Google Scholar] [CrossRef]
- Barakat, E.; Qayyum, M.; Hamed, M.; Al Rashed, S. Short-term peak demand forecasting in fast developing utility with inherit dynamic load characteristics. I. Application of classical time-series methods. II. Improved modelling of system dynamic load characteristics. IEEE Trans. Power Syst. 1990, 5, 813–824. [Google Scholar] [CrossRef] [PubMed]
- Fidalgo, J.; Lopes, J. Forecasting active and reactive power at substations transformers. In Proceedings of the 2003 IEEE Bologna Power Tech Conference Proceedings, Bologna, Italy, 23–26 June 2003. [Google Scholar]
- Bhatt, A.K.; Solanki, P.; Bhatt, A.; Cherukuri, R. A fast and efficient back propagation algorithm to forecast active and reactive power drawn by various capacity Induction Motors. In Proceedings of the International Conference on Circuits, Power and Computing Technologies (ICCPCT), Nagercoil, India, 20–21 March 2013. [Google Scholar]
- Khotanzad, A.; Afkhami-Rohani, R.; Lu, T.-L.; Abaye, A.; Davis, M.; Maratukulam, D. ANNSTLF-a neural-network-based electric load forecasting system. IEEE Trans. Neural Netw. 1997, 8, 835–846. [Google Scholar] [CrossRef]
- Park, D.; El-Sharkawi, M.; Marks, R.; Atlas, L.; Damborg, M. Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 1991, 6, 442–449. [Google Scholar] [CrossRef]
- Oonsivilai, A.; El-Hawary, M.E. Wavelet neural network based short term load forecasting of electric power system commercial load. In Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, Edmonton, AB, Canada, 9–12 May 1999. [Google Scholar]
- Taylor, J.; Buizza, R. Neural network load forecasting with weather ensemble predictions. IEEE Trans. Power Syst. 2002, 17, 626–632. [Google Scholar] [CrossRef]
- Chan, J.Y.; Milanovic, J.V.; Delahunty, A. Risk-Based Assessment of Financial Losses Due to Voltage Sag. IEEE Trans. Power Deliv. 2011, 26, 492–500. [Google Scholar] [CrossRef]
- Zambrano, X.; Hernandez, A.; Izzeddine, M.; de Castro, R.M. Estimation of Voltage Sags from a Limited Set of Monitors in Power Systems. IEEE Trans. Power Deliv. 2017, 32, 656–665. [Google Scholar] [CrossRef]
- Bollen, M.H.J. Understanding Power Quality Problems: Voltage Sags and Interruptions; Wiley: New York, NY, USA, 2000. [Google Scholar]
- Short, T.; Mansoor, A.; Sunderman, W.; Sundaram, A. Site variation and prediction of power quality. IEEE Trans. Power Deliv. 2003, 18, 1369–1375. [Google Scholar] [CrossRef]
- Espinosa-Juarez, E.; Hernández, A. A Method for Voltage Sag State Estimation in Power Systems. IEEE Trans. Power Deliv. 2007, 22, 2517–2526. [Google Scholar] [CrossRef]
- Brown, R.E. Electric Power Distribution Reliability (Power Engineering (Willis)), 2nd ed.; Willis, H.L., Ed.; CRC Press: Boca Raton, FL, USA, 2008. [Google Scholar]
- Li, W. Risk Assessment of Power Systems: Models, Methods, and Applications, 2nd ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2014. [Google Scholar]
- Myo Thu, A.; Milanovic, J.V. Stochastic prediction of voltage sags by considering the probability of the failure of the protection system. IEEE Trans. Power Deliv. 2006, 21, 322–329. [Google Scholar]
- IEEE Std 493-2007 (Revision of IEEE Std 493-1997); IEEE Recommended Practice for the Design of Reliable Industrial and Commercial Power Systems—Redline. The Institute of Electrical and Electronics Engineers: New York, NY, USA, 2007; pp. 1–426.
- Dugan, R.C.; McGranaghan, M.; Santoso, S.; Beaty, H.W. Electrical Power Systems Quality; McGraw-Hill: Berkshire, UK, 2003. [Google Scholar]
- Majidi, M.; Etezadi-Amoli, M.; Fadali, M.S. A sparse-data-driven approach for fault location in transmission networks. IEEE Trans. Smart Grid 2017, 8, 548–556. [Google Scholar] [CrossRef]
- Olguin, G.; Vuinovich, F.; Bollen, M. An Optimal Monitoring Program for Obtaining Voltage Sag System Indexes. IEEE Trans. Power Syst. 2006, 21, 378–384. [Google Scholar] [CrossRef]
- Espinosa-Juarez, E.; Hernandez, A. Neural Networks Applied to Solve the Voltage Sag State Estimation Problem: An Approach Based on the Fault Positions Concept. In Proceedings of the 2009 Electronics, Robotics and Automotive Mechanics Conference (CERMA), Washington, DC, USA, 22–25 September 2009. [Google Scholar]
- Dhupia, B.; Rani, M.U.; Alameen, A. The Role of Big Data Analytics in Smart Grid Management. In Emerging Research in Data Engineering Systems and Computer Communications; Venkata Krishna, P., Obaidat, M., Eds.; Advances in Intelligent Systems and Computing; Springer: Singapore, 2020; Volume 1054, pp. 403–412. [Google Scholar]
- Ma, Z.; Xie, J.; Li, H.; Sun, Q.; Si, Z.; Zhang, J.; Guo, J. The Role of Data Analysis in the Development of Intelligent Energy Networks. IEEE Netw. 2017, 31, 88–95. [Google Scholar] [CrossRef]
- Ahmad, T.; Chen, H.; Wang, J.; Guo, Y. Review of various modeling techniques for the detection of electricity theft in smart grid environment. Renew. Sustain. Energy Rev. 2018, 82, 2916–2933. [Google Scholar] [CrossRef]
- Jokar, P.; Arianpoo, N.; Leung, V.C.M. Electricity Theft Detection in AMI Using Customers’ Consumption Patterns. IEEE Trans. Smart Grid 2015, 7, 216–226. [Google Scholar] [CrossRef]
- Landa-Torres, I.; Unanue, I.; Angulo, I.; Russo, M.R.; Campolongo, C.; Maffei, A.; Srinivasan, S.; Glielmo, L.; Iannelli, L. The application of the data mining in the integration of RES in the smart grid: Consumption and generation forecast in the I3RES project. In Proceedings of the 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), Riga, Latvia, 11–13 May 2015. [Google Scholar]
- Mathumitha, R.; Rathika, P.; Manimala, K. Big Data Analytics and Visualization of Residential Electricity Consumption Behavior based on Smart Meter Data. In Proceedings of the 2022 International Conference on Breakthrough in Heuristics and Reciprocation of Advanced Technologies (BHARAT), Visakhapa, India, 7–8 April 2022. [Google Scholar]
- Koziel, S.; Hilber, P.; Ichise, R. Application of big data analytics to support power networks and their transition towards smart grids. In Proceedings of the 2019 IEEE International Conference on Big Data, Los Angeles, CA, USA, 9–12 December 2019; pp. 6104–6106. [Google Scholar]
- Zhou, K.; Fu, C.; Yang, S. Big data driven smart energy management: From big data to big insights. Renew. Sustain. Energy Rev. 2016, 56, 215–225. [Google Scholar] [CrossRef]
- Wamba, S.F.; Akter, S.; Edwards, A.; Chopin, G.; Gnanzou, D. How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 2015, 165, 234–246. [Google Scholar] [CrossRef]
Topics | References |
---|---|
Big Data Characteristics | [11,12,13,14,15,16,17,18,19,20,21,22] |
BDA techniques | ANN [5,8,9,27]; Deep Learning [28,30,31,32] |
BDA applications | Load Forecasting [8,9,38,40,41]; Fault or Outage Detection and Diagnosis [10]; Voltage Sag Estimation [43,44,45,46]; Power System Management [55,56,57,58,59] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liao, H.; Michalenko, E.; Vegunta, S.C. Review of Big Data Analytics for Smart Electrical Energy Systems. Energies 2023, 16, 3581. https://doi.org/10.3390/en16083581
Liao H, Michalenko E, Vegunta SC. Review of Big Data Analytics for Smart Electrical Energy Systems. Energies. 2023; 16(8):3581. https://doi.org/10.3390/en16083581
Chicago/Turabian StyleLiao, Huilian, Elizabeth Michalenko, and Sarat Chandra Vegunta. 2023. "Review of Big Data Analytics for Smart Electrical Energy Systems" Energies 16, no. 8: 3581. https://doi.org/10.3390/en16083581