Abstract
Power transformers are a vital component in microgrids, as they play a crucial role in energy transformation, transmission, and distribution. With the ongoing digital transition in the energy sector and the emergence of the concept of the smart grid in power systems, power transformers must also adapt to this shift towards a more intelligent state. This includes the integration of features such as autonomous diagnostics and reliability, smart sensor integration, online monitoring, prognostics, and cybercommunication. This study provides an overview of the current level of smartness in power transformers and presents an approach for integrating them into a larger smart energy management system in a typical microgrid. This approach utilizes a combination of multi-agent theory, machine learning, automation, SCADA, and dispatching systems, with the goal of designing a Multi-Agent Reinforcement Learning algorithm for smarter diagnostic of power transformers. This algorithm utilizes multiple types of agents that work together to detect critical failures in power transformers and to assess their health and prognostic management. The algorithm delivers the global state to a smart energy management system for load management and power factor adjustment in a microgrid.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
El Hadraoui, H., Zegrari, M., Chebak, A., Laayati, O., Guennouni, N.: A multi-criteria analysis and trends of electric motors for electric vehicles. World Electric Veh. J. 13, 65 (2022). https://doi.org/10.3390/wevj13040065
Laayati, O., Bouzi, M., Chebak, A.: Smart energy management system: design of a monitoring and peak load forecasting system for an experimental open-pit mine. Appl. Syst. Innovation 5, 18 (2022). https://doi.org/10.3390/asi5010018
Laayati, O., El Hadraoui, H., Bouzi, M., El-Alaoui, A., Kousta, A., Chebak, A.: Smart energy management system: blockchain-based smart meters in microgrids. In: 2022 4th Global Power, Energy and Communication Conference (GPECOM), pp. 580–585 (2022)
El Maghraoui, A., Ledmaoui, Y., Laayati, O., El Hadraoui, H., Chebak, A.: Smart energy management: a comparative study of energy consumption forecasting algorithms for an experimental open-pit mine. Energies 15, 4569 (2022). https://doi.org/10.3390/en15134569
Maghraoui, A.E., Hammouch, F.-E., Ledmaoui, Y., Chebak, A.: Smart energy management system: a comparative study of energy consumption prediction algorithms for a hotel building. In: 2022 4th Global Power, Energy and Communication Conference (GPECOM), pp. 529–534 (2022)
El Hadraoui, H., Zegrari, M., Hammouch, F.-E., Guennouni, N., Laayati, O., Chebak, A.: Design of a customizable test bench of an electric vehicle powertrain for learning purposes using model-based system engineering. Sustainability 14, 10923 (2022). https://doi.org/10.3390/su141710923
de Faria, H., Costa, J.G.S., Olivas, J.L.M.: A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis. Renew. Sustain. Energy Rev. 46, 201–209 (2015). https://doi.org/10.1016/j.rser.2015.02.052
Laayati, O., Hadraoui, H.E., Bouzi, M., Chebak A.: Smart energy management system: oil immersed power transformer failure prediction and classification techniques based on DGA data. In: 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–6 (2022)
Shintemirov, A., Tang, W., Wu, Q.H.: Power Transformer Fault Classification Based on Dissolved Gas Analysis by Implementing Bootstrap and Genetic Programming. IEEE Trans. Syst. Man Cybernet. Part C (Appl. Rev.) 39, 69–79 (2009). https://doi.org/10.1109/TSMCC.2008.2007253
Abdo, A., Liu, H., Zhang, H., Guo, J., Li, Q.: A new model of faults classification in power transformers based on data optimization method. Electric Power Syst. Res. 200, 107446 (2021). https://doi.org/10.1016/j.epsr.2021.107446
Zhang, X., et al.: Research on transformer fault diagnosis: Based on improved firefly algorithm optimized LPboost–classification and regression tree. IET Gener. Transm. Distrib. 15, 2926–2942 (2021). https://doi.org/10.1049/gtd2.12229
Zhang, L., Sheng, G., Hou, H., Zhou, N., Jiang, X.: An adaptive fault diagnosis method of power transformers based on combining oversampling and cost-sensitive learning. IET Smart Grid 4, 623–635 (2021). https://doi.org/10.1049/stg2.12044
Hu, H., Ma, X., Shang, Y.: A novel method for transformer fault diagnosis based on refined deep residual shrinkage network. IET Electr. Power Appl. 16, 206–223 (2022). https://doi.org/10.1049/elp2.12147
AriasVelásquez, R.M.: Support vector machine and tree models for oil and Kraft degradation in power transformers. Eng. Fail. Anal. 127, 105488 (2021). https://doi.org/10.1016/j.engfailanal.2021.105488
Lopes SM de, A., Flauzino, R.A., Altafim, R.A.C.: Incipient fault diagnosis in power transformers by data-driven models with over-sampled dataset. Electric Power Syst. Res. 201, 107519 (2021). https://doi.org/10.1016/j.epsr.2021.107519
Hao, X., Caixin, S.: Artificial immune network classification algorithm for fault diagnosis of power transformer. IEEE Trans. Power Delivery 22, 930–935 (2007). https://doi.org/10.1109/TPWRD.2007.893182
Xing, M., Ding, W., Li, H., Zhang, T.: A power transformer fault prediction method through temporal convolutional network on dissolved gas chromatography data. Secur. Commun. Networks 2022, e5357412 (2022). https://doi.org/10.1155/2022/5357412
Odongo, G., Musabe, R., Hanyurwimfura, D.: A multinomial DGA classifier for incipient fault detection in oil-impregnated power transformers. Algorithms 14, 128 (2021). https://doi.org/10.3390/a14040128
Lee, C.-T., Horng, S.-C.: Abnormality detection of cast-resin transformers using the fuzzy logic clustering decision tree. Energies 13, 2546 (2020). https://doi.org/10.3390/en13102546
Kazemi, Z., Naseri, F., Yazdi, M., Farjah, E.: An EKF-SVM machine learning-based approach for fault detection and classification in three-phase power transformers. IET Sci. Meas. Technol. 15, 130–142 (2021). https://doi.org/10.1049/smt2.12015
Madavan, R., Saroja, S.: Decision making on the state of transformers based on insulation condition using AHP and TOPSIS methods. IET Sci. Meas. Technol. 14, 137–145 (2020). https://doi.org/10.1049/iet-smt.2018.5337
Raichura, M.B., Chothani, N.G., Patel, D.D.: Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique. IET Sci. Meas. Technol. 14, 111–121 (2020). https://doi.org/10.1049/iet-smt.2019.0102
Zou, J., Chen, W., Wan, F., Fan, Z., Du, L.: Raman spectral characteristics of oil-paper insulation and its application to ageing stage assessment of oil-immersed transformers. Energies 9, 946 (2016). https://doi.org/10.3390/en9110946
Kim, M., Lee, S.: Power transformer voltages classification with acoustic signal in various noisy environments. Sensors 22, 1248 (2022). https://doi.org/10.3390/s22031248
Castro, B., et al.: Partial discharge monitoring in power transformers using low-cost piezoelectric sensors. Sensors 16, 1266 (2016). https://doi.org/10.3390/s16081266
Shamlou, A., RezaFeyzi, M., Behjat, V.: Winding deformation classification in a power transformer based on the time-frequency image of frequency response analysis using Hilbert-Huang transform and evidence theory. Int. J. Electr. Power Energy Syst. 129, 106854 (2021). https://doi.org/10.1016/j.ijepes.2021.106854
Laayati, O., Bouzi, M., Chebak, A.: Smart energy management system: SCIM diagnosis and failure classification and prediction using energy consumption data. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 1377–1386. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_125
El Hadraoui, H., Laayati, O., Guennouni, N., Chebak, A., Zegrari, M.: A data-driven model for fault diagnosis of induction motor for electric powertrain. In: 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), pp. 336–341 (2022)
Laayati, O., et al.: An AI-layered with multi-agent systems architecture for prognostics health management of smart transformers: a novel approach for smart grid-ready energy management systems. Energies 15, 7217 (2022). https://doi.org/10.3390/en15197217
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Laayati, O., El-Bazi, N., Hadraoui, H.E., Ennawaoui, C., Chebak, A., Bouzi, M. (2023). Toward Smarter Power Transformers in Microgrids: A Multi-agent Reinforcement Learning for Diagnostic. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-031-29860-8_65
Download citation
DOI: https://doi.org/10.1007/978-3-031-29860-8_65
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29859-2
Online ISBN: 978-3-031-29860-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)