Artificial intelligence integrated grid systems: Technologies, potential frameworks, challenges, and research directions
Open accessAbstract
Real-time monitoring and control are crucial for ensuring the resilient, coordinated, and optimal operation of next-generation power systems, such as virtual power plants and microgrids. Artificial intelligence (AI) technologies have great potential for improving the effectiveness of monitoring, controlling, optimizing, and managing energy systems. As such, integrating AI into energy systems is seen as a promising path for developing intelligent grids, especially given the rise of distributed and renewable energy sources and the shift toward net-zero systems. This research explores the latest advancements across various areas of energy systems, revealing the current capabilities of intelligent monitoring and fault detection, control and optimization strategies, and energy management systems. The study delves into the key challenges, methods, findings, and research gaps in these areas. It further outlines a framework and the potential benefits of intelligent grid systems, offering multiple directions for future research to address these gaps. Ultimately, this comprehensive review aims to guide industry experts in the practical application of these innovations. • Potential application of AI for intelligent grid systems. • Scopes and trends of the AI-based grid system on different domains. • Exploring intelligent fault detection, control, optimization, and management systems. • Challenges and potential solutions to adopt and implement the AI-based grid systems. • Future research directions based on the development of intelligent technology.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
As AI applications mature within specific domains, the challenge of coordinating across multiple energy vectors (e.g., electricity, gas, heat) has emerged as a key frontier.
“Concurrently, as AI applications mature within specific domains, the challenge of coordinating across multiple energy vectors (e.g., electricity, gas, heat) has emerged as a key frontier [36,38].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [36]
Legacy infrastructure often lacks necessary digital capabilities, requiring costly retrofits.
“Legacy infrastructure often lacks necessary digital capabilities [35,36], requiring costly retrofits”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [36]
These transitions often require new business models and ecosystems.
“These transitions often require new business models and ecosystems [36].”
From Addressing Challenges for the Effective Adoption of Artificial Intelligence in the Energy Sector (2025) · cites this paper as [36]
Cites
1 retracted
Al-Gabalawy, M. A. (2021) · International Journal of Energy Research · doi:10.1002/er.6764 · Cited by 8 · see citing works
Retracted2011 International Joint Conference on Neural Networks
(2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2102870 · Cited by 1,772 · see citing works
Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities
Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125834 · Cited by 1,258 · see citing works
A survey on smart grid technologies and applications
Dileep, G. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.08.092 · Cited by 1,054 · see citing works
The U.S. Department of Energy's Microgrid Initiative
Ton, D., & Smith, M. A. (2012) · The Electricity Journal · doi:10.1016/j.tej.2012.09.013 · Cited by 728 · see citing works
Zhao, Y., Li, T., Zhang, X., & Zhang, C. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.04.021 · Cited by 594 · see citing works
Machlev, R., Heistrene, L., Perl, M., Levy, K. Y., Belikov, J., Mannor, S., & Levron, Y. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100169 · Cited by 552 · see citing works
Ahmad, T., Zhang, H., & Yan, B. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102052 · Cited by 504 · see citing works
Shen, J., & Khaligh, A. (2015) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2015.2464690 · Cited by 470 · see citing works
Ethical framework for Artificial Intelligence and Digital technologies
Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102433 · Cited by 469 · see citing works
Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks
Yu, J. J. Q., Hou, Y., Lam, A. Y. S., & Li, V. O. K. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2776310 · Cited by 453 · see citing works
Hasan, M. K., Habib, A. K. M. A., Shukur, Z., Ibrahim, F., Islam, S., & Razzaque, M. A. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103540 · Cited by 355 · see citing works
Energy meters evolution in smart grids: A review
Avancini, D. B., Rodrigues, J. J. P. C., Martins, S. G. B., Rabêlo, R. A. L., Al-Muhtadi, J. F., & Šolić, P. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.01.229 · Cited by 331 · see citing works
Shi, Z., Yao, W., Li, Z., Zeng, L., Zhao, Y., Zhang, R., Tang, Y., & Wen, J. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115733 · Cited by 327 · see citing works
CNN-Based Transformer Model for Fault Detection in Power System Networks
Thomas, J. B., Chaudhari, S. G., Shihabudheen, K. V., & Verma, N. K. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3238059 · Cited by 289 · see citing works
Liu, Z., Sun, Y., Xing, C., Liu, J., He, Y., Zhou, Y., & Zhang, G. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100195 · Cited by 288 · see citing works
TENCON 2017 - 2017 IEEE Region 10 Conference
(2017) · doi:10.1109/tencon39482.2017 · Cited by 272 · see citing works
North American Power Symposium 2010
(2010) · doi:10.1109/naps17227.2010 · Cited by 263 · see citing works
2011 Asia-Pacific Power and Energy Engineering Conference
(2011) · doi:10.1109/appeec16702.2011 · Cited by 258 · see citing works
Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning
Liu, W., Zhuang, P., Liang, H., et al. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2801880 · Cited by 251 · see citing works
IEEE PES Innovative Smart Grid Technologies, Europe
(2014) · doi:10.1109/isgteurope32333.2014 · Cited by 247 · see citing works
Support Vector Machine-Based Islanding and Grid Fault Detection in Active Distribution Networks
Baghaee, H. R., Mlakic, D., Nikolovski, S., & Dragicevic, T. (2020) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2019.2916621 · Cited by 235 · see citing works
Renewable energy management in smart grids by using big data analytics and machine learning
Mostafa, N. A., Ramadan, H. S., & Elfarouk, O. (2022) · Machine Learning with Applications · doi:10.1016/j.mlwa.2022.100363 · Cited by 231 · see citing works
Panda, D. K., & Das, S. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126877 · Cited by 226 · see citing works
Artificial intelligence based smart energy community management: A reinforcement learning approach
Zhou, S., Hu, Z., Gu, W., et al. (2019) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2018.00840 · Cited by 190 · see citing works
Duan, J., Yi, Z., Shi, D., Lin, C., Lu, X. B., & Wang, Z. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2896618 · Cited by 188 · see citing works
Hua, W., Chen, Y., Qadrdan, M., Jiang, J. S., Sun, H., & Wu, J. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112308 · Cited by 187 · see citing works
Ahl, A., Yarime, M., Goto, M., Chopra, S. S., Kumar, N. M., Tanaka, K., & Sagawa, D. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109488 · Cited by 184 · see citing works
Olabi, A. G., Abdelghafar, A. A., Maghrabie, H. M., Sayed, E. T., Rezk, H., Radi, M. A., Obaideen, K., & Abdelkareem, M. A. (2023) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2023.101730 · Cited by 184 · see citing works
Pawar, P., TarunKumar, M., & Panduranga Vittal, K. (2019) · Measurement · doi:10.1016/j.measurement.2019.107187 · Cited by 183 · see citing works
Saleem, M., Usman, M. R., Usman, M. A., & Politis, C. (2022) · IEEE Access · doi:10.1109/access.2022.3147484 · Cited by 171 · see citing works
Salehi, N., García, H. M., Quesada, G. V., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3142810 · Cited by 164 · see citing works
Intelligent energy management systems: a review
Mischos, S., Dalagdi, E., & Vrakas, D. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10441-3 · Cited by 163 · see citing works
Karagiannopoulos, S., Aristidou, P., & Hug, G. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2905348 · Cited by 162 · see citing works
A Review of Agent and Service-Oriented Concepts Applied to Intelligent Energy Systems
Vrba, P., Mařı́k, V., Siano, P., Leitão, P., Zhabelova, G., Vyatkin, V. V., & Strasser, T. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2326411 · Cited by 158 · see citing works
2009 International Conference on Clean Electrical Power
(2009) · doi:10.1109/iccep14447.2009 · Cited by 157 · see citing works
Edge-cloud Computing Systems for Smart Grid: State-of-the-art, Architecture, and Applications
Li, J., Gu, C., Xiang, Y., & Li, F. (2022) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2021.000161 · Cited by 154 · see citing works
2006 Chinese Control Conference
(2006) · doi:10.1109/ccc11467.2006 · Cited by 153 · see citing works
IoT based real time energy monitoring system using Raspberry Pi
Mudaliar, M. D., & Sivakumar, N. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100292 · Cited by 151 · see citing works
Trivedi, R., & Khadem, S. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100147 · Cited by 147 · see citing works
Habib, A. K. M. A., Hasan, M. K., Alkhayyat, A. H., et al. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108638 · Cited by 142 · see citing works
Smart grid evolution: Predictive control of distributed energy resources—A review
Babayomi, O. O., Zhang, Z., Dragičević, T., Hu, J., & Rodríguez, J. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108812 · Cited by 141 · see citing works
Afridi, Y. S., Ahmad, K., & Hassan, L. (2022) · International Journal of Energy Research · doi:10.1002/er.7100 · Cited by 140 · see citing works
Kow, K. W., Wong, Y. W., Rajkumar, R. K., & Rajkumar, R. K. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.064 · Cited by 139 · see citing works
Jayamaha, D. K. J. S., Lidula, N. W. A., & Rajapakse, A. D. (2019) · IEEE Access · doi:10.1109/access.2019.2945397 · Cited by 138 · see citing works
Águila-León, J., Vargas‐Salgado, C., Chiñas‐Palacios, C., et al. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.115920 · Cited by 137 · see citing works
On the Use of Artificial Intelligence for High Impedance Fault Detection and Electrical Safety
Wang, S., & Dehghanian, P. (2020) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2020.3017698 · Cited by 127 · see citing works
Li, Y., Gao, W., Yan, W., Huang, S., Wang, R., Gevorgian, V., & Gao, W. (2021) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2020.000267 · Cited by 126 · see citing works
Zhang, B., Hu, W., Ghias, A. M. Y. M., Xu, X. S., & Chen, Z. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2022.116647 · Cited by 123 · see citing works
A load frequency control using a PSO-based ANN for micro-grids in the presence of electric vehicles
Safari, A., Babaei, F., & Farrokhifar, M. (2018) · International Journal of Ambient Energy · doi:10.1080/01430750.2018.1563811 · Cited by 121 · see citing works
Toward Distributed Energy Services: Decentralizing Optimal Power Flow With Machine Learning
Dobbe, R., Sondermeijer, O., Fridovich-Keil, D., Arnold, D., Callaway, D., & Tomlin, C. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2935711 · Cited by 120 · see citing works
2021 29th Iranian Conference on Electrical Engineering
(2021) · Iranian Conference on Electrical Engineering · doi:10.1109/icee52715.2021.9544127 · Cited by 120 · see citing works
Patnaik, B., Mishra, M., Bansal, R. C., et al. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116457 · Cited by 116 · see citing works
Srivastava, A., & Parida, S. K. (2021) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2021.3129982 · Cited by 115 · see citing works
Qays, M. O., Ahmad, I., Abu‐Siada, A., Hossain, M. L., & Yasmin, F. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.01.085 · Cited by 114 · see citing works
Self-citationEnergy-Storage-Based Intelligent Frequency Control of Microgrid With Stochastic Model Uncertainties
Mu, C., Zhang, Y., Jia, H., et al. (2019) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2942770 · Cited by 110 · see citing works
Quantum computing for smart grid applications
Ullah, M. H., Eskandarpour, R., Zheng, H., & Khodaei, A. (2022) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.12602 · Cited by 107 · see citing works
An Intelligent and Fast Controller for DC/DC Converter Feeding CPL in a DC Microgrid
Farsizadeh, H., Gheisarnejad, M., Mosayebi, M., et al. (2019) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2019.2928814 · Cited by 105 · see citing works
Rizeakos, V., Bachoumis, A., Andriopoulos, N., Birbas, M., & Birbas, A. N. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120932 · Cited by 102 · see citing works
Alam, M. M., Rahman, M. H., Ahmed, M. F., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-19147-y · Cited by 98 · see citing works
Self-citationIntelligent multi-objective control and management for smart energy efficient buildings
Shaikh, P. H., Nor, N. M., Nallagownden, P., Elamvazuthi, I., & Ibrahim, T. (2015) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2015.08.006 · Cited by 97 · see citing works
Wu, N., & Wang, H. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.09.052 · Cited by 97 · see citing works
Abubakar, A., Almeida, C. F. M., & Gemignani, M. M. F. (2021) · Machines · doi:10.3390/machines9120328 · Cited by 94 · see citing works
Al-Saadi, M., Al‐Greer, M., & Short, M. (2023) · Energies · doi:10.3390/en16041608 · Cited by 93 · see citing works
An Intelligent Data Mining-Based Fault Detection and Classification Strategy for Microgrid
Baloch, S., & Muhammad, M. S. (2021) · IEEE Access · doi:10.1109/access.2021.3056534 · Cited by 92 · see citing works
Electrical Microgrid Optimization via a New Recurrent Neural Network
Urias, M. E. G., Sanchez, E. N., & Ricalde, L. J. (2014) · IEEE Systems Journal · doi:10.1109/jsyst.2014.2305494 · Cited by 88 · see citing works
Goni, M. O. F., Nahiduzzaman, M., Anower, M. S., et al. (2023) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2023.100107 · Cited by 88 · see citing works
Intelligent control of a grid-connected wind-photovoltaic hybrid power systems
Hong, C.-M., & Chen, C.-H. (2013) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2013.10.024 · Cited by 86 · see citing works
Mellit, A., Benghanem, M., Kalogirou, S. A., & Pavan, A. M. (2023) · Renewable Energy · doi:10.1016/j.renene.2023.03.096 · Cited by 86 · see citing works
Predicting Smart Grid Stability with Optimized Deep Models
Breviglieri, P., Erdem, T., & Eken, S. (2021) · SN Computer Science · doi:10.1007/s42979-021-00463-5 · Cited by 83 · see citing works
Molokomme, D. N., Onumanyi, A. J., & Abu-Mahfouz, A. M. I. (2022) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan11030047 · Cited by 82 · see citing works
Abdolrasol, M. G. M., Hannan, M. A., Hussain, S. M. S., Ustun, T. S., Sarker, M. R., & Ker, P. J. (2021) · Energies · doi:10.3390/en14206507 · Cited by 81 · see citing works
Self-citationEnergy-Net: A Deep Learning Approach for Smart Energy Management in IoT-Based Smart Cities
Abdel‐Basset, M., Hawash, H., Chakrabortty, R. K., et al. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3063677 · Cited by 79 · see citing works
Kiehbadroudinezhad, M., Merabet, A., Abo-Khalil, A. G., Salameh, T. S. Z., & Ghenaï, C. (2022) · Energies · doi:10.3390/en15093359 · Cited by 77 · see citing works
Abdali, A., Mazlumi, K., & Noroozian, R. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.03.051 · Cited by 76 · see citing works
Sapountzoglou, N., Lago, J., Schutter, B. D., & Raison, B. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115299 · Cited by 76 · see citing works
Sahri, Y., Tamalouzt, S., Belaid, S. L., et al. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.01.021 · Cited by 75 · see citing works
Fault detection and diagnosis in grid-connected PV systems under irradiance variations
Hajji, M., Yahyaoui, Z., Mansouri, M. M., et al. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.03.033 · Cited by 74 · see citing works
Convolutional Neural Network‐Based Intelligent Protection Strategy for Microgrids
Bukhari, S. B. A., Kim, C., Mehmood, K. K., Haider, R., & Zaman, M. S. U. (2020) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2018.7049 · Cited by 73 · see citing works
A State of Art Survey for Intelligent Energy Monitoring Systems
Ageed, Z. S., Zeebaree, S. R. M., Sadeeq, M. A. M., Abdulrazzaq, M. B., Salim, B. W., Salih, A. A., Yasin, H. M., & Ahmed, A. M. (2021) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2021/v8i130192 · Cited by 68 · see citing works
İncı, M. (2023) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2023.08.228 · Cited by 68 · see citing works
Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey
Yuan, Z., Xiong, G., & Fu, X. (2022) · Energies · doi:10.3390/en15228693 · Cited by 64 · see citing works
Ali, M., Prakash, K., Hossain, M. A., & Pota, H. R. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.127904 · Cited by 64 · see citing works
Self-citationSIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications
Asef, P., Taheri, R., Shojafar, M., et al. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3165890 · Cited by 64 · see citing works
A novel SVM based adaptive scheme for accurate fault identification in microgrid
Aiswarya, R., Nair, D. S., Rajeev, T., et al. (2023) · Electric Power Systems Research · doi:10.1016/j.epsr.2023.109439 · Cited by 64 · see citing works
Gashteroodkhani, O. A., Majidi, M., & Etezadi-Amoli, M. (2020) · Electric Power Systems Research · doi:10.1016/j.epsr.2020.106239 · Cited by 63 · see citing works
A Control Strategy Based on Deep Reinforcement Learning Under the Combined Wind-Solar Storage System
Huang, S., Li, P., Yang, M. Y., Gao, Y., Yun, J., & Zhang, C. (2021) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2021.3105497 · Cited by 61 · see citing works
Deep reinforcement learning-based secondary control for microgrids in islanded mode
Barbalho, P. I. N., Lacerda, V. A., Fernandes, R. A. S., & Coury, D. V. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108315 · Cited by 61 · see citing works
Mehdi, H. M., Azeem, M. K., & Ahmad, I. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2022.106333 · Cited by 60 · see citing works
AI-Empowered Recommender System for Renewable Energy Harvesting in Smart Grid System
Patel, R. K., Kumari, A., Tanwar, S., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3152528 · Cited by 59 · see citing works
Fault location in electrical power systems using intelligent systems techniques
de Souza, J. C. S., Rodrigues, M. A. P., Schilling, M. T., et al. (2001) · IEEE Transactions on Power Delivery · doi:10.1109/61.905590 · Cited by 57 · see citing works
A Distributed Iterative Learning Framework for DC Microgrids: Current Sharing and Voltage Regulation
Liu, X., Jiang, H., Wang, Y., & He, H. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2018.2863747 · Cited by 57 · see citing works
Intelligent Fault Detection System for Microgrids
Cepeda, C., Orozco-Henao, C. A., Percybrooks, W., Rivera, J. D. P., Montoya, O. D., Gil-González, W., & Velez, J. C. (2020) · Energies · doi:10.3390/en13051223 · Cited by 56 · see citing works
Sedhom, B. E., El‐Saadawi, M. M., Hatata, A. Y., et al. (2019) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2019.105511 · Cited by 55 · see citing works
Mofidul, R. B., Alam, M. M., Rahman, M. H., & Jang, Y. M. (2022) · Sensors · doi:10.3390/s22228980 · Cited by 55 · see citing works
Self-citation2014 Clemson University Power Systems Conference
(2014) · doi:10.1109/psc33075.2014 · Cited by 53 · see citing works
Singh, P. K., & Lather, J. S. (2020) · International Journal of Ambient Energy · doi:10.1080/01430750.2020.1720811 · Cited by 52 · see citing works
Technoeconomic Analysis of Fuel Cell Vehicle‐to‐Grid (FCV2G) System Supported by Photovoltaic Energy
İncı, M. (2023) · Energy Technology · doi:10.1002/ente.202201162 · Cited by 52 · see citing works
Lee, W., Jeoung, H., Park, D., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3079903 · Cited by 51 · see citing works
Intelligent Energy Management for Off-Grid Renewable Hybrid System Using Multi-Agent Approach
Slama, S. B. (2020) · IEEE Access · doi:10.1109/access.2019.2963584 · Cited by 50 · see citing works
Bourakadi, D. E., Yahyaouy, A., & Boumhidi, J. (2022) · Sustainable Computing: Informatics and Systems · doi:10.1016/j.suscom.2022.100709 · Cited by 49 · see citing works
Fault Detection in DC Microgrids Using Short-Time Fourier Transform
Grcić, I., Pandžić, H., & Novosel, D. (2021) · Energies · doi:10.3390/en14020277 · Cited by 48 · see citing works
Panthi, M., & Das, T. K. (2022) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2022.100567 · Cited by 47 · see citing works
HVDC Fault Detection and Classification with Artificial Neural Network Based on ACO-DWT Method
Jawad, R. S., & Abid, H. (2023) · Energies · doi:10.3390/en16031064 · Cited by 47 · see citing works
Srivastava, A., & Parida, S. K. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.107889 · Cited by 44 · see citing works
Hybridized Intelligent Home Renewable Energy Management System for Smart Grids
Ma, Y.-H., & Li, B. (2020) · Sustainability · doi:10.3390/su12052117 · Cited by 43 · see citing works
2013 8th EUROSIM Congress on Modelling and Simulation
(2013) · doi:10.1109/eurosim31352.2013 · Cited by 43 · see citing works
Artificial Intelligence-Based Protection for Smart Grids
Bakkar, M., Bogarra, S., Córcoles, F., et al. (2022) · Energies · doi:10.3390/en15134933 · Cited by 43 · see citing works
EDLA-EFDS: A Novel Ensemble Deep Learning Approach For Electrical Fault Detection Systems
Elmasry, W., & Wadi, M. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.107834 · Cited by 42 · see citing works
Gush, T., Bukhari, S. B. A., Mehmood, K. K., Admasie, S., Kim, J., & Kim, C. (2019) · Energies · doi:10.3390/en12234504 · Cited by 42 · see citing works
Saadatmand, S., Shamsi, P., & Ferdowsi, M. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.106657 · Cited by 42 · see citing works
Liu, Z., Luo, Y. D., Zhuo, R., et al. (2017) · Journal of Modern Power Systems and Clean Energy · doi:10.1007/s40565-017-0323-y · Cited by 40 · see citing works
Machine learning tools for active distribution grid fault diagnosis
Shafiullah, M., AlShumayri, K. A., & Alam, M. S. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103279 · Cited by 40 · see citing works
Self-citationVigneysh, T., & Kumarappan, N. (2016) · International Journal of Computational Intelligence Systems · doi:10.1080/18756891.2016.1237183 · Cited by 39 · see citing works
Xu, J., Li, K., & Abusara, M. A. (2022) · Memetic Computing · doi:10.1007/s12293-022-00357-w · Cited by 37 · see citing works
Data-driven based optimal distributed frequency control for islanded AC microgrids
Mo, N.-L., Guan, Z., Zhang, Z., Cheng, X., Liu, Z., & Li, T. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.105904 · Cited by 37 · see citing works
Fault Classification with Convolutional Neural Networks for Microgrid Systems
Pan, P., Mandal, R. K., & Akanda, M. M. R. R. (2022) · International Transactions on Electrical Energy Systems · doi:10.1155/2022/8431450 · Cited by 37 · see citing works
Dutta, S., Dutta, S., Sahu, S. K., et al. (2023) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2023.101012 · Cited by 37 · see citing works
Younesi, A., Shayeghi, H., & Siano, P. (2020) · Energies · doi:10.3390/en13051250 · Cited by 35 · see citing works
Context-aware Edge Computing and Internet of Things in Smart Grids: A systematic mapping study
Aranda, J. A. S., Costa, R. D. S., de Vargas, V. W., et al. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107826 · Cited by 35 · see citing works
An adaptive compensation droop control strategy for reactive power sharing in islanded microgrid
Ding, X., Yao, R., Zhai, X., et al. (2019) · Electrical Engineering · doi:10.1007/s00202-019-00870-1 · Cited by 34 · see citing works
Development of Intelligent Controlled Microgrid for Power Sharing and Load Shedding
Lin, F., Tan, K., Chang, C.-F., et al. (2022) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2022.3152167 · Cited by 34 · see citing works
Intelligent Three Tie Contactor Switch Unit-Based Fault Detection and Isolation in DC Microgrids
Subramaniam, K., & Illindala, M. S. (2019) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2019.2948923 · Cited by 31 · see citing works
Toward an intelligent community microgrid energy management system based on optimal control schemes
Azaroual, M., Mbungu, N. T., Ouassaid, M., et al. (2022) · International Journal of Energy Research · doi:10.1002/er.8343 · Cited by 31 · see citing works
Artificial Intelligence-Based Fault Diagnosis for Condition Monitoring of Electric Motors
Sharma, A., Mathew, L., Chatterji, S., et al. (2020) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001420590430 · Cited by 30 · see citing works
Boujoudar, Y., Azeroual, M., Moussaoui, H. E., et al. (2020) · International Transactions on Electrical Energy Systems · doi:10.1002/2050-7038.12579 · Cited by 30 · see citing works
Intelligent Islanding Detection of Microgrids Using Long Short-Term Memory Networks
Bukhari, S. B. A., Mehmood, K. K., Wadood, A., & Park, H. (2021) · Energies · doi:10.3390/en14185762 · Cited by 30 · see citing works
Ghasemi, N., Ghanbari, M., & Ebrahimi, R. (2022) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108618 · Cited by 29 · see citing works
A review on smart grid telecommunication system
Sharma, D. K., Rapaka, G. K., Pasupulla, A. P., et al. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.05.581 · Cited by 28 · see citing works
AlShorman, O., & Alshorman, A. M. (2021) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v11i4.pp2820-2829 · Cited by 27 · see citing works
Shayan, M. E., Najafi, G., Ghobadian, B., et al. (2022) · International journal of energy and environmental engineering · doi:10.1007/s40095-022-00503-7 · Cited by 27 · see citing works
Ponukumati, B. K., Sinha, P., Maharana, M. K., et al. (2022) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.5107 · Cited by 26 · see citing works
An energy and leakage current monitoring system for abnormality detection in electrical appliances
Alam, M. M., Shahjalal, M. A., Rahman, M. H., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-22508-2 · Cited by 26 · see citing works
Self-citationla Ossa, J. A.-D., Orozco-Henao, C. A., & Marín-Quintero, J. (2023) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2022.108923 · Cited by 26 · see citing works
Jadidi, S., Badihi, H., & Zhang, Y. M. (2023) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2023.109062 · Cited by 26 · see citing works
Intelligent secondary control in smart microgrids: an on-line approach for islanded operations
Shokoohi, S., Golshannavaz, S., Khezri, R., & Bevrani, H. (2018) · Optimization and Engineering · doi:10.1007/s11081-018-9382-9 · Cited by 25 · see citing works
Mohammad, F., Ahmed, M. A., & Kim, Y.-C. (2021) · Energies · doi:10.3390/en14196161 · Cited by 25 · see citing works
Intelligent Energy Management System for Smart Grids Using Machine Learning Algorithms
Babu, K., Sivasubramanian, S., Nivetha, C. S., et al. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202338705004 · Cited by 25 · see citing works
Ardito, C. A., Deldjoo, Y., Noia, T. D., Sciascio, E. D., & Nazary, F. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118368 · Cited by 23 · see citing works
A distributed machine learning approach for the secondary voltage control of an Islanded micro-grid
Karim, M. A., Currie, J., & Lie, T. T. (2016) · IEEE Innovative Smart Grid Technologies-Asia · doi:10.1109/isgt-asia.2016.7796454 · Cited by 22 · see citing works
A multi-agent approach to intelligent monitoring in smart grids
Vallejo, D., Albusac, J. A., Glez-Morcillo, C., et al. (2013) · International Journal of Systems Science · doi:10.1080/00207721.2013.783644 · Cited by 21 · see citing works
Intelligent fault diagnosis framework of microgrid based on cloud–edge integration
Chen, W., Feng, B., Tan, Z., Wu, N., & Fen, S. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.01.151 · Cited by 20 · see citing works
Subanth Williams, A., Suja Mani Malar, R., & Ahilan, T. (2023) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.109103 · Cited by 20 · see citing works
Baseer, M. A., Alsaduni, I., & Zubair, M. (2021) · Energy Technology · doi:10.1002/ente.202000980 · Cited by 19 · see citing works
Majumder, I., Dhar, S., Dash, P. K., et al. (2020) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2019.1114 · Cited by 17 · see citing works
SARATHKUMAR, D., Srinivasan, M., Stonier, A. A., et al. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1055/1/012129 · Cited by 17 · see citing works
Sarangi, S., Sahu, B. K., & Rout, P. K. (2022) · Electrical Engineering · doi:10.1007/s00202-022-01658-6 · Cited by 16 · see citing works
Machine Learning Based Fault Diagnosis in HVDC Transmission Lines
Muzzammel, R. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-6052-7_43 · Cited by 15 · see citing works
Deep learning based optimal energy management framework for community energy storage system
Alam, M. M., & Jang, Y. M. (2022) · ICT Express · doi:10.1016/j.icte.2022.05.007 · Cited by 15 · see citing works
Self-citationAlam, M. M., Mofidul, R. B., & Jang, Y. M. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107100 · Cited by 15 · see citing works
Self-citationRadhakrishnan, N., Chakraborty, I., Xie, J., et al. (2020) · IET Smart Grid · doi:10.1049/iet-stg.2019.0261 · Cited by 14 · see citing works
Burger, C., & Weinmann, J. (2024) · Ubiquity Press eBooks · doi:10.5334/bda.d · Cited by 14 · see citing works
Intelligent Monitoring Analysis of Power Grid Monitoring Information Based on Big Data Mining
Lu, R., Liu, N., Li, D., et al. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1992/3/032132 · Cited by 12 · see citing works
Tran, Q. T. T., Roose, L. R., Van, B. D., et al. (2022) · Energies · doi:10.3390/en15165932 · Cited by 12 · see citing works
Smart Energy and Electric Power Systems
Padmanaban, S., Holm‐Nielsen, J. B., Padmanandam, K., et al. (2022) · Elsevier eBooks · doi:10.1016/c2021-0-00466-1 · Cited by 12 · see citing works
Empirical Wavelet Transform-Based Intelligent Protection Scheme for Microgrids
Bukhari, S. B. A., Wadood, A., Khurshaid, T., Mehmood, K. K., Rhee, S.-B., & Kim, K.-C. (2022) · Energies · doi:10.3390/en15217995 · Cited by 12 · see citing works
Research on online fault detection tool of substation equipment based on artificial intelligence
Xingxin, C., Xin, Z., & Gangming, W. (2022) · Journal of King Saud University - Science · doi:10.1016/j.jksus.2022.102149 · Cited by 11 · see citing works
An intelligent management of power flow in the smart grid system using hybrid NPO-ATLA approach
Dsouza, A. K., Thammaiah, A., & Venkatesh, L. K. M. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10158-9 · Cited by 10 · see citing works
Applications of AI and IOT in Renewable Energy
(2022) · Elsevier eBooks · doi:10.1016/c2021-0-00962-7 · Cited by 9 · see citing works
Ananthu, D. P., Neelshetty, K., & Venkateshkumar, M. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v26.i2.pp617-628 · Cited by 9 · see citing works
Electricity pricing, capacity, and predictive maintenance considering reliability
Tsao, Y., & Vu, T.-L. (2023) · Annals of Operations Research · doi:10.1007/s10479-023-05164-1 · Cited by 8 · see citing works
Intelligent Load Management System Development with Renewable Energy for Demand Side Management
Paul, W. U. H., Siddiqui, A. S., & Kirmani, S. (2023) · International Journal of Advanced Engineering and Management Research · doi:10.51505/ijaemr.2023.8213 · Cited by 8 · see citing works
Karim, M. A., Currie, J., & Lie, T. T. (2017) · IEEE Innovative Smart Grid Technologies-Asia · doi:10.1109/isgt-asia.2017.8378412 · Cited by 7 · see citing works
Smart Grid Sensors: Principles and Applications
Mohsenian‐Rad, H. (2021) · Cited by 7 · see citing works
PLC based Efficient Energy Management System in The Smart Grid
Sundaramahalingam, S., Jegadeeshwaran, S., Esakiraj, M., et al. (2022) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec54921.2022.9952126 · Cited by 7 · see citing works
Using hybrid-CBR for intelligence monitoring and decision-making systems on SMART grid
Glukhikh, D. I., Shchinnikov, I., & Glukhikh, I. N. (2022) · Intelligent Decision Technologies · doi:10.3233/idt-210239 · Cited by 5 · see citing works
Venkatesan, A. K., & Natarajan, S. K. (2021) · Distributed Generation & Alternative Energy Journal · doi:10.13052/dgaej2156-3306.37214 · Cited by 4 · see citing works
An Intelligent Energy Management System with an Efficient IoT based Deep Learning Framework
Wilfred, C. B., George, S. M., Sivaranjani, S., et al. (2022) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds53736.2022.9760757 · Cited by 4 · see citing works
Orozco-Henao, C. A., la Ossa, J. A. D., & Marín-Quintero, J. G. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4148612 · Cited by 4 · see citing works
Al-Quraan, M. M., Khan, A., Centeno, A., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.00060 · Cited by 4 · see citing works
Tiwari, S. P. (2022) · Distributed Generation & Alternative Energy Journal · doi:10.13052/dgaej2156-3306.3812 · Cited by 3 · see citing works
Fault Diagnosis Method of Islanded DC Microgrid Based on Wavelet Sliding Window Energy
Han, H., Shi, M., Zheng, X., et al. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-1532-1_88 · Cited by 1 · see citing works
Stefanidou-Voziki, P., Cardoner-Valbuena, D., Villafáfila‐Robles, R., et al. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4021696 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Codepori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology
Rasheed, Z., Malik, S., Kemell, K.-K., Muhammad, W., Saari, M., Systä, K., & Abrahamsson, P. (2024) · doi:10.2139/ssrn.4979510 · Cited by 27 · see citing works
- Authors
- Alam, M. M., Hossain, M. J., Habib, M. A., Arafat, M. Y., & Hannan, M. A.
- Affiliations
- University of Technology Sydney (AU)
- Sunway University (MY)
- Corresponding author
- Hossain, M.J.
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 211
- Pages
- 115251
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W4406164381
- Cited by
- 76
- References
- 179
- Citations by year
- 2026: 48 · 2025: 27 · 2024: 1
- FWCI
- 16.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Control and Systems Engineering › Microgrid Control and Optimization
- Keywords
- artificial intelligence, smart grid, power system monitoring, fault detection, grid control, energy optimization, energy management systems, virtual power plants, microgrids, distributed energy resources, renewable energy integration, net-zero energy systems, AI adoption barriers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
