Deep Learning Applications in Power Systems: A Review Study
DOI:
https://doi.org/10.70445/gjus.2.2.2025.130-151Keywords:
Deep Learning, Power systems, Smart grid, load forecasting, fault detection, renewable energy, neural networks, stability analysisAbstract
This review paper discusses the application of deep learning in modern power systems, and how it can be used to enhance forecasting, fault detection, stability analysis, and renewable energy integration. It addresses some of the basic concepts of deep learning and power system structure, then goes on to discuss some of the key techniques: CNNs, RNNs, LSTMs, and reinforcement learning. The study compares against the existing research showing that deep learning has superior performance over traditional methods but also indicates that deep learning has several challenges such as high cost of computation, dependence on data, and lack of interpretability. The directions in the future are smart grid integration, explainable AI, and hybrid models. Altogether, deep learning is a promising technology to improve the efficiency, reliability, and intelligence of power systems.
References
[1]. Ozcanli AK, Yaprakdal F, Baysal M. Deep learning methods and applications for electrical power systems: A comprehensive review. International Journal of Energy Research. 2020 Jul;44(9):7136-57.
[2]. Khodayar M, Liu G, Wang J, Khodayar ME. Deep learning in power systems research: A review. CSEE Journal of Power and Energy Systems. 2020 Nov 20;7(2):209-20.
[3]. Forootan MM, Larki I, Zahedi R, Ahmadi A. Machine learning and deep learning in energy systems: A review. Sustainability. 2022 Apr 18;14(8):4832.
[4]. Alimi OA, Ouahada K, Abu-Mahfouz AM. A review of machine learning approaches to power system security and stability. IEEE access. 2020 Jun 19;8:113512-31.
[5]. Kumbhar A, Dhawale PG, Kumbhar S, Patil U, Magdum P. A comprehensive review: Machine learning and its application in integrated power system. Energy Reports. 2021 Nov 1;7:5467-74.
[6]. Cheng L, Yu T. A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems. International Journal of Energy Research. 2019 May;43(6):1928-73.
[7]. Miraftabzadeh SM, Di Martino A, Longo M, Zaninelli D. Deep learning in power systems: A bibliometric analysis and future trends. IEEE Access. 2024 Nov 5;12:163172-96.
[8]. Moradzadeh A, Mansour-Saatloo A, Nazari-Heris M, Mohammadi-Ivatloo B, Asadi S. Introduction and literature review of the application of machine learning/deep learning to load forecasting in power system. Application of machine learning and deep learning methods to power system problems. 2021 Oct 21:119-35.
[9]. Zhang Z, Zhang D, Qiu RC. Deep reinforcement learning for power system applications: An overview. CSEE Journal of Power and Energy Systems. 2019 Oct 7;6(1):213-25.
[10]. Samanta IS, Panda S, Rout PK, Bajaj M, Piecha M, Blazek V, Prokop L. A comprehensive review of deep-learning applications to power quality analysis. Energies. 2023 May 30;16(11):4406.
[11]. Strielkowski W, Vlasov A, Selivanov K, Muraviev K, Shakhnov V. Prospects and challenges of the machine learning and data-driven methods for the predictive analysis of power systems: A review. Energies. 2023 May 11;16(10):4025.
[12]. Mishra M, Singh JG. A comprehensive review on deep learning techniques in power system protection: Trends, challenges, applications and future directions. Results in Engineering. 2025 Mar 1;25:103884.
[13]. Sadeghi S, Hesami Naghshbandy A, Moradi P, Rezaei N. Introduction and Literature Review of the Application of Machine Learning/Deep Learning to Control Problems of Power Systems. Application of Machine Learning and Deep Learning Methods to Power System Problems. 2021 Oct 21:83-117.
[14]. Massaoudi M, Abu-Rub H, Refaat SS, Chihi I, Oueslati FS. Deep learning in smart grid technology: A review of recent advancements and future prospects. IEEE access. 2021 Apr 5;9:54558-78.
[15]. Klaiber J, Van Dinther C. Deep learning for variable renewable energy: A systematic review. ACM Computing Surveys. 2023 Aug 25;56(1):1-37.
[16]. Vaish R, Dwivedi UD, Tewari S, Tripathi SM. Machine learning applications in power system fault diagnosis: Research advancements and perspectives. Engineering Applications of Artificial Intelligence. 2021 Nov 1;106:104504.
[17]. Huang B, Wang J. Applications of physics-informed neural networks in power systems-a review. IEEE Transactions on Power Systems. 2022 Mar 25;38(1):572-88.
[18]. Almalaq A, Edwards G. A review of deep learning methods applied on load forecasting. In2017 16th IEEE international conference on machine learning and applications (ICMLA) 2017 Dec 18 (pp. 511-516). IEEE.
[19]. Ardeshiri A, Lotfi A, Behkam R, Moradzadeh A, Barzkar A. Introduction and literature review of power system challenges and issues. Application of machine learning and deep learning methods to power system problems. 2021 Oct 21:19-43.
[20]. Khamparia A, Singh KM. A systematic review on deep learning architectures and applications. Expert Systems. 2019 Jun;36(3):e12400.
[21]. Talaei Khoei T, Ould Slimane H, Kaabouch N. Deep learning: systematic review, models, challenges, and research directions. Neural Computing and Applications. 2023 Nov;35(31):23103-24.
[22]. Hasan F, Kargarian A, Mohammadi A. A survey on applications of machine learning for optimal power flow. In2020 IEEE Texas Power and Energy Conference (TPEC) 2020 Feb 6 (pp. 1-6). IEEE.
[23]. Sedghi M, Zolfaghari M, Mohseni A, Nosratian-Ahour J. Real-time transient stability estimation of power system considering nonlinear limiters of excitation system using deep machine learning: An actual case study in Iran. Engineering Applications of Artificial Intelligence. 2024 Jan 1;127:107254.
[24]. Li Y, Cao J, Xu Y, Zhu L, Dong ZY. Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance. Renewable and Sustainable Energy Reviews. 2024 Jan 1;189:113913.
[25]. Aluko-Olokun P. Leveraging Deep Learning for Predictive Maintenance and Grid Stability in Renewable Energy-Integrated Power Systems. International Journal of Research Publication and Reviews. 2024;5:7984-8000.
[26]. Chandel SS, Gupta A, Chandel R, Tajjour S. Review of deep learning techniques for power generation prediction of industrial solar photovoltaic plants. Solar Compass. 2023 Dec 1;8:100061.
[27]. Li Y, Zhang M, Chen C. A deep-learning intelligent system incorporating data augmentation for short-term voltage stability assessment of power systems. Applied Energy. 2022 Feb 15;308:118347.
[28]. Mansouri M, Trabelsi M, Nounou H, Nounou M. Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhancement prospects. IEEE Access. 2021 Sep 7;9:126286-306.
[29]. Chen Y, Tan Y, Deka D. Is machine learning in power systems vulnerable?. In2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) 2018 Oct 29 (pp. 1-6). IEEE.
[30]. Soundappan SJ. AI-based fault detection and isolation for reliability in modern power systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM). 2022 Jul 1;5(4):7106-10.
[31]. Sayghe A, Hu Y, Zografopoulos I, Liu X, Dutta RG, Jin Y, Konstantinou C. Survey of machine learning methods for detecting false data injection attacks in power systems. IET Smart Grid. 2020 Oct;3(5):581-95.
[32]. Shamshirband S, Rabczuk T, Chau KW. A survey of deep learning techniques: application in wind and solar energy resources. IEEE access. 2019 Nov 8;7:164650-66.
[33]. Mansouri M, Trabelsi M, Nounou H, Nounou M. Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhancement prospects. IEEE Access. 2021 Sep 7;9:126286-306.
[34]. Khan S, Yairi T. A review on the application of deep learning in system health management. Mechanical systems and signal processing. 2018 Jul 1;107:241-65.
[35]. Wang W, Harrou F, Bouyeddou B, Senouci SM, Sun Y. A stacked deep learning approach to cyber-attacks detection in industrial systems: application to power system and gas pipeline systems. Cluster Computing. 2022 Feb;25(1):561-78.
[36]. Chandrasekaran R, Paramasivan SK. Advances in deep learning techniques for short-term energy load forecasting applications: A review. Archives of Computational Methods in Engineering. 2025 Mar;32(2):663-92.
[37]. Duchesne L, Karangelos E, Wehenkel L. Recent developments in machine learning for energy systems reliability management. Proceedings of the IEEE. 2020 May 13;108(9):1656-76.
[38]. Saleh A, Zulkifley MA, Harun HH, Gaudreault F, Davison I, Spraggon M. Forest fire surveillance systems: A review of deep learning methods. Heliyon. 2024 Jan 15;10(1).
[39]. Jafari M, Kavousi-Fard A, Dabbaghjamanesh M, Karimi M. A survey on deep learning role in distribution automation system: A new collaborative learning-to-learning (L2L) concept. IEEE Access. 2022 Jul 29;10:81220-38.
[40]. Mohan N, Soman KP, Vinayakumar R. Deep power: Deep learning architectures for power quality disturbances classification. In2017 international conference on technological advancements in power and energy (TAP Energy) 2017 Dec 21 (pp. 1-6). IEEE.
[41]. Handa A, Sharma A, Shukla SK. Machine learning in cybersecurity: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2019 Jul;9(4):e1306.
[42]. Almalaq A, Albadran S, Mohamed MA. Deep machine learning model-based cyber-attacks detection in smart power systems. Mathematics. 2022 Jul 25;10(15):2574.
[43]. Salkuti SR. A survey of big data and machine learning. International Journal of Electrical and Computer Engineering (IJECE). 2020 Feb 15;10(1):575-80.
[44]. Qiu S, Cui X, Ping Z, Shan N, Li Z, Bao X, Xu X. Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: A review. Sensors. 2023 Jan 23;23(3):1305.
[45]. Santos L, Santos FN, Oliveira PM, Shinde P. Deep learning applications in agriculture: A short review. InIberian Robotics conference 2019 Nov 20 (pp. 139-151). Cham: Springer International Publishing.
[46]. Li C, Ishak I, Ibrahim H, Zolkepli M, Sidi F, Li C. Deep learning-based recommendation system: systematic review and classification. IEEE Access. 2023 Oct 10;11:113790-835.
[47]. Araujo SO, Peres RS, Ramalho JC, Lidon F, Barata J. Machine learning applications in agriculture: current trends, challenges, and future perspectives. Agronomy. 2023 Dec 1;13(12):2976.
[48]. Yoon DH, Yoon J. Development of a real-time fault detection method for electric power system via transformer-based deep learning model. International Journal of Electrical Power & Energy Systems. 2024 Aug 1;159:110069.
[49]. Li C, Ishak I, Ibrahim H, Zolkepli M, Sidi F, Li C. Deep learning-based recommendation system: systematic review and classification. IEEE Access. 2023 Oct 10;11:113790-835.
[50]. Santos L, Santos FN, Oliveira PM, Shinde P. Deep learning applications in agriculture: A short review. InIberian Robotics conference 2019 Nov 20 (pp. 139-151). Cham: Springer International Publishing.
[51]. Kimanzi R, Kimanga P, Cherori D, Gikunda PK. Deep Learning algorithms used in intrusion detection systems--a review. arXiv preprint arXiv:2402.17020. 2024 Feb 26.
[52]. Yoon DH, Yoon J. Deep learning-based method for the robust and efficient fault diagnosis in the electric power system. IEEE Access. 2022 Apr 26;10:44660-8.
[53]. Mnyanghwalo D, Kundaeli H, Kalinga E, Hamisi N. Deep learning approaches for fault detection and classifications in the electrical secondary distribution network: Methods comparison and recurrent neural network accuracy comparison. Cogent Engineering. 2020 Jan 1;7(1):1857500.
[54]. Belagoune S, Bali N, Bakdi A, Baadji B, Atif K. Deep learning through LSTM classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems. Measurement. 2021 Jun 1;177:109330.
[55]. Badawy M, Ramadan N, Hefny HA. Healthcare predictive analytics using machine learning and deep learning techniques: a survey. Journal of Electrical Systems and Information Technology. 2023 Aug 29;10(1):40.
[56]. Elahe MF, Jin M, Zeng P. Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges. International Journal of Energy Research. 2021 Aug;45(10):14274-305.
[57]. Sharifani K, Amini M. Machine learning and deep learning: A review of methods and applications. World Information Technology and Engineering Journal. 2023;10(07):3897-904.
[58]. Chandrasekaran K, Kandasamy P, Ramanathan S. Deep learning and reinforcement learning approach on microgrid. International transactions on electrical energy systems. 2020 Oct;30(10):e12531.
[59]. Kumari P, Toshniwal D. Deep learning models for solar irradiance forecasting: A comprehensive review. Journal of Cleaner Production. 2021 Oct 10;318:128566.
[60]. Vivas E, Allende-Cid H, Salas R. A systematic review of statistical and machine learning methods for electrical power forecasting with reported mape score. Entropy. 2020 Dec 15;22(12):1412.
[61]. Sahoo S, Kumar S, Abedin MZ, Lim WM, Jakhar SK. Deep learning applications in manufacturing operations: a review of trends and ways forward. Journal of Enterprise Information Management. 2023 Jan 27;36(1):221-51.
[62]. Chen K, Huang C, He J. Fault detection, classification and location for transmission lines and distribution systems: a review on the methods. High voltage. 2016 Apr;1(1):25-33.
[63]. Hossain E, Khan I, Un-Noor F, Sikander SS, Sunny MS. Application of big data and machine learning in smart grid, and associated security concerns: A review. Ieee Access. 2019 Jan 24; 7:13960-88.
