Machine learning-based inertia estimation in power systems: a review of methods and challenges
Abstract The transformation of power systems is accelerating due to the widespread integration of renewable energy sources (RES) and the growing role of inverter-based generations (IBGs). This shift has significantly reduced rotational inertia, increasing the system’s vulnerability to frequency fluc...
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| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | Energy Informatics |
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| Online Access: | https://doi.org/10.1186/s42162-025-00496-7 |
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| author | Santosh Diggikar Arunkumar Patil Siddhant Satyapal Katkar Kunal Samad |
| author_facet | Santosh Diggikar Arunkumar Patil Siddhant Satyapal Katkar Kunal Samad |
| author_sort | Santosh Diggikar |
| collection | DOAJ |
| description | Abstract The transformation of power systems is accelerating due to the widespread integration of renewable energy sources (RES) and the growing role of inverter-based generations (IBGs). This shift has significantly reduced rotational inertia, increasing the system’s vulnerability to frequency fluctuations during disturbances. Consequently, the accurate and adaptive estimation of inertia has become crucial for maintaining frequency stability and grid reliability. Traditional estimation methods, though effective in certain scenarios, struggle to capture the non-linear and dynamic behaviors of modern power systems, necessitating the adoption of advanced solutions. This review comprehensively explores machine learning (ML)-based methodologies for inertia estimation, emphasizing their adaptability, scalability, and real-time capabilities compared to conventional approaches. The study categorizes ML techniques into supervised learning (SL), unsupervised learning (USL), semi-supervised learning (SSL), and reinforcement learning (RL), highlighting their applications, advantages, and limitations. Advanced methodologies, such as hybrid and ensemble models, are examined for their effectiveness in overcoming challenges posed by noisy data, dynamic behaviors, and complex grid configurations. Some advanced techniques demonstrate proficiency in analyzing complex datasets and providing real-time insights into the evolving dynamics of inertia. In addition to evaluating existing approaches, the review identifies key research gaps and emerging trends, offering strategic guidance and important considerations for the development of innovative ML-driven inertia estimation methods. By addressing these challenges, this study aims to support the creation of adaptive and reliable tools that ensure effective grid management in an energy ecosystem increasingly dominated by RES. Graphical abstract |
| format | Article |
| id | doaj-art-5d151f144a834bd1a01f67f2e3595869 |
| institution | DOAJ |
| issn | 2520-8942 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Energy Informatics |
| spelling | doaj-art-5d151f144a834bd1a01f67f2e35958692025-08-20T02:55:28ZengSpringerOpenEnergy Informatics2520-89422025-04-018114710.1186/s42162-025-00496-7Machine learning-based inertia estimation in power systems: a review of methods and challengesSantosh Diggikar0Arunkumar Patil1Siddhant Satyapal Katkar2Kunal Samad3Department of Electrical Engineering, Central University of KarnatakaDepartment of Electrical Engineering, Central University of KarnatakaDepartment of Electrical Engineering, Central University of KarnatakaDepartment of Electrical Engineering, Central University of KarnatakaAbstract The transformation of power systems is accelerating due to the widespread integration of renewable energy sources (RES) and the growing role of inverter-based generations (IBGs). This shift has significantly reduced rotational inertia, increasing the system’s vulnerability to frequency fluctuations during disturbances. Consequently, the accurate and adaptive estimation of inertia has become crucial for maintaining frequency stability and grid reliability. Traditional estimation methods, though effective in certain scenarios, struggle to capture the non-linear and dynamic behaviors of modern power systems, necessitating the adoption of advanced solutions. This review comprehensively explores machine learning (ML)-based methodologies for inertia estimation, emphasizing their adaptability, scalability, and real-time capabilities compared to conventional approaches. The study categorizes ML techniques into supervised learning (SL), unsupervised learning (USL), semi-supervised learning (SSL), and reinforcement learning (RL), highlighting their applications, advantages, and limitations. Advanced methodologies, such as hybrid and ensemble models, are examined for their effectiveness in overcoming challenges posed by noisy data, dynamic behaviors, and complex grid configurations. Some advanced techniques demonstrate proficiency in analyzing complex datasets and providing real-time insights into the evolving dynamics of inertia. In addition to evaluating existing approaches, the review identifies key research gaps and emerging trends, offering strategic guidance and important considerations for the development of innovative ML-driven inertia estimation methods. By addressing these challenges, this study aims to support the creation of adaptive and reliable tools that ensure effective grid management in an energy ecosystem increasingly dominated by RES. Graphical abstracthttps://doi.org/10.1186/s42162-025-00496-7Renewable energy sources (RES)Inverter-based generations (IBGs)InertiaInertia estimationMachine learning (ML)Power system stability |
| spellingShingle | Santosh Diggikar Arunkumar Patil Siddhant Satyapal Katkar Kunal Samad Machine learning-based inertia estimation in power systems: a review of methods and challenges Energy Informatics Renewable energy sources (RES) Inverter-based generations (IBGs) Inertia Inertia estimation Machine learning (ML) Power system stability |
| title | Machine learning-based inertia estimation in power systems: a review of methods and challenges |
| title_full | Machine learning-based inertia estimation in power systems: a review of methods and challenges |
| title_fullStr | Machine learning-based inertia estimation in power systems: a review of methods and challenges |
| title_full_unstemmed | Machine learning-based inertia estimation in power systems: a review of methods and challenges |
| title_short | Machine learning-based inertia estimation in power systems: a review of methods and challenges |
| title_sort | machine learning based inertia estimation in power systems a review of methods and challenges |
| topic | Renewable energy sources (RES) Inverter-based generations (IBGs) Inertia Inertia estimation Machine learning (ML) Power system stability |
| url | https://doi.org/10.1186/s42162-025-00496-7 |
| work_keys_str_mv | AT santoshdiggikar machinelearningbasedinertiaestimationinpowersystemsareviewofmethodsandchallenges AT arunkumarpatil machinelearningbasedinertiaestimationinpowersystemsareviewofmethodsandchallenges AT siddhantsatyapalkatkar machinelearningbasedinertiaestimationinpowersystemsareviewofmethodsandchallenges AT kunalsamad machinelearningbasedinertiaestimationinpowersystemsareviewofmethodsandchallenges |