Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review
Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of p...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/4/764 |
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| author | Mihail Senyuk Svetlana Beryozkina Murodbek Safaraliev Andrey Pazderin Ismoil Odinaev Viktor Klassen Alena Savosina Firuz Kamalov |
| author_facet | Mihail Senyuk Svetlana Beryozkina Murodbek Safaraliev Andrey Pazderin Ismoil Odinaev Viktor Klassen Alena Savosina Firuz Kamalov |
| author_sort | Mihail Senyuk |
| collection | DOAJ |
| description | Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research. |
| first_indexed | 2024-03-07T22:34:35Z |
| format | Article |
| id | doaj.art-7b474c5957774297ad6ba0045ccce3c9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-07T22:34:35Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7b474c5957774297ad6ba0045ccce3c92024-02-23T15:14:59ZengMDPI AGEnergies1996-10732024-02-0117476410.3390/en17040764Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art ReviewMihail Senyuk0Svetlana Beryozkina1Murodbek Safaraliev2Andrey Pazderin3Ismoil Odinaev4Viktor Klassen5Alena Savosina6Firuz Kamalov7Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, RussiaCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitDepartment of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, RussiaDepartment of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, RussiaDepartment of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, RussiaDepartment of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, RussiaDepartment of Electric Drive and Automation of Industrial Installations, Ural Federal University, 620002 Yekaterinburg, RussiaDepartment of Electrical Engineering, Canadian University Dubai, Dubai 117781, United Arab EmiratesModern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.https://www.mdpi.com/1996-1073/17/4/764power systembig datamachine learningemergency controlsynchronous generatorsmall signal stability |
| spellingShingle | Mihail Senyuk Svetlana Beryozkina Murodbek Safaraliev Andrey Pazderin Ismoil Odinaev Viktor Klassen Alena Savosina Firuz Kamalov Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review Energies power system big data machine learning emergency control synchronous generator small signal stability |
| title | Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review |
| title_full | Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review |
| title_fullStr | Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review |
| title_full_unstemmed | Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review |
| title_short | Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review |
| title_sort | bulk power systems emergency control based on machine learning algorithms and phasor measurement units data a state of the art review |
| topic | power system big data machine learning emergency control synchronous generator small signal stability |
| url | https://www.mdpi.com/1996-1073/17/4/764 |
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