Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parameters
Smart control techniques have been implemented to address fluctuating power levels within isolated microgrids, mitigating the risk of unstable frequencies and the potential degradation of power supply quality. However, a challenge lies in the fact that employing these computationally complex methods...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824000879 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846117613020643328 |
|---|---|
| author | Komeil Nosrati Vjatseslav Skiparev Aleksei Tepljakov Eduard Petlenkov Juri Belikov |
| author_facet | Komeil Nosrati Vjatseslav Skiparev Aleksei Tepljakov Eduard Petlenkov Juri Belikov |
| author_sort | Komeil Nosrati |
| collection | DOAJ |
| description | Smart control techniques have been implemented to address fluctuating power levels within isolated microgrids, mitigating the risk of unstable frequencies and the potential degradation of power supply quality. However, a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication delays. This study introduces a flexible real-time approach for the frequency control problem using an artificial neural network (ANN) constrained to stabilized regions. Our solution integrates stabilizing PID controllers, computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning (RL)-based selected constraints. First, we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion, specifically addressing communication delays. Next, we refine the controller parameters online through an automated process that identifies optimal coefficient combinations, leveraging a constrained ANN to manage frequency deviations within a restricted parameter range. Our approach is further enhanced by employing the RL technique, which trains the tuning system using an interpolated stability boundary curve to ensure both stability and performance. This one-of-a-kind combination of ANN, RL, and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC microgrids. The experiments show that our solution outperforms traditional methods due to its reduced parameter search space. In particular, the proposed method reduces transient and steady-state frequency deviations more than semi- and unconstrained methods. The improved metrics and stability analysis show that the method improves system performance and stability under changing conditions. |
| format | Article |
| id | doaj-art-0ebf943df6274a10bc165d6ffee22ab9 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-0ebf943df6274a10bc165d6ffee22ab92024-12-18T08:53:01ZengElsevierEnergy and AI2666-54682024-12-0118100421Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parametersKomeil Nosrati0Vjatseslav Skiparev1Aleksei Tepljakov2Eduard Petlenkov3Juri Belikov4Department of Computer Systems, Tallinn University of Technology, Tallinn, 12616, Estonia; Corresponding author.Department of Software Science, Tallinn University of Technology, Tallinn, 12616, EstoniaDepartment of Computer Systems, Tallinn University of Technology, Tallinn, 12616, EstoniaDepartment of Computer Systems, Tallinn University of Technology, Tallinn, 12616, EstoniaDepartment of Software Science, Tallinn University of Technology, Tallinn, 12616, EstoniaSmart control techniques have been implemented to address fluctuating power levels within isolated microgrids, mitigating the risk of unstable frequencies and the potential degradation of power supply quality. However, a challenge lies in the fact that employing these computationally complex methods without stability preservation might not suffice to handle the rapid changes of this highly dynamic environment in real-world scenarios over communication delays. This study introduces a flexible real-time approach for the frequency control problem using an artificial neural network (ANN) constrained to stabilized regions. Our solution integrates stabilizing PID controllers, computed through small-signal analysis and tuned via an automated search for optimal ANN weights and reinforcement learning (RL)-based selected constraints. First, we design stabilizing PID controllers by applying the stability boundary locus method and the Mikhailov criterion, specifically addressing communication delays. Next, we refine the controller parameters online through an automated process that identifies optimal coefficient combinations, leveraging a constrained ANN to manage frequency deviations within a restricted parameter range. Our approach is further enhanced by employing the RL technique, which trains the tuning system using an interpolated stability boundary curve to ensure both stability and performance. This one-of-a-kind combination of ANN, RL, and advanced PID tuning methods is a big step forward in how we handle frequency control problems in isolated AC microgrids. The experiments show that our solution outperforms traditional methods due to its reduced parameter search space. In particular, the proposed method reduces transient and steady-state frequency deviations more than semi- and unconstrained methods. The improved metrics and stability analysis show that the method improves system performance and stability under changing conditions.http://www.sciencedirect.com/science/article/pii/S2666546824000879AC microgridConstrained artificial neural networksFrequency controlReinforcement learningStabilizing controller |
| spellingShingle | Komeil Nosrati Vjatseslav Skiparev Aleksei Tepljakov Eduard Petlenkov Juri Belikov Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parameters Energy and AI AC microgrid Constrained artificial neural networks Frequency control Reinforcement learning Stabilizing controller |
| title | Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parameters |
| title_full | Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parameters |
| title_fullStr | Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parameters |
| title_full_unstemmed | Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parameters |
| title_short | Intelligent frequency control of AC microgrids with communication delay: An online tuning method subject to stabilizing parameters |
| title_sort | intelligent frequency control of ac microgrids with communication delay an online tuning method subject to stabilizing parameters |
| topic | AC microgrid Constrained artificial neural networks Frequency control Reinforcement learning Stabilizing controller |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824000879 |
| work_keys_str_mv | AT komeilnosrati intelligentfrequencycontrolofacmicrogridswithcommunicationdelayanonlinetuningmethodsubjecttostabilizingparameters AT vjatseslavskiparev intelligentfrequencycontrolofacmicrogridswithcommunicationdelayanonlinetuningmethodsubjecttostabilizingparameters AT alekseitepljakov intelligentfrequencycontrolofacmicrogridswithcommunicationdelayanonlinetuningmethodsubjecttostabilizingparameters AT eduardpetlenkov intelligentfrequencycontrolofacmicrogridswithcommunicationdelayanonlinetuningmethodsubjecttostabilizingparameters AT juribelikov intelligentfrequencycontrolofacmicrogridswithcommunicationdelayanonlinetuningmethodsubjecttostabilizingparameters |