Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis
This paper focus on the application of Physics Informed Neural Network (PINN) for extracting parameters of photovoltaic (PV), wind, and energy storage equipment models. Accurately extracting the parameters of these models is essential for effectively controlling and optimizing the overall stability...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10833613/ |
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| author | Renhai Feng Khan Wajid Muhammad Faheem Jiang Wang Fazal E. Subhan Muhammad Shoaib Bhutta |
| author_facet | Renhai Feng Khan Wajid Muhammad Faheem Jiang Wang Fazal E. Subhan Muhammad Shoaib Bhutta |
| author_sort | Renhai Feng |
| collection | DOAJ |
| description | This paper focus on the application of Physics Informed Neural Network (PINN) for extracting parameters of photovoltaic (PV), wind, and energy storage equipment models. Accurately extracting the parameters of these models is essential for effectively controlling and optimizing the overall stability of Chongqing power system (CPS). Despite numerous algorithms proposed to tackle this issue, accurately and reliably extracting the parameters of these remains a significant challenge. This paper proposed an improved PINN, named Uniform Physics Informed Neural Network (UPINN), with Proximal Policy Optimization (PPO) based reinforcement learning, for extortion of parameters of these models. The PINN difficulty is overcome in UPINN by configuring four strategies: feedback operator, GRU gating mechanisms, transfer operator with historic population, and modification factor with PPO aided reinforcement learning. UPINN models are trained iteratively to maximize parameters and reduce RMSE. UPINN accurately extracts parameters and describes the behavior of PV, wind, and energy storage equipment models as it converges towards optimal solutions through parameter adjustments and RMSE evaluations. The UPINN was implemented for real-time voltage stability monitoring of CPS. The results show that UPINN performs better than other neural network models in respect of accuracy and stability, demonstrating the effectiveness of improved strategies. Moreover, its emphasis the importance of computed and estimated indices obtained through UPINN for predicting voltage collapse occurrences within the system. |
| format | Article |
| id | doaj-art-af4c7c062deb4f16b76d458e8218fdaf |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-af4c7c062deb4f16b76d458e8218fdaf2025-01-15T00:03:24ZengIEEEIEEE Access2169-35362025-01-01138110812610.1109/ACCESS.2025.352704710833613Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability AnalysisRenhai Feng0https://orcid.org/0000-0001-7194-6889Khan Wajid1Muhammad Faheem2https://orcid.org/0009-0000-2274-6821Jiang Wang3Fazal E. Subhan4https://orcid.org/0000-0003-1620-5986Muhammad Shoaib Bhutta5School of Electrical and Information Engineering, Tianjin University, Tianjin, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin, ChinaVTT Technical Research Center of Finland Ltd., Espoo, FinlandState Grid Chongqing Electric Power Company, Chongqing, ChinaPerformance Engineering Laboratory, School of Electronic Engineering, Dublin City University, Dublin, IrelandSchool of Automobile Engineering, Guilin University of Aerospace Technology, Guilin, ChinaThis paper focus on the application of Physics Informed Neural Network (PINN) for extracting parameters of photovoltaic (PV), wind, and energy storage equipment models. Accurately extracting the parameters of these models is essential for effectively controlling and optimizing the overall stability of Chongqing power system (CPS). Despite numerous algorithms proposed to tackle this issue, accurately and reliably extracting the parameters of these remains a significant challenge. This paper proposed an improved PINN, named Uniform Physics Informed Neural Network (UPINN), with Proximal Policy Optimization (PPO) based reinforcement learning, for extortion of parameters of these models. The PINN difficulty is overcome in UPINN by configuring four strategies: feedback operator, GRU gating mechanisms, transfer operator with historic population, and modification factor with PPO aided reinforcement learning. UPINN models are trained iteratively to maximize parameters and reduce RMSE. UPINN accurately extracts parameters and describes the behavior of PV, wind, and energy storage equipment models as it converges towards optimal solutions through parameter adjustments and RMSE evaluations. The UPINN was implemented for real-time voltage stability monitoring of CPS. The results show that UPINN performs better than other neural network models in respect of accuracy and stability, demonstrating the effectiveness of improved strategies. Moreover, its emphasis the importance of computed and estimated indices obtained through UPINN for predicting voltage collapse occurrences within the system.https://ieeexplore.ieee.org/document/10833613/Physics informed neural networkreinforcement learningmicrogridvoltage stability |
| spellingShingle | Renhai Feng Khan Wajid Muhammad Faheem Jiang Wang Fazal E. Subhan Muhammad Shoaib Bhutta Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis IEEE Access Physics informed neural network reinforcement learning microgrid voltage stability |
| title | Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis |
| title_full | Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis |
| title_fullStr | Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis |
| title_full_unstemmed | Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis |
| title_short | Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis |
| title_sort | uniform physics informed neural network framework for microgrid and its application in voltage stability analysis |
| topic | Physics informed neural network reinforcement learning microgrid voltage stability |
| url | https://ieeexplore.ieee.org/document/10833613/ |
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