An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods
Abstract This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with a Genetic Algorithm-optimized PID (GA-PID) controller. Traditional PID controllers, while extensively used,...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-85639-2 |
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| author | Ritesh Dash Kalvakurthi Jyotheeswara Reddy Bhabasis Mohapatra Mohit Bajaj Ievgen Zaitsev |
| author_facet | Ritesh Dash Kalvakurthi Jyotheeswara Reddy Bhabasis Mohapatra Mohit Bajaj Ievgen Zaitsev |
| author_sort | Ritesh Dash |
| collection | DOAJ |
| description | Abstract This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with a Genetic Algorithm-optimized PID (GA-PID) controller. Traditional PID controllers, while extensively used, often face limitations in handling the nonlinearities and uncertainties inherent in interconnected power systems, leading to slower settling time and higher overshoot during load disturbances. The LSTM + GA-PID controller mitigates these issues by utilizing LSTM’s capacity to learn from historical data by using gradient descent to forecast the future disturbances, while the GA optimizes the PID parameters in real time, ensuring dynamic adaptability and improved control precision. The proposed controller’s performance is rigorously tested against both classical PID and GA-PID controllers through simulations conducted in MATLAB/Simulink. The results reveal that the LSTM + GA-PID controller achieves a 2.33-fold reduction in settling time compared to the GA-PID controller and a 4.07-fold reduction compared to the classical PID controller. Additionally, the controller exhibits a 3.27% reduction in overshoot and mitigates mechanical power output perturbations by 3.43% during transient load changes. Hardware validation has been carried out to show the robustness of the model. |
| format | Article |
| id | doaj-art-59784359c8064758accd58d9a5609ecc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-59784359c8064758accd58d9a5609ecc2025-01-12T12:16:24ZengNature PortfolioScientific Reports2045-23222025-01-0115112710.1038/s41598-025-85639-2An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methodsRitesh Dash0Kalvakurthi Jyotheeswara Reddy1Bhabasis Mohapatra2Mohit Bajaj3Ievgen Zaitsev4School of EEE, REVA UniversitySchool of EEE, REVA UniversityDepartment of Electrical Engineering, ITER, Siksha ‘O’ Anusandhan (Deemed to be University)Department of Electrical Engineering, Graphic Era (Deemed to be University)Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of UkraineAbstract This paper proposes an advanced Load Frequency Control (LFC) strategy for two-area hydro-wind power systems, using a hybrid Long Short-Term Memory (LSTM) neural network combined with a Genetic Algorithm-optimized PID (GA-PID) controller. Traditional PID controllers, while extensively used, often face limitations in handling the nonlinearities and uncertainties inherent in interconnected power systems, leading to slower settling time and higher overshoot during load disturbances. The LSTM + GA-PID controller mitigates these issues by utilizing LSTM’s capacity to learn from historical data by using gradient descent to forecast the future disturbances, while the GA optimizes the PID parameters in real time, ensuring dynamic adaptability and improved control precision. The proposed controller’s performance is rigorously tested against both classical PID and GA-PID controllers through simulations conducted in MATLAB/Simulink. The results reveal that the LSTM + GA-PID controller achieves a 2.33-fold reduction in settling time compared to the GA-PID controller and a 4.07-fold reduction compared to the classical PID controller. Additionally, the controller exhibits a 3.27% reduction in overshoot and mitigates mechanical power output perturbations by 3.43% during transient load changes. Hardware validation has been carried out to show the robustness of the model.https://doi.org/10.1038/s41598-025-85639-2Load frequency controlLSTMGenetic algorithmPID controllerPower system controlFrequency regulation |
| spellingShingle | Ritesh Dash Kalvakurthi Jyotheeswara Reddy Bhabasis Mohapatra Mohit Bajaj Ievgen Zaitsev An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods Scientific Reports Load frequency control LSTM Genetic algorithm PID controller Power system control Frequency regulation |
| title | An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods |
| title_full | An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods |
| title_fullStr | An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods |
| title_full_unstemmed | An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods |
| title_short | An approach for load frequency control enhancement in two-area hydro-wind power systems using LSTM + GA-PID controller with augmented lagrangian methods |
| title_sort | approach for load frequency control enhancement in two area hydro wind power systems using lstm ga pid controller with augmented lagrangian methods |
| topic | Load frequency control LSTM Genetic algorithm PID controller Power system control Frequency regulation |
| url | https://doi.org/10.1038/s41598-025-85639-2 |
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