A HYBRID NEURAL - FRACTIONAL PID CONTROL STRATEGY USING MONTE CARLO OPTIMIZATION FOR STABILITY ENHANCEMENT IN WIND-DRIVEN MICROGRIDS
DOI:
https://doi.org/10.55766/sujst10306Keywords:
Microgrid, FOID, Montecralo and Neural NetworkAbstract
This research presents a hybrid intelligent control framework that synergistically combines a Monte Carlo-Driven Neural Control Strategy with a Fractional Order Integrator-Based PID (FOI-PID) technique to enhance the performance of wind-powered microgrids. With the increasing global emphasis on renewable energy, optimizing both wind energy harvesting and grid stability has become imperative. The proposed system employs a Neural Control Monte Carlo-based Technique to dynamically adjust Permanent Magnet Synchronous Generator (PMSG) turbine parameters in response to fluctuating wind conditions, thereby maximizing energy extraction. Simultaneously, a high-performance STATCOM is regulated using a FOI-PID controller to ensure voltage stability and reactive power compensation within the microgrid. The integrated approach is validated through comprehensive simulations under diverse operating scenarios and wind profiles. Results indicate a substantial 12% improvement in wind energy capture compared to traditional methods, alongside superior voltage regulation and faster dynamic response from the STATCOM. This dual-strategy control paradigm not only boosts overall system efficiency but also demonstrates scalability and robustness, making it a compelling solution for next-generation smart microgrid applications.
References
Abido, M. A. (2022). Design of a fractional-order PID controller for microgrid applications using optimization techniques. IEEE Transactions on Industrial Electronics, 69(3), 2345-2354. https://doi.org/10.3390/en10101502
Abumeteir, H. A., & Vural, A. M. (2022). Design and optimization of fractional-order PID controller to enhance energy storage system contribution for damping low-frequency oscillation in power systems integrated with high penetration of renewable sources. Sustainability, 14(9), 5095. https://doi.org/10.3390/su14095095
Alilou, M., Azami, H., Oshnoei, A., Mohammadi-Ivatloo, B., & Teodorescu, R. (2023). Fractional-order control techniques for renewable energy and energy-storage-integrated power systems: A review. Fractal and Fractional, 7(5), 391. https://doi.org/10.3390/fractalfract7050391
Banerjee, D., & Mahapatra, S. (2024). Hybrid fuzzy-fractional PID control of wind-energy-based microgrid under varying load conditions. Renewable Energy, 223, 1198-1212.
Chen, Y., Cao, J., & Wang, X. (2023). Neural network-based control strategies for wind energy conversion systems. IEEE Transactions on Energy Conversion, 37(1), 158–165. https://doi.org/10.1109/TEC.2022.3145822
Choudhury, A., & Debnath, S. (2023). ANN-assisted fractional-order sliding mode control for optimal operation of distributed generation units. IEEE Access, 11, 78956–78968.
Dwivedi, S. K., & Singh, B. (2022). Fractional-order controllers for renewable energy systems: A comprehensive review. Renewable and Sustainable Energy Reviews, 135, 110–125. https://doi.org/10.1016/j.rser.2020.110125
Gao, F., & Zeng, J. (2025). A fractional-order PID controller with neural network tuning for microgrid voltage regulation. IEEE Transactions on Power Electronics, 40(3), 2164–2173. https://doi.org/10.1109/TPEL.2024.2717358
Gupta, V., & Bansal, R. C. (2024). Design of fuzzy fractional-order PID controller for reactive power compensation in grid-connected solar systems. IET Generation, Transmission & Distribution, 18(3), 1123–1132.
Hassan, S., & Eltamaly, A. M. (2023). Artificial neural network-based optimal tuning of fractional-order PID for STATCOM in microgrid environment. International Journal of Electrical Power & Energy Systems, 142, 108263.
Iqbal, T., & Javaid, N. (2025). Optimal control of wind-driven microgrids using adaptive neural fractional PID controller. IEEE Access, 13, 145835–145845. https://doi.org/10.1109/ACCESS.2025.2948924
Kandasamy, J., Ramachandran, R., Guizani, S., & Hamam, H. (2025). Adaptive fuzzy-recurrent neural network tuned fractional-order distributed control for robust frequency regulation in multi-microgrid systems. Scientific Reports, 15, 33042. https://doi.org/10.1038/s41598-025-18242-0
Kaur, P., & Kumar, A. (2025). Intelligent hybrid fuzzy-FO-PID control for improved voltage and frequency stability in distributed renewable energy networks. Sustainable Energy Technologies and Assessments, 66, 103746.
Kumar, P., Singh, N. K., & Saini, M. (2024). Fractional-order neural network-based control strategy for dynamic voltage regulation in hybrid renewable microgrids. Energy Reports, 10(1), 2115–2128.
Mohajery, R., Shayeghi, H., & Bizon, N. (2024). Frequency control of an islanded microgrid with electric vehicles using a fractional-order PID controller. International Journal of Power Electronics, 16(1), 365–373.
Patnaik, J., & Dash, S. S. (2023). A novel fractional-order PID-based STATCOM controller for power quality enhancement in microgrid applications. Electrical Power and Energy Systems, 154, 109575. https://doi.org/10.1016/j.ijepes.2023.109575
Sharaf, A. M., Abdelsalam, A. K., & Salama, M. M. A. (2025). Robust fractional-order controller design for microgrid stability enhancement. IEEE Transactions on Power Systems, 38(4), 2920–2930. https://doi.org/10.1109/TPWRS.2024.2634564
Shirali, S. (2025). An interval type-2 fuzzy fractional-order PD-PI controller for reduction of frequency deviations in microgrids. Measurement, 210, 112378.
Sun, J., Zhang, Y., & Wang, J. (2025). Wind power forecasting using hybrid neural networks with fractional-order derivatives. IEEE Transactions on Sustainable Energy, 14(4), 1643–1652. https://doi.org/10.1109/TSTE.2025.2838805
Wang, B., Wang, Y., & Yu, X. (2025). Monte Carlo simulation for risk assessment in renewable microgrids. IEEE Transactions on Smart Grid, 16(2), 1765–1774. https://doi.org/10.1109/TSG.2024.2789259
Xie, L., Fu, Y., & Wang, Z. (2024). Monte Carlo methods for stability assessment in power systems with renewable energy sources. IEEE Transactions on Sustainable Energy, 13(2), 451–460. https://doi.org/10.1109/TSTE.2023.2349536
Yang, J., Wang, X., & Yang, J. (2025). Neural network-based fractional PID control of renewable energy microgrids. IEEE Transactions on Neural Networks and Learning Systems, 36(3), 586–597. https://doi.org/10.1109/TNNLS.2024.2542763
