Vol. 17, no. 6, 2025
РусскийEnglish
RADIOELECTRONICS
Intelligent Control and Stability Analysis of Smart Grids Using CNN-LSTM Network and Model Predictive Controller
Fadhel A. Jumaa
Al-Furat Al-Awsat Technical University, https://atu.edu.iq/
Kufa-Najaf Al-Ashraf–54001, Iraq
Email: dr-fadhela.jumaa@atu.edu.iq
Aws M. Abdullah
University of Baghdad, https://en.uobaghdad.edu.iq
Baghdad 10071, Iraq
Email:aws.abd@cois.uobaghdad.edu.iq
Ahmed Haitham Najim
Al-Imam Al-Adham University College, https://imamaladham.edu.iq/en/
Baghdad 10054, Iraq
Email: ahmed.al.adhami82@gmail.com
Anas Fouad Ahmed
Al-Iraqia University, https://en.aliraqia.edu.iq
Al Adhmia-Haiba Khaton, 6029, Baghdad, Iraq
Email: anas.ahmed@aliraqia.edu.iq
Received July 10, 2025, peer-reviewed July 24, 2025, accepted September 27, 2025, published December 17, 2025
Abstract: It is important that real time stability in smart grids is ensured as the integration of renewables and the complexity of the systems grows. In this paper, we provide a solid architecture, which combines a Residual CNNLSTM deep neural network predictor, FPGA-accelerated Model Predictive Control (MPC), and SHAP-based explainability. The proposed method predicted with 99.8% accuracy using the Electrical grid Stability Simulated Dataset (UCI) and minimized the instability rates surpassing 85 percent in all operating conditions. Meeting real-time operating needs, FPGA deployment on a Xilinx Zynq UltraScale+ provided 3.1 ms latency and 5 times reduced energy consumption against CPU processing. By emphasizing bus voltage and frequency as major instability drivers, SHAP analysis improved openness for operators. To our knowledge, this is the first framework that ensures predictive accuracy, real-time corrective control, hardware feasibility, and interpretability simultaneously, as compared to ten other cutting-edge approaches. These results suggest the promise of integrated AI–MPC–FPGA techniques for dependable and transparent smart grid operations.
Keywords: Model Predictive Control (MPC); Intelligent Control Systems; Residual CNN–LSTM; Real-time Grid Monitoring; SHAP Explainability
UDC 621.316.925:004.8:681.513
RENSIT, 2025, 17(5):667-674e DOI: 10.17725/j.rensit.2025.17.667
Full-text electronic version of this article - web site http://en.rensit.ru/vypuski/article/718/17(6)667-674e.pdf