Optimized CNN-BiLSTM framework for reactive power management and voltage profile improvement in renewable energy based power grids

Abstract This article describes a method for improving power grid voltage profiles by more effectively regulating reactive power through the integration of hybrid renewable energy systems (HRES) in smart grids. The unpredictable nature of renewable energy sources RES, such as wind turbines and solar systems, causes an unstable voltage profile throughout the grid, underscoring the problem of voltage fluctuation in power grids. This article proposes DSTATCOM, a reactive power adjustment device, to address these voltage fluctuations and provide the grid with the required var. DSTATCOM assists in preserving voltage stability by consistently lowering the voltage drop, which guarantees an increase in active power flow. Therefore, the overall voltage profile throughout the electrical grid gets improved. Convolutional neural networks (CNN) with bidirectional long short-term memory (BiLSTM) combined to form the proposed solution, which controls and maximizes DSTATCOM performance. These advanced artificial intelligence (AI) methods helps in dynamic reactive power management, improving the grid’s voltage profile and DSTATCOM’s performance. In smart grid situations, this method works well for real-time voltage regulation since CNNs are employed for feature extraction and BiLSTM helps capture temporal dependencies in the grid’s power behavior. The CNN-BiLSTM network’s weights are also adjusted using an adaptive parrot optimizer (APO). The proposed approach was implemented using the MATLAB/Simulink environment, and three different scenarios were used to assess its performance. Simulation results confirm that the method achieved up to 33.4% loss reduction, improved voltage stability index (VSI) to 1.02 p.u, minimized total harmonic distortion (THD) below 1.7%, and cut settling time to 0.075 s. The hybrid PV/wind setup ensured superior voltage stability, while the model attained high prediction accuracy with an R2 of 0.9672 and RMSE of 3.0094. By controlling reactive power balance, the created system assures grid stability, improves the voltage profile, and reduces power loss.