Density‐Based Congestion Control Protocol With GPR‐GRNN Congestion Prediction and Enhanced Security Using the LFSRE Algorithm for Active Distribution Networks

ABSTRACT Active distribution network (ADN) plays a vital role in the smart power grid implementation process. Congestion prediction and avoidance are essential in ADN to prevent overloads, improve efficiency, and ensure system stability. Various existing approaches are developed to alleviate congestion in ADN, but they are unable to predict the accurate congestion and require more time to generate appropriate control commands for power dispatch. Therefore, more advanced and adaptive methods are needed for accurate congestion management in modern power systems. This work implements a deep learning algorithm combined with an efficient cryptography model to ensure secure data transmission in ADN. Initially, network characteristics are collected during data transmission from the distributed network for attack detection and preprocessed using Two‐Step Sparse Switchable Normalization (TSSN‐Net) to normalize the data and Self‐Attention‐based Imputation for Time Series (SAITS) based missing value imputation to improve quality by filling in the blanks. The preprocessed data were used to select the features using redundancy analysis and interaction weight (RAIW). The Gaussian Process Reference‐based General Regression Neural Network (GPR‐GRNN) is then used to anticipate congestion using the selected features. Once the congestion is predicted, it is avoided using the Density‐Based Congestion Control Protocol (DBCCP), which reduces the loss of packets to avoid the network congestion. After that, the gathered data are securely stored in the cloud server through linear feedback shift register encryption (LFSRE) based encryption algorithm. The proposed approach achieves an accuracy of 97.60%, PPV of 96.70%, selectivity of 97.20%, and NPV of 96.10%. The proposed approach enables significant advancement in modern power systems focusing on intelligent forecasting and uncertainty‐aware congestion management for long‐term success.