An Improved Framework for Enhancing Security And Efficiency In Reading Smart Grid Technologies

The advent of Smart Grid technologies has revolutionized the energy landscape by introducing advanced monitoring and control capabilities. However, as these technologies have become integral to our energy infrastructure, ensuring the security and efficiency of data transmission and management has become a paramount concern. This paper presents a comprehensive framework designed to address these concerns, focusing on the monitoring of critical parameters in the Smart Grid, including Photovoltaic (PV) voltage and current, Double Fed Induction Generator (DFIG) voltage, grid voltage, and current. To facilitate real-time data monitoring, Internet of Things (IoT) devices are deployed within the Smart Grid organization. These devices collect and transmit vital data to a centralized cloud platform through Node MCU. Security in data transmission is a primary concern, especially in critical infrastructure systems like the Smart Grid. To address this, the proposed framework incorporates a novel approach that leverages Red Deer Optimized (RDO) Artificial Neural Networks (ANNs). Red Deer Optimization is employed to optimize the parameters of ANN thereby enhancing data security during transmission, effectively safeguarding sensitive information from potential threats. This added layer of security ensures that data integrity is maintained throughout the entire transmission process. The proposed framework offers a holistic solution that not only protects sensitive information but also optimizes the overall performance of smart grid systems. The comparison analysis has the efficiency of the proposed optimization is 90%.