EFFICIENT ARTIFICIAL NEURAL NETWORKS FOR ENHANCING SMART GRID STABILITY: A COMPREHENSIVE REVIEW OF TECHNIQUES AND APPLICATIONS - Nasir , Kazi Kalim; Shinde, Jitendra N; Sairise, Raju M (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Renewable energy sources and better control systems are what make smart grids unique. To keep them stable and efficient, they need strong and flexible solutions. Artificial Neural Networks (ANNs) have become very important in solving these problems because they can make smart predictions and decisions in real time, which is very important for smart grids. This in-depth review looks at different ANN designs and how they can be used to make smart grids more stable. First, we talk about the things that make smart grids different, like spread production and changing load needs, which make security more difficult. Then, we talk about how ANNs can help predict demand and green output, focussing on how they can change to changing grid conditions and learn from data that isn’t straight and has a lot of dimensions. The review talks about a few important techniques, such as deep learning and reinforcement learning, that have shown a lot of promise in predictive analytics and real-time control tasks. We also look at examples of how ANNs have been used successfully to predict problems, control energy flow, and improve grid operations, showing real improvements in the stability and efficiency of the grid. It also talks about the problems with integrating ANN solutions and the way forward for using them on a larger scale in smart grids, with a focus on privacy, security, and interoperability. This review combines recent research and real-world applications to give a basic knowledge and encourage more new ideas in this area that is changingquickl.