Deep Learning Applications in Power Systems: A Review Study

This review paper discusses the application of deep learning in modern power systems, and how it can be used to enhance forecasting, fault detection, stability analysis, and renewable energy integration. It addresses some of the basic concepts of deep learning and power system structure, then goes on to discuss some of the key techniques: CNNs, RNNs, LSTMs, and reinforcement learning. The study compares against the existing research showing that deep learning has superior performance over traditional methods but also indicates that deep learning has several challenges such as high cost of computation, dependence on data, and lack of interpretability. The directions in the future are smart grid integration, explainable AI, and hybrid models. Altogether, deep learning is a promising technology to improve the efficiency, reliability, and intelligence of power systems.