Deep Learning Techniques for Overcoming Interoperability Challenges in Smart Grid Systems

The application of deep learning techniques to design and develop smart grid systems present opportunities to counteract interoperability barriers ultimately improving the smart grid usability, reliability, stability and security. This paper highlights the fairly new robust Deep Learning techniques and how they can be used to solve various smart grid interoperation challenges. In this regard, following a step-by-step approach, we to detail on how data are collected and pre-processed, as well as, how deep learning models are selected and trained with corresponding performance metrics made on how the same models can be deployed in some prominent applications such as; predictive maintenance, energy consumption prediction, and anomaly detection, and lastly in cybersecurity. In addition, we continue the description of works in the field of grids standardization and protocol to achieve unification of communication between grid entities. The findings shown here prove that deep learning can enhance computational grid administration, security, and interaction ideals. There is still a need to address the issues related to the complexity of deep learning models as well as their response to dynamic gird systems in the literature; Future work should also consider the applicability of new architectures in modern grid challenges like integration of renewable energy systems and impact of EVs. In conclusion, the application of deep learning as part of the techniques offers a promising approach to improving smart grid systems and moving towards the progressive development of energy infrastructure with increased reliability and efficiency.