Fault diagnosis of smart grids based on deep learning approach

The smart grid systems allow power and data flows between suppliers and consumers to optimize the grid in terms of sustainability, reliability, and efficiency. The estimate of time-based energy consumption and generation plays a crucial role in future energy systems. The electric generation systems are changing fast. The complexity of electric power systems has been growing because of renewables, inverter-based control devices and integration of electrical vehicles. The most important challenge in a smart grid is how to reduce the cost of power with a large amount of user participation. The developing smart grid featured by monitoring, security and renewable energy demand-generation control is facing difficulties because of enormously large data sets. Different types of faults have occurred in smart grids that cause major maintenance costs. Effective monitoring tools and predictive approaches can help to maintain a reliable supply of electric energy. Based on the large data sets collected by measurement devices, many data analytics methods are applied in the smart grid to solve various problems. This paper presents a fault prediction approach and load forecasting approach. The approach of data analytics and deep learning for smart grids (SGs) and their applications are proposed in this paper, which seeks to provide a transition to future energy systems while providing significant knowledge of existing smart grids.