A Survey of Machine Learning Applications for Power System Analytics

Recent advances in computing technologies and the availability of large amounts of heterogeneous data in power grids are opening the way for the application of state-of-art machine learning techniques. Compared to traditional computational approaches, machine learning algorithms could gain advantage from their intrinsic generalization capability, by also providing accurate short-term power flow forecasts from distributed measurement units, with a greater computational efficiency and scalability. Several studies in the literature investigated the use of suitable machine learning models to address different issues in the field of power grid operation and management. Furthermore, the ongoing transition towards smart grids is generating new research opportunities for the real-time application of machine learning algorithms in power systems. In this paper, a literature survey on the application of different machine learning techniques in power systems is presented and critically reviewed, by evaluating the main advantages and criticalities of each technique for the selected applications.