Data Analytics of Smart Grid Environment

The future of the power industry heavily relies on the use of modern electric grids integrated with information and communication technology (ICT). Such grids are commonly known as smart grids. The advantage of using smart grids is that they provide a better quality of service in terms of better resource and asset management, detecting faults in the system, efficient energy consumption by reducing the demand and supply gap, and peak load reduction. Data analytics has already been applied extensively in the power sector to provide various services such as-demand forecasting, revenue protection, and data visualization. However, there are still many areas which can be benefited by using data analytical techniques. One such area is the demand response management in the smart grid environment where data analytics can be effective in order to manage the overall load on the grid. The entities involved in the smart grid comprise of power generation units, transmission and distribution units, and end-users. The end-users may belong to the different sectors such as commercial, residential and transportation. The consumption data related to these users can be analyzed to provide many ancillary services in the smart grid and to improve the overall quality of service for the users. Keeping this in mind, the major focus of this thesis is on data analytics in the smart grid environment along with the demand response management of the connected loads. To achieve these tasks, four different schemes have been proposed in this thesis with an emphasis on data analytics and demand response management in smart grid. In the first technique, a top-down approach is designed to detect electricity theft in the power network based on decision tree (DT) and support vector machine (SVM) Unlike the existing schemes, the proposed scheme detects and locates the real-time electricity theft at every level in power transmission and distribution.