Shape Data Analysis for Machine Learning in Power Systems Applications

This dissertation proposes the use of the shape of data as a new feature to improve and develop new in machine learning and deep learning algorithms utilized for different power systems applications. The new features are obtained through Shape Data Analysis (SDA), an emerging field in Statistics. SDA is used to obtain the shape of the data structure to observe different patterns developed under distribution networks abnormal conditions, as well as determining the shape of load curves to improve existing electrical load forecasting algorithms. Specifically, shape-based data analysis is implemented and developed for two different applications: electrical fault detection and electrical consumption short-term load forecasting. The algorithms proposed are implemented on data collected from Intelligent Electronic Devices (IEDs), Phasor Measurement Units (PMUs), and Supervisory Control and Data Acquisition (SCADA) systems in power distribution networks.