Deep learning and big data applications in electrical power systems

Abstract

Power systems are massive and complex electrical engineering systems. The power system analysis and decision-making has been dependent only on physical modeling, numerical calculations, and some statistical inferences. Contemporary smart grids have bidirectional power flow, and uncertainty on the random nature of renewable energy availability, also a geographical dispersion of mobile loads, with a partial observability of power quality issues. A new generation of power-electronics-enabled power systems hardware, electrical circuits instrumentation, communications, intelligent control, and real-time performance is shaping the present and future development of smart-grids. Engineers must develop the technology for smarter power systems in order to build smart-grids, and big data applications are a requirement for such modernization. The analysis of transmission and distribution has been traditionally conducted as completely decoupled infrastructures, in which the design engineer will select a section and apply a model. Integrated co-simulation and analysis on both transmission and distribution sides of the grid can be conducted using machine learning (ML) and other AI methods. Aggregation techniques can be used with new instantaneous power theories and advanced signal processing. Deep learning models can be used in updating generator and load setpoints, based on the load forecasts, as well as incorporating online estimation algorithms about weather, storage, net-metering, possibilities of natural disasters, approximate generation schedules over the next hour, and information about transmission line faults that may severely disrupt the currents flowing on the distribution side. Load control algorithms can be tuned whether the distribution grid depends on the transmission grid or on dispersed generation.