Knowledge discovery for smart grid operation, control, and situation awareness — a big data visualization platform

Visualization of Multi-Fidelity Approximations of Stochastic Economic Dispatch

“…While it is out of scope to provide a thorough survey of visualizations designed for grid operations, it is notable to mention visualization is an accepted approach for understanding the various aspects of the grid and has been in use for many years. Visualization tasks range from general analyses of power flow and voltages [13,15,17,27], to monitoring operational status and sources of failures [9,11,16], and smart grid operations using sensor networks [8,12,26], among others. We look to these works for inspiration, and also leverage the spatio-temporal aspects of our data to design our visualization dashboard.…”

The Paradigm Revolution in the Distribution Grid: The Cutting-Edge and Enabling Technologies

“…Fault location identification from the measured data is a critical application of data analytics techniques. The GridCloud [78] An open-source platform for real-time data acquisition, sharing, and monitoring FNET/GridEye [75] Visualization of system stress 3D visualization using AVS Express7.3 [79] Visualize real-time power system information and relationship utilizing SCADA data Tableau [80,81] SG data visualization Microsoft Power BI [82] Smart meter data visualization Animation loops [83] Combine periodic snapshots of the grid into time-lapse videos defined across geographic areas Sparklines [83] Summarize trends in time-varying PMU data as word-sized line plots Google Earth [64,84] SG data visualization Cartography and spatial analysis techniques [85] Visualize, yield and performance map of grid-connected PV systems GIS [77] To display real-time operational data…”

Data Cleaning Algorithms for Power Information Communication Assets Data Based on Self-coding

“…AND COMMUNICATION ASSETS Compared with the traditional big data technology, the large data technology for power information and communication assets needs higher efficiency and accuracy, so there is a higher requirement for data integrity. Before data processing, data need to be cleaned [9]- [11] . At present, the research on big data cleaning at home and abroad mainly includes clustering and association analysis, conditional function dependence, Markov model, etc.…”