FNET

Abstract: Frequency dynamics is one of the most important measures of an electrical power system status. To better understand power system dynamics, an accurately measured wide-area frequency is needed. The concept of building an Internet-based real-time GPS-synchronized wide-area frequency monitoring network (FNET) was proposed in 2000 by Qiu et al., and this concept has been realized. The FNET system consists of frequency disturbance recorders and an information management system. The FNET has made the synchronized observations of the entire U.S. power network possible with very little cost for the first time. This paper summarizes the implementation of the FNET system and shows some preliminary observations and analyses of the data that have been collected from the FNET.

Abstract: Recent developments in smart grid technology have spawned interest in the use of phasor measurement units to help create a reliable power system transmission and distribution infrastructure. Wide-area monitoring systems (WAMSs) utilizing synchrophasor measurements can help with understanding, forecasting, or even controlling the status of power grid stability in real-time. A power system frequency monitoring network (FNET) was first proposed in 2001 and was established in 2004. As a pioneering WAMS, it serves the entire North American power grid through advanced situational awareness techniques, such as real-time event alerts, accurate event location estimation, animated event visualization, and post event analysis. Several papers published in the past several years discussed the FNET structure and its functionality. This paper presents some of the latest implementations of FNET's applications, which add significant capacities to this system for observing power system problems.

Abstract: We show that Transformer encoder architectures can be massively sped up, with limited accuracy costs, by replacing the self-attention sublayers with simple linear transformations that "mix" input tokens. These linear transformations, along with standard nonlinearities in feed-forward layers, prove competent at modeling semantic relationships in several text classification tasks. Most surprisingly, we find that replacing the self-attention sublayer in a Transformer encoder with a standard, unparameterized Fourier Transform achieves 92-97% of the accuracy of BERT counterparts on the GLUE benchmark, but trains nearly seven times faster on GPUs and twice as fast on TPUs. The resulting model, FNet, also scales very efficiently to long inputs. Specifically, when compared to the "efficient" Transformers on the Long Range Arena benchmark, FNet matches the accuracy of the most accurate models, but is faster than the fastest models across all sequence lengths on GPUs (and across relatively shorter lengths on TPUs). Finally, FNet has a light memory footprint and is particularly efficient at smaller model sizes: for a fixed speed and accuracy budget, small FNet models outperform Transformer counterparts.

Abstract: The electric power grid wide-area monitoring system (WAMS) has been extended from the transmission to distribution level. As the first WAMS deployed at the distribution level, the frequency monitoring network FNET/GridEye uses global positioning system-time-synchronized monitors, called frequency disturbance recorders, to capture dynamic grid behaviors. In this paper, the latest developments of monitor design and the state-of-the-art data analytics applications of FNET/GridEye are introduced. Its innovations and uniqueness are also discussed. Thanks to its low cost, easy installation, and multifunctionalities, FNET/GridEye works as a cost-effective situational awareness tool for power grid operators and pioneers the development of WAMS in electric power grids.