The Circular Variance as a Visual Summary of Synchronized Voltage Angle Measurements

Phasor measurement units (PMUs) allow voltage angle differences across power grids to be monitored to identify sudden shifts associated with system disturbances. The Eastern Interconnection Situational Awareness and Monitoring System (ESAMS) was developed to identify such wide-area disturbances and summarize them in reports released the following day. Demonstration of ESAMS in North America's Eastern Interconnection revealed the need for an effective visual summary of the disturbance's impact on voltage angle pairs. This paper proposes the use of the circular variance, a measure of dispersion applicable to angular data, for this purpose. Results based on PMU data from North America's Eastern and Western interconnections indicate that the circular variance provides useful summaries of wide-area voltage angle measurements. They also show that the circular variance may have potential uses when applied to historical data to identify unusual grid conditions

Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network

An automatic system that utilizes data analytics and machine learning to identify adult American eel in data obtained by imaging sonars is created in this study. Wavelet transform has been applied to de-noise the ARIS sonar data and a convolutional neural network model has been built to classify eels and non-eel objects. Because of the unbalanced amounts of data in laboratory and field experiments, a transfer learning strategy is implemented to fine-tune the convolutional neural network model so that it performs well for both the laboratory and field data. The proposed system can provide important information to develop mitigation strategies for safe passage of out-migrating eels at hydroelectric facilities

Power Grid Data Quality Filter and Machine Learning for Event Classification

The stability and reliability of the power grid are of great importance to the economy and national security. The power grid is a complex system that has many interconnected networks. With the advent of phasor measurement unit (PMU) data, system operators can for the first time view the status of the power systems from a wide area interconnection level. Since there are constant disturbances happening in the power grid, data analytics techniques could be valuable for applications on PMU data that inform the operators regarding significant or interesting power system events. In this study, we develop a data processing and machine learning approach that handles streaming PMU data for detecting and classifying power system events. This paper provides details regarding the techniques we use for filtering out common PMU data quality issues. Also presented is a machine learning classifier that can distinguish between generator trips and other type of power system events. Among the other type of power system events, we also develop metrics based on k-means clustering for the characterization of such events in order to discover interesting ones. Testing conducted on real world PMU data shows that the approach achieves satisfactory results

An Open-Source Library of Phasor Measurement Unit Data Capturing Real Bulk Power Systems Behavior

This paper describes an open-source library of transmission-level synchrophasor measurements, curated with the aim of accelerating data-driven research and development in the power systems domain. This dataset contains measurements describing both disturbances and ambient conditions, spans two years in time, and is sourced from electric utilities across the United States. Comprised of 1694 unique events, this is the largest open-source repository of real transmission-level phasor measurement unit (PMU) data to date, and will be invaluable for benchmarking new algorithms, testing tools and approaches developed by vendors and researchers, and developing educational tools for university students and control room operators. This paper additionally highlights several potential applications of the dataset that may be useful to the research community