Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification

This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time series of event data to a two-dimensional image for learning. Two main DL algorithms, i.e. CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) are tested and compared with two widely used data mining tools, the Support Vector Machine and Decision Tree. The test results demonstrate the superiority of the both DL algorithms over other methods in the application of power system transient disturbance classification.Comment: An updated version of this manuscript has been accepted by the 2018 IEEE International Conference on Energy Internet (ICEI), Beijing, Chin

Fast Sequence Component Analysis for Attack Detection in Synchrophasor Networks

Modern power systems have begun integrating synchrophasor technologies into part of daily operations. Given the amount of solutions offered and the maturity rate of application development it is not a matter of "if" but a matter of "when" in regards to these technologies becoming ubiquitous in control centers around the world. While the benefits are numerous, the functionality of operator-level applications can easily be nullified by injection of deceptive data signals disguised as genuine measurements. Such deceptive action is a common precursor to nefarious, often malicious activity. A correlation coefficient characterization and machine learning methodology are proposed to detect and identify injection of spoofed data signals. The proposed method utilizes statistical relationships intrinsic to power system parameters, which are quantified and presented. Several spoofing schemes have been developed to qualitatively and quantitatively demonstrate detection capabilities.Comment: 8 pages, 4 figures, submitted to IEEE Transaction

Learning from power system data stream: phasor-detective approach

Assuming access to synchronized stream of Phasor Measurement Unit (PMU) data over a significant portion of a power system interconnect, say controlled by an Independent System Operator (ISO), what can you extract about past, current and future state of the system? We have focused on answering this practical questions pragmatically - empowered with nothing but standard tools of data analysis, such as PCA, filtering and cross-correlation analysis. Quite surprisingly we have found that even during the quiet "no significant events" period this standard set of statistical tools allows the "phasor-detective" to extract from the data important hidden anomalies, such as problematic control loops at loads and wind farms, and mildly malfunctioning assets, such as transformers and generators. We also discuss and sketch future challenges a mature phasor-detective can possibly tackle by adding machine learning and physics modeling sophistication to the basic approach

Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Methodology and Tools for Field Testing of Synchrophasor Systems

The electrical power grid, as one of today’s most critical infrastructures, requires constant monitoring by operators to be aware of and react to any threats to the system’s condition. With control centers typically located far away from substations and other physical grid equipment, field measurement data forms the basis for a vast majority of control decisions in power system operation. For that reason, it is imperative to ensure the highest level of data integrity as erroneous data may lead to inappropriate control actions with potentially devastating consequences. Performance of one of the most advanced monitoring systems, the synchrophasor system, is the focus of this thesis. This research will look at testing techniques used for performance assessment of synchrophasor system performance in the field. Existing methods will be reviewed and evaluated for deficiencies in capturing system performance regarding data quality. The focus of this work will be on improving synchrophasor data quality, by introducing new testing methodology that utilizes a nested testing approach for end-to-end testing in the field using a portable test set and associated software tools. The capability of such methods and these tools to fully characterize and evaluate the performance of synchrophasor systems in the field will be validated through implementation in a large-scale testbed. The purpose of this research is to specify, develop and implement a methodology and associated tools for field-testing of synchrophasor systems. To this day, there is no dedicated standard for field-testing of synchrophasor systems. This resulted in an inability to define widely accepted procedures to detect deterioration of system performance due to poor data quality and caused communication failures, unacceptable device and subsystem accuracy, or loss of calibration. This work will demonstrate how the new approach addresses the mentioned performance assessment gap. The feasibility of implementation of the proposed test procedures will be demonstrated using different test system configurations available in a large-scale testbed. The proposed method is fully leveraging the benefits of a portable device specifically developed for field-testing, which may be used for improvement of commissioning, maintenance and troubleshooting tests for existing installations. Use Cases resulting from this work will illustrate the practical benefits of the proposed methodology and associated tools

Application of Advanced Early Warning Systems with Adaptive Protection

This project developed and field-tested two methods of Adaptive Protection systems utilizing synchrophasor data. One method detects conditions of system stress that can lead to unintended relay operation, and initiates a supervisory signal to modify relay response in real time to avoid false trips. The second method detects the possibility of false trips of impedance relays as stable system swings “encroach” on the relays’ impedance zones, and produces an early warning so that relay engineers can re-evaluate relay settings. In addition, real-time synchrophasor data produced by this project was used to develop advanced visualization techniques for display of synchrophasor data to utility operators and engineers

Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset using Big Data Analytics

A novel method for fast extraction of fundamental frequency events (FFE) based on measurements of frequency and rate of change of frequency by Phasor Measurement Units (PMU) is introduced. The method is designed to work with exceptionally large historical PMU datasets. Statistical analysis was used to extract the features and train Random Forest and Catboost classifiers. The method is capable of fast extraction of FFE from a historical dataset containing measurements from hundreds of PMUs captured over multiple years. The reported accuracy of the best algorithm for classification expressed as Area Under the receiver operating Characteristic curve reaches 0.98, which was obtained in out-of-sample evaluations on 109 system-wide events over 2 years observed at 43 PMUs. Then Minimum Volume Enclosing Ellipsoid Algorithm was used to further analyze the events. 93.72% events were correctly characterized, where average duration of the event as seen by the PMU was 9.93 sec

Measurement Platform for Latency Characterization of Wide Area Monitoring, Protection and Control Systems

Wide area monitoring, protection and control (WAMPAC) systems have emerged as a critical technology to improve the reliability, resilience, and stability of modern power grids. They are based on phasor measurement unit (PMU) technology and synchronized monitoring on a wide area. Since these systems are required to make rapid decisions and control actions on the grid, they are characterized by stringent time constraints. For this reason, the latency of WAMPAC systems needs to be appropriately assessed. Following this necessity, this article presents the design and implementation of a measurement platform that allows latency characterization of different types of WAMPAC systems in several operating conditions. The proposed WAMPAC Characterizer has been metrologically characterized through a WAMPAC Emulator and then used to measure the latency of a WAMPAC system based on an open-source platform frequently used by transmission system operators (TSOs) for the implementation of their PMU-based wide area systems