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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Multi-View Convolutional Neural Network for Data Spoofing Cyber-Attack Detection in Distribution Synchrophasors

Security of Distribution Synchrophasors Data (DSD) is of paramount importance as the data is used for critical smart grid applications including situational awareness, advanced protection, and dynamic control. Unfortunately, the DSD are attractive targets for malicious attackers aiming to damage grid. Data spoofing is a new class of deceiving attack, where the DSD of one Phasor Measurement Units (PMUs) is tampered by other PMUs thereby spoiling measurement based applications. In order to address this issue, a source authentication based data spoofing attack detection method is proposed using Multi-view Convolutional Neural Network (MCNN). First, common components embedded in raw frequency measurements from DSD are removed by Savitzky-Golay (SG) filter. Second, fast S transform (FST) is utilized to extract representative spatial fingerprints via time frequency analysis. Third, the spatial fingerprint is fed to MCNN, which combines dilated and standard convolutions for automatic feather extraction and source identification. Finally, according to the output of MCNN, spoofing attack detection is performed via threshold criterion. Extensive experiments with actual DSD from multiple locations in FNET/Grideye are conducted to verify the effectiveness of the proposed method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗
DOE OSTI2021

Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features

The internetworking of grid-connected power electronics converters (PECs) in photovoltaic (PV) farms has inevitably expanded the cyber-attack surfaces. Here this paper presents a comprehensive study on cyber-attack detection and diagnosis for PEC-enabled PV farms via single waveform sensor to distinguish between normal conditions, open-circuit faults, short-circuit faults, and cyber-attacks. To our knowledge, this has not been attempted before. Firstly, we propose frequency-domain magnitude-based residuals to identify short-circuit faults and a time-domain mean current vector-based feature to distinguish open-circuit faults from other threats. These features can fully reflect the specific physical characteristics of PV farms during threat duration. Secondly, unlike micro phasor measurement units (µPMU) and raw electric waveform-based methods, the proposed innovative features can address novel cyber-attacks that are excluded from the training process. Thirdly, an online hardware-in-the-loop (HIL) testbed using the OPAL-RT real-time digital simulator has verified the effectiveness. The monitoring system runs in real-time while using HIL as an operational solar farm and a National Instruments (NI) data acquisition card as the electric waveform sensor at the point of coupling.

42 ENGINEERING↗
DOE OSTI2023

Learning Distribution Grid Topologies: A Tutorial

Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutorial summarizes, contrasts, and establishes useful links between recent works on topology identification and detection schemes that have been proposed for power distribution grids. The primary focus is to highlight methods that overcome the limited availability of measurement devices in distribution grids, while enhancing topology estimates using conservation laws of power-flow physics and structural properties of feeders. Grid data from phasor measurement units or smart meters can be collected either passively in the traditional way, or actively, upon actuating grid resources and measuring the feeder's voltage response. Analytical claims on feeder identifiability and detectability are reviewed under disparate meter placement scenarios. Such topology learning claims can be attained exactly or approximately so via algorithmic solutions with various levels of computational complexity, ranging from least-squares fits to convex optimization problems, and from polynomial-time searches over graphs to mixed-integer programs. Although the emphasis is on radial single-phase feeders, extensions to meshed and/or multiphase circuits are sometimes possible and discussed. Here this tutorial aspires to provide researchers and engineers with knowledge of the current state-of-the-art in tractable distribution grid learning and insights into future directions of work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Synchrophasor Measurements-based Events Detection Using Deep Learning

Deep learning algorithms have been developed for phasor measurement units (PMUs) analysis aiming at providing grid operators to observe and react to significant real-time changes in the grid associated with multiple factors (e.g., power generation and load variations, different type of faults, and equipment mailfunction), or for offline post-event system diagnostics. In this study, a Long Short-Term Memory (LSTM)-based deep neural network (DNN) is adopted and evaluated to identify the most appropriate model configurations for event detection and longer-term anomalous pattern extraction. The proposed DNN model shows the potential on long-term predictions with the ability to capture nonlinear and nonstationary mixture complex patterns in PMU datasets. Real-world PMU in the WECC system were used for model development and validation.

deep learning↗
DOE OSTI2021

SoDa: An Irradiance-Based Synthetic Solar Data Generation Tool (SoDa) v0.1

SoDa is an irradiance-based synthetic Solar Data generation tool to generate realistic sub-minute solar photovoltaic (PV) power time series, that emulate the weather pattern for a certain geographical location. Our tool relies on the National Solar Radiation Database (NSRDB) to obtain irradiance and weather data patterns for the site. Irradiance is mapped onto a PV model estimate of a solar plant's 30-min power output, based on the configuration of the panel. We use a stochastic model with a switching behavior due to different weather regimes as provided by the cloud type label in the NSRDB, with parameters for the cloudy states trained on the high-resolution solar power measurements from a Phasor Measurement Unit (PMU).

Carreno, IgnacioLosada↗
DOE OSTI2020

Machine Learning for Synchrophasor Analysis

The report presents results from the development of a cloud-based, Big Data analysis framework for power systems. The computational pipeline uses the Apache Spark framework running in an OpenStack cloud infrastructure. A real-world phasor measurement unit (PMU) dataset has been used to carry out the analysis. Several Machine Learning (ML) methods have been developed and implemented for event and anomaly detection and classification. Actual examples of power system events detection and analysis using synchrophasor data are presented. It has been shown that applications of the cloud-based computing environment and the Apache Spark framework enable a significant increase in the computational efficiency of large-scale PMU data analysis.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Machine Learning Guided Operational Intelligence from Synchrophasors (Final Report)

Schweitzer Engineering Laboratories (SEL) and Oregon State University (OSU) received over 27 terabytes of electrical power system phasor measurement unit (PMU) data for the Eastern, Western, and ERCOT interconnections. The dataset includes measurements spread across 446 PMUs from early 2016 to mid 2018 depending on the interconnect. The full dataset was split into a training and test (holdout) dataset by PNNL. All data was received in the Apache Parquet format. The overarching goal of this project is to develop and execute a strategy to mitigate data anomalies, perform analysis on the dataset, and detect anomalous events in the data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Event Screening Methods for the Eastern Interconnection Situational Awareness and Monitoring System (ESAMS)

The Eastern Interconnection Situational Awareness and Monitoring System (ESAMS) is a tool developed to explore the applications and advantages of sharing phasor measurement unit (PMU) data among operating entities. An initial conclusion from testing was that detected events required further screening before being reported to ensure that events would be of interest to system operators. The methods described in this report were integrated into ESAMS to meet this need. First, an event is screened for whether the change in voltage angle measurements indicates that the detected disturbance is sufficiently large. Next, an event is classified to determine if it was related to a loss of generation. Finally, a neural network is used to determine if the system’s response to the disturbance is normal or abnormal. These methods were tested with one month of field-measured PMU data. Results indicate that the additional information provided by these additional screening tools will improve selection of events for inclusion in the daily event reports automatically generated by ESAMS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data

As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.

Synchrophasors, Machine Learning, System-wide Even↗
DOE OSTI2022

End-to-end Analytics for Grid Arch Design & All-hazard Assessment

Resiliency, reliability, and security of the next-generation smart grid depend upon leveraging advanced communication and computing technologies, integrating them with physical power systems, and developing real-time, fast, data-based applications to help in wide-area monitoring and control of the grid. Using a high sampling data rate from phasor measurement units (PMUs) to develop applications has opened the door to achieving the next-generation grid requirements. The North American Synchrophasor Initiative Network (NASPlnet) was developed in 2007-09 to create a standard and guide for PMU data exchanges. With the advancement in both networking and grid requirements, it is necessary to evaluate the performance of different NASPInet versions and their impact on applications. Therefore, we need a cyber-power cosimulation framework that supports very large-scale co-simulation capable of running in parallel, high-performance computing platforms and capturing real-life network behavior. This work presents a cyber-physical co-simulation testbed using NS3 to model the communication network, GridPACK to model the power grid, and HELICS as a co-simulation engine. Comparative analysis of latency in synchrophasor networks and a performance evaluation of a power system stabilizer application based on PMU data in an Institute of Electrical and Electronics Engineers 39-bus test system is presented using this co-simulation testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Accelerated Data Analytics for Power-System Time-Series (ADAPT) [Final Project Report]

Over the past decade, electric utilities have made significant progress in deploying networks of phasor measurement units (PMUs). The high-speed measurements provided by PMUs are valuable for offline analysis, but the sheer volume of data presents a challenge for utilities. The Accelerated Data Analytics for Power-System Time-Series (ADAPT) project was created to address this challenge by developing SciSync, an open-source software tool that enables utilities to rapidly read, process, analyze, and review grid measurements. SciSync was designed to provide the capabilities of Archive Walker, a research tool developed by PNNL, along with the reliability and performance of commercial-grade software.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Eastern Interconnection Wide-Area Oscillation Assessment and Study Report

To meet the government mandates such as Renewable Portfolio Standards (RPS), conventional synchronous generators with rotating mass are being displaced by inverter-based resources (IBRs), leading to high penetration of renewable energy sources (RES). As a result, the inherent properties and characteristics of the transformed generation mix may significantly impact the grid behaviour, and it is of paramount significance that the impact and the consequences of the resource mix change is thoroughly understood to adopt measures to maintain reliable grid operations. Of great concern is the inter-area low-frequency oscillation, which usually propagates through a large region and has a system-wide impact. Such oscillations may lead to unnecessary or inadvertent tripping of generators, that may be simply reacting to the oscillation originating from geographically remote sites. Such tripping of generators can lead to cascading outages, system split and load loss events. Over the years, several system-wide oscillation events have been observed across all three North American interconnections. This project titled Wide-area Oscillation Assessment and Trending Study, sponsored by the Office of Electricity (OE) of the Department of Energy (DOE), aims to conduct the required research to capture and investigate the potential changes in wide-area oscillatory performance of the system as a result of resource mix transition. The Pacific Northwest National Laboratory (PNNL) conducted this research and technical staff of Federal Energy Regulatory Commission (FERC) served as advisors to the project. The primary objective of this study is to assess whether there are significant trends in the power system wide-area oscillatory behaviours, as a result of the generation-mix changes due to the increased penetration of RES in the U.S. Eastern Interconnection (EI). The analysis presented in this report can serve as reference for future grid planning and operation of the EI system. This research conducted in this project evaluates the wide-area oscillatory behaviour of the EI, using a model-based approach for potential future resource mixes, and the measurement-based analysis with 21-month phasor measurement unit (PMU) time series. The oscillation modes in EI system along with the impact of the changes of the generation mix on the frequency and damping ratio (DR) of the oscillations have been studied.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Synchrophasor-Based Zonal Current Differential Protection for Secondary Low Voltage Networks

This report describes an approach to utilizing phasor measurement unit (PMU) data from multiple Intelligent Electronics Devices (IEDs) in a low-voltage network to produce a differential scheme for protecting the medium-voltage feeder and low-voltage network transformers. The proposed protection scheme is designed and prototyped on a real-time automation controller. Its performance is evaluated using real-time controller hardware-in-the-loop simulation. Lab testing results indicate that the proposed protection scheme allows significant distributed energy resources (DER) backfeed and enables selective and fast protection of medium voltage feeders.

42 ENGINEERING↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

Danovo Energy Solution's presented its paper named: Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events at the 2026 Georgia Tech Fault & Disturbance Analysis Conference. The full paper can be found at OSTI ID# 3169150 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danova Energy Solutions]↗
DOE OSTI2021

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.

Baembitov, Rashid↗
DOE OSTI2022

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

24 POWER TRANSMISSION AND DISTRIBUTION↗