PMU-data-driven Event Classification in Power Transmission Grids

Niazazari, Iman; Liu, Yunchuan; Ghasenikhani, Amir; Biswas, Shuchismita; Livani, Hanif; Yang, Lei; Centeno, Virgilio A.

doi:10.1109/isgt49243.2021.9372227

Title: PMU-data-driven Event Classification in Power Transmission Grids

Abstract

This paper presents an event classification in transmission grids. The convolutional neural network (CNN)-based classifier is proposed to capture the temporal similarity of time-synchronized data stream from phasor measurement units (PMUs). The proposed CNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset from the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, phase angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The results show that ROCOF gives the best classification performance compared to other PMU outputs. In addition, it is shown that the classifier trained with a larger dataset has higher accuracy. Moreover, the superiority of the proposed method is validated through comparison with other state-of-the-art classification methods.

Authors:: Niazazari, Iman; Liu, Yunchuan; Ghasenikhani, Amir; Biswas, Shuchismita; Livani, Hanif; Yang, Lei; Centeno, Virgilio A.

Publication Date:: 2021-02-16

Research Org.:: Univ. of Nevada, Reno, NV (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1958827

DOE Contract Number:: OE0000911

Resource Type:: Conference

Journal Name:: 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)

Additional Journal Information:: Conference: 2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT),Washington, DC, USA,16-18 February 2021

Country of Publication:: United States

Language:: English

Citation Formats


                    Niazazari, Iman, Liu, Yunchuan, Ghasenikhani, Amir, Biswas, Shuchismita, Livani, Hanif, Yang, Lei, and Centeno, Virgilio A. PMU-data-driven Event Classification in Power Transmission Grids.  United States: N. p., 2021. 
        Web.  doi:10.1109/isgt49243.2021.9372227.

Copy to clipboard


                    Niazazari, Iman, Liu, Yunchuan, Ghasenikhani, Amir, Biswas, Shuchismita, Livani, Hanif, Yang, Lei, & Centeno, Virgilio A. PMU-data-driven Event Classification in Power Transmission Grids.  United States.  https://doi.org/10.1109/isgt49243.2021.9372227

Copy to clipboard


                    Niazazari, Iman, Liu, Yunchuan, Ghasenikhani, Amir, Biswas, Shuchismita, Livani, Hanif, Yang, Lei, and Centeno, Virgilio A. 2021.  
        "PMU-data-driven Event Classification in Power Transmission Grids".  United States.  https://doi.org/10.1109/isgt49243.2021.9372227.  https://www.osti.gov/servlets/purl/1958827.

Copy to clipboard


                    
@article{osti_1958827,

  title        = {PMU-data-driven Event Classification in Power Transmission Grids},

  author       = {Niazazari, Iman and Liu, Yunchuan and Ghasenikhani, Amir and Biswas, Shuchismita and Livani, Hanif and Yang, Lei and Centeno, Virgilio A.},

  abstractNote = {This paper presents an event classification in transmission grids. The convolutional neural network (CNN)-based classifier is proposed to capture the temporal similarity of time-synchronized data stream from phasor measurement units (PMUs). The proposed CNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset from the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, phase angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The results show that ROCOF gives the best classification performance compared to other PMU outputs. In addition, it is shown that the classifier trained with a larger dataset has higher accuracy. Moreover, the superiority of the proposed method is validated through comparison with other state-of-the-art classification methods.},

  doi          = {10.1109/isgt49243.2021.9372227},

  url          = {https://www.osti.gov/biblio/1958827},
  journal      = {2021 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2021},

  month        = {2}

}

Copy to clipboard

Conference:

View Conference (0.46 MB)

https://doi.org/10.1109/isgt49243.2021.9372227

Other availability

Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

Study of a new method for power system transients classification based on wavelet entropy and neural network
journal, March 2011

Zhengyou, He; Shibin, Gao; Xiaoqin, Chen
International Journal of Electrical Power & Energy Systems, Vol. 33, Issue 3
https://doi.org/10.1016/j.ijepes.2010.10.001

Expert system for classification and analysis of power system events
journal, April 2002

Styvaktakis, E.; Bollen, M. H. J.; Gu, I. Y. H.
IEEE Transactions on Power Delivery, Vol. 17, Issue 2
https://doi.org/10.1109/61.997911

Locating Switched Capacitor Using Wavelet Transform and Hybrid Principal Component Analysis Network
journal, April 2007

Hong, Ying-Yi; Chen, Bo-Yuan
IEEE Transactions on Power Delivery, Vol. 22, Issue 2
https://doi.org/10.1109/TPWRD.2007.893435

A decision tree method for building energy demand modeling
journal, October 2010

Yu, Zhun; Haghighat, Fariborz; Fung, Benjamin C. M.
Energy and Buildings, Vol. 42, Issue 10
https://doi.org/10.1016/j.enbuild.2010.04.006

Feature selection of power system transient stability assessment based on random forest and recursive feature elimination
conference, October 2016

Zhang, Chun; Li, Yansong; Yu, Zhihong
2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)
https://doi.org/10.1109/APPEEC.2016.7779696

PMU Placement for Line Outage Identification via Multinomial Logistic Regression
journal, January 2018

Kim, Taedong; Wright, Stephen J.
IEEE Transactions on Smart Grid, Vol. 9, Issue 1
https://doi.org/10.1109/TSG.2016.2546339

Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM
journal, January 2018

Zhang, Senlin; Wang, Yixing; Liu, Meiqin
IEEE Access, Vol. 6
https://doi.org/10.1109/ACCESS.2017.2785763

Cause identification of electromagnetic transient events using spatiotemporal feature learning
journal, December 2020

Niazazari, Iman; Jalilzadeh Hamidi, Reza; Livani, Hanif
International Journal of Electrical Power & Energy Systems, Vol. 123
https://doi.org/10.1016/j.ijepes.2020.106255

Extensions of recurrent neural network language model
conference, May 2011

Mikolov, Tomas; Kombrink, Stefan; Burget, Lukas
ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
https://doi.org/10.1109/ICASSP.2011.5947611

Disruptive event classification using PMU data in distribution networks
conference, July 2017

Niazazari, I.; Livani, H.
2017 IEEE Power & Energy Society General Meeting (PESGM)
https://doi.org/10.1109/PESGM.2017.8274154

A PMU-data-driven disruptive event classification in distribution systems
journal, April 2018

Niazazari, Iman; Livani, Hanif
Electric Power Systems Research, Vol. 157
https://doi.org/10.1016/j.epsr.2017.12.021

Electromagnetic transient events (EMTE) classification in transmission grids
conference, July 2017

Khoshdeli, M.; Niazazari, I.; Hamidi, R. Jalilzadeh
2017 IEEE Power & Energy Society General Meeting (PESGM)
https://doi.org/10.1109/PESGM.2017.8273984

Variational Mode Decomposition and Decision Tree Based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation System
journal, July 2018

Achlerkar, Pankaj D.; Samantaray, S. R.; Sabarimalai Manikandan, M.
IEEE Transactions on Smart Grid, Vol. 9, Issue 4
https://doi.org/10.1109/TSG.2016.2626469

Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approach
conference, February 2020

Niazazari, Iman; Livani, Hanif
2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
https://doi.org/10.1109/ISGT45199.2020.9087649

Similar records in OSTI.GOV collections:

Robust Event Classification Using Imperfect Real-world PMU Data

Journal Article Liu, Yunchuan; Yang, Lei; Ghasemkhani, Amir; ... - IEEE Internet of Things Journal (Online)

Here, this paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grainedmore »« less
https://doi.org/10.1109/jiot.2022.3177686
Weakly Supervised Event Classification Using Imperfect Real-world PMU Data with Scarce Labels

Conference Liu, Yunchuan; Yang, Lei - 2022 IEEE Power & Energy Society General Meeting (PESGM)

This paper studies event classification using imperfect real-world phasor measurement unit (PMU) data with scarce event types (labels). By investigating the real-world PMU data, it is observed that most real-world PMU data's event type is unknown, which makes it challenging to directly use such dataset to build event classifiers as existing classification techniques require high-quality training data with known event type (i.e., label). To address this challenge, a weakly supervised learning based event classification approach is developed, which can use noisy and low-quality PMU data for the training. First, data quality issues are fixed using data preprocessing techniques and thenmore »« less
https://doi.org/10.1109/pesgm48719.2022.9917176

Full Text Available
Final Project Report: Discovery of Signatures, Anomalies, and Precursors in Synchrophasor Data with Matrix Profile and Deep Recurrent Neural Networks

Technical Report Yu, Nanpeng; Yamashita, Koji; Foggo, Brandon; ...

The widespread deployment of phasor measurement unit (PMU) across the U.S. together with the burgeoning machine learning technology made it possible to develop data-driven PMU data analytics to improve grid security and reliability in a more insightful and effective manner. Although PMU applications have been explored for over a decade, the representative PMU usage is limited to the bulk power system monitoring mainly due to the data integrity issues associated with PMUs (typically missing, fragmented, and wrongly amplified data). To forge a breakthrough on this stalemate and embrace PMUs for power system control and protection as well, we applied variousmore »« less
https://doi.org/10.2172/1874793

Full Text Available
Power System Event Identification Based on Deep Neural Network With Information Loading

Journal Article Shi, Jie; Foggo, Brandon; Yu, Nanpeng - IEEE Transactions on Power Systems

Online power system event identification and classification are crucial to enhancing the reliability of transmission systems. In this study, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strikemore »« less
https://doi.org/10.1109/tpwrs.2021.3080279

Full Text Available
Line Faults Classification Using Machine Learning on Three Phase Voltages Extracted from Large Dataset of PMU Measurements

Journal Article Otudi, Hussain; Dokic, Tatjana; Mohamed, Taif; ... - Proceedings of the Annual Hawaii International Conference on System Sciences

An end-to-end supervised learning method is developed to classify transmission line faults in a twoyear field-recorded dataset that includes synchronized measurements of three-phase voltages recorded by 38 Phasor Measurement Units (PMU) sparsely located in in the US Western Grid interconnection. Statistical analysis is performed to extract features from this large dataset to train Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) classifiers initially. The training further leverages a simulated dataset from a synthetic grid with 12 PMUs to increase the number of faults of types infrequently seen in the field-recorded dataset. Training the classification models withmore »« less
https://doi.org/10.24251/hicss.2022.425

Full Text Available

Similar Records

Title: PMU-data-driven Event Classification in Power Transmission Grids

Abstract

Citation Formats

Study of a new method for power system transients classification based on wavelet entropy and neural network journal, March 2011

Expert system for classification and analysis of power system events journal, April 2002

Locating Switched Capacitor Using Wavelet Transform and Hybrid Principal Component Analysis Network journal, April 2007

A decision tree method for building energy demand modeling journal, October 2010

Feature selection of power system transient stability assessment based on random forest and recursive feature elimination conference, October 2016

PMU Placement for Line Outage Identification via Multinomial Logistic Regression journal, January 2018

Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM journal, January 2018

Cause identification of electromagnetic transient events using spatiotemporal feature learning journal, December 2020

Extensions of recurrent neural network language model conference, May 2011

Disruptive event classification using PMU data in distribution networks conference, July 2017

A PMU-data-driven disruptive event classification in distribution systems journal, April 2018

Electromagnetic transient events (EMTE) classification in transmission grids conference, July 2017

Variational Mode Decomposition and Decision Tree Based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation System journal, July 2018

Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approach conference, February 2020

Study of a new method for power system transients classification based on wavelet entropy and neural network
journal, March 2011

Expert system for classification and analysis of power system events
journal, April 2002

Locating Switched Capacitor Using Wavelet Transform and Hybrid Principal Component Analysis Network
journal, April 2007

A decision tree method for building energy demand modeling
journal, October 2010

Feature selection of power system transient stability assessment based on random forest and recursive feature elimination
conference, October 2016

PMU Placement for Line Outage Identification via Multinomial Logistic Regression
journal, January 2018

Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM
journal, January 2018

Cause identification of electromagnetic transient events using spatiotemporal feature learning
journal, December 2020

Extensions of recurrent neural network language model
conference, May 2011

Disruptive event classification using PMU data in distribution networks
conference, July 2017

A PMU-data-driven disruptive event classification in distribution systems
journal, April 2018

Electromagnetic transient events (EMTE) classification in transmission grids
conference, July 2017

Variational Mode Decomposition and Decision Tree Based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation System
journal, July 2018

Attack on Grid Event Cause Analysis: An Adversarial Machine Learning Approach
conference, February 2020