A Dynamic Behavior-Based Bulk Power System Event Signature Library With Empirical Clustering

The grid reinforcement, advanced grid stabilizing systems, and inverter-interfaced loads have varied power system dynamics. The changing trends of various dynamic phenomena need to be scrutinized to ensure future grid reliability. A dynamic behavior-based event signature library of phasor measuremen...

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Main Authors: Koji Yamashita, Brandon Foggo, Xianghao Kong, Yuanbin Cheng, Jie Shi, Nanpeng Yu
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9882120/
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author Koji Yamashita
Brandon Foggo
Xianghao Kong
Yuanbin Cheng
Jie Shi
Nanpeng Yu
author_facet Koji Yamashita
Brandon Foggo
Xianghao Kong
Yuanbin Cheng
Jie Shi
Nanpeng Yu
author_sort Koji Yamashita
collection DOAJ
description The grid reinforcement, advanced grid stabilizing systems, and inverter-interfaced loads have varied power system dynamics. The changing trends of various dynamic phenomena need to be scrutinized to ensure future grid reliability. A dynamic behavior-based event signature library of phasor measurement unit (PMU) data has great potential to discover new and unprecedented event signatures. This paper presents an event signature library design that further defines more granular event categories within the major event categories (e.g., frequency, voltage, and oscillation events) provided by electric utilities and regional transmission organizations. The proposed library design embraces a supervised machine learning approach with a deep neural network (DNN) model and manually-generated labels. The input of the model uses representative PMUs that evidently express dominant event signatures. The performance of the event categorization module was evaluated, via information entropy, against labels generated automatically from clustering analyses. We applied the event signature library design to two years of over 1000 actual events in the bulk U.S. power system. The module obtains remarkable event discrimination capability.
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spelling doaj.art-fe4f4503fbcb4bf1a9bf9d8b736dbe102022-12-22T02:04:28ZengIEEEIEEE Access2169-35362022-01-0110963079632110.1109/ACCESS.2022.32053219882120A Dynamic Behavior-Based Bulk Power System Event Signature Library With Empirical ClusteringKoji Yamashita0https://orcid.org/0000-0002-1892-2455Brandon Foggo1https://orcid.org/0000-0002-8547-391XXianghao Kong2Yuanbin Cheng3Jie Shi4https://orcid.org/0000-0002-1760-0462Nanpeng Yu5https://orcid.org/0000-0001-5086-5465Department of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USADepartment of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USADepartment of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USADepartment of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USADepartment of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USADepartment of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA, USAThe grid reinforcement, advanced grid stabilizing systems, and inverter-interfaced loads have varied power system dynamics. The changing trends of various dynamic phenomena need to be scrutinized to ensure future grid reliability. A dynamic behavior-based event signature library of phasor measurement unit (PMU) data has great potential to discover new and unprecedented event signatures. This paper presents an event signature library design that further defines more granular event categories within the major event categories (e.g., frequency, voltage, and oscillation events) provided by electric utilities and regional transmission organizations. The proposed library design embraces a supervised machine learning approach with a deep neural network (DNN) model and manually-generated labels. The input of the model uses representative PMUs that evidently express dominant event signatures. The performance of the event categorization module was evaluated, via information entropy, against labels generated automatically from clustering analyses. We applied the event signature library design to two years of over 1000 actual events in the bulk U.S. power system. The module obtains remarkable event discrimination capability.https://ieeexplore.ieee.org/document/9882120/Classifierclusteringdeep neural networkevent librarypower systemPMU
spellingShingle Koji Yamashita
Brandon Foggo
Xianghao Kong
Yuanbin Cheng
Jie Shi
Nanpeng Yu
A Dynamic Behavior-Based Bulk Power System Event Signature Library With Empirical Clustering
IEEE Access
Classifier
clustering
deep neural network
event library
power system
PMU
title A Dynamic Behavior-Based Bulk Power System Event Signature Library With Empirical Clustering
title_full A Dynamic Behavior-Based Bulk Power System Event Signature Library With Empirical Clustering
title_fullStr A Dynamic Behavior-Based Bulk Power System Event Signature Library With Empirical Clustering
title_full_unstemmed A Dynamic Behavior-Based Bulk Power System Event Signature Library With Empirical Clustering
title_short A Dynamic Behavior-Based Bulk Power System Event Signature Library With Empirical Clustering
title_sort dynamic behavior based bulk power system event signature library with empirical clustering
topic Classifier
clustering
deep neural network
event library
power system
PMU
url https://ieeexplore.ieee.org/document/9882120/
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