Pub Date : 2022-05-24DOI: 10.1109/SGSMA51733.2022.9805998
A. Yablokov, I. Ivanov, F. Kulikov, A. Tychkin, A. Panaschatenko, A. Zhukov, D. Dubinin
Synchrophasor measurements were not meant to capture fast electromagnetic transients. However, quite a few algorithms have already been proposed to make use of phasor measurement unit (PMU) data for fault location on overhead transmission lines. In most of the papers, there are solely mathematical models with little or no consideration of the real PMU behavior. Instead of pure modeling, this research employs a lab testbed with PMUs configured as "Class M" defined in IEEE C37.118. By using a number of impedance-based fault location expressions (this time–with current and voltage data from PMUs), it is shown that good estimates could be reached within at most four cycles into the fault. Since the fault location accuracy can be reduced by incorrect transmission line data, a new fault location method is developed with built-in line parameter estimation. Preliminary test results with Class M phasors from the lab equipment can be considered as promising.
{"title":"Synchrophasor-based Fault Location with Class M Fault Capture and Built-in Line Parameter Estimation","authors":"A. Yablokov, I. Ivanov, F. Kulikov, A. Tychkin, A. Panaschatenko, A. Zhukov, D. Dubinin","doi":"10.1109/SGSMA51733.2022.9805998","DOIUrl":"https://doi.org/10.1109/SGSMA51733.2022.9805998","url":null,"abstract":"Synchrophasor measurements were not meant to capture fast electromagnetic transients. However, quite a few algorithms have already been proposed to make use of phasor measurement unit (PMU) data for fault location on overhead transmission lines. In most of the papers, there are solely mathematical models with little or no consideration of the real PMU behavior. Instead of pure modeling, this research employs a lab testbed with PMUs configured as \"Class M\" defined in IEEE C37.118. By using a number of impedance-based fault location expressions (this time–with current and voltage data from PMUs), it is shown that good estimates could be reached within at most four cycles into the fault. Since the fault location accuracy can be reduced by incorrect transmission line data, a new fault location method is developed with built-in line parameter estimation. Preliminary test results with Class M phasors from the lab equipment can be considered as promising.","PeriodicalId":256954,"journal":{"name":"2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128079143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/SGSMA51733.2022.9805850
Haoran Li, Zhihao Ma, Yang Weng, E. Farantatos
Machine Learning (ML) models are continuously introduced to power systems in domains like state estimation and event identification. However, training an ML model usually requires a lot of data. For data-limited grids, we propose a transfer learning framework to transfer knowledge from a source grid with rich Phasor Measurement Unit (PMU) data for the event-type differentiation problem. The goal is challenging due to (1) different dimensionalities of the source and the target measurement spaces, (2) dissimilar data distributions, and (3) redundant PMU’s information. Thus, we project the source and the target measurement space into a latent feature space, which reduces and aligns the dimensionality of input measurements, maintains close data distributions in the latent space, and enables the transferability from the source domain to the target domain. Then, we introduce transfer learning in supervised learning by vectorizing each PMU’s measurement window as one training sample, forming the latent space. We theoretically show that our approach minimizes the upper bound of misclassification rate and experimentally demonstrates the high performance on various synthetic datasets.
{"title":"Transfer Learning for Event-Type Differentiation on Power Systems","authors":"Haoran Li, Zhihao Ma, Yang Weng, E. Farantatos","doi":"10.1109/SGSMA51733.2022.9805850","DOIUrl":"https://doi.org/10.1109/SGSMA51733.2022.9805850","url":null,"abstract":"Machine Learning (ML) models are continuously introduced to power systems in domains like state estimation and event identification. However, training an ML model usually requires a lot of data. For data-limited grids, we propose a transfer learning framework to transfer knowledge from a source grid with rich Phasor Measurement Unit (PMU) data for the event-type differentiation problem. The goal is challenging due to (1) different dimensionalities of the source and the target measurement spaces, (2) dissimilar data distributions, and (3) redundant PMU’s information. Thus, we project the source and the target measurement space into a latent feature space, which reduces and aligns the dimensionality of input measurements, maintains close data distributions in the latent space, and enables the transferability from the source domain to the target domain. Then, we introduce transfer learning in supervised learning by vectorizing each PMU’s measurement window as one training sample, forming the latent space. We theoretically show that our approach minimizes the upper bound of misclassification rate and experimentally demonstrates the high performance on various synthetic datasets.","PeriodicalId":256954,"journal":{"name":"2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121648965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/SGSMA51733.2022.9806022
Dahlia Saba, M. Rusch, A. V. Meier, D. Laverty
With the increasing usage of power electronic devices that contribute to harmonic distortion in power systems, analysis of harmonic components has become important for maintaining power quality. Moreover, time-synchronized point-on-wave measurements are of growing interest for analyzing dynamic phenomena in power systems, where synchrophasors that report a magnitude and angle of the estimated fundamental component of a signal are insufficient. This paper proposes a new method for estimating harmonic content by applying quadrature demodulation iteratively to estimate a synchrophasor for each harmonic component of a power system signal. We first apply the method to simulated signals to verify its accuracy, then we demonstrate its effectiveness at reconstructing a signal from data measured from real power distribution systems.
{"title":"Iterative Quadrature Demodulation for Harmonic Synchrophasor Estimation","authors":"Dahlia Saba, M. Rusch, A. V. Meier, D. Laverty","doi":"10.1109/SGSMA51733.2022.9806022","DOIUrl":"https://doi.org/10.1109/SGSMA51733.2022.9806022","url":null,"abstract":"With the increasing usage of power electronic devices that contribute to harmonic distortion in power systems, analysis of harmonic components has become important for maintaining power quality. Moreover, time-synchronized point-on-wave measurements are of growing interest for analyzing dynamic phenomena in power systems, where synchrophasors that report a magnitude and angle of the estimated fundamental component of a signal are insufficient. This paper proposes a new method for estimating harmonic content by applying quadrature demodulation iteratively to estimate a synchrophasor for each harmonic component of a power system signal. We first apply the method to simulated signals to verify its accuracy, then we demonstrate its effectiveness at reconstructing a signal from data measured from real power distribution systems.","PeriodicalId":256954,"journal":{"name":"2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121923071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/SGSMA51733.2022.9805997
S. Blair, Jason J. A. Costello
Fundamental changes in power grids due to decarbonization require advanced monitoring and automated analysis. Capturing synchronized waveform data from voltage and current sensors, sometimes referred to Continuous Point on Wave (CPOW) monitoring, offers several capabilities beyond synchrophasors from Phasor Measurement Units (PMUs). However, the obvious drawbacks in manipulating, transferring, and storing waveform are the high data bandwidth and storage requirements. Therefore, access to streaming synchronized waveform data is typically restricted to substation local area networks (LANs). This paper reports on a platform to address these issues and therefore to deliver wide-area waveform monitoring in a way which is convenient and practical. It is shown how a lossless data compression method designed for streaming waveform data can significantly reduce data bandwidth requirements and improve end-to-end efficiency and latency. Data bandwidth requirements can be reduced to 5-15% of the original size. The same approach can be applied to both real-time streaming and offline data storage, with reduced file size compared to other industry formats such as COMTRADE and PQDIF. It supports any sampling rate, any number of samples per message, and arbitrary configurations of measurement quantities to be sent. An implementation of the scheme, called Slipstream, has been open sourced to enable industry adoption.
{"title":"Slipstream: High-Performance Lossless Compression for Streaming Synchronized Waveform Monitoring Data","authors":"S. Blair, Jason J. A. Costello","doi":"10.1109/SGSMA51733.2022.9805997","DOIUrl":"https://doi.org/10.1109/SGSMA51733.2022.9805997","url":null,"abstract":"Fundamental changes in power grids due to decarbonization require advanced monitoring and automated analysis. Capturing synchronized waveform data from voltage and current sensors, sometimes referred to Continuous Point on Wave (CPOW) monitoring, offers several capabilities beyond synchrophasors from Phasor Measurement Units (PMUs). However, the obvious drawbacks in manipulating, transferring, and storing waveform are the high data bandwidth and storage requirements. Therefore, access to streaming synchronized waveform data is typically restricted to substation local area networks (LANs). This paper reports on a platform to address these issues and therefore to deliver wide-area waveform monitoring in a way which is convenient and practical. It is shown how a lossless data compression method designed for streaming waveform data can significantly reduce data bandwidth requirements and improve end-to-end efficiency and latency. Data bandwidth requirements can be reduced to 5-15% of the original size. The same approach can be applied to both real-time streaming and offline data storage, with reduced file size compared to other industry formats such as COMTRADE and PQDIF. It supports any sampling rate, any number of samples per message, and arbitrary configurations of measurement quantities to be sent. An implementation of the scheme, called Slipstream, has been open sourced to enable industry adoption.","PeriodicalId":256954,"journal":{"name":"2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128418215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/SGSMA51733.2022.9806001
T. Xygkis, G. Korres
The introduction of synchrophasor technology in power distribution sector is an emerging trend in an effort to meet the intense requirements for high quality monitoring and control of active distribution grids. Given the appreciable resources needed to develop a phasor measurement unit infrastructure, this paper presents a new optimization-based method for the deployment of distribution-level phasor measurement units in order to improve state estimation accuracy considering the associated investment costs and the pre-existing conventional metering system. The related problem is formulated as a mixed integer semidefinite programming model aiming to minimize the worst case coordinate state estimation error while satisfying predetermined budget constraints. The proposed method is assessed via numerical simulations on the IEEE 34-node test feeder and an actual 64 node distribution network. The obtained results show that it can represent a reliable design tool for the installation of phasor measurement units in distribution grids, making an optimal trade-off between precision and cost.
{"title":"Optimizing D-PMU deployment for distribution system state estimation","authors":"T. Xygkis, G. Korres","doi":"10.1109/SGSMA51733.2022.9806001","DOIUrl":"https://doi.org/10.1109/SGSMA51733.2022.9806001","url":null,"abstract":"The introduction of synchrophasor technology in power distribution sector is an emerging trend in an effort to meet the intense requirements for high quality monitoring and control of active distribution grids. Given the appreciable resources needed to develop a phasor measurement unit infrastructure, this paper presents a new optimization-based method for the deployment of distribution-level phasor measurement units in order to improve state estimation accuracy considering the associated investment costs and the pre-existing conventional metering system. The related problem is formulated as a mixed integer semidefinite programming model aiming to minimize the worst case coordinate state estimation error while satisfying predetermined budget constraints. The proposed method is assessed via numerical simulations on the IEEE 34-node test feeder and an actual 64 node distribution network. The obtained results show that it can represent a reliable design tool for the installation of phasor measurement units in distribution grids, making an optimal trade-off between precision and cost.","PeriodicalId":256954,"journal":{"name":"2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125368908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/SGSMA51733.2022.9806007
E. Satsuk, A. Zhukov, D. Dubinin, I. Ivanov, A. Murzin
Synchrophasor measurements enable the validation of transmission line parameters utilized by state estimation software, relays and fault locators. Despite quite a few algorithms proposed earlier, practical implementation results are almost absent, and only simulated phasors are often taken for testing. This work provides a simple regression-based approach for positive sequence line parameter estimation through line terminal PMU data. The key feature is thorough testing of the proposed technique on real phasor archives collected from four different lines located in two distinct power systems. Promising results obtained in such a "black-box" rather than software-only impractical simulations serve as an indicator of the algorithm’s validity. We also highlight an attempt of zero sequence line parameter estimation based on PMU archives collected under power system imbalance. The derived results can be thought of as a first step towards putting PMU-driven parameter estimation methods into practice.
{"title":"Analytical Approach to Phasor-based Line Parameter Estimation Verified Through Real PMU Data","authors":"E. Satsuk, A. Zhukov, D. Dubinin, I. Ivanov, A. Murzin","doi":"10.1109/SGSMA51733.2022.9806007","DOIUrl":"https://doi.org/10.1109/SGSMA51733.2022.9806007","url":null,"abstract":"Synchrophasor measurements enable the validation of transmission line parameters utilized by state estimation software, relays and fault locators. Despite quite a few algorithms proposed earlier, practical implementation results are almost absent, and only simulated phasors are often taken for testing. This work provides a simple regression-based approach for positive sequence line parameter estimation through line terminal PMU data. The key feature is thorough testing of the proposed technique on real phasor archives collected from four different lines located in two distinct power systems. Promising results obtained in such a \"black-box\" rather than software-only impractical simulations serve as an indicator of the algorithm’s validity. We also highlight an attempt of zero sequence line parameter estimation based on PMU archives collected under power system imbalance. The derived results can be thought of as a first step towards putting PMU-driven parameter estimation methods into practice.","PeriodicalId":256954,"journal":{"name":"2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126655117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/SGSMA51733.2022.9805853
Yuru Wu, H. Yin, Yilu Liu, S. Gao
This paper introduces a new distribution level Phasor Measurement Unit (PMU) which adopts advanced hardware components and structure. The hardware parameters from the new PMU and the existing PMU are used to build a simulation model to predict the PMU performance. Therefore, a real-world testbench is built and four distribution level PMUs are tested under the steady-state and dynamic tests. The quantitative experiment result confirms the prediction model which could guide future PMU design, and also verifies the accuracy of the new PMU on the synchrophasor and frequency measurements in multiple scenarios.
{"title":"Dynamic Performance Comparison and Prediction based on Distribution-level Phasor Measurement Units","authors":"Yuru Wu, H. Yin, Yilu Liu, S. Gao","doi":"10.1109/SGSMA51733.2022.9805853","DOIUrl":"https://doi.org/10.1109/SGSMA51733.2022.9805853","url":null,"abstract":"This paper introduces a new distribution level Phasor Measurement Unit (PMU) which adopts advanced hardware components and structure. The hardware parameters from the new PMU and the existing PMU are used to build a simulation model to predict the PMU performance. Therefore, a real-world testbench is built and four distribution level PMUs are tested under the steady-state and dynamic tests. The quantitative experiment result confirms the prediction model which could guide future PMU design, and also verifies the accuracy of the new PMU on the synchrophasor and frequency measurements in multiple scenarios.","PeriodicalId":256954,"journal":{"name":"2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116611334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/SGSMA51733.2022.9805847
M. Kamal, Wenting Li, Deepjyoti Deka, Hamed Mohsenian-Rad
A novel method is proposed to address the issue of low-observability in Distribution System State Estimation (DSSE). We first use the historical data at the unobservable locations to construct and train proper Generative Adversarial Network (GAN) models to compensate for lack of direct real-time measurements. We then integrate the trained GAN models, together with the direct synchronized measurements at the observable locations, into the formulation of the DSSE problem. In this regard, we simultaneously take advantage of the forecasting capabilities of the GAN models, the available real-time synchronized measurements, and the DSSE formulations based on physical laws in the power system. As a result, on one hand we conduct a physics-conditioned estimation of the unknown power injections at the unobservable locations; and on the other hand, we also achieve a complete DSSE solution for the understudy low-observable active power distribution system.
{"title":"Physics-Conditioned Generative Adversarial Networks for State Estimation in Active Power Distribution Systems with Low Observability","authors":"M. Kamal, Wenting Li, Deepjyoti Deka, Hamed Mohsenian-Rad","doi":"10.1109/SGSMA51733.2022.9805847","DOIUrl":"https://doi.org/10.1109/SGSMA51733.2022.9805847","url":null,"abstract":"A novel method is proposed to address the issue of low-observability in Distribution System State Estimation (DSSE). We first use the historical data at the unobservable locations to construct and train proper Generative Adversarial Network (GAN) models to compensate for lack of direct real-time measurements. We then integrate the trained GAN models, together with the direct synchronized measurements at the observable locations, into the formulation of the DSSE problem. In this regard, we simultaneously take advantage of the forecasting capabilities of the GAN models, the available real-time synchronized measurements, and the DSSE formulations based on physical laws in the power system. As a result, on one hand we conduct a physics-conditioned estimation of the unknown power injections at the unobservable locations; and on the other hand, we also achieve a complete DSSE solution for the understudy low-observable active power distribution system.","PeriodicalId":256954,"journal":{"name":"2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124978033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/SGSMA51733.2022.9805843
R. Reyes, J. de la O, M. Paternina, J. Chow, A. Zamora, J. Ortiz
This paper presents a user-friendly graphical user interface (GUI) that is embedded in a Python toolbox to identify electromechanical modes emerging after large power system disturbances. This GUI is able to read xslx or csv files that contain the information of several phasor measurement units (PMUs) or a data file with a proper read format. Besides this GUI incorporates three well-known methods that stand out to perform ringdown analysis in power systems such as Prony’s method (PM), eigensystem realization algorithm (ERA), and matrix pencil (MP). A straightforward implementation is adopted to capture dynamic parameters by processing single or multiple channels. Numerical and graphical results demonstrate the usability of the GUI, even in real system events1.
{"title":"A Python-based Ringdown Analysis Toolbox for Electromechanical Modes Identification","authors":"R. Reyes, J. de la O, M. Paternina, J. Chow, A. Zamora, J. Ortiz","doi":"10.1109/SGSMA51733.2022.9805843","DOIUrl":"https://doi.org/10.1109/SGSMA51733.2022.9805843","url":null,"abstract":"This paper presents a user-friendly graphical user interface (GUI) that is embedded in a Python toolbox to identify electromechanical modes emerging after large power system disturbances. This GUI is able to read xslx or csv files that contain the information of several phasor measurement units (PMUs) or a data file with a proper read format. Besides this GUI incorporates three well-known methods that stand out to perform ringdown analysis in power systems such as Prony’s method (PM), eigensystem realization algorithm (ERA), and matrix pencil (MP). A straightforward implementation is adopted to capture dynamic parameters by processing single or multiple channels. Numerical and graphical results demonstrate the usability of the GUI, even in real system events1.","PeriodicalId":256954,"journal":{"name":"2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129745023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/SGSMA51733.2022.9805845
J. Follum, J. Eto
Oscillation monitoring and mitigation are important aspects of reliable bulk power system operation. Forced oscillations, which occur when a piece of equipment injects oscillations into the system, can at times be observed across wide areas, making identification of the source challenging. A phasor measurement unit (PMU)-based wide-area monitoring system capable of identifying the region of the grid containing the source of a forced oscillation was recently deployed for testing in the United State’s Eastern Interconnection (EI). The system was designed to operate under real-world constraints, such as PMU data being permanently or temporarily unavailable from some locations. The impact of unavailable data was reflected in a confidence assessment that accompanied oscillation notifications. The metrics used to form this assessment are presented in this paper and validated using thousands of trials from a publicly available test case library of simulated measurements. The results demonstrate that the proposed metrics can help system operators evaluate the veracity of notifications from the source localization system.
{"title":"Confidence Metrics for Regional Forced Oscillation Source Localization","authors":"J. Follum, J. Eto","doi":"10.1109/SGSMA51733.2022.9805845","DOIUrl":"https://doi.org/10.1109/SGSMA51733.2022.9805845","url":null,"abstract":"Oscillation monitoring and mitigation are important aspects of reliable bulk power system operation. Forced oscillations, which occur when a piece of equipment injects oscillations into the system, can at times be observed across wide areas, making identification of the source challenging. A phasor measurement unit (PMU)-based wide-area monitoring system capable of identifying the region of the grid containing the source of a forced oscillation was recently deployed for testing in the United State’s Eastern Interconnection (EI). The system was designed to operate under real-world constraints, such as PMU data being permanently or temporarily unavailable from some locations. The impact of unavailable data was reflected in a confidence assessment that accompanied oscillation notifications. The metrics used to form this assessment are presented in this paper and validated using thousands of trials from a publicly available test case library of simulated measurements. The results demonstrate that the proposed metrics can help system operators evaluate the veracity of notifications from the source localization system.","PeriodicalId":256954,"journal":{"name":"2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121078649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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