Pub Date : 2022-09-12DOI: 10.1109/OJIM.2022.3205680
Jie Lin;Song Chen;Enping Lin;Yu Yang
Deep learning neural network serves as a powerful tool for visual anomaly detection (AD) and fault diagnosis, attributed to its strong abstractive interpretation ability in the representation domain. The deep features from neural networks that are pretrained on the ImageNet classification task have been proved to be useful for AD based on Gaussian discriminant analysis. However, with the ever-increasing complexity of deep learning neural networks, the set of deep features becomes massive where redundancy appears to be inevitable. The redundant features increase computational cost and degrade the performance of the AD method. In this article, we discuss the deep feature selection for the AD task and show how to reduce the redundancy in the representation domain. We propose a horizontal selection (dimensional reduction) method of features with subspace decomposition and a vertical selection to identify the most effective network layer for AD and fault diagnosis. We test the proposed method on two public datasets, one for AD task and the other for fault diagnosis of bearings. We show the significance of different network layers and feature subspaces on AD tasks and prove the effectiveness of the feature selection strategy.
{"title":"Deep Feature Selection for Anomaly Detection Based on Pretrained Network and Gaussian Discriminative Analysis","authors":"Jie Lin;Song Chen;Enping Lin;Yu Yang","doi":"10.1109/OJIM.2022.3205680","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3205680","url":null,"abstract":"Deep learning neural network serves as a powerful tool for visual anomaly detection (AD) and fault diagnosis, attributed to its strong abstractive interpretation ability in the representation domain. The deep features from neural networks that are pretrained on the ImageNet classification task have been proved to be useful for AD based on Gaussian discriminant analysis. However, with the ever-increasing complexity of deep learning neural networks, the set of deep features becomes massive where redundancy appears to be inevitable. The redundant features increase computational cost and degrade the performance of the AD method. In this article, we discuss the deep feature selection for the AD task and show how to reduce the redundancy in the representation domain. We propose a horizontal selection (dimensional reduction) method of features with subspace decomposition and a vertical selection to identify the most effective network layer for AD and fault diagnosis. We test the proposed method on two public datasets, one for AD task and the other for fault diagnosis of bearings. We show the significance of different network layers and feature subspaces on AD tasks and prove the effectiveness of the feature selection strategy.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09887794.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50426832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-12DOI: 10.1109/OJIM.2022.3205649
Ming Zhang;Duo Wang;Nasser Amaitik;Yuchun Xu
With the rapid development of information and sensor technology, the data-driven remaining useful lifetime (RUL) prediction methods have been acquired a successful development. Nowadays, the data-driven RUL methods are focused on estimating the RUL value. However, it is more important to quantify the uncertainty associated with the RUL value. This is because increasingly complex industrial systems would arise various sources of uncertainty. This article proposes a novel distributional RUL prediction method, which aims at quantifying the RUL uncertainty by identifying the confidence interval with the cumulative distribution function (CDF). The proposed learning method has been built based on quantile regression and implemented from a distributional perspective under the deep neural network framework. The results of the run-to-failure degradation experiments of rolling bearing demonstrate the effectiveness and good performance of the proposed method compared to other state-of-the-art methods. The visualization results obtained by $t{text{-}}SNE$ technology have been investigated to further verify the effectiveness and generalization ability of the proposed method.
{"title":"A Distributional Perspective on Remaining Useful Life Prediction With Deep Learning and Quantile Regression","authors":"Ming Zhang;Duo Wang;Nasser Amaitik;Yuchun Xu","doi":"10.1109/OJIM.2022.3205649","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3205649","url":null,"abstract":"With the rapid development of information and sensor technology, the data-driven remaining useful lifetime (RUL) prediction methods have been acquired a successful development. Nowadays, the data-driven RUL methods are focused on estimating the RUL value. However, it is more important to quantify the uncertainty associated with the RUL value. This is because increasingly complex industrial systems would arise various sources of uncertainty. This article proposes a novel distributional RUL prediction method, which aims at quantifying the RUL uncertainty by identifying the confidence interval with the cumulative distribution function (CDF). The proposed learning method has been built based on quantile regression and implemented from a distributional perspective under the deep neural network framework. The results of the run-to-failure degradation experiments of rolling bearing demonstrate the effectiveness and good performance of the proposed method compared to other state-of-the-art methods. The visualization results obtained by \u0000<inline-formula> <tex-math>$t{text{-}}SNE$ </tex-math></inline-formula>\u0000 technology have been investigated to further verify the effectiveness and generalization ability of the proposed method.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09887803.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50426833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-12DOI: 10.1109/OJIM.2022.3205672
Teimour Heidari;Seyed Hossein Hesamedin Sadeghi
This article proposes an efficient phenomenological inversion method to determine the depth profile of a surface-breaking crack in a metal from the output signal of an alternating current field measurement (ACFM) probe. The proposed method utilizes a conjugate gradient algorithm to minimize an objective function, representing the difference between the probe predicted and actual signals in an iterative manner. The objective function is derived explicitly in terms of crack depth variables by considering a polynomial function for the field distribution in the depth direction and applying appropriate Green’s functions. This approach enhances the accuracy and computational efficiency of the inversion process, regardless of the choice of the initial crack depth profile or the presence of noise in the measurement system. The validity and efficiency of the proposed method are demonstrated by comparing the reconstructed depth profiles of several simulated and machine-made cracks with their actual data, and those obtained using the conventional phenomenological approach based on an efficient stochastic optimization scheme along with a fast pseudo-analytic ACFM probe output simulator.
{"title":"An Efficient Phenomenological Inversion Method for Reconstruction of Crack Depth Profile in a Metal From ACFM Probe Output Signals","authors":"Teimour Heidari;Seyed Hossein Hesamedin Sadeghi","doi":"10.1109/OJIM.2022.3205672","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3205672","url":null,"abstract":"This article proposes an efficient phenomenological inversion method to determine the depth profile of a surface-breaking crack in a metal from the output signal of an alternating current field measurement (ACFM) probe. The proposed method utilizes a conjugate gradient algorithm to minimize an objective function, representing the difference between the probe predicted and actual signals in an iterative manner. The objective function is derived explicitly in terms of crack depth variables by considering a polynomial function for the field distribution in the depth direction and applying appropriate Green’s functions. This approach enhances the accuracy and computational efficiency of the inversion process, regardless of the choice of the initial crack depth profile or the presence of noise in the measurement system. The validity and efficiency of the proposed method are demonstrated by comparing the reconstructed depth profiles of several simulated and machine-made cracks with their actual data, and those obtained using the conventional phenomenological approach based on an efficient stochastic optimization scheme along with a fast pseudo-analytic ACFM probe output simulator.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09887788.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67899534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-05DOI: 10.1109/OJIM.2022.3203449
Paolo Attilio Pegoraro;Carlo Sitzia;Antonio Vincenzo Solinas;Sara Sulis
A primary requirement for the transmission system operator is an accurate knowledge of grid parameters. Moreover, the availability of effective and accurate monitoring tools allows the proper operation of power transmission grids. However, in spite of the now widespread possibility of having monitoring systems based on synchronized measurements, the monitoring applications can be affected not only by the inevitable uncertainty sources but also by the simplified or incomplete modeling of the network components. For this reason, the impact on power system monitoring and control applications of tap-changing transformer models is a key point. In this scenario, this article presents a method to estimate simultaneously line parameters, tap changer ratios, and systematic measurement errors associated with the instrument transformers. The method exploits a flexible model of the tap-changing transformer based on a parameter representing the ratio between the two winding impedances of the transformer. The proposal is based also on the suitable modeling of the measurement chain and on the constraints introduced by the equations of involved transmissions lines and transformers. The validation has been carried out by means of tests performed on the IEEE 14 Bus Test Case.
{"title":"Estimation of Line Parameters, Tap Changer Ratios, and Systematic Measurement Errors Based on Synchronized Measurements and a General Model of Tap-Changing Transformers","authors":"Paolo Attilio Pegoraro;Carlo Sitzia;Antonio Vincenzo Solinas;Sara Sulis","doi":"10.1109/OJIM.2022.3203449","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3203449","url":null,"abstract":"A primary requirement for the transmission system operator is an accurate knowledge of grid parameters. Moreover, the availability of effective and accurate monitoring tools allows the proper operation of power transmission grids. However, in spite of the now widespread possibility of having monitoring systems based on synchronized measurements, the monitoring applications can be affected not only by the inevitable uncertainty sources but also by the simplified or incomplete modeling of the network components. For this reason, the impact on power system monitoring and control applications of tap-changing transformer models is a key point. In this scenario, this article presents a method to estimate simultaneously line parameters, tap changer ratios, and systematic measurement errors associated with the instrument transformers. The method exploits a flexible model of the tap-changing transformer based on a parameter representing the ratio between the two winding impedances of the transformer. The proposal is based also on the suitable modeling of the measurement chain and on the constraints introduced by the equations of involved transmissions lines and transformers. The validation has been carried out by means of tests performed on the IEEE 14 Bus Test Case.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09878122.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68078013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/OJIM.2022.3203452
Nicolle Vigil;Behrouz Movahhed Nouri;Henrique C. Fernandes;Clemente Ibarra-Castanedo;Xavier P. V. Maldague;Bardia Yousefi
Thermographic has proven to be effective for the early detection of breast cancer and with clinical breast examination (CBE). There are many matrix factorization methods developed for computational thermography that can be used to extract thermal variations across the acquisition time. These methods are often used to summarize thermographic sequences and simultaneously highlight predominant thermal patterns. Finding a single predominant infrared image capturing the prevalent patterns of changes remains a challenging task in the field. This study presents the applications of convex factor analysis combined with the bell-curve membership function embedding approach to tackle this task and generate one image to represent the entire sequence. This low-dimensional (LD) representation of a thermal sequence was then used to extract thermomics and train tuned hyperparameters random forest model for early breast cancer diagnosis. A comparative analysis of different embedding methods and factorization approaches is also provided. The results of the proposed method combining clinical information, and demographics yield 78.9% (75.7% and 85.9%), while the convex-nonnegative matrix factorization (NMF) alone gave 76.9% (73.7% and 86.1%). The result of the proposed method suggests that the embedding can help preserve important thermal patterns, which significantly aid CBE and early detection of breast cancer.
{"title":"Convex Factorization Embedding Thermography for Breast Cancer Diagnostic","authors":"Nicolle Vigil;Behrouz Movahhed Nouri;Henrique C. Fernandes;Clemente Ibarra-Castanedo;Xavier P. V. Maldague;Bardia Yousefi","doi":"10.1109/OJIM.2022.3203452","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3203452","url":null,"abstract":"Thermographic has proven to be effective for the early detection of breast cancer and with clinical breast examination (CBE). There are many matrix factorization methods developed for computational thermography that can be used to extract thermal variations across the acquisition time. These methods are often used to summarize thermographic sequences and simultaneously highlight predominant thermal patterns. Finding a single predominant infrared image capturing the prevalent patterns of changes remains a challenging task in the field. This study presents the applications of convex factor analysis combined with the bell-curve membership function embedding approach to tackle this task and generate one image to represent the entire sequence. This low-dimensional (LD) representation of a thermal sequence was then used to extract thermomics and train tuned hyperparameters random forest model for early breast cancer diagnosis. A comparative analysis of different embedding methods and factorization approaches is also provided. The results of the proposed method combining clinical information, and demographics yield 78.9% (75.7% and 85.9%), while the convex-nonnegative matrix factorization (NMF) alone gave 76.9% (73.7% and 86.1%). The result of the proposed method suggests that the embedding can help preserve important thermal patterns, which significantly aid CBE and early detection of breast cancer.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09873947.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67900151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-01DOI: 10.1109/OJIM.2022.3203444
Daisy H. Green;Peter A. Lindahl;Steven B. Leeb
Electromechanical systems experience both gradual and sudden fault conditions. Power monitoring provides a valuable approach for detecting faults, essentially turning a machine into its own sensor for observing developing and abrupt failures. Machines can be monitored individually or nonintrusively (as a collection of loads) and signal processing can tease out relevant indicators of operational status and health. Load identification and diagnostics with aggregate electrical monitoring rely on the correct physical interpretation of measurements. Specifically, ties between the observed measurements and the actual physical task performed by a load ensure the relevance of a measurement, or a feature space derived from the measurement, for reliable identification and diagnostics. This article examines three-phase mathematical relationships for different load configurations, specifically with an eye toward selecting a feature space useful for automated diagnostics. The utility of these three-phase measurement representations is demonstrated with experimental data from several microgrid systems.
{"title":"Three-Phase Electrical Measurement Representations for Nonintrusive Load Diagnostics","authors":"Daisy H. Green;Peter A. Lindahl;Steven B. Leeb","doi":"10.1109/OJIM.2022.3203444","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3203444","url":null,"abstract":"Electromechanical systems experience both gradual and sudden fault conditions. Power monitoring provides a valuable approach for detecting faults, essentially turning a machine into its own sensor for observing developing and abrupt failures. Machines can be monitored individually or nonintrusively (as a collection of loads) and signal processing can tease out relevant indicators of operational status and health. Load identification and diagnostics with aggregate electrical monitoring rely on the correct physical interpretation of measurements. Specifically, ties between the observed measurements and the actual physical task performed by a load ensure the relevance of a measurement, or a feature space derived from the measurement, for reliable identification and diagnostics. This article examines three-phase mathematical relationships for different load configurations, specifically with an eye toward selecting a feature space useful for automated diagnostics. The utility of these three-phase measurement representations is demonstrated with experimental data from several microgrid systems.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09873924.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50426411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-30DOI: 10.1109/OJIM.2022.3202555
Anand Shankar;Samarendra Dandapat;Shovan Barma
Discrimination of types of seizure using the Electroencephalogram (EEG) signal has always been a challenging task due to minuscule differences among different types of seizures. In this regard, deep learning (DL) which has already evidenced notable performance in image recognition could be suitable. However, a few attempts have been made so far in this regard mainly by constructing 2D input images for DL from 1D EEG signals directly using various techniques. Besides, the quality of the generated images has not been verified. Therefore, in this work, 2D images for the DL pipeline have been generated from brain rhythms, which already displayed remarkable performance in analyzing various brain activities. For this purpose, the Markov transition field transformation technique has been employed for 2D image construction by preserving statistical dynamics characteristics of EEG signals, which are very important during the discrimination of different types of seizures. And, a convolution neural network (CNN) has been used for classification. Further, the quality of the 2D image along with appropriate brain rhythms have also been investigated. For experimental validation, EEG recordings of six different types of seizure that are provided by the Temple University EEG dataset (TUH v1.5.2) has been taken into account. The proposed method has achieved the highest classification accuracy and weighted ${F}1$ -score up to 91.1% and 91.0% respectively. Further analysis shows that higher image resolution can provide the best classification accuracy. In addition, the $delta $ rhythm has been found the most suitable in seizure type classification. In a comparative study, the proposed idea demonstrated its superiority by displaying the uppermost classification performance.
{"title":"Discrimination of Types of Seizure Using Brain Rhythms Based on Markov Transition Field and Deep Learning","authors":"Anand Shankar;Samarendra Dandapat;Shovan Barma","doi":"10.1109/OJIM.2022.3202555","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3202555","url":null,"abstract":"Discrimination of types of seizure using the Electroencephalogram (EEG) signal has always been a challenging task due to minuscule differences among different types of seizures. In this regard, deep learning (DL) which has already evidenced notable performance in image recognition could be suitable. However, a few attempts have been made so far in this regard mainly by constructing 2D input images for DL from 1D EEG signals directly using various techniques. Besides, the quality of the generated images has not been verified. Therefore, in this work, 2D images for the DL pipeline have been generated from brain rhythms, which already displayed remarkable performance in analyzing various brain activities. For this purpose, the Markov transition field transformation technique has been employed for 2D image construction by preserving statistical dynamics characteristics of EEG signals, which are very important during the discrimination of different types of seizures. And, a convolution neural network (CNN) has been used for classification. Further, the quality of the 2D image along with appropriate brain rhythms have also been investigated. For experimental validation, EEG recordings of six different types of seizure that are provided by the Temple University EEG dataset (TUH v1.5.2) has been taken into account. The proposed method has achieved the highest classification accuracy and weighted \u0000<inline-formula> <tex-math>${F}1$ </tex-math></inline-formula>\u0000-score up to 91.1% and 91.0% respectively. Further analysis shows that higher image resolution can provide the best classification accuracy. In addition, the \u0000<inline-formula> <tex-math>$delta $ </tex-math></inline-formula>\u0000 rhythm has been found the most suitable in seizure type classification. In a comparative study, the proposed idea demonstrated its superiority by displaying the uppermost classification performance.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09870676.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67900148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-26DOI: 10.1109/OJIM.2022.3201933
Weiping Guo;Hui Wang;Zhiwei Han;Junping Zhong;Zhigang Liu
The catenary system is essential for ensuring the stable energy transmission of trains in high-speed railways. The non-contact catenary detection is a promising monitoring method, where the catenary image is captured by an industrial camera mounted on the inspection vehicle. The image quality is susceptible to the limitations of the environment and equipment, which adversely affects the catenary location and detection accuracy. In this paper, we propose an unsupervised learning-based catenary image enhancement method for improving localization accuracy. First, the enhancement model is optimized to enhance the catenary image quality, making it sharper and more conducive to detection. Subsequently, an advanced small target location approach, called TPH-YOLOv5, is used to locate the catenary components. Finally, we compared the localization performance of the enhanced image with the low-light image. The experiment results show that the proposed method can effectively enhance the quality of low-light catenary images and improve the positioning accuracy.
{"title":"A Novel Low-Light Catenary Image Enhancement Approach for CSCs Detection in High-Speed Railways","authors":"Weiping Guo;Hui Wang;Zhiwei Han;Junping Zhong;Zhigang Liu","doi":"10.1109/OJIM.2022.3201933","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3201933","url":null,"abstract":"The catenary system is essential for ensuring the stable energy transmission of trains in high-speed railways. The non-contact catenary detection is a promising monitoring method, where the catenary image is captured by an industrial camera mounted on the inspection vehicle. The image quality is susceptible to the limitations of the environment and equipment, which adversely affects the catenary location and detection accuracy. In this paper, we propose an unsupervised learning-based catenary image enhancement method for improving localization accuracy. First, the enhancement model is optimized to enhance the catenary image quality, making it sharper and more conducive to detection. Subsequently, an advanced small target location approach, called TPH-YOLOv5, is used to locate the catenary components. Finally, we compared the localization performance of the enhanced image with the low-light image. The experiment results show that the proposed method can effectively enhance the quality of low-light catenary images and improve the positioning accuracy.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09868101.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50426827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-16DOI: 10.1109/OJIM.2022.3198473
Gabriella Crotti;Giovanni D’Avanzo;Carmine Landi;Palma Sara Letizia;Mario Luiso
The measurement of Power Quality (PQ), generally performed at Low Voltage (LV) level, is gaining more and more importance also at the Medium Voltage (MV) level, due to the increasing presence of switching power converters (both loads or generators) directly connected to MV grids. In this case, the use of Voltage Transformers (VTs) is unavoidable to scale voltage down to amplitudes compatible with the input ranges of PQ instruments. However, the current absence of an international standard dealing with VTs used for PQ measurements leaves the manufacturers and the users in a situation of complete uncertainty, since different products can have performance specifications tested and stated in completely different ways. This paper aims at defining an integrated approach for the evaluation of VTs accuracy used for PQ measurements, focusing on harmonics and interharmonics. The paper provides experimental results of the tests performed on a MV inductive VT according to the proposed procedure. As a result, it is demonstrated that VT accuracy in the measurement of a specific phenomenon should be evaluated with complex waveforms including the contemporary presence of different PQ phenomena.
{"title":"Evaluation of Voltage Transformers’ Accuracy in Harmonic and Interharmonic Measurement","authors":"Gabriella Crotti;Giovanni D’Avanzo;Carmine Landi;Palma Sara Letizia;Mario Luiso","doi":"10.1109/OJIM.2022.3198473","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3198473","url":null,"abstract":"The measurement of Power Quality (PQ), generally performed at Low Voltage (LV) level, is gaining more and more importance also at the Medium Voltage (MV) level, due to the increasing presence of switching power converters (both loads or generators) directly connected to MV grids. In this case, the use of Voltage Transformers (VTs) is unavoidable to scale voltage down to amplitudes compatible with the input ranges of PQ instruments. However, the current absence of an international standard dealing with VTs used for PQ measurements leaves the manufacturers and the users in a situation of complete uncertainty, since different products can have performance specifications tested and stated in completely different ways. This paper aims at defining an integrated approach for the evaluation of VTs accuracy used for PQ measurements, focusing on harmonics and interharmonics. The paper provides experimental results of the tests performed on a MV inductive VT according to the proposed procedure. As a result, it is demonstrated that VT accuracy in the measurement of a specific phenomenon should be evaluated with complex waveforms including the contemporary presence of different PQ phenomena.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2022-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09857960.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68078014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-15DOI: 10.1109/OJIM.2022.3198470
Marco Pau;Ferdinanda Ponci;Antonello Monti;Carlo Muscas;Paolo Attilio Pegoraro
The real-time monitoring of electric distribution grids via state estimation is a fundamental requirement to deploy smart automation and control in the distribution system. Due to the large size of distribution networks and the poor coverage of measurement instrumentation on the field, designing fast state estimation algorithms and achieving accurate results are two major challenges associated to distribution system state estimation. In this paper, an efficient and accurate solution for performing state estimation in multi-feeder radial distribution grids is presented. The proposed algorithm is based on a two-step approach. In the first step, state estimation is performed in parallel on the different feeders suitably processing the available measurements and pseudo-measurements and taking into account their uncertainty characteristics. In the second step, the results on each feeder are post-processed to refine the estimations and to improve the accuracy performance. To this purpose, the second step considers how measurement uncertainties propagate towards the final estimates and how measurements shared among the feeders could adversely affect the final estimation. Performed tests show that the conceived design leads to accuracy performance very close to those achievable by running state estimation on the full grid. At the same time, the parallelization of the estimation process on the different feeders allows decentralizing the state estimation problem, with the associated benefits in terms of computation time and distribution of the communication and storage requirements.
{"title":"Distributed State Estimation for Multi-Feeder Distribution Grids","authors":"Marco Pau;Ferdinanda Ponci;Antonello Monti;Carlo Muscas;Paolo Attilio Pegoraro","doi":"10.1109/OJIM.2022.3198470","DOIUrl":"https://doi.org/10.1109/OJIM.2022.3198470","url":null,"abstract":"The real-time monitoring of electric distribution grids via state estimation is a fundamental requirement to deploy smart automation and control in the distribution system. Due to the large size of distribution networks and the poor coverage of measurement instrumentation on the field, designing fast state estimation algorithms and achieving accurate results are two major challenges associated to distribution system state estimation. In this paper, an efficient and accurate solution for performing state estimation in multi-feeder radial distribution grids is presented. The proposed algorithm is based on a two-step approach. In the first step, state estimation is performed in parallel on the different feeders suitably processing the available measurements and pseudo-measurements and taking into account their uncertainty characteristics. In the second step, the results on each feeder are post-processed to refine the estimations and to improve the accuracy performance. To this purpose, the second step considers how measurement uncertainties propagate towards the final estimates and how measurements shared among the feeders could adversely affect the final estimation. Performed tests show that the conceived design leads to accuracy performance very close to those achievable by running state estimation on the full grid. At the same time, the parallelization of the estimation process on the different feeders allows decentralizing the state estimation problem, with the associated benefits in terms of computation time and distribution of the communication and storage requirements.","PeriodicalId":100630,"journal":{"name":"IEEE Open Journal of Instrumentation and Measurement","volume":"1 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9552935/9687502/09855829.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67899444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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