[ { "key": "LI57ICHD", "version": 324, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/LI57ICHD", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/LI57ICHD", "type": "text/html" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "creatorSummary": "Chun et al.", "parsedDate": "2024-09-27", "numChildren": 1 }, "data": { "key": "LI57ICHD", "version": 324, "itemType": "journalArticle", "title": "Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer", "creators": [ { "creatorType": "author", "firstName": "Lin", "lastName": "Chun" }, { "creatorType": "author", "firstName": "Denghuan", "lastName": "Wang" }, { "creatorType": "author", "firstName": "Liqiong", "lastName": "He" }, { "creatorType": "author", "firstName": "Donglun", "lastName": "Li" }, { "creatorType": "author", "firstName": "Zhiping", "lastName": "Fu" }, { "creatorType": "author", "firstName": "Song", "lastName": "Xue" }, { "creatorType": "author", "firstName": "Xinliang", "lastName": "Su" }, { "creatorType": "author", "firstName": "Jing", "lastName": "Zhou" } ], "abstractNote": "Prophylactic dissection of paratracheal lymph nodes in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC) remains controversial. This study aims to integrate preoperative and intraoperative variables to compare traditional nomograms and machine learning (ML) models, developing and validating an interpretable predictive model for paratracheal lymph node metastasis (PLNM) in cN0 PTC patients. We retrospectively selected 3213 PTC patients treated at the First Affiliated Hospital of Chongqing Medical University from 2016 to 2020. They were randomly divided into the training and test datasets with a 7:3 ratio. The 533 PTC patients treated at the Guangyuan Central Hospital from 2019 to 2022 were used as an external test sets. We developed and validated nine ML models using 10-fold cross-validation and grid search for hyperparameter tuning. The predictive performance was evaluated using ROC curves, decision curve analysis (DCA), calibration curves, and precision-recall curves. The best model was compared to a traditional logistic regression-based nomogram. The XGBoost model achieved AUC values of 0.935, 0.857, and 0.775 in the training, validation, and test sets, respectively, significantly outperforming the traditional nomogram model with AUCs of 0.85, 0.844, and 0.769, respectively. SHapley Additive exPlanations (SHAP)-based visualization identified the top 10 predictive features of the XGBoost model, and a web-based calculator was created based on these features. ML is a reliable tool for predicting PLNM in cN0 PTC patients. The SHAP method provides valuable insights into the XGBoost model, and the resultant web-based calculator is a clinically useful tool to assist in the surgical planning for paratracheal lymph node dissection.", "publicationTitle": "Scientific Reports", "publisher": "", "place": "", "date": "2024-09-27", "volume": "14", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "22361", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Sci Rep", "DOI": "10.1038/s41598-024-73837-3", "citationKey": "", "url": "https://www.nature.com/articles/s41598-024-73837-3", "accessDate": "2025-02-10T10:47:55Z", "PMID": "", "PMCID": "", "ISSN": "2045-2322", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.nature.com", "callNumber": "", "rights": "2024 The Author(s)", "extra": "Publisher: Nature Publishing Group", "tags": [ { "tag": "Cancer", "type": 1 }, { "tag": "Cancer models", "type": 1 }, { "tag": "Head and neck cancer", "type": 1 }, { "tag": "Surgical oncology", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2025-02-10T10:48:03Z", "dateModified": "2025-02-10T10:48:04Z" } }, { "key": "95JAEMKW", "version": 323, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/95JAEMKW", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/95JAEMKW", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/142085/items/LI57ICHD", "type": "application/json" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. 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Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "MWHWJ5YK", "version": 321, "parentItem": "VR6TZ225", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2025-01-31T12:11:06Z", "url": "https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Krizhevsky et al. - 2012 - ImageNet Classification with Deep Convolutional Ne.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2025-01-31T12:11:06Z", "dateModified": "2025-01-31T12:11:06Z" } }, { "key": "VR6TZ225", "version": 321, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/VR6TZ225", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/VR6TZ225", "type": "text/html" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "creatorSummary": "Krizhevsky et al.", "parsedDate": "2012", "numChildren": 1 }, "data": { "key": "VR6TZ225", "version": 321, "itemType": "conferencePaper", "title": "ImageNet Classification with Deep Convolutional Neural Networks", "creators": [ { "creatorType": "author", "firstName": "Alex", "lastName": "Krizhevsky" }, { "creatorType": "author", "firstName": "Ilya", "lastName": "Sutskever" }, { "creatorType": "author", "firstName": "Geoffrey E", "lastName": "Hinton" } ], "abstractNote": "We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 39.7\\% and 18.9\\% which is considerably better than the previous state-of-the-art results. The neural network, which has 60 million parameters and 500,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and two globally connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of convolutional nets. To reduce overfitting in the globally connected layers we employed a new regularization method that proved to be very effective.", "proceedingsTitle": "Advances in Neural Information Processing Systems", "conferenceName": "", "publisher": "Curran Associates, Inc.", "place": "", "date": "2012", "eventPlace": "", "volume": "25", "issue": "", "numberOfVolumes": "", "pages": "", "series": "", "seriesNumber": "", "DOI": "", "ISBN": "", "citationKey": "", "url": "https://proceedings.neurips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html", "accessDate": "2025-01-31T12:11:02Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Neural Information Processing Systems", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "WV2CV5VC" ], "relations": {}, "dateAdded": "2025-01-31T12:11:02Z", "dateModified": "2025-01-31T12:11:02Z" } }, { "key": "MWEYIV9E", "version": 319, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/MWEYIV9E", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/MWEYIV9E", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/142085/items/I9QKVI7Y", "type": "application/json" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. 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Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "creatorSummary": "Rai et al.", "parsedDate": "2021-03-01", "numChildren": 1 }, "data": { "key": "D67AKZJW", "version": 318, "itemType": "journalArticle", "title": "Fault classification in power system distribution network integrated with distributed generators using CNN", "creators": [ { "creatorType": "author", "firstName": "Praveen", "lastName": "Rai" }, { "creatorType": "author", "firstName": "Narendra D.", "lastName": "Londhe" }, { "creatorType": "author", "firstName": "Ritesh", "lastName": "Raj" } ], "abstractNote": "Fault detection is the critical stage of the relaying system and their successful completion in minimum time is expected for fault clearance. With the increasing usage of distributed generators (DGs) in a distribution network, the conventional relaying methods are becoming inappropriate due to changing fault current levels. This paper presents a deep learning algorithm i.e. Convolutional Neural Network (CNN) customized for fault classification in the distributed networks integrated with DGs. This is first time that CNN has been used for fault detection using raw and sampled-data of three-phase voltage and current signals of various fault classes and no-fault class. The 10-fold cross-validation is used to demonstrate the performance of the proposed model in terms of different metrics such as accuracy, sensitivity, specificity, precision, and F1 score. The proposed model has attended an average 10-fold cross-validation accuracy of 99.52% for all the tested fault cases. This featureless proposed method has been compared with conventional approaches from literature and has shown better performance in terms of accuracy and computation burden. Further, a similar fault study is conducted on a mixed transmission line and distribution network with PV as DG using the proposed method and found performance accuracy of 99.92% and 99.97%, respectively.", "publicationTitle": "Electric Power Systems Research", "publisher": "", "place": "", "date": "2021-03-01", "volume": "192", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "106914", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Electric Power Systems Research", "DOI": "10.1016/j.epsr.2020.106914", "citationKey": "", "url": "https://www.sciencedirect.com/science/article/pii/S0378779620307124", "accessDate": "2024-12-14T08:53:27Z", "PMID": "", "PMCID": "", "ISSN": "0378-7796", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Convolutional Neural Network", "type": 1 }, { "tag": "Deep learning", "type": 1 }, { "tag": "Distributed generators", "type": 1 }, { "tag": "Distribution network", "type": 1 }, { "tag": "Fault classification", "type": 1 }, { "tag": "Power system", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2024-12-14T08:53:27Z", "dateModified": "2024-12-14T08:53:27Z" } }, { "key": "B69N9EHB", "version": 313, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/B69N9EHB", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/B69N9EHB", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/142085/items/I9QKVI7Y", "type": "application/json" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. 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This paper presents a deep learning algorithm i.e. Convolutional Neural Network (CNN) customized for fault classification in the distributed networks integrated with DGs. This is first time that CNN has been used for fault detection using raw and sampled-data of three-phase voltage and current signals of various fault classes and no-fault class. The 10-fold cross-validation is used to demonstrate the performance of the proposed model in terms of different metrics such as accuracy, sensitivity, specificity, precision, and F1 score. The proposed model has attended an average 10-fold cross-validation accuracy of 99.52% for all the tested fault cases. This featureless proposed method has been compared with conventional approaches from literature and has shown better performance in terms of accuracy and computation burden. Further, a similar fault study is conducted on a mixed transmission line and distribution network with PV as DG using the proposed method and found performance accuracy of 99.92% and 99.97%, respectively.", "publicationTitle": "Electric Power Systems Research", "publisher": "", "place": "", "date": "2021-03-01", "volume": "192", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "106914", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Electric Power Systems Research", "DOI": "10.1016/j.epsr.2020.106914", "citationKey": "", "url": "https://www.sciencedirect.com/science/article/pii/S0378779620307124", "accessDate": "2024-12-04T15:15:49Z", "PMID": "", "PMCID": "", "ISSN": "0378-7796", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Convolutional Neural Network", "type": 1 }, { "tag": "Deep learning", "type": 1 }, { "tag": "Distributed generators", "type": 1 }, { "tag": "Distribution network", "type": 1 }, { "tag": "Fault classification", "type": 1 }, { "tag": "Power system", "type": 1 } ], "collections": [ "WV2CV5VC" ], "relations": {}, "dateAdded": "2024-12-04T15:15:49Z", "dateModified": "2024-12-04T15:15:49Z" } }, { "key": "A38XJRSH", "version": 310, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/A38XJRSH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/A38XJRSH", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/142085/items/2JU3UUDR", "type": "application/json" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "A38XJRSH", "version": 310, "parentItem": "2JU3UUDR", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2024-12-04T15:14:50Z", "url": "https://www.mdpi.com/1996-1073/12/1/43/pdf?version=1545648279", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Mei et al. - 2019 - Online Recognition Method for Voltage Sags Based o.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2024-12-04T15:14:50Z", "dateModified": "2024-12-04T15:14:50Z" } }, { "key": "2JU3UUDR", "version": 310, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/2JU3UUDR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/2JU3UUDR", "type": "text/html" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "creatorSummary": "Mei et al.", "parsedDate": "2019-01", "numChildren": 1 }, "data": { "key": "2JU3UUDR", "version": 310, "itemType": "journalArticle", "title": "Online Recognition Method for Voltage Sags Based on a Deep Belief Network", "creators": [ { "creatorType": "author", "firstName": "Fei", "lastName": "Mei" }, { "creatorType": "author", "firstName": "Yong", "lastName": "Ren" }, { "creatorType": "author", "firstName": "Qingliang", "lastName": "Wu" }, { "creatorType": "author", "firstName": "Chenyu", "lastName": "Zhang" }, { "creatorType": "author", "firstName": "Yi", "lastName": "Pan" }, { "creatorType": "author", "firstName": "Haoyuan", "lastName": "Sha" }, { "creatorType": "author", "firstName": "Jianyong", "lastName": "Zheng" } ], "abstractNote": "Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.", "publicationTitle": "Energies", "publisher": "", "place": "", "date": "2019/1", "volume": "12", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "43", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.3390/en12010043", "citationKey": "", "url": "https://www.mdpi.com/1996-1073/12/1/43", "accessDate": "2024-12-04T15:14:47Z", "PMID": "", "PMCID": "", "ISSN": "1996-1073", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.mdpi.com", "callNumber": "", "rights": "http://creativecommons.org/licenses/by/3.0/", "extra": "Number: 1\nPublisher: Multidisciplinary Digital Publishing Institute", "tags": [ { "tag": "deep belief network", "type": 1 }, { "tag": "online recognition", "type": 1 }, { "tag": "voltage sag", "type": 1 } ], "collections": [ "WV2CV5VC" ], "relations": {}, "dateAdded": "2024-12-04T15:14:47Z", "dateModified": "2024-12-04T15:14:47Z" } }, { "key": "YRP2IDIZ", "version": 308, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/YRP2IDIZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/YRP2IDIZ", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/142085/items/R5ZR5Q8Q", "type": "application/json" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. 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In this paper, a novel method based on stacked sparse autoencoder is proposed to predict the post-fault transient stability status of the power system directly after clearing the fault. A dataset is generated off-line to train a stacked sparse autoencoder, and then the trained stacked sparse autoencoder is used in an online application of predicting any transient instability. The stacked sparse autoencoder is fed by the inputs, which are specific points extracted from the fault-on voltage magnitude measurements collected from the phasor measurement units. The effectiveness of the proposed method is demonstrated and compared with the conventional approaches that adopt multilayer perceptrons or post-fault measurements as it is applied to the 127-bus WSCC test system and to the Turkish power system.", "publicationTitle": "Electric Power Systems Research", "publisher": "", "place": "", "date": "2018-11-01", "volume": "164", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "243-252", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Electric Power Systems Research", "DOI": "10.1016/j.epsr.2018.08.009", "citationKey": "", "url": "https://www.sciencedirect.com/science/article/pii/S0378779618302529", "accessDate": "2024-12-04T15:13:34Z", "PMID": "", "PMCID": "", "ISSN": "0378-7796", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Deep learning", "type": 1 }, { "tag": "Phasor measurement units", "type": 1 }, { "tag": "Prediction", "type": 1 }, { "tag": "Sparse autoencoder", "type": 1 }, { "tag": "Transient stability", "type": 1 }, { "tag": "Wide area measurements", "type": 1 } ], "collections": [ "WV2CV5VC" ], "relations": {}, "dateAdded": "2024-12-04T15:13:34Z", "dateModified": "2024-12-04T15:13:34Z" } }, { "key": "3GUQRDJ2", "version": 305, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/3GUQRDJ2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/3GUQRDJ2", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/142085/items/E5NE8Z4W", "type": "application/json" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. 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Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "4CAZA5XH", "version": 304, "parentItem": "E5NE8Z4W", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2024-12-04T14:22:35Z", "url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/er.5331", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Ozcanli et al. - 2020 - Deep learning methods and applications for electri.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2024-12-04T14:22:35Z", "dateModified": "2024-12-04T14:22:35Z" } }, { "key": "E5NE8Z4W", "version": 303, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/E5NE8Z4W", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/E5NE8Z4W", "type": "text/html" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "creatorSummary": "Ozcanli et al.", "parsedDate": "2020", "numChildren": 2 }, "data": { "key": "E5NE8Z4W", "version": 303, "itemType": "journalArticle", "title": "Deep learning methods and applications for electrical power systems: A comprehensive review", "creators": [ { "creatorType": "author", "firstName": "Asiye K.", "lastName": "Ozcanli" }, { "creatorType": "author", "firstName": "Fatma", "lastName": "Yaprakdal" }, { "creatorType": "author", "firstName": "Mustafa", "lastName": "Baysal" } ], "abstractNote": "Over the past decades, electric power systems (EPSs) have undergone an evolution from an ordinary bulk structure to intelligent flexible systems by way of advanced electronics and control technologies. Moreover, EPS has become a more complex, unstable and nonlinear structure with the integration of distributed energy resources in comparison with traditional power grids. Unlike classical approaches, physical methods, statistical approaches and computer calculation techniques are commonly used to solve EPS problems. Artificial intelligent (AI) techniques have especially been used recently in many fields. Deep neural networks have become increasingly attractive as an AI approach due to their robustness and flexibility in handling nonlinear complex relationships on large scale data sets. Major deep learning concepts addressing some problems in EPS have been reviewed in the present study by a comprehensive literature survey. The practices of deep learning and its combinations are well organized with up-to-date references in various fields such as load forecasting, wind and solar power forecasting, power quality disturbances detection and classifications, fault detection power system equipment, energy security, energy management and energy optimization. Furthermore, the difficulties encountered in implementation and the future trends of this method in EPS are discussed subject to the findings of current studies. It concludes that deep learning has a huge application potential on EPS, due to smart technologies integration that will increase considerably in the future.", "publicationTitle": "International Journal of Energy Research", "publisher": "", "place": "", "date": "2020", "volume": "44", "issue": "9", "section": "", "partNumber": "", "partTitle": "", "pages": "7136-7157", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1002/er.5331", "citationKey": "", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/er.5331", "accessDate": "2024-12-04T14:22:30Z", "PMID": "", "PMCID": "", "ISSN": "1099-114X", "archive": "", "archiveLocation": "", "shortTitle": "Deep learning methods and applications for electrical power systems", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2020 John Wiley & Sons Ltd", "extra": "_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.5331", "tags": [ { "tag": "CNN", "type": 1 }, { "tag": "DBM", "type": 1 }, { "tag": "RNN", "type": 1 }, { "tag": "SAE", "type": 1 }, { "tag": "deep learning", "type": 1 }, { "tag": "forecasting", "type": 1 }, { "tag": "power systems", "type": 1 }, { "tag": "smart grid", "type": 1 } ], "collections": [ "WV2CV5VC" ], "relations": {}, "dateAdded": "2024-12-04T14:22:30Z", "dateModified": "2024-12-04T14:22:30Z" } }, { "key": "LDFHHZHX", "version": 301, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/LDFHHZHX", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/LDFHHZHX", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/142085/items/X768PBWX", "type": "application/json" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. 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Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "DRXLHPM9", "version": 301, "parentItem": "X768PBWX", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2024-12-04T14:17:35Z", "url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=9763506&ref=", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Yoon y Yoon - 2022 - Deep Learning-Based Method for the Robust and Effi.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2024-12-04T14:17:35Z", "dateModified": "2024-12-04T14:17:35Z" } }, { "key": "X768PBWX", "version": 300, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/X768PBWX", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/X768PBWX", "type": "text/html" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "creatorSummary": "Yoon and Yoon", "parsedDate": "2022", "numChildren": 2 }, "data": { "key": "X768PBWX", "version": 300, "itemType": "journalArticle", "title": "Deep Learning-Based Method for the Robust and Efficient Fault Diagnosis in the Electric Power System", "creators": [ { "creatorType": "author", "firstName": "Dong-Hee", "lastName": "Yoon" }, { "creatorType": "author", "firstName": "Jonghee", "lastName": "Yoon" } ], "abstractNote": "The robust and efficient diagnosis of power quality disturbances (PQDs) in electric power systems (EPSs) is one of the most important steps to protect a power system with minimal damage. However, the conventional fault detection methods used in the EPS mainly rely on heavy mathematical calculations, resulting in delayed actions against PQDs. To overcome these limitations, deep learning has been recently proposed to diagnose PQDs in the EPS, which allows the extraction of features from a huge amount of data to delineate subtle differences in electrical waveforms under faulty conditions. In this study, a deep learning-based diagnostic method for PQDs was proposed by exploiting a convolutional neural network (CNN) and simulated realistic three-phase voltage and current waveforms obtained from the PSCAD/EMTDC software. Specifically, PQDs related to various faults in EPSs were assessed to demonstrate the applicability of the deep-learning method as a fault diagnostic method. The proposed CNN model, trained by end-to-end learning and supervised learning approaches, successfully classified the type and location of the faults. Moreover, we found that simulated data obtained at the sampling rate of 50 Hz also accurately diagnosed the faults with an accuracy of over 99%; therefore, the proposed method could be a potential diagnostic tool in practice.", "publicationTitle": "IEEE Access", "publisher": "", "place": "", "date": "2022", "volume": "10", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "44660-44668", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/ACCESS.2022.3170685", "citationKey": "", "url": "https://ieeexplore.ieee.org/abstract/document/9763506", "accessDate": "2024-12-04T14:17:29Z", "PMID": "", "PMCID": "", "ISSN": "2169-3536", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "Conference Name: IEEE Access", "tags": [ { "tag": "Artificial intelligence", "type": 1 }, { "tag": "Computational modeling", "type": 1 }, { "tag": "Data models", "type": 1 }, { "tag": "Feature extraction", "type": 1 }, { "tag": "Forecasting", "type": 1 }, { "tag": "Load modeling", "type": 1 }, { "tag": "Power systems", "type": 1 }, { "tag": "Training", "type": 1 }, { "tag": "convolutional neural network", "type": 1 }, { "tag": "deep learning", "type": 1 }, { "tag": "electric power system", "type": 1 }, { "tag": "fault detection", "type": 1 }, { "tag": "power quality disturbance", "type": 1 } ], "collections": [ "WV2CV5VC" ], "relations": {}, "dateAdded": "2024-12-04T14:17:29Z", "dateModified": "2024-12-04T14:17:29Z" } }, { "key": "U5EDJNZ9", "version": 298, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/U5EDJNZ9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/U5EDJNZ9", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/142085/items/726CJDDJ", "type": "application/json" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "U5EDJNZ9", "version": 298, "parentItem": "726CJDDJ", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2024-12-04T14:15:07Z", "url": "https://www.mdpi.com/1996-1073/14/21/7017/pdf?version=1635324996", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Yang et al. - 2021 - Fault Diagnosis of Electric Motors Using Deep Lear.pdf", "md5": null, "mtime": null, "tags": [], "relations": {}, "dateAdded": "2024-12-04T14:15:07Z", "dateModified": "2024-12-04T14:15:07Z" } }, { "key": "726CJDDJ", "version": 298, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/726CJDDJ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/726CJDDJ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "creatorSummary": "Yang et al.", "parsedDate": "2021-01", "numChildren": 1 }, "data": { "key": "726CJDDJ", "version": 298, "itemType": "journalArticle", "title": "Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review", "creators": [ { "creatorType": "author", "firstName": "Yuanyuan", "lastName": "Yang" }, { "creatorType": "author", "firstName": "Md Muhie Menul", "lastName": "Haque" }, { "creatorType": "author", "firstName": "Dongling", "lastName": "Bai" }, { "creatorType": "author", "firstName": "Wei", "lastName": "Tang" } ], "abstractNote": "Electric motors are used extensively in numerous industries, and their failure can result not only in machine damage but also a slew of other issues, such as financial loss, injuries, etc. As a result, there is a significant scope to use robust fault diagnosis technology. In recent years, interesting research results on fault diagnosis for electric motors have been documented. Deep learning in the fault detection of electric equipment has shown comparatively better results than traditional approaches because of its more powerful and sophisticated feature extraction capabilities. This paper covers four traditional types of deep learning models: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN), and highlights their use in detecting faults of electric motors. Finally, the issues and obstacles that deep learning encounters in the fault detection mechanism as well as the prospects are discussed and summarized.", "publicationTitle": "Energies", "publisher": "", "place": "", "date": "2021/1", "volume": "14", "issue": "21", "section": "", "partNumber": "", "partTitle": "", "pages": "7017", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.3390/en14217017", "citationKey": "", "url": "https://www.mdpi.com/1996-1073/14/21/7017", "accessDate": "2024-12-04T14:15:05Z", "PMID": "", "PMCID": "", "ISSN": "1996-1073", "archive": "", "archiveLocation": "", "shortTitle": "Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application", "language": "en", "libraryCatalog": "www.mdpi.com", "callNumber": "", "rights": "http://creativecommons.org/licenses/by/3.0/", "extra": "Number: 21\nPublisher: Multidisciplinary Digital Publishing Institute", "tags": [ { "tag": "autoencoders", "type": 1 }, { "tag": "convolutional neural networks", "type": 1 }, { "tag": "deep belief network", "type": 1 }, { "tag": "deep learning", "type": 1 }, { "tag": "electric motors", "type": 1 }, { "tag": "fault diagnosis", "type": 1 }, { "tag": "recurrent neural network", "type": 1 } ], "collections": [ "WV2CV5VC" ], "relations": {}, "dateAdded": "2024-12-04T14:15:05Z", "dateModified": "2024-12-04T14:15:05Z" } }, { "key": "NCHFWZRZ", "version": 296, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/NCHFWZRZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/NCHFWZRZ", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/142085/items/T6K9YZ4V", "type": "application/json" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. 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Julio Gutiérrez", "links": { "alternate": { "href": "https://www.zotero.org/jtpguruj", "type": "text/html" } } }, "creatorSummary": "Ince et al.", "parsedDate": "2016-11", "numChildren": 2 }, "data": { "key": "T6K9YZ4V", "version": 295, "itemType": "journalArticle", "title": "Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks", "creators": [ { "creatorType": "author", "firstName": "Turker", "lastName": "Ince" }, { "creatorType": "author", "firstName": "Serkan", "lastName": "Kiranyaz" }, { "creatorType": "author", "firstName": "Levent", "lastName": "Eren" }, { "creatorType": "author", "firstName": "Murat", "lastName": "Askar" }, { "creatorType": "author", "firstName": "Moncef", "lastName": "Gabbouj" } ], "abstractNote": "Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.", "publicationTitle": "IEEE Transactions on Industrial Electronics", "publisher": "", "place": "", "date": "2016-11", "volume": "63", "issue": "11", "section": "", "partNumber": "", "partTitle": "", "pages": "7067-7075", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/TIE.2016.2582729", "citationKey": "", "url": "https://ieeexplore.ieee.org/abstract/document/7501527", "accessDate": "2024-12-04T14:05:48Z", "PMID": "", "PMCID": "", "ISSN": "1557-9948", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "Conference Name: IEEE Transactions on Industrial Electronics", "tags": [ { "tag": "Convolution", "type": 1 }, { "tag": "Convolutional neural networks (CNNs)", "type": 1 }, { "tag": "Fault detection", "type": 1 }, { "tag": "Feature extraction", "type": 1 }, { "tag": "Induction motors", "type": 1 }, { "tag": "Mathematical model", "type": 1 }, { "tag": "Neural networks", "type": 1 }, { "tag": "Real-time systems", "type": 1 }, { "tag": "motor current signature analysis (MCSA)", "type": 1 } ], "collections": [ "WV2CV5VC" ], "relations": {}, "dateAdded": "2024-12-04T14:05:48Z", "dateModified": "2024-12-04T14:05:48Z" } }, { "key": "QNNHANG6", "version": 293, "library": { "type": "group", "id": 142085, "name": "Análisis de faltas", "links": { "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/142085/items/QNNHANG6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/anlisis_de_faltas/items/QNNHANG6", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/142085/items/S8CE8P5F", "type": "application/json" } }, "meta": { "createdByUser": { "id": 274627, "username": "jtpguruj", "name": "J. 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