{"id":1528,"date":"2022-03-16T14:53:13","date_gmt":"2022-03-16T14:53:13","guid":{"rendered":"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/?page_id=1528"},"modified":"2022-04-22T17:15:03","modified_gmt":"2022-04-22T16:15:03","slug":"publications-3","status":"publish","type":"page","link":"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/?page_id=1528","title":{"rendered":"Conference paper, published on 2021.04.19"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1528\" class=\"elementor elementor-1528\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-96610ac elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"96610ac\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-fb909a8\" data-id=\"fb909a8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c417315 elementor-widget elementor-widget-heading\" data-id=\"c417315\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.9.0 - 06-12-2022 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Publication: A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4edf581 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4edf581\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3ebd23e\" data-id=\"3ebd23e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2127c2a elementor-widget elementor-widget-text-editor\" data-id=\"2127c2a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.9.0 - 06-12-2022 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#818a91;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#818a91;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>April 19, 2021<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9a1b528 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9a1b528\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-57ff5fd\" data-id=\"57ff5fd\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5f23092 elementor-widget elementor-widget-text-editor\" data-id=\"5f23092\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Abstract:\u00a0<\/strong><span style=\"font-style: inherit; font-weight: inherit; background-color: var(--ast-global-color-5);\">Automatic identification and classification of power quality disturbances (PQDs) is crucial for maintaining efficiency and safety of electrical systems and equipment condition. In recent years emerging deep learning techniques have shown potential in performing classification of PQDs. This paper proposes two novel deep learning models, called CNN(AE)-LSTM and CNN-LSTM(AE) that automatically distinguish between normal power system behaviour and three types of PQDs: voltage sags, voltage swells and interruptions. The CNN-LSTM(AE) model achieved the highest average classification accuracy with a 65:35 train-test split. The Adam optimiser and a learning rate of 0.001 were used for ten epochs with a batch size of 64. Both models are trained using real world data and outperform models found in literature. This work demonstrates the potential of deep learning in classifying PQDs and hence paves the way to effective implementation of AIbased automated quality monitoring to identify disturbances and reduce failures in real world power systems.<\/span><\/p><p><strong>\u00a0<\/strong><\/p><p><strong>Citation:\u00a0<\/strong>C. O\u2019Donovan, C. Giannetti and\u00a0<u>G. Todeschini<\/u>: \u2018A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders\u2019,\u00a0<i>Presented at the International Conference on Artificial Intelligence (ICAART),\u00a0<\/i>3-5 February 2021.<\/p><p>\u00a0<\/p><p><strong>Weblink:<\/strong> <a title=\"Original URL: https:\/\/www.scitepress.org\/Papers\/2021\/103471\/103471.pdf. Click or tap if you trust this link.\" href=\"https:\/\/eur03.safelinks.protection.outlook.com\/?url=https%3A%2F%2Fwww.scitepress.org%2FPapers%2F2021%2F103471%2F103471.pdf&amp;data=04%7C01%7CJill.Palmer%40Swansea.ac.uk%7C8784b418fc754cda97c008d8ef9c8982%7Cbbcab52e9fbe43d6a2f39f66c43df268%7C0%7C0%7C637522802887447722%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&amp;sdata=VQi8U5R3s1bbulQ3Ht0E868yNnHtRcjCRHmVvR9EvGI%3D&amp;reserved=0\" target=\"_blank\" rel=\"noopener noreferrer\" data-auth=\"Verified\" data-linkindex=\"4\">https:\/\/www.scitepress.org\/Papers\/2021\/103471\/103471.pdf<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Publication: A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders April 19, 2021 Abstract:\u00a0Automatic identification and classification of power quality disturbances (PQDs) is crucial for maintaining efficiency and safety of electrical systems and equipment condition. In recent years emerging deep learning techniques have shown potential in performing classification of PQDs. This paper proposes &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/?page_id=1528\"> <span class=\"screen-reader-text\">Conference paper, published on 2021.04.19<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-global-header-display":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":""},"_links":{"self":[{"href":"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/index.php?rest_route=\/wp\/v2\/pages\/1528"}],"collection":[{"href":"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/index.php?rest_route=\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1528"}],"version-history":[{"count":28,"href":"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/index.php?rest_route=\/wp\/v2\/pages\/1528\/revisions"}],"predecessor-version":[{"id":2352,"href":"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/index.php?rest_route=\/wp\/v2\/pages\/1528\/revisions\/2352"}],"wp:attachment":[{"href":"https:\/\/future-power-grid.sites.er.kcl.ac.uk\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1528"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}