{"id":817,"date":"2021-12-17T13:29:00","date_gmt":"2021-12-17T13:29:00","guid":{"rendered":"https:\/\/site.dlaboratory.com\/?p=817"},"modified":"2024-12-02T12:37:20","modified_gmt":"2024-12-02T12:37:20","slug":"dlab-introduces-key-performance-indicators-for-grid-performance","status":"publish","type":"post","link":"https:\/\/dlaboratory.com\/en\/dlab-introduces-key-performance-indicators-for-grid-performance\/","title":{"rendered":"dLab introduces Key Performance Indicators for grid performance"},"content":{"rendered":"\n<p><strong>Dlaboratory Sweden AB (publ) has developed a method for quantifying health status of an electric power grid based on deviations and disturbances. The approach provides an opportunity to quantify performance seamlessly from individual feeder levels up to an aggregated level that forms a single KPI that describes the health of the entire power grid. An&nbsp;<\/strong><strong>indicator that can be used to easily compare individual feeders, stations, or entire clusters of stations, with each other, and over time.<\/strong><\/p>\n\n\n\n<p>The common way to determine the reliability of an electric power grid is based on two parameters. Partly by looking at the duration of power supply interruptions over a given time. This value can be translated to an index based on for example the number of customers affected by the interruptions (SAIDI). Partly by looking at the number of interruptions, and in the same manner translating this value to various indices (e.g. SAIFI).<\/p>\n\n\n\n<p>&#8211; A problem with this approach of determining the reliability of an electric power grid, is that the parameters used, for example number of minutes per interruption or number of interruptions, are binary \u2013 either something has gone so wrong that the power supply is interrupted, or everything is seemingly completely fine, says Fredrik Akke, CTO.<\/p>\n\n\n\n<p>dLab\u2019s Deviation Based Grid Health Index addresses this problem. By evaluating all events deviating from normal operation in the grid, regardless of whether the events lead to an interruption or not, a more comprehensive indicator can be presented. This also increases the possibilities to act proactive, in order to prohibit an interruption.<\/p>\n\n\n\n<p>dLab\u2019s application dAnalyzer is using patented algorithms to automatically analyze disturbances and deviations based on their specific wave forms. The way in which dAnalyzer then further categorize and prioritize the severity of a disturbance, is by mimicking the physical protection relays virtually and calculate how far the function time has progressed \u2013 the closer the timer is to reaching the pre-set threshold for tripping the circuit breaker (and hence interrupting the power supply), the more severe the disturbance is.<\/p>\n\n\n\n<p>dLab\u2019s method for evaluating the Deviation Based Grid Health index includes, among other things, transforming this timer value to a negative score, which can be seen as a negative impact on the overall health. And since all events that could potentially lead to an interruption are evaluated based on the function time, the index is independent of what kind of fault occurred and what kind of grid the fault occurred in.<\/p>\n\n\n\n<p>How does one translate individual incidents and their evaluated scores into a more encompassing indicator for grid reliability? dLab\u2019s method rates the health from 0% to 100%, where 100 % represents a grid completely incident free, where the rate is calculated according to an ingenious algorithm even allowing for a recovery over time. This provides grid operators a better understanding of the current health of their grid, and the evolvement over time indicates maintenance or investment needs for error-prone feeders and stations, and a possibility to become more proactive.<\/p>\n\n\n\n<p>This makes dLab\u2019s Deviation Based Grid Health Index a universal grid performance indicator that can be used to compare individual feeders, stations or entire clusters of stations with each other, and over time.<\/p>\n\n\n\n<p>Stay tuned for an upcoming webinar where we will describe this more in-depth!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Dlaboratory Sweden AB (publ) has developed a method for quantifying health status of an electric power grid based on deviations and disturbances. The approach provides an opportunity to quantify performance seamlessly from individual feeder levels up to an aggregated level that forms a single KPI that describes the health of the entire power grid. An&nbsp;indicator [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"wp-custom-template-news","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[19,24],"tags":[],"class_list":["post-817","post","type-post","status-publish","format-standard","hentry","category-news","category-product-news"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.8 (Yoast SEO v27.8) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>dLab introduces Key Performance Indicators for grid performance - dLab<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dlaboratory.com\/en\/dlab-introduces-key-performance-indicators-for-grid-performance\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"dLab introduces Key Performance Indicators for grid performance\" \/>\n<meta property=\"og:description\" content=\"Dlaboratory Sweden AB (publ) has developed a method for quantifying health status of an electric power grid based on deviations and disturbances. The approach provides an opportunity to quantify performance seamlessly from individual feeder levels up to an aggregated level that forms a single KPI that describes the health of the entire power grid. 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