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{"id":3089,"date":"2020-12-22T05:12:46","date_gmt":"2020-12-22T05:12:46","guid":{"rendered":"https:\/\/www.dsgenterprisesltd.com\/product\/import-placeholder-for-26793\/"},"modified":"2022-01-06T10:53:47","modified_gmt":"2022-01-06T10:53:47","slug":"probabilistic-reliability-analysis-zu-siemens-pss-sincal-massive-and-parallelized-analysis-modules","status":"publish","type":"product","link":"https:\/\/dsgenterprisesltd.com\/product\/probabilistic-reliability-analysis-zu-siemens-pss-sincal-massive-and-parallelized-analysis-modules\/","title":{"rendered":"Probabilistic Reliability Analysis (ZU) Siemens PSS SINCAL Massive and Parallelized Analysis Modules"},"content":{"rendered":"Probabilistic Reliability Analysis (ZU) Siemens PSS SINCAL Massive and Parallelized Analysis Modules<\/h1>\n
Probabilistic Reliability Analysis (ZU)<\/strong><\/p>\nRequirements and Use Cases<\/strong><\/p>\nThe aim of the probabilistic reliability analysis is to draw conclusions about the future occurrence of supply interruptions based on failures observed in network operation in the past. For this purpose, the indices of supply reliability of the entire network under observation or of individual customers are calculated from the operating behavior of the components and their interaction in the network based on failure models.<\/p>\n
\n- Determination of the supply reliability considering the statistical relevance of equipment failures in contrast to the classical application of the deterministic (n-1) criterion<\/li>\n
- Quantitative comparison of system or network concepts that do not differ according to the (n-1) criterion<\/li>\n
- Determination of reliability indices for load nodes, network areas or the entire network as well as contributions from equipment<\/li>\n
- Analysis and derivation of measures to increase the reliability of supply<\/li>\n<\/ul>\n
Solution<\/strong><\/p>\nThe probabilistic reliability analysis (ZU) in PSS?SINCAL is a significant extension of the contingency analysis and resupply (CA) module and enables the calculation of reliability indicators based on the topological and electrical network model, probabilistic failure models and operating behavior of components as well as restoration of supply measures in case of supply interruptions<\/p>\n
\n- Detailed and quantitative description of supply reliability in the observed network area<\/li>\n
- Identification of the contributions of individual components to the reliability level and analysis of weak points in the network<\/li>\n
- Calculation of reliability indices per network node to which load elements are connected (“consumer indices”), as well as reliability indices for the entire network (“network indices”) such as the interruption frequency, average interruption duration and the product of both indices, unavailability of supply per network customer and year<\/li>\n<\/ul>\n
PSS?SINCAL Massive and Parallelized Analysis Modules<\/strong><\/p>\nExtend the core calculation modules to run massive and parallelized simulations over the whole network, and address many of the emerging analysis needs around renewables and e-mobility, from Contingency Analysis and Resupply, to Network Stress Test and Maximum Hosting Capacity.<\/p>\n","protected":false},"excerpt":{"rendered":"
Probabilistic Reliability Analysis (ZU) Siemens PSS SINCAL Massive and Parallelized Analysis Modules Probabilistic Reliability Analysis (ZU) Requirements and Use Cases The aim of the probabilistic reliability analysis is to draw conclusions about the future occurrence of supply interruptions based on failures observed in network operation in the past. For this purpose, the indices of supply […]<\/p>\n","protected":false},"featured_media":3035,"comment_status":"open","ping_status":"closed","template":"","meta":{"ocean_post_layout":"","ocean_both_sidebars_style":"","ocean_both_sidebars_content_width":0,"ocean_both_sidebars_sidebars_width":0,"ocean_sidebar":"0","ocean_second_sidebar":"0","ocean_disable_margins":"enable","ocean_add_body_class":"","ocean_shortcode_before_top_bar":"","ocean_shortcode_after_top_bar":"","ocean_shortcode_before_header":"","ocean_shortcode_after_header":"","ocean_has_shortcode":"","ocean_shortcode_after_title":"","ocean_shortcode_before_footer_widgets":"","ocean_shortcode_after_footer_widgets":"","ocean_shortcode_before_footer_bottom":"","ocean_shortcode_after_footer_bottom":"","ocean_display_top_bar":"default","ocean_display_header":"default","ocean_header_style":"","ocean_center_header_left_menu":"0","ocean_custom_header_template":"0","ocean_custom_logo":0,"ocean_custom_retina_logo":0,"ocean_custom_logo_max_width":0,"ocean_custom_logo_tablet_max_width":0,"ocean_custom_logo_mobile_max_width":0,"ocean_custom_logo_max_height":0,"ocean_custom_logo_tablet_max_height":0,"ocean_custom_logo_mobile_max_height":0,"ocean_header_custom_menu":"0","ocean_menu_typo_font_family":"0","ocean_menu_typo_font_subset":"","ocean_menu_typo_font_size":0,"ocean_menu_typo_font_size_tablet":0,"ocean_menu_typo_font_size_mobile":0,"ocean_menu_typo_font_size_unit":"px","ocean_menu_typo_font_weight":"","ocean_menu_typo_font_weight_tablet":"","ocean_menu_typo_font_weight_mobile":"","ocean_menu_typo_transform":"","ocean_menu_typo_transform_tablet":"","ocean_menu_typo_transform_mobile":"","ocean_menu_typo_line_height":0,"ocean_menu_typo_line_height_tablet":0,"ocean_menu_typo_line_height_mobile":0,"ocean_menu_typo_line_height_unit":"","ocean_menu_typo_spacing":0,"ocean_menu_typo_spacing_tablet":0,"ocean_menu_typo_spacing_mobile":0,"ocean_menu_typo_spacing_unit":"","ocean_menu_link_color":"","ocean_menu_link_color_hover":"","ocean_menu_link_color_active":"","ocean_menu_link_background":"","ocean_menu_link_hover_background":"","ocean_menu_link_active_background":"","ocean_menu_social_links_bg":"","ocean_menu_social_hover_links_bg":"","ocean_menu_social_links_color":"","ocean_menu_social_hover_links_color":"","ocean_disable_title":"default","ocean_disable_heading":"default","ocean_post_title":"","ocean_post_subheading":"","ocean_post_title_style":"","ocean_post_title_background_color":"","ocean_post_title_background":0,"ocean_post_title_bg_image_position":"","ocean_post_title_bg_image_attachment":"","ocean_post_title_bg_image_repeat":"","ocean_post_title_bg_image_size":"","ocean_post_title_height":0,"ocean_post_title_bg_overlay":0.5,"ocean_post_title_bg_overlay_color":"","ocean_disable_breadcrumbs":"default","ocean_breadcrumbs_color":"","ocean_breadcrumbs_separator_color":"","ocean_breadcrumbs_links_color":"","ocean_breadcrumbs_links_hover_color":"","ocean_display_footer_widgets":"default","ocean_display_footer_bottom":"default","ocean_custom_footer_template":"0"},"product_cat":[141],"product_tag":[],"class_list":{"0":"post-3089","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-siemens-simulation-softwares-pss-sincal","8":"entry","9":"has-media","11":"first","12":"instock","13":"shipping-taxable","14":"product-type-simple","15":"has-product-nav","16":"col","17":"span_1_of_3","18":"owp-content-center","19":"owp-thumbs-layout-horizontal","20":"owp-btn-normal","21":"owp-tabs-layout-horizontal","22":"has-no-thumbnails"},"yoast_head":"\n
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