{"id":3321,"date":"2025-01-10T12:51:12","date_gmt":"2025-01-10T12:51:12","guid":{"rendered":"https:\/\/jita-au.com\/?p=3321"},"modified":"2025-07-14T13:33:42","modified_gmt":"2025-07-14T13:33:42","slug":"new-neural-pll-architecture","status":"publish","type":"post","link":"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/","title":{"rendered":"New neural PLL Architecture"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3321\" class=\"elementor elementor-3321\" data-elementor-settings=\"{&quot;ha_cmc_init_switcher&quot;:&quot;no&quot;}\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e232701 elementor-section-height-min-height elementor-section-full_width elementor-hidden-tablet elementor-hidden-mobile elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"e232701\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\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-cb97594\" data-id=\"cb97594\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a18c791 elementor-widget__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"a18c791\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 14 No. 2 (2024): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b2fff26 elementor-widget elementor-widget-heading\" data-id=\"b2fff26\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Vladimir V. \u0110oki\u0107, Dragana \u0110oki\u0107<b><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-52044bb elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"52044bb\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c82606 elementor-widget elementor-widget-heading\" data-id=\"1c82606\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">New neural PLL Architecture<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c297c9e elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"c297c9e\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-455c8d6 e-grid e-con-full wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"455c8d6\" data-element_type=\"container\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-dcea8af elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"dcea8af\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Review paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0579899 elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"0579899\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: https:\/\/doi.org\/10.7251\/JIT2402150DJ<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f3b63f elementor-align-center elementor-widget elementor-widget-button\" data-id=\"2f3b63f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/01\/Pages-from-JITA_Vol-14_Issue-2-WEB-8.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb7c4cb elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"cb7c4cb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-61aebe3 elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"61aebe3\" 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\t\t<p>A PLL or phase-locked loop is a control system that creates an output signal whose phase is related to the phase-locked loop and represents controlled input signal. The goal of this research is to first investigate the functioning of new PLL neural networks and then, in the research section, explore an approach involving the extraction of neural symmetrical voltage components. The architectural characteristics of phase-locked loops (PLLs) typically include capture and lock ranges, bandwidth, and transient response. The new neural PLL architecture offers several advantages, such as low noise performance, reduced silicon area, and compatibility with low supply voltages. However, it may also present disadvantages, including hardware dependency and potential design complexity compared to traditional PLL architectures. Evaluating these factors is crucial, depending on the specific needs of the application.\u00a0<\/p><p>In this paper, we present the scientific research included in the experimental part where we investigate the performance of the proposed neural PLL, for which experimental comparisons with the conventional PLL in a distorted reference frame are necessary. Structural columns or structural circles will be used for graphic display.\u00a0<\/p><p>The following research methods and techniques will be applied: instruments, basic methods and data processing procedures &#8211; if they are foreseen. What makes this work a scientific research work is a descriptive method that will be used.\u00a0<\/p><p>To better understand how PLLs work, we propose an original three-phase neural approach for components of the system\u2019s phase and symmetry. The quality of the electricity can be assessed and managed with this framework. Our study shows that the full neural architecture may be applied to three-phase power systems because it is based on DSP supplies. Additionally, we present the performance of the PLL system in a three-phase power supply context. Different regulators, such as PI and RST based on phase logic, are incorporated into the PLL scheme. The results suggest that the neural PLL could make a significant contribution in applications where the quality and efficiency of three-phase power systems are essential.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c5f19e elementor-widget elementor-widget-heading\" data-id=\"5c5f19e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: Neural Phase-Locked Loop (PLL), electroenergetic system, neural network, neural architecture<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11c9492 elementor-widget elementor-widget-heading\" data-id=\"11c9492\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Paper received:<\/b> 24.10.2024.<br><b>Paper accepted:<\/b> 19.11.2024.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6dd494a wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"6dd494a\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb049d1 elementor-widget elementor-widget-shortcode\" data-id=\"cb049d1\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3321 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">1,204<\/span>\r\n\t\t\t<\/div><\/div>\n\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<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-511d0df elementor-section-height-min-height elementor-section-full_width elementor-hidden-mobile elementor-hidden-desktop elementor-hidden-laptop elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"511d0df\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\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-0acee9c\" data-id=\"0acee9c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c0e98b3 elementor-widget__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"c0e98b3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 14 No. 1 (2024): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e6b43c5 elementor-widget elementor-widget-heading\" data-id=\"e6b43c5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><strong>Vladimir Radovanovi\u0107, Olja Kr\u010dadinac, Jasmina Peri\u0161i\u0107, Marina Milovanovi\u0107, \u017deljko Stankovi\u0107 <\/strong>\n <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-68f1849 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"68f1849\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-186a6b2 elementor-widget elementor-widget-heading\" data-id=\"186a6b2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Comparison Of Agile And Devops Methodologies: Analysis Of Efficiency, Flexibility, And Application In Software Development<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b2f501 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"7b2f501\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5027126 elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"5027126\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Review paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a5312c elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"4a5312c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: Https:\/\/Doi.Org\/10.7251\/JIT2401078R<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8d7a912 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"8d7a912\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/06\/Pages-from-JITA_Vol-14_Issue-1-WEB.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-580ffd3 elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"580ffd3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-211717e elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"211717e\" 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\t\t<p>This paper provides a concise overview of Agile and DevOps methodologies in software engineering. It aims to introduce readers to the fundamental principles of Agile and DevOps, accompanied by brief descriptions and practical examples. The advantages and disadvantages of each methodology are discussed, followed by a comparative analysis highlighting key differences. Understanding these methodologies is crucial in today\u2019s IT landscape, as they are commonly employed in various organizations, impacting project management, team collaboration, and product delivery. This paper serves as a valuable resource for individuals seeking a basic understanding of Agile and DevOps methodologies in software engineering.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62b72c3 elementor-widget elementor-widget-heading\" data-id=\"62b72c3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: Agile Methodology, DevOps Methodology, Software Engineering, Comparative Analysis, Software Development<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-68df115 elementor-widget elementor-widget-heading\" data-id=\"68df115\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Paper received:<\/b> 24.10.2024.<br><b>Paper accepted:<\/b> 19.11.2024.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f4dcd16 wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"f4dcd16\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0f73093 elementor-widget elementor-widget-shortcode\" data-id=\"0f73093\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3321 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">1,204<\/span>\r\n\t\t\t<\/div><\/div>\n\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<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2d980b8 elementor-section-height-min-height elementor-section-full_width elementor-hidden-tablet elementor-hidden-desktop elementor-hidden-laptop elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"2d980b8\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\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-d772afa\" data-id=\"d772afa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5597455 elementor-widget__width-inherit elementor-widget-mobile__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"5597455\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 14 No. 2 (2024): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f3e70ee elementor-widget elementor-widget-heading\" data-id=\"f3e70ee\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><strong>Vladimir V. \u0110oki\u0107, Dragana \u0110oki\u0107<\/strong>\n <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b0a72e2 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"b0a72e2\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-87a3678 elementor-widget elementor-widget-heading\" data-id=\"87a3678\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">New neural PLL Architecture<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96936f4 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"96936f4\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f04febc elementor-widget__width-initial elementor-widget-mobile__width-inherit elementor-widget elementor-widget-heading\" data-id=\"f04febc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Review paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-389228b elementor-widget__width-initial elementor-widget-mobile__width-inherit elementor-widget elementor-widget-heading\" data-id=\"389228b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: Https:\/\/Doi.Org\/10.7251\/JIT2402150DJ<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73b8917 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"73b8917\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/01\/Pages-from-JITA_Vol-14_Issue-2-WEB-8.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-80faf79 elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"80faf79\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8cefffd elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"8cefffd\" 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\t\t<p>A PLL or phase-locked loop is a control system that creates an output signal whose phase is related to the phase-locked loop and represents controlled input signal. The goal of this research is to first investigate the functioning of new PLL neural networks and then, in the research section, explore an approach involving the extraction of neural symmetrical voltage components. The architectural characteristics of phase-locked loops (PLLs) typically include capture and lock ranges, bandwidth, and transient response. The new neural PLL architecture offers several advantages, such as low noise performance, reduced silicon area, and compatibility with low supply voltages. However, it may also present disadvantages, including hardware dependency and potential design complexity compared to traditional PLL architectures. Evaluating these factors is crucial, depending on the specific needs of the application.<\/p><p>In this paper, we present the scientific research included in the experimental part where we investigate the performance of the proposed neural PLL, for which experimental comparisons with the conventional PLL in a distorted reference frame are necessary. Structural columns or structural circles will be used for graphic display.<\/p><p>The following research methods and techniques will be applied: instruments, basic methods and data processing procedures \u2013 if they are foreseen. What makes this work a scientific research work is a descriptive method that will be used.<\/p><p>To better understand how PLLs work, we propose an original three-phase neural approach for components of the system\u2019s phase and symmetry. The quality of the electricity can be assessed and managed with this framework. Our study shows that the full neural architecture may be applied to three-phase power systems because it is based on DSP supplies. Additionally, we present the performance of the PLL system in a three-phase power supply context. Different regulators, such as PI and RST based on phase logic, are incorporated into the PLL scheme. The results suggest that the neural PLL could make a significant contribution in applications where the quality and efficiency of three-phase power systems are essential.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0c5e44b elementor-widget elementor-widget-heading\" data-id=\"0c5e44b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: Agile Methodology, DevOps Methodology, Software Engineering, Comparative Analysis, Software Development<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba4c1a6 elementor-widget elementor-widget-heading\" data-id=\"ba4c1a6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Paper received:<\/b> 24.10.2024.<br><b>Paper accepted:<\/b> 19.11.2024.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dee52ab wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"dee52ab\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5cb9871 elementor-widget elementor-widget-shortcode\" data-id=\"5cb9871\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3321 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">1,204<\/span>\r\n\t\t\t<\/div><\/div>\n\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<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Vol. 14 No. 2 (2024): JITA &#8211; APEIRON Vladimir V. \u0110oki\u0107, Dragana \u0110oki\u0107 New neural PLL Architecture Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2402150DJ Download Article PDF Abstract A PLL or phase-locked loop is a control system that creates an output signal whose phase is related to the phase-locked loop and represents controlled input signal. The goal of this research is to first investigate the functioning of new PLL neural networks and then, in the research section, explore an approach involving the extraction of neural symmetrical voltage components. The architectural characteristics of phase-locked loops (PLLs) typically include capture and lock ranges, bandwidth, and transient response. The new neural PLL architecture offers several advantages, such as low noise performance, reduced silicon area, and compatibility with low supply voltages. However, it may also present disadvantages, including hardware dependency and potential design complexity compared to traditional PLL architectures. Evaluating these factors is crucial, depending on the specific needs of the application.\u00a0 In this paper, we present the scientific research included in the experimental part where we investigate the performance of the proposed neural PLL, for which experimental comparisons with the conventional PLL in a distorted reference frame are necessary. Structural columns or structural circles will be used for graphic display.\u00a0 The following research methods and techniques will be applied: instruments, basic methods and data processing procedures &#8211; if they are foreseen. What makes this work a scientific research work is a descriptive method that will be used.\u00a0 To better understand how PLLs work, we propose an original three-phase neural approach for components of the system\u2019s phase and symmetry. The quality of the electricity can be assessed and managed with this framework. Our study shows that the full neural architecture may be applied to three-phase power systems because it is based on DSP supplies. Additionally, we present the performance of the PLL system in a three-phase power supply context. Different regulators, such as PI and RST based on phase logic, are incorporated into the PLL scheme. The results suggest that the neural PLL could make a significant contribution in applications where the quality and efficiency of three-phase power systems are essential. Keywords: Neural Phase-Locked Loop (PLL), electroenergetic system, neural network, neural architecture Paper received: 24.10.2024.Paper accepted: 19.11.2024. Vol. 14 No. 1 (2024): JITA &#8211; APEIRON Vladimir Radovanovi\u0107, Olja Kr\u010dadinac, Jasmina Peri\u0161i\u0107, Marina Milovanovi\u0107, \u017deljko Stankovi\u0107 Comparison Of Agile And Devops Methodologies: Analysis Of Efficiency, Flexibility, And Application In Software Development Review paper DOI: Https:\/\/Doi.Org\/10.7251\/JIT2401078R Download Article PDF Abstract This paper provides a concise overview of Agile and DevOps methodologies in software engineering. It aims to introduce readers to the fundamental principles of Agile and DevOps, accompanied by brief descriptions and practical examples. The advantages and disadvantages of each methodology are discussed, followed by a comparative analysis highlighting key differences. Understanding these methodologies is crucial in today\u2019s IT landscape, as they are commonly employed in various organizations, impacting project management, team collaboration, and product delivery. This paper serves as a valuable resource for individuals seeking a basic understanding of Agile and DevOps methodologies in software engineering. Keywords: Agile Methodology, DevOps Methodology, Software Engineering, Comparative Analysis, Software Development Paper received: 24.10.2024.Paper accepted: 19.11.2024. Vol. 14 No. 2 (2024): JITA &#8211; APEIRON Vladimir V. \u0110oki\u0107, Dragana \u0110oki\u0107 New neural PLL Architecture Review paper DOI: Https:\/\/Doi.Org\/10.7251\/JIT2402150DJ Download Article PDF Abstract A PLL or phase-locked loop is a control system that creates an output signal whose phase is related to the phase-locked loop and represents controlled input signal. The goal of this research is to first investigate the functioning of new PLL neural networks and then, in the research section, explore an approach involving the extraction of neural symmetrical voltage components. The architectural characteristics of phase-locked loops (PLLs) typically include capture and lock ranges, bandwidth, and transient response. The new neural PLL architecture offers several advantages, such as low noise performance, reduced silicon area, and compatibility with low supply voltages. However, it may also present disadvantages, including hardware dependency and potential design complexity compared to traditional PLL architectures. Evaluating these factors is crucial, depending on the specific needs of the application. In this paper, we present the scientific research included in the experimental part where we investigate the performance of the proposed neural PLL, for which experimental comparisons with the conventional PLL in a distorted reference frame are necessary. Structural columns or structural circles will be used for graphic display. The following research methods and techniques will be applied: instruments, basic methods and data processing procedures \u2013 if they are foreseen. What makes this work a scientific research work is a descriptive method that will be used. To better understand how PLLs work, we propose an original three-phase neural approach for components of the system\u2019s phase and symmetry. The quality of the electricity can be assessed and managed with this framework. Our study shows that the full neural architecture may be applied to three-phase power systems because it is based on DSP supplies. Additionally, we present the performance of the PLL system in a three-phase power supply context. Different regulators, such as PI and RST based on phase logic, are incorporated into the PLL scheme. The results suggest that the neural PLL could make a significant contribution in applications where the quality and efficiency of three-phase power systems are essential. Keywords: Agile Methodology, DevOps Methodology, Software Engineering, Comparative Analysis, Software Development Paper received: 24.10.2024.Paper accepted: 19.11.2024.<\/p>\n","protected":false},"author":1,"featured_media":534,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3321","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>New neural PLL Architecture - JITA -Journal of Information Technology and Application<\/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:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New neural PLL Architecture - JITA -Journal of Information Technology and Application\" \/>\n<meta property=\"og:description\" content=\"Vol. 14 No. 2 (2024): JITA &#8211; APEIRON Vladimir V. \u0110oki\u0107, Dragana \u0110oki\u0107 New neural PLL Architecture Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2402150DJ Download Article PDF Abstract A PLL or phase-locked loop is a control system that creates an output signal whose phase is related to the phase-locked loop and represents controlled input signal. The goal of this research is to first investigate the functioning of new PLL neural networks and then, in the research section, explore an approach involving the extraction of neural symmetrical voltage components. The architectural characteristics of phase-locked loops (PLLs) typically include capture and lock ranges, bandwidth, and transient response. The new neural PLL architecture offers several advantages, such as low noise performance, reduced silicon area, and compatibility with low supply voltages. However, it may also present disadvantages, including hardware dependency and potential design complexity compared to traditional PLL architectures. Evaluating these factors is crucial, depending on the specific needs of the application.\u00a0 In this paper, we present the scientific research included in the experimental part where we investigate the performance of the proposed neural PLL, for which experimental comparisons with the conventional PLL in a distorted reference frame are necessary. Structural columns or structural circles will be used for graphic display.\u00a0 The following research methods and techniques will be applied: instruments, basic methods and data processing procedures &#8211; if they are foreseen. What makes this work a scientific research work is a descriptive method that will be used.\u00a0 To better understand how PLLs work, we propose an original three-phase neural approach for components of the system\u2019s phase and symmetry. The quality of the electricity can be assessed and managed with this framework. Our study shows that the full neural architecture may be applied to three-phase power systems because it is based on DSP supplies. Additionally, we present the performance of the PLL system in a three-phase power supply context. Different regulators, such as PI and RST based on phase logic, are incorporated into the PLL scheme. The results suggest that the neural PLL could make a significant contribution in applications where the quality and efficiency of three-phase power systems are essential. Keywords: Neural Phase-Locked Loop (PLL), electroenergetic system, neural network, neural architecture Paper received: 24.10.2024.Paper accepted: 19.11.2024. Vol. 14 No. 1 (2024): JITA &#8211; APEIRON Vladimir Radovanovi\u0107, Olja Kr\u010dadinac, Jasmina Peri\u0161i\u0107, Marina Milovanovi\u0107, \u017deljko Stankovi\u0107 Comparison Of Agile And Devops Methodologies: Analysis Of Efficiency, Flexibility, And Application In Software Development Review paper DOI: Https:\/\/Doi.Org\/10.7251\/JIT2401078R Download Article PDF Abstract This paper provides a concise overview of Agile and DevOps methodologies in software engineering. It aims to introduce readers to the fundamental principles of Agile and DevOps, accompanied by brief descriptions and practical examples. The advantages and disadvantages of each methodology are discussed, followed by a comparative analysis highlighting key differences. Understanding these methodologies is crucial in today\u2019s IT landscape, as they are commonly employed in various organizations, impacting project management, team collaboration, and product delivery. This paper serves as a valuable resource for individuals seeking a basic understanding of Agile and DevOps methodologies in software engineering. Keywords: Agile Methodology, DevOps Methodology, Software Engineering, Comparative Analysis, Software Development Paper received: 24.10.2024.Paper accepted: 19.11.2024. Vol. 14 No. 2 (2024): JITA &#8211; APEIRON Vladimir V. \u0110oki\u0107, Dragana \u0110oki\u0107 New neural PLL Architecture Review paper DOI: Https:\/\/Doi.Org\/10.7251\/JIT2402150DJ Download Article PDF Abstract A PLL or phase-locked loop is a control system that creates an output signal whose phase is related to the phase-locked loop and represents controlled input signal. The goal of this research is to first investigate the functioning of new PLL neural networks and then, in the research section, explore an approach involving the extraction of neural symmetrical voltage components. The architectural characteristics of phase-locked loops (PLLs) typically include capture and lock ranges, bandwidth, and transient response. The new neural PLL architecture offers several advantages, such as low noise performance, reduced silicon area, and compatibility with low supply voltages. However, it may also present disadvantages, including hardware dependency and potential design complexity compared to traditional PLL architectures. Evaluating these factors is crucial, depending on the specific needs of the application. In this paper, we present the scientific research included in the experimental part where we investigate the performance of the proposed neural PLL, for which experimental comparisons with the conventional PLL in a distorted reference frame are necessary. Structural columns or structural circles will be used for graphic display. The following research methods and techniques will be applied: instruments, basic methods and data processing procedures \u2013 if they are foreseen. What makes this work a scientific research work is a descriptive method that will be used. To better understand how PLLs work, we propose an original three-phase neural approach for components of the system\u2019s phase and symmetry. The quality of the electricity can be assessed and managed with this framework. Our study shows that the full neural architecture may be applied to three-phase power systems because it is based on DSP supplies. Additionally, we present the performance of the PLL system in a three-phase power supply context. Different regulators, such as PI and RST based on phase logic, are incorporated into the PLL scheme. The results suggest that the neural PLL could make a significant contribution in applications where the quality and efficiency of three-phase power systems are essential. Keywords: Agile Methodology, DevOps Methodology, Software Engineering, Comparative Analysis, Software Development Paper received: 24.10.2024.Paper accepted: 19.11.2024.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/\" \/>\n<meta property=\"og:site_name\" content=\"JITA -Journal of Information Technology and Application\" \/>\n<meta property=\"article:published_time\" content=\"2025-01-10T12:51:12+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-14T13:33:42+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cover_issue_949_en_US.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"595\" \/>\n\t<meta property=\"og:image:height\" content=\"793\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\/\/jita-au.com\/#\/schema\/person\/d4becda53cfcbc99c449927eabf3877f\"},\"headline\":\"New neural PLL Architecture\",\"datePublished\":\"2025-01-10T12:51:12+00:00\",\"dateModified\":\"2025-07-14T13:33:42+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/\"},\"wordCount\":905,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/jita-au.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cover_issue_949_en_US.jpg\",\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/\",\"url\":\"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/\",\"name\":\"New neural PLL Architecture - 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JITA -Journal of Information Technology and Application","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/","og_locale":"en_US","og_type":"article","og_title":"New neural PLL Architecture - JITA -Journal of Information Technology and Application","og_description":"Vol. 14 No. 2 (2024): JITA &#8211; APEIRON Vladimir V. \u0110oki\u0107, Dragana \u0110oki\u0107 New neural PLL Architecture Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2402150DJ Download Article PDF Abstract A PLL or phase-locked loop is a control system that creates an output signal whose phase is related to the phase-locked loop and represents controlled input signal. The goal of this research is to first investigate the functioning of new PLL neural networks and then, in the research section, explore an approach involving the extraction of neural symmetrical voltage components. The architectural characteristics of phase-locked loops (PLLs) typically include capture and lock ranges, bandwidth, and transient response. The new neural PLL architecture offers several advantages, such as low noise performance, reduced silicon area, and compatibility with low supply voltages. However, it may also present disadvantages, including hardware dependency and potential design complexity compared to traditional PLL architectures. Evaluating these factors is crucial, depending on the specific needs of the application.\u00a0 In this paper, we present the scientific research included in the experimental part where we investigate the performance of the proposed neural PLL, for which experimental comparisons with the conventional PLL in a distorted reference frame are necessary. Structural columns or structural circles will be used for graphic display.\u00a0 The following research methods and techniques will be applied: instruments, basic methods and data processing procedures &#8211; if they are foreseen. What makes this work a scientific research work is a descriptive method that will be used.\u00a0 To better understand how PLLs work, we propose an original three-phase neural approach for components of the system\u2019s phase and symmetry. The quality of the electricity can be assessed and managed with this framework. Our study shows that the full neural architecture may be applied to three-phase power systems because it is based on DSP supplies. Additionally, we present the performance of the PLL system in a three-phase power supply context. Different regulators, such as PI and RST based on phase logic, are incorporated into the PLL scheme. The results suggest that the neural PLL could make a significant contribution in applications where the quality and efficiency of three-phase power systems are essential. Keywords: Neural Phase-Locked Loop (PLL), electroenergetic system, neural network, neural architecture Paper received: 24.10.2024.Paper accepted: 19.11.2024. Vol. 14 No. 1 (2024): JITA &#8211; APEIRON Vladimir Radovanovi\u0107, Olja Kr\u010dadinac, Jasmina Peri\u0161i\u0107, Marina Milovanovi\u0107, \u017deljko Stankovi\u0107 Comparison Of Agile And Devops Methodologies: Analysis Of Efficiency, Flexibility, And Application In Software Development Review paper DOI: Https:\/\/Doi.Org\/10.7251\/JIT2401078R Download Article PDF Abstract This paper provides a concise overview of Agile and DevOps methodologies in software engineering. It aims to introduce readers to the fundamental principles of Agile and DevOps, accompanied by brief descriptions and practical examples. The advantages and disadvantages of each methodology are discussed, followed by a comparative analysis highlighting key differences. Understanding these methodologies is crucial in today\u2019s IT landscape, as they are commonly employed in various organizations, impacting project management, team collaboration, and product delivery. This paper serves as a valuable resource for individuals seeking a basic understanding of Agile and DevOps methodologies in software engineering. Keywords: Agile Methodology, DevOps Methodology, Software Engineering, Comparative Analysis, Software Development Paper received: 24.10.2024.Paper accepted: 19.11.2024. Vol. 14 No. 2 (2024): JITA &#8211; APEIRON Vladimir V. \u0110oki\u0107, Dragana \u0110oki\u0107 New neural PLL Architecture Review paper DOI: Https:\/\/Doi.Org\/10.7251\/JIT2402150DJ Download Article PDF Abstract A PLL or phase-locked loop is a control system that creates an output signal whose phase is related to the phase-locked loop and represents controlled input signal. The goal of this research is to first investigate the functioning of new PLL neural networks and then, in the research section, explore an approach involving the extraction of neural symmetrical voltage components. The architectural characteristics of phase-locked loops (PLLs) typically include capture and lock ranges, bandwidth, and transient response. The new neural PLL architecture offers several advantages, such as low noise performance, reduced silicon area, and compatibility with low supply voltages. However, it may also present disadvantages, including hardware dependency and potential design complexity compared to traditional PLL architectures. Evaluating these factors is crucial, depending on the specific needs of the application. In this paper, we present the scientific research included in the experimental part where we investigate the performance of the proposed neural PLL, for which experimental comparisons with the conventional PLL in a distorted reference frame are necessary. Structural columns or structural circles will be used for graphic display. The following research methods and techniques will be applied: instruments, basic methods and data processing procedures \u2013 if they are foreseen. What makes this work a scientific research work is a descriptive method that will be used. To better understand how PLLs work, we propose an original three-phase neural approach for components of the system\u2019s phase and symmetry. The quality of the electricity can be assessed and managed with this framework. Our study shows that the full neural architecture may be applied to three-phase power systems because it is based on DSP supplies. Additionally, we present the performance of the PLL system in a three-phase power supply context. Different regulators, such as PI and RST based on phase logic, are incorporated into the PLL scheme. The results suggest that the neural PLL could make a significant contribution in applications where the quality and efficiency of three-phase power systems are essential. Keywords: Agile Methodology, DevOps Methodology, Software Engineering, Comparative Analysis, Software Development Paper received: 24.10.2024.Paper accepted: 19.11.2024.","og_url":"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/","og_site_name":"JITA -Journal of Information Technology and Application","article_published_time":"2025-01-10T12:51:12+00:00","article_modified_time":"2025-07-14T13:33:42+00:00","og_image":[{"width":595,"height":793,"url":"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cover_issue_949_en_US.jpg","type":"image\/jpeg"}],"author":"admin","twitter_card":"summary_large_image","twitter_misc":{"Written by":"admin","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/#article","isPartOf":{"@id":"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/"},"author":{"name":"admin","@id":"https:\/\/jita-au.com\/#\/schema\/person\/d4becda53cfcbc99c449927eabf3877f"},"headline":"New neural PLL Architecture","datePublished":"2025-01-10T12:51:12+00:00","dateModified":"2025-07-14T13:33:42+00:00","mainEntityOfPage":{"@id":"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/"},"wordCount":905,"commentCount":0,"publisher":{"@id":"https:\/\/jita-au.com\/#organization"},"image":{"@id":"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/#primaryimage"},"thumbnailUrl":"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cover_issue_949_en_US.jpg","inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/","url":"https:\/\/jita-au.com\/index.php\/2025\/01\/10\/new-neural-pll-architecture\/","name":"New neural PLL Architecture - 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