[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122655-en":3,"doc-seo-122655-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122655,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Enhancing power system security using soft computing and machine learning","The work proposes a machine-learning-based approach to enhance power system security by predicting single transmission-line outages and calculating contingency ratings. A mathematical model using particle swarm optimization is applied to stability analysis with and without unified power flow controller and interline power flow controller, while evaluating associated costs. The method targets rapid identification of the most affected line and the location for compensation under contingencies such as line failures and load changes, supporting safer transmission operation.","UDC 621.3 [https://doi.org/10.20998/2074-272X.2023.4.13](https://doi.org/10.20998/2074-272X.2023.4.13)  \n[P. Venkatesh](P. Venkatesh), N. Visali  \nEnhancing power system security using soft computing and machine learning  \nPurpose. To guarantee proper operation of the system, the suggested method infers the loss of a single transmission line in order to calculate a contingency rating. Methods. The proposed mathematical model with the machine learning with particle swarm optimization algorithm has been used to observe the stability analysis with and without the unified power flow controller and interline power flow controller, as well as the associated costs. This allows for rapid prediction of the most affected transmission line and the location for compensation. Results. Many contingency conditions, such as the failure of a single transmission line and change in the load, are built into the power system. The single transmission line outage and load fluctuation used to determine the contingency ranking are the primary emphasis of this work. Practical value. In order to set up a safe transmission power system, the suggested stability analysis has been quite helpful. References 16, figures 9.  \nKey words: machine learning, particle swarm optimization, power system security, interline power flow controller, unified power flow controller.  \nМета. Щоб гарантувати правильну роботу системи, запропонований метод передбачає втрату однієї лінії передачірозрахунку рейтингу непередбачених обставин. Методи. Запропонована математична модель з алгоритмом машинногонавчання з оптимізацією рою частинок використовувалася для спостереження за аналізом стійкості з уніфікованимрегулятором потоку потужності та міжлінійним регулятором потоку потужності та без нього, а також з відповіднимивитратами. Це дозволяє швидко передбачити найбільш постраждалу лінію передачі та місце для компенсації. Результати.Багато позаштатних ситуацій, таких як відмова однієї лінії електропередачі та зміна навантаження, вбудовані венергосистему. Основна увага у цій роботі приділяється відключенню однієї лінії електропередачі та коливаннямнавантаження, які використовуються для визначення рейтингу непередбачених обставин. Практична цінність. Пропонованийаналіз стійкості виявився дуже корисним до створення безпечної системи передачі електроенергії. Бібл . 16, рис . 9.Ключові слова: машинне навчання, оптимізація рою частинок, безпека енергосистеми, вбудований контролер потоку  \nпотужності, уніфікований контролер потоку потужності.  \nIntroduction. Multiple renewable and non-renewable power sources have been added to the grid in recent years inan effort to keep up with rising demand. Generators, transmission lines, and distribution networks already have it rough, and transient load changes make matters worse. Investigating the most appropriate load modeling is necessary for predicting the system’s features. When paired with contingency criteria and constant-impedance, constantcurrent, and constant-power loads, the ZIP load model creates accurate and durable representations of loads overextended time periods (ZIP is a common acronym for the polynomial load model – constant impedance Z, constant current I, constant active power P).  \nEven the most basic contemporary lives require complex electrical systems. Therefore, it is crucial to keep the electrical system reliable. A power system’s users, infrastructure, and bottom line must all be safeguarded if the system is to be considered secure. The failure of a transmission line or generator, an unexpected increase in load demand, the destruction of a transformer, etc. are just a few examples of the kinds of occurrences that might make such a power system useless. Maintaining the safety of the power system is an intriguing problem. Power outages have increased as a consequence of system instability. Many companies go bankrupt, and the lives of regular people are disrupted. Because this is the source of the blackout, taking decisive act","cbCaijrF1kJtPt2b","https://ap.wps.com/l/cbCaijrF1kJtPt2b","pdf",1087323,1,5,"English","en",105,"# Introduction\n## Power system security challenges and contingency analysis\n## Load modeling and ZIP/polynomial models\n## FACTS devices and indices for stability assessment\n# Methodology and proposed model\n## Particle swarm optimization with machine learning for contingency rating\n## Stability analysis with UPFC and interline power flow controller\n# Results and practical value\n## Contingency conditions and contingency ranking\n## Compensation location prediction","[{\"question\":\"What is the main goal of the proposed method for power system security?\",\"answer\":\"Guarantee proper system operation by inferring the loss of a single transmission line and calculating a contingency rating for security assessment.\"},{\"question\":\"Which optimization and machine learning technique is used in the proposed model?\",\"answer\":\"The approach uses a mathematical model with machine learning based on a particle swarm optimization algorithm.\"},{\"question\":\"How does the study incorporate FACTS controllers in the stability analysis?\",\"answer\":\"Stability analysis is performed with and without the unified power flow controller and the interline power flow controller, including evaluation of associated costs.\"}]","Enhancing power system security using soft computing and machine learning | 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