[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126627-en":3,"doc-seo-126627-105":30,"detail-sidebar-cat-0-en-105":91},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":11,"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},126627,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","MACHINE LEARNING BASED GRID SAFETY ASSESSMENT THROUGH SIMULATION OF UNEXPECTED CONTINGENCIES DURING MAINTENANCE - Research paper","Effective maintenance coordination is essential for reliable electricity supply in power systems driven primarily by renewable energy. Operational security standards create high computational complexity, making existing planning tools difficult to scale. This work applies the machine learning model lightning search optimised random forest (LSORF) to predict contingency analysis outcomes quickly and effectively. The method is tested on Belgium’s regional transmission grid spanning 200 kV to 50 kV voltage levels, where LSORF shows consistent improvements over benchmarks and informs maintenance feasibility under renewable growth projections.","Vol. 05, No. S1 (2023) 86-92, doi: 10.24874/PES.SI.01.011  \nProceedings on Engineering Sciences  \n[www.pesjournal.net](www.pesjournal.net)  \nMACHINE LEARNING BASED GRID SAFETY ASSESSMENT THROUGH SIMULATION OF UNEXPECTED CONTINGENCIES DURING  \nMAINTENANCE  \nKamalraj R.1  \nVikas Verma Received 28.04.2023.  \nManish Joshi Accepted 29.06.2023.  \nKeywords:  \nRenewable energy, machine learning (ML), lightning search optimised random forest (LSORF), contingency.  \nA B S T R A C T  \nEffective maintenance coordination has become essential to ensuring a reliable electricity supply in power systems primarily powered by renewable sources. On the other hand, the computational complexity of the operational security standards presents difficulties for the existing planning tools. To solve this problem, a research paper suggests applying the machine learning (ML) method known as lightning search optimised random forest (LSORF) to anticipate the results of contingency analyses rapidly and effectively. The entire regional transmission system of Belgium (BE), which includes voltage ranges of 200 kV to 50 kV, is the subject of the study. Results show that LSORF regularly outperforms other benchmarks. The results demonstrate that LSORF consistently outperforms other benchmark methods. Furthermore, the study highlights the impact of projected growth in renewable energy on maintenance feasibility. This strategy provides useful insights for improving maintenance planning in renewable energy systems.  \n© 2023 Published by Faculty of Engineering  \n1. INTRODUCTION  \nEnsuring the security and reliability of power supply is a paramount concern in modern power systems. Regular maintenance activities are crucial in ensuring the grid infrastructure's smooth functioning (Peyghami et al.(2019)) . Maintenance activities are essential for ensuring power systems' reliability and smooth operation. However, unexpected contingencies during scheduled maintenance pose significant challenges to system security (Wu et al. (2019)) . These contingencies can range from equipment failures and unplanned outages to unpredictable changes in demand or the intermittent  \nnature of renewable energy sources. When such contingencies arise, they can jeopardize the ability of the grid to safely accommodate these unexpected events (Duman et al. (2023)) .  \nThe management of unexpected contingencies during maintenance requires careful planning and consideration. Maintaining operational security standards and ensuring the grid can swiftly and effectively respond to unforeseen events (Dudurych (2021)) . However, current planning tools used for maintenance coordination in power systems often struggle with tractability issues when incorporating operational security standards. Considering the impact of  \nunexpected contingencies significantly increases the computational burden. To ensure the safety and reliability of the grid, planners must simulate numerous scenarios to evaluate the system's ability to withstand contingencies during maintenance. This requires extensive computational resources and poses challenges in time and efficiency (Medina et al. (2022)) .  \nOne of the key challenges in maintenance planning for renewable-dominated power systems lies in addressing unexpected contingencies. These contingencies can arise from various factors, such as extreme weather events, equipment failures, or grid disturbances. When a contingency occurs during a scheduled maintenance period, it can disrupt the system's normal functioning and lead to potential reliability and security issues. Therefore, ensuring that the grid can safely accommodate any unexpected contingencies that may arise during maintenance activities becomes crucial.  \nInnovative approaches are being explored to alleviate these computational burdens and enhance maintenance coordination. Using ML models to anticipate contingency analysis results quickly and accurately isone relevant system. By incorporating ML into maintenance planni","cbCaigAl4W5l70bs","https://ap.wps.com/l/cbCaigAl4W5l70bs","pdf",630284,3,1,"English","en",105,"# Introduction\n## Security and reliability concerns in maintenance\n## Challenges of unexpected contingencies and computational burden\n## Proposed ML-based methodology\n# Related Works\n## Reinforcement learning for power grid operation and maintenance\n## Deep reinforcement learning for microgrid energy management","[{\"question\":\"Why is machine learning used for grid safety assessment during maintenance?\",\"answer\":\"Machine learning is used to rapidly and effectively predict contingency analysis results, addressing the computational complexity of operational security standards in existing planning tools.\"},{\"question\":\"What model is proposed in the study?\",\"answer\":\"The study applies the lightning search optimised random forest (LSORF) model to anticipate outcomes of contingency analyses during maintenance windows.\"},{\"question\":\"On which power system and voltage ranges is the approach tested?\",\"answer\":\"The approach is tested on Belgium’s entire regional transmission system, covering voltage levels from 200 kV down to 50 kV.\"}]","MACHINE LEARNING BASED GRID SAFETY ASSESSMENT THROUGH SIMULATION OF UNEXPECTED CONTINGENCIES DURING MAINTENANCE - 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