变温油浸式电力设备可靠性与状态评估技术综述

IF 5.1 3区 工程技术 Q2 ENERGY & FUELS
Raheel Ahmed , Ji Liu , Mingze Zhang , Xianhao Fan
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引用次数: 0

摘要

油浸式电力变压器是现代电网的重要组成部分,其绝缘系统的完整性在很大程度上取决于油浸式电力变压器。它由纤维素基固体绝缘和矿物或酯基绝缘流体组成。由于电、热、机械和环境压力,绝缘材料会随着时间的推移而降解。研究人员在实验室中进行了加速老化测试,以模拟现实生活条件,研究油浸设备如何随着时间的推移而退化。有价值的技术,以减少保温系统的寿命,使研究材料的行为和预测保温系统将承受多长时间。本文综述了传统的和先进的变压器绝缘评估技术,特别强调了在寒冷气候环境中的有效性。它包括聚合度(DP),糠醛分析,溶解气体分析(DGA)以及先进的介电响应方法,包括FDS和PDC测试。本文还讨论了低温绝缘材料的精度、挑战和可靠性问题。结合人工智能、机器学习和光学传感技术的新兴诊断方法为早期故障检测和状态监测提供了有希望的改进。老化过程的加速可能导致绝缘过早失效,增加运行风险。此外,它还讨论了人工智能和机器学习的集成,以加强监测,并开发耐寒材料和流体,以提高极端环境下变压器的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reliability and condition assessment techniques for oil-immersed power equipment under varying temperatures: A review
Oil-immersed power transformers are important components in modern power grids, relying heavily on the integrity of their insulation systems. It consists of cellulose-based solid insulation and mineral or ester based insulating fluids. The insulation materials degrade over time due to electrical, thermal, mechanical and environmental pressures. Researchers have conducted accelerated aging tests in the lab to replicate real-life conditions to investigate how oil-immersed equipment degrades over time. The valuable techniques to reduce the lifespan of the insulation system enable the study of the material behavior and predict how long the insulation system will withstand. This review examines conventional and advanced transformer insulation assessment techniques, particularly emphasizing efficacy in cold-climate environments. It includes the degree of polymerization (DP), furfural analysis, and dissolved gas analysis (DGA) as well as advanced dielectric response methodologies, including FDS and PDC testing. The review also discusses low-temperature insulating material precision, challenges, and dependability issues. Emerging diagnostic approaches incorporating artificial intelligence, machine learning and optical sensing technologies offer promising improvements in early fault detection and condition monitoring. The acceleration of the deterioration process can lead to premature insulation failure and increased operational risks. Additionally, it discusses the integration of AI and machine learning for enhanced monitoring and the development of cold-resistant materials and fluids for improved transformer reliability in extreme environments.
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来源期刊
Energy Reports
Energy Reports Energy-General Energy
CiteScore
8.20
自引率
13.50%
发文量
2608
审稿时长
38 days
期刊介绍: Energy Reports is a new online multidisciplinary open access journal which focuses on publishing new research in the area of Energy with a rapid review and publication time. Energy Reports will be open to direct submissions and also to submissions from other Elsevier Energy journals, whose Editors have determined that Energy Reports would be a better fit.
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