基于机器学习的变压器寿命估计应用
Machine Learning Applications in Estimating Transformer Loss of Life
AI总结：
本文利用ANFIS方法建立数据驱动静态模型，用于小时级变压器寿命损失估计，验证其有效性与准确性。
摘要：
变压器寿命评估和故障诊断一直是电力公司的重要问题。环境温度和负载曲线是影响变压器绝缘老化的主要因素，进而影响变压器寿命。IEEE Std. C57.911995提供了一个基于环境温度和变压器负载计算变压器寿命损失的模型。本文利用该标准开发了一个数据驱动的静态模型，用于每小时估计变压器寿命损失。在开发此静态模型的各种机器学习方法中，选择了自适应网络模糊推理系统（ANFIS）。数值模拟证明了所提出ANFIS方法在解决此问题方面的有效性和准确性，与其它相关机器学习方法相比。
英文摘要：
Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.911995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
