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Root cause analysis improved with machine learning for failure analysis in power transformers

机译：电力变压器故障分析的机器学习改进了根本原因分析

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The root cause analysis, diagnosis and classification of faults in power transformers with high accuracy and efficiency is the fundamental key to ensure reliability and power quality with least interruptions. In this research, a new proposal was developed for an intelligent Genetic Algorithm tuned artificial neural network (ANN) classifier for transformer faults for to improve the root cause analysis, in this case, this new proposal is able to segregate all fault types using Dissolved Gas Analysis (DGA) samples from power transformers of a large range of providers and from other research papers, these input data have been pre-processed using the Fast Decision tree learner (FDTL), tree learner advanced, and M5 Rule (M5R) algorithm and NN. We replace the conventional action selection procedure of Reinforcement Learning (RL) by a machine learning based optimizer. In this research, a new proposal for computationally least expensive incomparison to other approaches is presented. Our proposed classifier could serve as an important tool in ensuring healthy operation of power transformers, the correlation is higher than 0.98 with tree learner classifier, with a validation of the over-fitting perspective.
机译：具有高精度和效率的电力变压器故障的根本原因分析，诊断和分类是确保可靠性和功率质量最小中断的基本键。在本研究中，为智能遗传算法调整的人工神经网络（ANN）分类器开发了一个新的提案，用于改善根本原因分析，在这种情况下，这种新的提案能够使用溶解气体分离所有故障类型来自大量提供商和其他研究论文的电力变压器的分析（DGA）样本，这些输入数据已经使用快速决策树学习者（FDTL），树立学习者高级和M5规则（M5R）算法预处理了这些输入数据nn。我们通过基于机器学习的优化器取代了加强学习（RL）的传统动作选择程序。在这项研究中，提出了一种对其他方法的计算最便宜的廉价效果的新提案。我们所提出的分类器可以作为确保电力变压器健康运行的重要工具，相关性高于树立学习者分类器，验证过度拟合的视角。

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