基于ANFIS的变压器寿命预测和状态评估

变压器寿命和运行状态准确的评估对其检修策略的制定有着重要的指导意义。为了实现对变压器寿命和状态进行客观的、科学的评估,文中提出了基于自适应模糊神经网络(ANFIS)的多特征诊断参数的变压器寿命预测和状态评估方法。提取影响变压器寿命的特征参数,通过自适应模糊神经网络对这些特征参数进行学习,利用反向传播算法解决权重的自适应动态调整,构建变压器的寿命预测模型;在其基础上结合油中溶解气体建立一种变压器综合健康状态评估模型。通过实验数据研究论证,该模型能够准确有效地诊断变压器寿命和状态,同时相比传统方法有更高的预测精度和评估精度,是一种新的有效的变压器状态评估方法。

Transformer Life Estimation and State Assessment Based on ANFIS

In Accurate assessment of transformer life and operating conditions has important guiding significance for the formulation of its maintenance strategy. In order to achieve objective and scientific assessment of transformer life estimation and condition assessment, this paper constructs a transformer life prediction and condition assessment method based on multi-feature diagnostic parameters of adaptive fuzzy neural network (ANFIS). First extract the characteristic parameters that affect the life of the transformer, learn these characteristic parameters through an adaptive fuzzy neural network, use the back-propagation algorithm to solve the adaptive dynamic adjustment of the weights, build a life prediction model of the transformer, and then build a A comprehensive health assessment model for transformers. Through experimental data research and demonstration, this model can accurately and effectively diagnose the life and state of transformers, and at the same time has higher prediction accuracy and assessment accuracy than traditional methods. It is a new and effective method for transformer state assessment.