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Power transformer fault diagnosis based on multi-class multi-kernel learning relevance vector machine

机译:基于多类多核学习相关向量机的电力变压器故障诊断

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摘要

Diagnosis of potential faults concealed inside power transformers is the key of ensuring power system safety. The existing transformer diagnosis methods only infer based on single informative data and it is difficult to detect transformer faults more correctly. In this paper, fault diagnosis based on multi-class multi-kernel learning relevance vector machine (MMKL-RVM) is proposed which can integrate the informative data that can indicate the existence of fault. MMKL-RVM achieves sparsity without the constraint of having a binary class problem and provides probabilistic outputs for class membership instead of the hard binary decisions given by the traditional SVM. Most importantly, MMKL-RVM enables informative integration of possibly heterogeneous informative data or feature spaces in a multitude of ways, from the simple summation of feature expansions to weighted product of kernels. Additionally, Genetic Algorithm (GA) combined with K-fold Cross Validation (K-CV) method is adopted to optimize the kernels parameters in order to enhance the performance of the MMKL-RVM. Experimental results show that MMKL-RVM is capable of more excellent diagnosis accuracy to BP neural network and SVM.
机译:诊断隐藏在电力变压器内部的潜在故障是确保电力系统安全的关键。现有的变压器诊断方法仅基于单个信息数据进行推断,因此难以更正确地检测变压器故障。本文提出了一种基于多类多核学习相关性向量机(MMKL-RVM)的故障诊断方法,该方法可以集成表明故障存在的信息数据。 MMKL-RVM实现了稀疏性而没有二进制类问题的约束,并为类成员资格提供了概率输出,而不是传统SVM给出的硬二进制决策。最重要的是,MMKL-RVM支持从可能的特征扩展的简单总和到内核的加权乘积,以多种方式对可能异构的信息数据或特征空间进行信息集成。另外,采用遗传算法(GA)和K-fold交叉验证(K-CV)方法相结合来优化内核参数,以增强MMKL-RVM的性能。实验结果表明,MMKL-RVM对BP神经网络和SVM的诊断准确性更高。

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