基于布谷鸟搜索算法和DBN模型的变压器故障识别
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1.三峡大学;2.西安交通大学

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TM ???

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湖北省重点研发计划项目2020BAB110


Transformer Fault Identification Based On Cuckoo Search Algorithm And DBN Model
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1.China Three Gorges University;2.Xi’an Jiaotong University

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    摘要:

    依据油中溶解气体含量特征准确识别变压器故障类型,对其安全运行具有重要意义。深度信念网络(DBN)对样本数据中的特征提取有独特优势,本文采用DBN作为故障识别模型,将变压器油中溶解气体原始数据集直接输入至训练模型,并分别通过三种智能搜索算法对DBN中批处理、梯度下降学习率、层神经单元数三个重要参数进行智能寻优,来解决少量原始样本数据直接输入时,故障识别率不高的问题。结果显示,采用布谷鸟搜索算法(CS)优化DBN模型时,比常用的粒子群搜索算法(PSO)和遗传算法搜索(GA)优化下的效果更好,CS-DBN故障总识别率比GA-DBN高4.2%,比PSO-DBN提高2.5%同时进化效率提高56.2%;CS-DBN模型的泛化性能也比另两种更好。

    Abstract:

    Accurately identifying the type of transformer fault based on the characteristics of dissolved gas content in the oil is of great significance to its safe operation. Deep Belief Network (DBN) has unique advantages in feature extraction from sample data. In this paper, DBN is used as a fault identification model. The original data set of dissolved gas in transformer oil is directly input to the training model, and the DBN is analyzed through three intelligent search algorithms. The three important parameters of mid-batch processing, gradient descent learning rate, and number of neural units are intelligently optimized to solve the problem of low fault recognition rate when a small amount of raw sample data is directly input. The results show that when using the Cuckoo Search Algorithm (CS) to optimize the DBN model, the effect is better than the commonly used particle swarm search algorithm (PSO) and genetic algorithm search (GA) optimization. The fault total recognition rate of CS-DBN is 4.2% higher than that of GA-DBN, 2.5% higher than PSO-DBN and 56.2% higher in evolution efficiency; the generalization performance of CS-DBN model is also better than the other two.

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  • 收稿日期:2021-03-10
  • 最后修改日期:2021-05-30
  • 录用日期:2021-06-18
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