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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TM85

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湖北省重点研发计划(2020BAB110)


Transformer fault identification based on the cuckoo search algorithm and DBN model
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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,对变压器的安全运行具有重要意义。考虑深度信念网络(DBN)对样本数据中的特征提取有独特优势,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,并通过3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3个重要参数分别进行智能寻优,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。仿真结果表明,采用布谷鸟搜索算法(CS)优化DBN模型时比粒子群搜索算法(PSO)和遗传算法搜索(GA)优化的效果更好,CS -DBN模型的故障总识别率比GA-DBN的高4.2%,比PSO-DBN的提高2.5%,同时进化效率提高56.2%;CS-DBN模型的泛化性能也比另2种更好。

    Abstract:

    The fault identification of transformer based on the characteristics of dissolved gas contents in the oil is of great significance to its safe operation. The deep belief network (DBN) is selected as a fault identification model in this paper since it has unique for extracting features from sample data. Firstly, the original data set of dissolved gas in transformer oil is directly deployed as the inputs of the training model, and the DBN is processed through three intelligent search algorithms.Three important parameters of mid-batch processing, gradient descent learning rate, and number of neural units are intelligently optimized to solve the problem of a low fault recognition rate when input raw sample data are limited. It is shown that the performance of the proposed method is better than the particle swarm search (PSO) algorithm and genetic algorithm (GA) search optimization. The 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. It also has a good generalization performance.

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刘展程,王爽,唐波.基于布谷鸟搜索算法和DBN模型的变压器故障识别[J].电力科学与技术学报,2022,(2):3-11. LIU Zhancheng, WANG Shuang, TANG Bo. Transformer fault identification based on the cuckoo search algorithm and DBN model[J]. Journal of Electric Power Science and Technology,2022,(2):3-11.

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  • 在线发布日期: 2022-05-26
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