基于蚁群算法的变压器故障诊断研究
发布时间：2019-05-08 22:19
【摘要】：电力变压器是电力系统中最重要的设备之一,它的运行状态直接影响到整个电网的输变电状态。在电力行业不断发展的今天,由于诸多因素的影响,传统的DGA方法已经无法准确地判别出变压器的故障类型,满足不了现今对变压器故障判别准确率的要求。因此,DGA与智能方法结合的组合模型己成为变压器故障诊断的一种必然发展趋势。目前,最常用的诊断方法是DGA与BP神经网络的组合模型。在这一模型中,BP网络本身存在的自适应学习、并行处理、联想记忆和非线性映射等特性可以完善普通DGA方法存在的缺陷。然而,若收集到的故障样本数目过于庞大且对故障诊断的检测精度要求较高时,BP网络本身的缺陷将延长网络达到收敛时所需要的时间,甚至使得网络不收敛,易将局部最小值当作全局最优值,从而导致故障诊断的准确率降低。所以,DGA与BP神经网络的组合模型在变压器故障诊断方面仍然存在不足,为了进一步完善该方法,有必要利用其它优化方法对BP神经网络进行改进。蚁群算法(ACA)是一种新型的仿生态算法,它具有全局优化能力和启发式搜索特性,将其与BP网络结合可以改进BP网络的性能。本文中提出利用ACA调节BP神经网络权值,达到提高BP网络性能的要求,并利用改进后的BP网络对变压器故障进行再诊断,以此验证新方法的优越性。首先构建出结构为5-8-5的BP神经网络对变压器进行仿真与故障识别,利用MATLAB编写程序,得出结果,证明单纯的BP神经网络能够对变压器故障进行识别,但准确率不高。其次阐述利用ACA优化BP网络的基本原理,说明其核心思想就是利用ACA优化BP神经网络的权值参数,提出基于ACA优化BP神经网络的变压器故障诊断方法(ACA-BP方法),利用ACA-BP方法对变压器进行仿真分析和故障识别,对比单纯BP网络诊断变压器故障的结果,证明ACA-BP方法能够有效防止训练过程中诊断结果陷入局部最优,加快网络收敛速度,缩短学习时间,提高故障辨识的准确度,更准确地反应出变压器的实际故障。文章最后总结了ACA-BP方法的优越性及进一步改进的方向。
[Abstract]:Power transformer is one of the most important equipments in power system, its running state directly affects the transmission and transformation state of the whole power network. With the continuous development of electric power industry, due to the influence of many factors, the traditional DGA method has been unable to accurately identify the fault types of transformers, and can not meet the requirements of the accuracy of transformer fault discrimination. Therefore, the combination model of DGA and intelligent method has become an inevitable trend of transformer fault diagnosis. At present, the most commonly used diagnostic method is the combination model of DGA and BP neural network. In this model, the self-adaptive learning, parallel processing, associative memory and nonlinear mapping of BP networks can perfect the defects of ordinary DGA methods. However, if the number of fault samples collected is too large and the detection accuracy of fault diagnosis is high, the defect of the BP network itself will prolong the time required for the network to reach convergence, even make the network not converge. The local minimum value is easy to be regarded as the global optimal value, which leads to the reduction of the accuracy of fault diagnosis. Therefore, the combination model of DGA and BP neural network still has shortcomings in transformer fault diagnosis. In order to further improve the method, it is necessary to use other optimization methods to improve the BP neural network. Ant colony algorithm (ACA) is a new ecological-like algorithm, which has global optimization ability and heuristic search characteristics. Combining it with BP network can improve the performance of BP network. In this paper, ACA is used to adjust the weights of BP neural network to improve the performance of BP network, and the improved BP network is used to re-diagnose transformer faults, so as to verify the superiority of the new method. Firstly, the BP neural network with the structure of 5 ~ 8 ~ 5 is constructed to simulate and identify the transformer fault. The program is programmed by MATLAB, and the result shows that the simple BP neural network can identify the transformer fault, but the accuracy is not high. Secondly, the basic principle of optimizing BP network by ACA is described, and the core idea is to optimize the weight parameters of BP neural network by ACA. A fault diagnosis method for transformer based on ACA optimized BP neural network (ACA-BP method) is put forward. The ACA-BP method is used to simulate the transformer and identify the faults. Compared with the results of simple BP network diagnosis, it is proved that the ACA-BP method can effectively prevent the diagnosis results from falling into the local optimum in the course of training. The network convergence speed is accelerated, the learning time is shortened, the accuracy of fault identification is improved, and the actual fault of transformer is reflected more accurately. Finally, the advantages of ACA-BP method and the direction of further improvement are summarized.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM407;TP18
，
本文编号：2472262
[Abstract]:Power transformer is one of the most important equipments in power system, its running state directly affects the transmission and transformation state of the whole power network. With the continuous development of electric power industry, due to the influence of many factors, the traditional DGA method has been unable to accurately identify the fault types of transformers, and can not meet the requirements of the accuracy of transformer fault discrimination. Therefore, the combination model of DGA and intelligent method has become an inevitable trend of transformer fault diagnosis. At present, the most commonly used diagnostic method is the combination model of DGA and BP neural network. In this model, the self-adaptive learning, parallel processing, associative memory and nonlinear mapping of BP networks can perfect the defects of ordinary DGA methods. However, if the number of fault samples collected is too large and the detection accuracy of fault diagnosis is high, the defect of the BP network itself will prolong the time required for the network to reach convergence, even make the network not converge. The local minimum value is easy to be regarded as the global optimal value, which leads to the reduction of the accuracy of fault diagnosis. Therefore, the combination model of DGA and BP neural network still has shortcomings in transformer fault diagnosis. In order to further improve the method, it is necessary to use other optimization methods to improve the BP neural network. Ant colony algorithm (ACA) is a new ecological-like algorithm, which has global optimization ability and heuristic search characteristics. Combining it with BP network can improve the performance of BP network. In this paper, ACA is used to adjust the weights of BP neural network to improve the performance of BP network, and the improved BP network is used to re-diagnose transformer faults, so as to verify the superiority of the new method. Firstly, the BP neural network with the structure of 5 ~ 8 ~ 5 is constructed to simulate and identify the transformer fault. The program is programmed by MATLAB, and the result shows that the simple BP neural network can identify the transformer fault, but the accuracy is not high. Secondly, the basic principle of optimizing BP network by ACA is described, and the core idea is to optimize the weight parameters of BP neural network by ACA. A fault diagnosis method for transformer based on ACA optimized BP neural network (ACA-BP method) is put forward. The ACA-BP method is used to simulate the transformer and identify the faults. Compared with the results of simple BP network diagnosis, it is proved that the ACA-BP method can effectively prevent the diagnosis results from falling into the local optimum in the course of training. The network convergence speed is accelerated, the learning time is shortened, the accuracy of fault identification is improved, and the actual fault of transformer is reflected more accurately. Finally, the advantages of ACA-BP method and the direction of further improvement are summarized.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM407;TP18
，
本文编号：2472262
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