| 周亚中,何怡刚,邢致恺,邵凯旋,李紫豪,雷蕾潇.基于 IDBO-ARIMA 的电力变压器振动信号预测[J].电子测量与仪器学报,2023,37(8):11-20 |
| 基于 IDBO-ARIMA 的电力变压器振动信号预测 |
| Power transformer vibration signal prediction based on IDBO-ARIMA |
| |
| DOI： |
| 中文关键词: 电力变压器 振动信号预测 时间序列 回归分析 蜣螂优化算法 |
| 英文关键词:power transformer vibration signal prediction time series regression analysis dung beetle optimizer algorithm |
| 基金项目:国家重点研发计划专项( 2020YFB0905905)、国家重点研发计划( 2016YFF0102200)、国家自然科学基金( 51977153,51977161,51577046)、中央高校基本 科 研 业 务 费 专 项 资 金 ( 2042021kf0233)、 国 家 自 然 科 学 基 金 重 点 项 目 ( 51637004 )、 装 备 预 先 研 究 重 点 项 目
(41402040301)、湖北省重点研发计划项目(2021BEA162)、武汉市局科技计划项目(20201G01)资助 |
|
|
| Author | Institution |
| Zhou Yazhong | 1. State Key Laboratory of Power Grid Environmental Protection, Wuhan University,2. School of Electrical Engineering and Automation, Wuhan University |
| He Yigang | 1. State Key Laboratory of Power Grid Environmental Protection, Wuhan University,2. School of Electrical Engineering and Automation, Wuhan University |
| Xing Zhikai | 1. State Key Laboratory of Power Grid Environmental Protection, Wuhan University,2. School of Electrical Engineering and Automation, Wuhan University |
| Shao Kaixuan | 1. State Key Laboratory of Power Grid Environmental Protection, Wuhan University,2. School of Electrical Engineering and Automation, Wuhan University |
| Li Zihao | 1. State Key Laboratory of Power Grid Environmental Protection, Wuhan University,2. School of Electrical Engineering and Automation, Wuhan University |
| Lei Leixiao | 1. State Key Laboratory of Power Grid Environmental Protection, Wuhan University,2. School of Electrical Engineering and Automation, Wuhan University |
|
| 摘要点击次数: 459 |
| 全文下载次数: 396 |
| 中文摘要: |
| 为解决电力变压器振动信号因非平稳特性而导致难以预测的问题,提出一种基于改进蜣螂优化算法的差分整合移动平
均自回归预测模型。 首先,利用 ADF 检验和 KPSS 检验对变压器原始振动信号进行平稳性检验,若不平稳则进行差分处理直至
信号平稳。 其次,通过在蜣螂优化算法中引入周期突变机制以提升算法的寻优能力,并利用改进后的蜣螂优化算法对差分整合
移动平均自回归模型参数 p 和 q 进行定阶,实现对变压器振动信号的预测。 最后,利用某个 0. 4- / 0. 4-kV,15-kVA 三相双绕组
干式变压器实际采集的振动数据,验证所提出模型的有效性。 仿真结果表明,该模型的平均绝对百分比误差可达 3. 77%,而差
分整合移动平均自回归模型、长短时记忆网络、循环神经网络和卷积神经网络的平均绝对百分比误差分别为 5. 34%、4. 74%、
5. 03%、5. 40%。 因此,所提出的模型可以实现变压器振动信号的精准预测。 |
| 英文摘要: |
| To solve the problem that power transformer vibration signals is difficult to predict because of the non-stationary characteristic,
an autoregressive integrated moving average prediction model based on improved dung beetle optimizer algorithm is proposed. Firstly,
ADF test and KPSS test are used to check the stationary of the transformer original vibration signal, and if it is not stationary, differential
processing is performed until the signal is stationary. Secondly, the periodic mutation mechanism is introduced into dung beetle optimizer
algorithm to improve the optimization ability of the algorithm, and the parameters p and q of autoregressive integrated moving average
model are determined by improved dung beetle optimizer algorithm to realize the prediction of transformer vibration signal. Finally, the
validity of the proposed model is verified by using the actual collected vibration data of a 0. 4- / 0. 4-kV, 15-kVA three-phase doublewinding dry-type transformer. The simulation result shows that the mean absolute percentage error of the model can reach 3. 77%, while
the mean absolute percentage error of the autoregressive integrated moving average model, long short-term memory network, recurrent
neural network and convolutional neural network are 5. 34%, 4. 74%, 5. 03% and 5. 40%, respectively. Therefore, the proposed model
can achieve accurate prediction of transformer vibration signal. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
