| Author Name | Affiliation | Postcode | | Li Bian* | College of Electronic and Information Engineering, Guangdong Ocean University, Guangdong Zhanjiang 524088, China | 524088 | | Hui He | College of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China | 150022 | | Hongna Sun | College of Electrical and Control Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China | 150022 | | Wenjing Liu | Handan Power Supply Company, Hebei Handan 056002, China | 150022 |
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| Abstract: |
| The original fault data of oil immersed transformer often contains a large number of unnecessary attributes, which greatly increases the elapsed time of the algorithm and reduces the classification accuracy, leading to the rise of the diagnosis error rate. Therefore, in order to obtain high quality oil immersed transformer fault attribute data sets, an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction. The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms. Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25% and a reduction accuracy of 98%. By using BP neural network to classify the reduction results, the accuracy was 86.25%, and the overall effect was better than those of the original data and other algorithms. Hence, the proposed method is effective for fault attribute reduction of oil immersed transformer. |
| Key words: transformer fault improved imperialist competitive algorithm rough set attribute reduction BP neural network |
| DOI：10.11916/j.issn.1005-9113.19030 |
| Clc Number:TM407 |
| Fund: |
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| Descriptions in Chinese: |
| 油浸式变压器原始故障数据往往具有较多的不必要属性，这大大增加了算法的运行时间，同时也降低分类精度，导致变压器故障错误诊断。因此，为获得更优质的油浸式变压器故障属性数据集，提出利用改进后的帝国竞争算法优化粗糙集来对原始故障数据集进行属性约简。通过仿真实验，并与其他智能算法进行对比分析来验证其可行性，结果表明：该算法在迭代到第27次时就已经趋于稳定，其约简率高达56.25%，约简精度为98%，使用BP神经网络计算其分类准确率为86.25%，整体效果优于原始数据和其他算法。因此该算法对于油浸式变压器故障属性约简十分有效。 |
