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量子粒子群优化核极限学习机的船舶变压器故障诊断

谢佩军1,高婷婷1,叶宏武1,2   

  1. 1. 浙江纺织服装职业技术学院机电与轨道交通学院,宁波 315211;  2. 中国科学院宁波材料技术与工程研究所, 宁波 315201
  • 出版日期:2021-07-25 发布日期:2021-10-14

谢佩军, 高婷婷, 叶宏武. 量子粒子群优化核极限学习机的船舶变压器故障诊断[J]. 系统科学与数学, 2021, 41(7): 1807-1816.

XIE Peijun, GAO Tingting, YE Hongwu. Marine Transformer Fault Diagnosis Based on Quantum-Behaved Particle Swarm Optimization and Kernel Extreme Learning Machine[J]. Journal of Systems Science and Mathematical Sciences, 2021, 41(7): 1807-1816.

Marine Transformer Fault Diagnosis Based on Quantum-Behaved Particle Swarm Optimization and Kernel Extreme Learning Machine

XIE Peijun1 ,GAO Tingting1 ,YE Hongwu1,2   

  1. 1. School of Mechatronics and Rail Transit, Zhejiang Fashion Institute of Technology, Ningbo 315211; 2. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201
  • Online:2021-07-25 Published:2021-10-14
船舶变压器是船舶电力系统的关键部件,变压器故障易导致电力系 统异常甚至引发船体及生命安全事故.针对基本智能算法故障诊断模型的局限性,文章 采用寻优能力强搜索效率高的QPSO优化KELM建立船舶变压器故障诊断模型.采用UCI 数据集对SVM、ELM、KLEM、PSO-KELM和QPSO-KELM进行对比实验,结果表明QPSO-KELM具有更好的 分类性能和计算效率.最后,通过船舶变压器真实故障数据集对QPSO-KELM进行测试分析,测试集 和训练集的平均正确率分别达92.15$\%$和97.65$\%$, 标准差分别为3.75$\%$和1.42$\%$,充分证明了QPSO-KELM具有优秀的诊断精度和算法稳定性,对 各类船舶具有一定的实际应用价值.
Marine transformer is the key component of marine power system, transformer fault may lead to power system abnormality, and even cause ship and life safety accidents. In view of the limitations of the transformer fault diagnosis models based on basic intelligent algorithm, a transformer fault diagnosis model with strong optimization ability and high computational efficiency, which based on Quantum-behaved Particle Swarm Optimization (QPSO) and Kernel Extreme Learning Machine (KELM) was proposed. The UCI data set was used to test the models, experimental results showed that compared to SVM, ELM, KLEM, PSO-KELM and QPSO-KELM, QPSO-KELM had better classification performance and computational efficiency. Finally, the data set of real marine transformer faults were used to test QPSO-KELM, the average accuracy of testing set and training set were 92.15$\%$ and 97.65$\%$, and the standard deviations were 3.75$\%$ and 1.42$\%$, which fully proved that QPSO-KELM had excellent diagnostic accuracy and algorithm stability, and had a certain practical value for ships.
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