船用配电系统故障分类

Mohd Abdul Talib Mat Yusoh, Ahmad Farid Abidin, Nadiah Binti Mohamad Bakar Mohd Basri, Nor Zulaily Mohamad, N. H. Nik Ali, Choong Chin Aun
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引用次数: 0

摘要

本文讨论了船舶电能质量问题及其对船舶安全的影响。随着船舶系统电能生产和利用新技术的引入,电能质量(PQ)的重要性日益提高。本文的目的是利用集合袋树、最近邻和支持向量机对船舶上的PQ干扰进行分类。在此基础上,根据实测数据建立了船舶配电系统的电气模型。在该分类方案中考虑的PQ扰动类型是电压凹陷、电压膨胀以及电压膨胀和电压瞬态的组合。为了在分类方案中获得较高的性能,选择s变换(ST)来提取分类器使用的重要特征。在这种情况下,总数据的60%用于训练,剩余的数据将用于测试。结果表明,与最近邻和支持向量机相比,集成袋树的准确率高达91.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Faults on the Shipboard Distribution Power System
This paper discusses the issue of electrical power quality and how it affects the ship safety. Since the new techniques for producing and utilizing electrical energy in the ship systems have been introduced, there is a need to consider the increase in the significance of Power Quality (PQ). The objective of this paper is to classify the PQ disturbances on the ship using Ensemble Bagged Tree, Nearest Neighbors and Support Vector Machine. Hence, the electrical model on the ship distribution system is develop based on the real measurement data. The types of PQ disturbances that has been considered in this classification scheme are voltage sag, voltage swell, and combination of voltage swell and voltage transient. In order to get the high performances in the classification scheme, the S-Transform (ST) is chosen to extract the significant features used by the classifiers. In this case, 60% of total data is used for training and the remaining data will be used for testing. The results shows that the Ensemble Bagged Tree presents high accuracy rate of 91.7% compared to the Nearest Neighbors and Support Vector Machine.
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