针对舰船综合电力系统，提出基于大数据的继电保护定值校核方法。首先，针对舰船综合电力系统产生的海量动态数据，利用SOM聚类算法挖掘电力系统的运行数据。然后，采用基于部分失真的SOM聚类算法提高聚类效率，在计算连接权向量后，判断当前最小欧氏距离与该向量的欧氏距离大小，再决定是否继续聚类；最后，依据电力系统运行数据，采用基于Ward等值的故障计算方法获取故障计算结果，参考定值校核规则，完成继电保护定值校核。实验表明，该方法通过SOM聚类处理，能够准确分类出船舶综合电力系统电流以及电压数据，且能够准确获取继电保护线路的定值校核结果，可以为提高舰船综合电力系统安全性提供支持。
A relay protection setting verification method based on big data is proposed for the integrated power system of ships. Firstly, in response to the massive dynamic data generated by the integrated power system of ships, the SOM clustering algorithm is used to mine the operational data of the power system. Then, the SOM clustering algorithm based on partial distortion is adopted to improve clustering efficiency. After calculating the connection weight vector, the current minimum Euclidean distance and the Euclidean distance of the vector are determined, and then the decision is made whether to continue clustering; Finally, based on the operating data of the power system, a fault calculation method based on Word equivalence is adopted to obtain the fault calculation results. Referring to the fixed value verification rules, the relay protection fixed value verification is completed. The experiment shows that this method can accurately classify the current and voltage data of the ship's integrated power system through SOM clustering processing, and can accurately obtain the calibration results of relay protection lines, which can provide support for improving the safety of the ship's integrated power system.
2024,46(8): 153-156 收稿日期：2024-1-30
DOI：10.3404/j.issn.1672-7649.2024.08.028
分类号：TM588
基金项目：山东省自然科学基金资助项目（ZR2020MF127）
作者简介：吴相坛(1998-),男,硕士研究生,助理工程师,研究方向为电气工程及其自动化
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