A novel load prediction method for hybrid electric ship based on working condition classification

被引：8
作者：
Gao, Diju [1 ]
Jiang, Yao [1 ]
Zhao, Nan [2 ]
机构：
[1] Shanghai Maritime Univ, Minist Transport, Key Lab Marine Technol & Control Engn, 1550 Haigang Ave, Shanghai 201306, Peoples R China
[2] Univ Coll Dublin, Sch Elect & Elect Engn, Dublin, Ireland
来源：
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL | 2022年 / 44卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Hybrid power ship; condition classification; prediction; load demand; ENERGY MANAGEMENT; MODEL; SVM; VEHICLE;
D O I：
10.1177/0142331220923767
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to effectively optimize the load distribution between power sources during the navigation of hybrid ships, a method for predicting ship load demand based on real-time classification according to different working conditions is proposed. The k-means clustering algorithm is used to quantify the voyage history data to classify the ship's navigation conditions into fast-changing conditions and slow-changing conditions. Some characteristic parameters related to working conditions are selected as input. Then, input and the category of working conditions are put into least squares support vector machine to learn and train to get an online working condition classifier. The genetic algorithm is used to optimize the radial-based neural network to predict the load demand under fast-changing conditions, use the Markov chain model to predict the load demand under slow-changing conditions, so as to obtain the most accurate future load demand of the ship. The simulation results show that the proposed prediction models under different conditions have higher precision, which is an effective means of predicting the load demand for hybrid power ships.
引用
收藏
页码：5 / 14
页数：10
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