摘要
船舶电站发电系统主要由发电机、主配电板以及各类辅助设备组成,船舶电站是船舶安全可靠航运的关键系统,如果在船舶航运中出现系统故障,将严重影响船舶航运整体安全。进行负荷分配自动转移时,由于故障诊断精度较低,存在转移效率与性能较差的问题,对此,研究了船舶电站负荷分配自动转移故障分析及解决办法。首先,利用GA-BP神经网络的模型,通过训练学习船舶电站的故障数据,实现故障的自动识别和诊断。其次,建立了多目标优化模型,并通过NSGA-Ⅱ算法实现自动转移路径的优化设计。实验结果表明,所提方法最高检测精度达到98%,自动转移路径时间最长为4 min,技术水平和应用价值较高。通过这些措施,可以有效地解决船舶电站负荷分配自动转移故障,保障船舶电力系统的稳定运行。
The power generation system of ship power stations mainly consists of generators,main distribution boards and various auxiliary equipment.Ship power stations are key systems for safe and reliable shipping of ships.If system failures occur during shipping,it will seriously affect the overall safety of ship shipping.When conducting automatic transfer of load distribution,due to the low accuracy of fault diagnosis,there are problems with poor transfer efficiency and performance.Therefore,the fault analysis and solutions for automatic transfer of load distribution in ship power plants are studied.Firstly,using the GA-BP neural network model,automatic fault identification and diagnosis can be achieved by training and learning the fault data of ship power plants.Secondly,a multi-objective optimization model is established and the optimization design of automatic transfer paths is achieved through the NSGA-Ⅱ algorithm.The experimental results show that the highest detection accuracy of the proposed method is 98%,and the longest automatic transfer path time is 4 min,demonstrating the high technical level and application value of the method.Through these measures,the automatic transfer fault of load distribution in ship power stations can be effectively solved,ensuring the stable operation of the ship's power system.
作者
秦瑞卿
葛涛
Qin Ruiqing;Ge Tao(The Third Bureau directly under the China Maritime Police Bureau,Guangzhou 510715,China;School of Marine Engineering,Guangzhou Institute of Navigation,Guangzhou 510725,China)
出处
《机电工程技术》
2024年第8期279-283,共5页
Mechanical & Electrical Engineering Technology
关键词
船舶电站
故障诊断
负荷分配自动转移
NSGA-Ⅱ算法
ship power station
fault diagnosis
automatic transfer of load distribution
NSGA-Ⅱ algorithm
