为降低能源消耗，设计船舶机械设备启动过程的能耗监测系统，提升能耗监测效果。采集层利用采集器，控制质量流量计与流向传感器等，采集船舶机械设备启动过程的能耗数据，以及设备运行数据与环境数据；网络层利用ZigBee模块与现场可编程门阵列控制器，预处理采集层采集的数据，剔除明显错误与不合理的数据，并传输预处理后的数据至数据存储层。数据存储层通过数据库服务器存储网络层传输的预处理后的数据，应用层中能耗监测模块通过调用数据存储层存储的数据，实时呈现能耗监测结果。能耗预测模块利用灰色径向基函数神经网络，依据数据存储层存储的数据，得到能耗预测结果。通过能耗超标预警模块对超标的能耗数据进行预警，提醒船员变更启动方式，达到节能降耗的目的。实验证明，该系统可有效采集与处理船舶机械设备启动过程的相关数据；该系统可有效监测机械设备启动过程能耗，并精准预测启动能耗。
In order to reduce energy consumption, an energy consumption monitoring system is designed for the startup process of ship mechanical equipment to improve the energy consumption monitoring effect. The acquisition layer uses the collector to control the mass flowmeter and flow direction sensor to collect the energy consumption data, equipment operation data and environmental data during the startup of the ship's mechanical equipment. The network layer uses ZigBee module and field programmable gate array controller to preprocess the data collected by the acquisition layer, eliminate obvious errors and unreasonable data, and transmit the preprocessed data to the data storage layer. The data storage layer stores the pre processed data transmitted by the network layer through the database server. The energy consumption monitoring module in the application layer presents the energy consumption monitoring results in real time by calling the data stored in the data storage layer. The energy consumption prediction module uses the gray radial basis function neural network to obtain the energy consumption prediction results according to the data stored in the data storage layer. The excessive energy consumption data is warned by the excessive energy consumption warning module to remind the driver to change the starting mode, so as to achieve the purpose of energy saving and consumption reduction. The experiment proves that the system can effectively collect and process the relevant data of the starting process of the ship's mechanical equipment. The system can effectively monitor the energy consumption of mechanical equipment during startup and accurately predict the startup energy consumption.
2022,44(24): 173-176 收稿日期：2022-08-15
DOI：10.3404/j.issn.1672-7649.2022.24.037
分类号：TP227
作者简介：晁红芬(1981-),女,本科,讲师,研究方向为机电一体化
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