基于数据融合的船舶电网参数测量研究

采用单传感器的传统船舶电气参数测量方法存在很多缺陷，会受到传感器的测量噪声和电网中的电磁干扰等影响，从而影响参数测量的精度和测量结果的稳定性。为此，应用状态估计技术和多传感器数据融合理论，提出了一种新的船舶电气参数测量方法。首先建立电压和电流的状态模型，将其连续的动态方程离散化，用于数字信号处理器（DSP）中。然后采用卡尔曼滤波和无反馈分布式融合来对离散化后的采样数据进行融合，从而得到全局数据融合的最优估计。最后，经过一个周期的采集数据估计值进行有效值计算，在液晶屏中显示出来。与单个传感器的检测方法相比，该检测方法具有更高的精度和更好的稳定性。仿真结果和实验测试结果都证明了本检测方法的有效性和优越性。

Electric Parameter Monitoring of Marine Power System Based on Data Fusion Theory

Because the traditional electric parameter monitoring methods of marine power system based on single measurement sensor possess many disadvantages, such as measurement value includs various noises produced by electric and magnetic field and measurement device etc. , which will greatly influence the accuracy of monitoring result and increase the uncertainty of result. A new parameter monitoring method of marine electric network based on data fusion theory applied state estimation technology and multisensor data fusion theory are proposed. The state models of the voltage and the current are established. In DSP the continuous signals from voltage and current sensor respectively are discreted; then Kalman filter and the distributed fusion without feedback are used to fuse these discrete measurement values, and the global fusion estimation can be got. Compared with the traditional method based on single measurement device, the method possesses better monitoring accuracy and stronger stability. Finally computer simulation results and test results show the validity and superiority of the monitoring method in the paper.