摘要
为了消除传感器测量中的干扰及不确定性,获得更准确、更可靠的测量结果,提出一种新型的基于量子遗传算法的数据级融合算法,并将其应用于高压断路器机械振动或分合闸线圈电流等测量数据的融合。该算法以各传感器的加权因子为优化变量,以总的样本方差最小为目标函数,采用量子遗传算法进行优化求解,求得融合后的最优输出。通过各种比较,证明了该算法的正确性和实用性。该算法可推广应用于其他设备的数据融合。
In order to eliminate the interference and uncertainty of ble measurement results,a new data level fusion algorithm based on sensor measurement and obtain more accurate and relia- Quantum Genetic Algorithm （QGA） is presented. It is applied to the fusion of measurement parameters on mechanical vibration or switch coil current of high - voltage circuit breaker, Each sensor＇s weighted factor is selected as optimization variable. The minimum of total sample variance is regarded as optimized funetion. Optimization solution with QGA is carried out to obtain the optimal fusion output. The correctness and practi cality are proved by comparision. This algorithm can be applied to data fusion of other equipment.
出处
《现代电子技术》
2010年第4期166-169,共4页
Modern Electronics Technique
基金
常州市输配电及节电技术重点实验室开放课题(CS0904)
关键词
QGA数据级融合算法
高压断路器
传感器测量
QGA
data level fusion algorithm
high- voltage circuit breaker
measurement of sensor
