物联网环境下变电站信号可并行识别的改进帧时隙ALOHA算法

Improved Framed Slot ALOHA Algorithm of Parallel Identification for Substation Signals in IoT Environment

为了解决山地变电站监测信号的碰撞问题,该文首先构建山地变电站物联网监测系统,研究传统帧时隙ALOHA算法并进行仿真和识别效率分析,鉴于该算法吞吐率较低、局限性的弊端,提出改进帧时隙ALOHA算法。然而当监测信号量持续增大时,会导致信号识别成功率急剧降低产生安全运行问题。因此,该文对该算法进行了二次改进,研究了可并行识别的改进帧时隙ALOHA算法,搭建了变电站物联网测试平台,最后经过仿真验证,该改进算法保证了吞吐率随着信号量增大时保持相对稳定,提高了山地变电站监测信号被成功识别的概率,保障了变电站的安全。

This paper first constructed a mountain substation monitoring system in IoT environment,studied the traditional frame slot ALOHA algorithm and carried out simulation and efficiency analysis in order to solve the collision problem of monitoring signals in mountain substations.In view of the low throughput and limitation of the algorithm,the improved frame slot ALOHA algorithm was studied and simulation and recognition efficiency analysis was carried out.However,when the monitoring semaphore continues to increase,the success rate of signal recognition will be reduced sharply,resulting in a safe operation problem.Then the second improved frame slot ALOHA algorithm was studied,and the substation IoT test platform was built.Finally,the simulation proved that the throughput remains relatively stable as the semaphore increases,so the probability of successful identification with monitoring signal of mountain substation is improved,and the safety of the substation is guaranteed.