单片机交流采样问题
大家好 我现在做一个功能要采集漏电值 AD采过来的数据应该怎么处理呢 我现在在主循环里一直采样,取最大值和最小值,40ms也就是2个周期进行一次处理 出来的结果浮动特别大 为啥呀。 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***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 本帖最后由 ningling_21 于 2021-10-27 10:54 编辑交流信号本来就是幅度随时间变化的信号,不过有的交流信号周期性,需连续采样一个周期再进行处理,如果幅度不够还要加运放把信号放大到合适采样的范围 什么样的信号?采样率多少?采集后的数据准备用来干啥? 单片机采样不是随便设置采样时刻的,要根据你需要设置采样时刻,如果你在大循环里面随便采,数值当然就是乱的 单片机交流采样,最好是使用ADC-DMA运用,持续采样一周期的信号,这样才能采集到比较准确的数据 如果交流波形是固定不变的,只是振幅不同,那么可以使用周期内采集足够数量的点 ,通过计算应该是可以获得,有效值的,还是建议是用模拟电路进行解决。如果对成本没有要求,比如AD637,可以比较简单的解决这个问题 这种情况,应该使用DMA连续采样,再分析波形。采样周期要足够小。 其实我采样很小了,在WHILE循环里采 没有什么其他时长多的任务感觉是硬件问题
单片机交流采样问题
既然是交流电,就需要在一个周期采样10次以上,为什么是40而不是4 ms呢?搜索复制
另外,一周采样10次选出你需要的数据:例如最大、最小等,然后也可以计算有效值。
我是1ms定时器中断里采集,取一定时间内的极值,只要输入RC滤波,结果还是很稳定的 如果单片机运算能力不错,可以一周期采样40次以上,再进行均方根运算。
单片机交流采样问题
我也不知道 采样率太低,而且信号的包络变化周期是多少呢?单片机交流采样问题
学习了单片机交流采样问题
对于交流采样首先是分析你的交流是多少Hz的,然后确定你的采样率。举个栗子:50Hz的交流电压,周期是20ms,为了满足我的采样需求,我定一个周期内采集500个点,那么我的采样率为:25kHz。知道了这个以后,然后计算交流量的有效值,也就是我们平时所说的均方根。
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