Evaluation of the Effect of Nonlinearity of the Successive Approximation ADC to the Measurement Error of RMS

Currently, the digital measurement methods are most often used to measure the root mean square (RMS) value of a signal. Among the digital measurement methods, the most popular method is based on averaging of the squre of input samples. If the measurement time is select as a multiple to the period of the input signal, the additional frequency error of this method is equal to zero. To implement digital measurement methods, the use of analog-to-digital converters (ADCs) is necessary. The conversion function of the real ADC is nonlinear, which results to the measurement error. The nonlinearity of the ADC built by the successive approximation architecture in its form is close to a random function. For this reason, the existing methods for estimating the RMS measurement error from the nonlinearity of the ADC results to a much too high to the error estimate relative to the results of simulation. In this paper, we propose a method for estimating the measurement error of the RMS caused by the nonlinearity of the successive approximation ADC presented as a random function. The comparison of the proposed error estimation technique with the popular method of `worst case' is performed. Simulation modeling by Matlab software package is performed.