Implementation of the adaptive quantization method in digitally controlled measuring generators

Abstract

Measuring generators with digital control, in particular power calibrators, used to calibrate electricity meters, contain a digital-to-analog converter (DAC) that converts codes of the generated signal into voltage. Signal codes are stored in the generator memory. A truncation discreteness error (quantization noise) arises caused by sampling (quantization) in time and by the level of signal samples in the DAC. A relative value of the quantization noise depends on the amplitude of the generated signal (relative to the reference voltage of the DAC): the larger the amplitude, the more significant bits of the DAC are involved in the conversion process, and the less the relative value of the noise. In generators, where the amplitude of the output signal changes over a wide range (high dynamic range) by changing the digital samples of the signal, the quantization noise at low signal amplitudes can become unacceptably large. This situation occurs in power calibrators where the output current changes hundreds of times since the error of the verified electricity meter is normalized in a wide range of current flowing through it. A new algorithm for generating samples of a sinusoidal signal in measuring generators with digital control called adaptive quantization is proposed. Adaptive quantization can significantly improve one of the selected signal parameters (the so-called optimality criterion), for example, reduce the error in reproduction of the first harmonic, or reduce the value of higher harmonic components. In addition, the proposed algorithm reduces the dependence of the selected parameter on the sampling frequency and on the number of DAC bits used, which makes it possible to expand the dynamic range of the generator (in the current channel) without using additional amplifiers with programmable gain (PGA). Studies carried out using computer simulation have confirmed the efficiency of the adaptive quantization algorithm.