## 6 Conclusions  
The presented experiments show that the proposed method is effective and suitable for the assigned task. The expected relative error in estimating the variance of the dynamic error signal is close to zero in all analyzed scenarios, where both the measurement window and the correction method for the DFT algorithm output were used. The variability of this effectiveness indicator depended directly on the application conditions mainly on the noise level in the processed measurement signal. Using a measurement window during the application of the proposed method is necessary to mitigate the effects of spectral leakage. The measurement window used should have good dynamic characteristics, with resolution being a secondary consideration in this case. Because the use of the \"Bartlett\" measurement window during the experiments produced results very similar to those of the \"Hamming\" window, it is recommended for cases where lower computational complexity is important. Using a low-dynamic measurement window increases the expected value of the relative error in estimating the variance of the dynamic error signal. Correcting the amplitude value of the DFT algorithm coefficients before applying the method is necessary when random error signals are present in the processed measurement signal. This correction algorithm allows obtaining an expected value of the estimatedquantity close to zero, while its variability is directly proportional to the ratio of noise power to useful signal power. The operation of the proposed correction algorithm does not affect the effectiveness of the proposed method when these error signals are absent. The experiments showed that the simplifications proposed in Equation (30) do not significantly affect the effectiveness of the proposed method. This is an important property because the form of Equation (27) is much more complex. This property holds both when spectral leakage occurs and when noise is present in the measurement signal. Therefore, the presented method makes it possible to estimate the parameters of the dynamic error signal associated with the input quantities of digital signal processing algorithms, enabling the development of an uncertainty budget for the output quantities without prior knowledge of the spectrum or nature of the processed signal. Applications of the proposed method may include cases where wavelet transformation algorithms [5; 6; 7; 10; 11; 16; 18] are used in the measurement chain or multiplicative algorithms used during power and energy measurement [8; 9]. The use of the proposed method enables the development of the uncertainty budget of the output quantities of the measurement chain, using various types of data processing algorithms. This operation is extremely important when, based on the analysis of the values of these quantities, a decision related to the task being performed is made, for example, on the activation of protections related to the detection of a short circuit or a fault in the transmission network [10; 11]. This work, together with the works [16; 18; 23; 51], is a comprehensive guide to metrological analysis in the described cases. It should be noted here that this analysis is most often omitted, mainly due to the high degree of complexity or the lack of appropriate mathematical models. Another approach to solving the problem presented in this paper may be to use the [PERSON]'s method [54] to estimate the spectrum of the signal processed by the measurement chain. This method is resistant to the phenomenon of spectrum leakage and can be used when the processed signal contains noise. It should be noted, however, that the discussed method requires an appropriate selection of the model order and is also characterized by greater computational complexity. A significant disadvantage of the [PERSON]'s method is also the need to solve a system of equations, but in the case of the CMSIS-DSP [4] library available for the ARM platform, there is no ready implementation of the algorithm enabling this operation. Data curation, L.D.