Adjustable window for amplitude estimation considering the time-varying frequency of power systems signals
Thiago R. F. Mendonca, Milena F. Pinto, Carlos A. Duque
Abstract
Open-access reader
Thiago R. F. Mendonca, Milena F. Pinto, Carlos A. Duque
Abstract
Open-access reader
The development of signal processing techniques that allows the estimation of parameters from the power system are crucial to keep the grid within a safe margin of operation. Some methods require a specific sampling rate in order to avoid asynchronous sampling, which may result in errors in estimation algorithms. Due to unbalances between demand and supply, the power signal is time-varying in nature, hindering the selection of an optimal fixed window length. In this work is proposed a technique in which the window length adapts to the actual value of estimated frequency, achieving better performance. The filter utilized to test the method was a moving average filter, but it can be expanded for other algorithms. Results have shown good performance when changing the filter coefficients according to frequency estimation.
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The development of signal processing techniques that allows the estimation of parameters from the power system are crucial to keep the grid within a safe margin of operation. Some methods require a specific sampling rate in order to avoid asynchronous sampling, which may result in errors in estimation algorithms. Due to unbalances between demand and supply, the power signal is time-varying in nature, hindering the selection of an optimal fixed window length. In this work is proposed a technique in which the window length adapts to the actual value of estimated frequency, achieving better performance. The filter utilized to test the method was a moving average filter, but it can be expanded for other algorithms. Results have shown good performance when changing the filter coefficients according to frequency estimation.
Key concepts: Computer science, Filter (signal processing), Window (computing), Sampling (signal processing), Power (physics), Control theory (sociology), Asynchronous communication, Margin (machine learning)