An Adaptive LMS-Based Dynamic Frequency Estimation Method for Power Systems with Uncertain Noise

As the large-scale integration of renewable energy sources increases, the operational uncertainty and the degree of noise pollution within the power system are also on the rise, setting higher standards for the accuracy and adaptability of traditional frequency estimation methods. To ensure the stability and reliability of the power system, this study proposes a dynamic frequency estimation method based on the Adaptive Least Mean Squares (LMS) algorithm, targeting power systems experiencing uncertain noise and dynamic changes. This method adaptively adjusts filter coefficients and utilizes the iterative nature of the LMS algorithm to minimize the mean square error of the output, thereby enhancing the precision of frequency estimation. Additionally, to optimize the response speed and stability of the algorithm, this research introduces the concept of variable step-size and has designed a new adaptive parameter adjustment strategy that uses bounded Arctangent parameter values to counter the effects of step-size jumps. Research findings indicate that this adaptive strategy significantly improves the robustness of the algorithm under uncertain grid conditions, ensuring the accuracy and real-time performance of frequency estimation.