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Diffusion quantized augmented complex MEEF with variable center algorithm for cooperative frequency estimation in power systems
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DOI：10.1016/j.jfranklin.2026.109006.png)
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Abstract
En 中文
Accurate and robust frequency estimation is pivotal for distributed power systems with high renewable penetration. However, traditional single-node adaptive filtering-based frequency methods, which lack spatial cooperation and consider measurement noise under Gaussian distribution assumptions, are inherently vulnerable to local disturbances and remain incapable of adapting to the asymmetric, non-Gaussian measurement noise introduced by renewables. To overcome these challenges, this paper develops a diffusion-based robust adaptive filtering algorithm termed Diffusion Quantized Augmented Complex Minimum Error Entropy with Fiducial Points and Variable Center (DQACMEEF-VC). A variable-center mechanism is first introduced into the augmented complex minimum error entropy with fiducial points criterion (MEEF) to dynamically adapt to asymmetric error distributions, and a novel robust cost, called ACMEEF-VC, is defined to overcome the inflexibility of fixed kernels against asymmetric non-Gaussian noise. Then the ACMEEF-VC is embedded into a diffusion Adapt-Then-Combine cooperative framework to develop a diffusion ACMEEF-VC (DACMEEF-VC) algorithm, enabling spatial noise averaging across nodes to suppress local disturbances and enhance tracking speed, while an adaptive quantization strategy is incorporated to significantly reduce computational complexity. Furthermore, theoretical analyses confirm the existence of local extremum points, establish local mean stability, and derive a closed-form expression for the steady-state global mean square deviation. Simulations under diverse network topologies and operational scenarios validate the theoretical findings and demonstrate the superior accuracy, robustness, and tracking capability of the proposed method compared to existing approaches.
Keywords:
Frequency estimation
Diffusion
Minimum error entropy with fiducial points
Variable center
Quantized method
Journal
J
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3.7
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6.4K
Citations:
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