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A robust technique based on maximum correntropy criterion (MCC) is proposed for frequency estimation in an unbalanced power system using error in phase angle. This is achieved by replacing the magnitude error used in the standard cost function of the MCC with the phase error and by extending the concept of the augmented (widely linear) complex statistics for the processing of unbalanced voltages and other abnormal system conditions. The idea emanates from the facts that: (i) useful information is primarily conveyed over the phase in power system frequency estimation, and (ii) the performance degradation of the well‐known minimum mean squared error criterion based adaptive filters in impulsive noise environments can be overcome by the MCC due to the non‐quadratic and higher order moments imbedded in its cost function. The proposed phase error‐based MCC method also utilises all the second‐order information within the complex valued system voltage through the Clarke's transformation. Simulation studies conducted using both synthetic and experimental data verify that the proposed algorithm provides better estimations compared to the considered algorithms. [ABSTRACT FROM AUTHOR]

An incremental least‐mean M‐estimate algorithm is proposed for distributed network, which minimises a robust M‐estimator‐based cost function against impulsive noise via the gradient descent method. Moreover, based on the same frame for implementation, an incremental distributed least‐mean p‐power algorithm is presented for combating impulsive noise. Simulations verify the superiority of the proposed algorithms in the presence of impulsive noise relative to some existing incremental distributed algorithms. [ABSTRACT FROM AUTHOR]

A new class of bias‐compensated affine‐projection‐like (APL) algorithms is proposed, in which a bias‐compensation vector is derived to eliminate the bias caused by the noisy input. In addition, a new estimation method for the input noise variance is proposed which does not require the input–output noise variance ratio in advance. Simulations in a system identification context show that the proposed algorithms achieve significant improvements in steady‐state misalignment as compared with the conventional APL algorithms. [ABSTRACT FROM AUTHOR]