An efficient harmonic estimator design based on Augmented Crow Search Algorithm in noisy environment

In this paper, an intelligent harmonic estimator based on an improved version of crow search algorithm is proposed for identification of inter, sub and power harmonics. The algorithm is named as Augmented Crow Search Algorithm (ACSA). Harmonic estimation design problem is considered as an estimation problem of phase and amplitude component of harmonic signals. It is a known fact that certain modifications are essential to make an algorithm compatible for real applications. Keeping this fact in consideration, an acceleration factor driven crow search algorithm is proposed that also incorporates opposition-based learning in initialization phase. Further, an error function is derived from the mean square values of difference of real and estimated values of the amplitude and phase components. An iterative optimization process is followed for identification of these components. A fair comparison is carried out on the set of four test benches of diverse properties and shapes. Comparison of different contemporary algorithms and a few of crow search algorithm variants is carried out to showcase efficacy of the proposed version of the crow search. It is observed that proposed version is competitive.

Automated summary

In this paper, an intelligent harmonic estimator based on an improved version of crow search algorithm is proposed for identification of inter, sub and power harmonics. The proposed algorithm, named ACSA, incorporates acceleration factor driven and opposition-based learning for optimization. Experimental results show that the proposed version is competitive compared to other contemporary algorithms and crow search variants.