Fast and accurate measurement of harmonic parameters employing hybrid adaptive linear neural network and filtered-x least mean square algorithm

Abstract

This study proposes a novel algorithm for fast and accurate measurement of fundamental, harmonics, sub- and inter-harmonic parameters of a distorted current signal with additive noise. A novel hybrid technique called adaptive linear neural network and filtered-x least mean square (ADALINE-FXLMS) algorithm is employed for amplitude and phase estimation. The ADALINE-FXLMS algorithm is the advancement of ADALINE least mean square (ADALINE-LMS) algorithm. With the help of this proposed algorithm, the adaptive step-size parameter is expanded up to the upperbound limit that results in further improvement of system stability. The convergence behaviour of the mean square errors for the noisy input signal is derived in details. By applying the proposed algorithm, the dynamic and steadystate response are analysed with different signal-to-noise ratio values. The simulated results obtained from the proposed algorithm are compared with the results generated by the variable step-size ADALINE-LMS algorithm in order to demonstrate the tracking capability. Finally, a laboratory prototype model is developed to justify the efficacy of the analytical results. The motivation of applying the proposed hybrid algorithm is due to its successful implementation in a real-time environment, faster convergence and simplicity structure.