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

• Published in: IET generation, transmission & distribution Vol. 10; no. 2; pp. 421 - 436
• Main Authors: Garanayak, Priyabrat, Panda, Gayadhar
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 04.02.2016
• Subjects: ADALINE least mean square algorithm; ADALINE‐FXLMS algorithm; Adaptive algorithms; adaptive step size parameter; additive noise; Algorithms; amplitude estimation; Computer simulation; distorted current signal; filtered‐X least mean square algorithm; filtering theory; harmonic parameter measurement; Harmonics; hybrid adaptive linear neural network; Least mean squares; Least mean squares algorithm; least mean squares methods; Mathematical models; neural nets; Neural networks; phase estimation; power engineering computing; power system harmonics; upper bound limit; power system harmonics; least mean squares methods; upper bound limit; ADALINE least mean square algorithm; filtering theory; filtered-X least mean square algorithm; amplitude estimation; adaptive step size parameter; harmonic parameter measurement; power engineering computing; distorted current signal; phase estimation; additive noise; hybrid adaptive linear neural network; neural nets; ADALINE-FXLMS algorithm
• ISSN: 1751-8687; 1751-8695
• DOI: 10.1049/iet-gtd.2015.0684

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 upper-bound 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 steady-state 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.