An adaptive linear neural network with least mean M-estimate weight updating rule employed for harmonics identification and power quality monitoring

Abstract

This paper describes a combined adaptive linear neural network and least mean M-estimate (ADALINE-LMM) algorithm for estimating the amplitude and phase of the individual harmonic contained in a distorted power system current signal. The weight vector of the ADALINE is updated iteratively by LMM algorithm. A Hampel’s three parts redescending M-estimator function is incorporated in the instantaneous cost function to provide thresholds for identifying and eliminating the effect of temporary fluctuation owing to the presence of impulsive noise. This type of combined approach shows more accurate and faster tracking capability than the combined ADALINE and variable step size least mean square (ADALINE-VSLMS) algorithm. In addition to this, the proposed algorithm is suggested in shunt hybrid active power filter (SHAPF) for extracting the harmonics and reactive power components from the distorted load currents. Extensive time domain simulation is carried out to evaluate the performance of the SHAPF for maintaining the power quality of a system under various demanding situations. Moreover, an experimental setup is developed in the laboratory for verification of the proposed control technique in a real-time application using a Spartan 3A DSP processor.

Acceleration Harmonic Estimation for Hydraulic Servo Shaking Table Based on Multi-Innovation Stochastic Gradient Algorithm

“…e LMS algorithm suffers from poor convergence and being failure in the case of signal drifting and changing conditions [13]. e initialization parameter for the RLS algorithm has a great impact on time varying dynamic signals [14]. e Kalman filter algorithm is capable enough to estimate harmonic parameter in presence of noise and other nonlinearities presented in the response signal [15].…”

“…Besides, many hybridized algorithm-based metaheuristic technique and classical optimization method have been reported to estimate the parameters of harmonics [14]. Because the harmonic is linear in amplitude and nonlinear in phase, the hybridized algorithm using the metaheuristic technique estimates the phase, whereas the classical optimization method estimates the amplitude [17].…”

Comprehensive Evaluation of Power Quality Based on an Improved TOPSIS Method Considering the Correlation between Indices

“…However, the current PQ evaluation methods have not considered the correlation between the PQ indices, or there are some shortcomings such as the weighting methods being simple, a large amount of calculation, and a lack of practical engineering guidance [9][10][11]. The artificial neural network method proposed in [12] requires a large amount of sample data to train the network. The fuzzy comprehensive evaluation method proposed in [13][14][15] relies too much on experience and is too subjective in the process of determining membership function and obtaining weights.…”

Photovoltaic tied unified power quality conditioner topology based on a novel notch filter utilized control algorithm for power quality improvement

“…These power conditioners are allowed to operate in a controlled manner for elimination of voltage and current power quality issues. Power conditioners development in various categories such as shunt active power filter, series active power filter and hybrid active power filters have been widely studied and implemented in the literature (Corasaniti VF et al, 2009; Dash and Ray, 2017a; Fallah et al, 2017; Garnayak and Panda, 2016, 2018; Panda et al, 2013) to prove the effectiveness. The aforementioned power conditioners have been used specifically for various nature of power quality means used separately for voltage and current quality issues.…”