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Currently, research is being devoted towards the development of fast and precise maximum power point tracking (MPPT) methods for various photovoltaic (PV) applications. Due to rapidly varying solar irradiation and cell temperature, traditional MPPT algorithms are unable to track the optimum power from PV modules. In this paper, an analog circuitry-based fast and robust MPPT method utilizing a boost DC/DC converter is presented to improve the tracking capability. The mathematical model of a PV module and design expressions for converter elements are presented. To trace the desired maximum power point (MPP), a control law is derived by synthesizing the PV characteristic curves. The steady-state and transient responses of the PV-integrated boost converter are demonstrated under various conditions of source and load using the MATLAB/Simulink platform. Furthermore, a laboratory prototype is developed to validate the proposed control strategy in the real-time application. A satisfactory agreement has been exhibited among simulation and experimental results. The superiority of the proposed MPP tracker over different existing methods is investigated. Additionally, the proposed controller distributes the energy spectrum over a wider range of frequencies and simultaneously reduces the energy concentration at the clock frequency and its multiples, so that the effect of electromagnetic interference (EMI) is reduced for certain range of loads.

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.

This paper presents a power system harmonic elimination using the mixed adaptive linear neural network and variable step-size leaky least mean square (ADALINE-VSSLLMS) control algorithm based active power filter (APF). The weight vector of ADALINE along with the variable step-size parameter and leakage coefficient of the VSSLLMS algorithm are automatically adjusted to eliminate harmonics from the distorted load current. For all iteration, the VSSLLMS algorithm selects a new rate of convergence for searching and runs the computations. The adopted shunt-hybrid APF (SHAPF) consists of an APF and a series of 7 th tuned passive filter connected to each phase. The performance of the proposed ADALINE-VSSLLMS control algorithm employed for SHAPF is analyzed through a simulation in a MATLAB/Simulink environment. Experimental results of a real-time prototype validate the efficacy of the proposed control algorithm.

This paper presents a unique twofold adaptive linear neural network (ADALINE) for fast and accurate measurement of fundamental, harmonics, sub-harmonics, inter-harmonics and decaying DC components of a distorted current signal with additive noise. The preceding parallel approach is termed as Master-Slave ADALINE (MS ADALINE). The Slave-ADALINE adopts least mean square (LMS) algorithm with a fixed and large step-size for weight vector adjustment. During the training interval or transients, this filter performs a significant role. On the other hand, the Master-ADALINE uses a variable step-size LMS algorithm for achieving a small steady-state error. At the end of each iteration, the local averages of the squared errors of both the ADALINE's are calculated and weights of the Master-ADALINE are updated accordingly. The amplitudes and phases of desired frequency components can be worked out from Master-ADALINE's weights. The proposed architecture improves the convergence speed by establishing an independent control action between the steady-state error and the speed of convergence. The simulation results of this method under various operating situations are analyzed and compared with single fold ADALINE structure that obeys dynamic step-size LMS (DSSLMS) adaptation rule. Eventually, a scaled laboratory prototype has been developed for the validation of the proposed technique in real-time utilization. This innovative research finding makes the power system smart and precise. INDEX TERMS Adaptive linear neural network (ADALINE), dynamic step-size least mean square (DSSLMS), harmonic estimation, power quality assessment, master-slave (MS).

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