Photovoltaic (PV) modules require maximum power point tracking (MPPT) algorithms to ensure that the amount of power extracted is maximized. In this paper, we propose a low-complexity MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. Namely, the expression for the output current of the NN model is used to derive the analytical, iterative rules for determining the maximal power point (MPP) voltage and irradiance estimation. In this way, the computational complexity is reduced compared to the other NN-based MPPT methods, in which the optimal voltage is predicted directly from the measurements. The proposed algorithm cannot instantaneously determine the optimal voltage, but it contains a tunable parameter for controlling the trade-off between the tracking speed and computational complexity. Numerical results indicate that the relative error between the actual maximum power and the one obtained by the proposed algorithm is less than 0.1%, which is up to ten times smaller than in the available algorithms.

Lithium-ion battery stands for vital segment of the hybrid-electrical vehicles (HEV). Accurate monitoring of battery status, which is the main task of battery management system (BMS), ensures reliable operation and preserve battery performance. Successful management of battery relies on accurate estimation of state-of-charge (SoC) parameter. Proposed methods for SoC estimation tend to consider nonlinear nature of battery system with time and temperature dependent parameters. Kalman-based filters have been widely used for SoC estimation. In this paper, two Kalman-based filters have been used fo SoC estimation, Extended Kalman filter (EKF) and Uscented Kalman filter (UKF). These two methods have been compared and simulation results are presented.

A new iterative method for frequency estimation in the unbalanced three-phase power system is proposed. The proposed solution is based on the transformation of unbalanced three-phase voltage signal into a complex exponential by using the orthogonal constant modulus algorithm. In addition, based on the transformed signal, a simple adaptive algorithm for frequency estimation is proposed. Beside the proposed approach, any frequency estimator designed for a single-tone complex exponential can be used here, which is also the advantage of the proposed method. Simulation results confirm that the proposed algorithm exhibits better performance compared to the considered algorithms.

The finite impulse response (FIR) harmonic estimators based on the Taylor signal model have recently received increased attention, due to their capability to accurately estimate harmonic phasors under dynamic conditions of a power system. However, these filters may produce a large estimation error when the frequency deviation from the nominal value is high. Online calculation of the FIR filter bank coefficients may improve the estimation accuracy, but at a cost of the increased computational complexity. To address this issue, in this paper we propose a dynamic harmonic estimation algorithm, which aims to enhance measurement accuracy at both nominal and off-nominal frequencies, and has a reduced computational complexity. The proposed algorithm estimates the harmonic parameters by means of the Taylor-based bandpass filter, while compensating the effects of the neighbouring spectral components. Numerical simulation results show that the proposed algorithm outperforms the available methods.