This study proposes a new algorithm for parameter estimation of the electric circuit model of Lithium (Li)-ion battery. The first-order battery-electric circuit model is considered in this work that resembles battery charging and discharging behaviors. The battery circuit element values have been modeled as polynomial equations with unknown coefficients. An accurate estimation of the battery circuit element values is profound to accurately find the battery State of Charge (SoC), an immeasurable quantity required in battery management systems (BMS). The ElectroStatic discharge algorithm (ESDA) is used in this study to estimate the unknown polynomial coefficients and, in turn, the values of the battery circuit elements. The accuracy of the proposed ESDA in estimating the battery circuit element values is compared to the recently proposed Artificial Hummingbird Optimization Technique (AHOT), Chameleon Swarm Algorithm (CSA), and Tuna Swarm Optimization (TSO). The results demonstrate the superiority of the proposed algorithm for charging and discharging in battery parameters estimation over the other algorithms with an accuracy gain of at least 10%.

Photovoltaic (PV) arrays, when subjected to Partial Shading (PS), exhibit several power losses due to diminished current across the array. Therefore, bypass diodes are connected across array modules to avoid the PS effect. Although they reduce the PS effect, the bypass diodes make the Power versus Voltage (P- V) relation of a PV non-convex. This paper investigates the Maximum Power Point Tracking (MPPT) problem under PS conditions to track the PV array's Maximum Power Point (MPP). Because previously proposed algorithms for this problem either failed to track the MPP or were computationally expensive, we propose a modified version of the Bat metaheuristic algorithm with dynamically narrowing search space (DNSS) exploration to avoid exploring low-power regions. The results show around 35 % gain in terms of rapidity and efficiency of the proposed metaheuristic approach in mitigating power losses compared to other existing algorithms.

Photovoltaic (PV) forecasting plays a major role in residential and industrial PV installation as well as penetration with the grid. An inaccurate PV power forecasting may result in increased monetary and energy losses. This study proposes a metaheuristic-based strategy for accurate PV power forecasting using a heuristic-based data-driven PV model. The proposed algorithm integrates a dense explorative strategy with the existing PV equation knowledge by a multilayer perceptron (MLP) network with Sigmoid activation functions to predict the best coefficients for the inputs of the data-driven PV model. The proposed method is compared to a recently proposed metaheuristic algorithm, the artificial hummingbird optimizer algorithm (AHOA). The comparison is performed for inside distribution (ID) and out-of-distribution (OOD) irradiance datasets and with varying temperatures. The results prove that the proposed NN-based algorithm achieves higher accuracy in PV power parameter prediction and hence forecasting.

Electricity generation using photovoltaic (PV) technology has become highly popular recently. However, natural barriers such as trees, buildings, bird drops, etc., cause partial shading (PS) on the PV surface resulting in high power losses. Bypass diodes used to mitigate the PS effect cause multiple peaks in the PV power delivery. The tracking of the optimal power peak can be considered an optimization problem with a continuously changing objective function due to different insolation conditions. All optimization strategies applied in previous works spanning from mathematical programming techniques to Machine Learning and the recently proposed Nature-inspired algorithms led to either sub-optimal maximum power or required extensive computations. This work presents an algorithm that combines the advantages of the previous works and avoids their loopholes. Experimental results indicate the superiority of the proposed algorithm over the state-of-the-art algorithm for the Maximum Power Peak Tracking problem.

This paper focusses on three phase Cascaded H bridge Multi-level Inverter (MLI) topology fed by solar Photovoltaic (PV) module. For maintaining maximum power as well as constant voltage for MLI, Perturb and Observe (P&O) technique is applied. Sine Pulse Width Modulation (SPWM) is implemented along with unipolar and bipolar switching scheme. Performance parameters like Total Harmonic Distortion (THD), switching loss, switching stress, Root Mean Square (RMS) fundamental voltage and efficiency of MLI output voltage is calculated and graphically compared for both unipolar as well as bipolar schemes. It is found that unipolar scheme outperforms bipolar scheme in all parameters and hence is better than the later. The control algorithm is also validated in Hardware-in-the-Loop using Typhoon HIL 402 emulator and results are presented and discussed.