Due to the high penetration of grid-connected photovoltaic (GCPV) systems, the network operators are regularly updating the grid codes to ensure that the operation of GCPV systems will assist in maintaining grid stability. Among these, low-voltage-ride-through (LVRT) is an essential attribute of PV inverters that allows them to remain connected with the grid during short-term disturbances in the grid voltage. Hence, PV inverters are equipped with control strategies that secure their smooth operation through this ride-through period as per the specified grid code. However, during the injection of reactive power under LVRT condition, various challenges have been observed, such as inverter overcurrent, unbalance phase voltages at the point of common coupling (PCC), overvoltage in healthy phases, oscillations in active, reactive power and dc-link voltage, distortion in injected currents and poor dynamic response of the system. Several strategies are found in the literature to overcome these challenges associated with LVRT. This paper critically reviews the recent challenges and the associated strategies under LVRT conditions in GCPV inverters. The drawbacks associated with the conventional current control strategies are investigated in MATLAB/Simulink environment. The advanced LVRT control strategies are categorized and analyzed under different types of grid faults. The work categorizes the state-of-the-art LVRT techniques on the basis of the synchronization methods, current injection techniques and dc-link voltage control strategies. It is found that state-of-the-art control strategies like OVSS/OCCIDGS provides improved voltage support and current limitation, which results in smooth LVRT operation by injecting currents of enhanced power quality.

This paper formulates a maximum power point tracking (MPPT) technique that accurately tracks the maximum power point of photovoltaic (PV), which undergoes simultaneous or independent sudden changes in load resistance and irradiance. The proposed algorithm is fundamentally divided into three parts; current and voltage perturbation algorithm (IPA/VPA), perturbation step-size reduction algorithm (PSSRA) and a deviation avoidance loop. The use of dual perturbation parameters using IPA and VPA ensures high tracking speed. PSSRA iteratively reduces the perturbation step-size of IPA and VPA and helps in reducing the power oscillations around MPP. Finally, a deviation avoidance loop is developed to detect a change in irradiance by examining the sign of the slope of the two power curves, namely P-V and P-I. The algorithm compares the per unit change in voltage and current of PV, to determine a simultaneous change in both irradiance and load resistance. The proposed algorithm is compared with two recently developed MPPT algorithms. The results show that the proposed technique can track the MPP with high speed and low steady-state oscillations and does not deviate from the MPP tracking path regardless of fast changes in irradiance and load resistance.

Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum ( $6.83\times 10 ^{-4}$ , $2.06\times 10 ^{-4}$ , and $4.48\times 10 ^{-6}$ ) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach.

In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), and whale optimization (WOA). The comparison of results broadly demonstrates that the TSA algorithm outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and convergence rate. Furthermore, the statistical results confirm that the TSA algorithm is a better algorithm in terms of average robustness and precision. The Friedman ranking test is also carried out to demonstrate the competency and reliability of the implemented approach.

In this paper, a new hybrid TSA-PSO algorithm is proposed that combines tunicate swarm algorithm (TSA) with the particle swarm optimization (PSO) technique for efficient maximum power extraction from a photovoltaic (PV) system subjected to partial shading conditions (PSCs). The performance of the proposed algorithm was enhanced by incorporating the PSO algorithm, which improves the exploitation capability of TSA. The response of the proposed TSA-PSO-based MPPT was investigated by performing a detailed comparative study with other recently published MPPT algorithms, such as tunicate swarm algorithm (TSA), particle swarm optimization (PSO), grey wolf optimization (GWO), flower pollination algorithm (FPA), and perturb and observe (P&O). A quantitative and qualitative analysis was carried out based on three distinct partial shading conditions. It was observed that the proposed TSA-PSO technique had remarkable success in locating the maximum power point and had quick convergence at the global maximum power point. The presented TSA-PSO MPPT algorithm achieved a PV tracking efficiency of 97.64%. Furthermore, two nonparametric tests, Friedman ranking and Wilcoxon rank-sum, were also employed to validate the effectiveness of the proposed TSA-PSO MPPT method.