Adaptive hill-climbing MPPT algorithms have superior performance as opposed to their conventional counterparts under medium-high irradiance. However, the performance of these hill-climbing algorithms remains mostly unknown under low irradiance condition. The low irradiance conditions are prominent in tropical countries during rainy seasons and niche PV applications. Additionally, several thin-film photovoltaic (PV) technologies have better efficiency under low irradiance conditions. Hence, the optimum operation of MPPT algorithms under low irradiance conditions is vital. In the real-time implementation, MPPT algorithms can fail to detect the incremental changes in voltage and current under low irradiance conditions. Hence, analog to digital converter (ADC) resolution becomes a critical constraint that governs the performance of hill-climbing (HC) MPPT algorithms. This work entails a detailed calculation to determine the perturbation step-sizes of the MPPT algorithms under a wide range of irradiance. Two distinct perturbation step-sizes are determined corresponding to the minimum and optimum change in voltage and current due to perturbation, that is sensed by the ADC. The authors also defined a general expression to determine the optimum digitized step-size for duty-based perturb and observe algorithm under low irradiance condition. This expression is formulated by considering the resolution of the ADC and the desirability of keeping the power oscillations at an acceptable level. Finally, the performance of eight hill-climbing algorithms for two distinct step-sizes is analyzed on a small-scale experimental prototype under both uniform and sudden changes in low values of irradiance. The statistical analysis validates that the adaptive HC drift-free MPPT algorithm outperforms other HC algorithms when implemented with the optimum perturbation step-size under low irradiance conditions.

Experimental Analysis of hill-climbing MPPT algorithms under low irradiance levels

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.

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A Comprehensive Review of Control Strategies to Overcome Challenges During LVRT in PV Systems

This article contains a review of essential control techniques for maximum power point tracking (MPPT) to be applied in photovoltaic (PV) panel systems. These devices are distinguished by their capability to transform solar energy into electricity without emissions. Nevertheless, the efficiency can be enhanced provided that a suitable MPPT algorithm is well designed to obtain the maximum performance. From the analyzed MPPT algorithms, four different types were chosen for an experimental evaluation over a commercial PV system linked to a boost converter. As the reference that corresponds to the maximum power is depended on the irradiation and temperature, an artificial neural network (ANN) was used as a reference generator where a high accuracy was achieved based on real data. This was used as a tool for the implementation of sliding mode controller (SMC), fuzzy logic controller (FLC) and model predictive control (MPC). The outcomes allowed different conclusions where each controller has different advantages and disadvantages depending on the various factors related to hardware and software.

Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications

An effective maximum power point tracking (MPPT) technique plays a crucial role in improving the efficiency and performance of grid-connected renewable energy sources (RESs). This paper uses the African Vulture Optimization Algorithm (AVOA), a metaheuristic technique inspired by nature, to tune the proportional–integral (PI)-based MPPT controllers for hybrid RESs of solar photovoltaic (PV) and wind systems, as well as the PI controllers in a storage system that are used to smooth the output fluctuations of those RESs in a hybrid system. The performance of the AVOA is compared with that of the widely used the particle swarm optimization (PSO) technique, which is commonly acknowledged as the foundation of swarm intelligence. As a result, this technique is introduced in this study to draw a comparison. It is observed that the proposed algorithm outperformed the PSO algorithm in terms of the tracking speed, robustness, and best convergence to the minimum value. A MATLAB/Simulink model was built, and optimization and simulation for the proposed system were carried out to verify the introduced algorithms. In conclusion, the optimization and simulation results showed that the AVOA is a promising method for solving a variety of engineering problems.

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African Vulture Optimization Algorithm-Based PI Controllers for Performance Enhancement of Hybrid Renewable-Energy Systems

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&amp;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. 

https://www.mdpi.com/1996-1073/15/9/3164/pdf?version=1651037266

A Novel TSA-PSO Based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions

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.

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Voltage and Current Reference Based MPPT Under Rapidly Changing Irradiance and Load Resistance

Black Soybean is highly nutritious legume of Uttarakhand but its hard seed coat reduces the nutrient availability to consumer. Attempt was made in this experiment to study effect of processing method on proximate composition of black soybean. Four Kilograms of black soybean was taken and subjected to three different processing methods like germination, roasting and cow urine treatment while one group was taken as control. Results indicated highly significant (Pd”0.01) differences in dry matter, crude protein, ether extract, ash and crude fibre content of black soybean among different groups compared to control. All processing methods were effective in improving nutritional value of black soybean thereby increasing its utilization in foods and food formulations.

Effect of processing method on nutritional quality of black soybean

The power electronics interface plays a vital role in controlling the amount and quality of power components that are injected into the grid. Thus, a well-designed current control strategy is essential which governs the performance of the grid-connected PV (GCPV) system under normal or fault conditions. The selection of appropriate control strategies also helps in fulfilling various control objectives. This paper evaluates the performance of various current control techniques of GCPV systems, when subjected to sudden variation in irradiance under unbalanced grid faults. The simulation results helped in comparing the peak current amplitude in faulty phase of these four conventional strategies. Finally, the THD of these control strategies are compared to determine their overall performance.

Jyoti Joshi

Papers

Experimental Analysis of hill-climbing MPPT algorithms under low irradiance levels

A Comprehensive Review of Control Strategies to Overcome Challenges During LVRT in PV Systems

Voltage and Current Reference Based MPPT Under Rapidly Changing Irradiance and Load Resistance

Effect of processing method on nutritional quality of black soybean

Performance Evaluation of Current Control Techniques for Grid Connected PV System under Unbalanced Grid Faults