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.
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.
Under grid voltage sags, over current protection and exploiting the maximum capacity of the inverter are the two main goals of grid-connected PV inverters. To facilitate low-voltage ride-through ...(LVRT), it is imperative to ensure that inverter currents are sinusoidal and remain within permissible limits throughout the inverter operation. An improved LVRT control strategy for a two-stage three-phase grid-connected PV system is presented here to address these challenges. To provide over current limitation as well as to ensure maximum exploitation of the inverter capacity, a control strategy is proposed, and performance the strategy is evaluated based on the three generation scenarios on a 2-kW grid connected PV system. An active power curtailment (APC) loop is activated only in high power generation scenario to limit the current's amplitude below the inverter's rated current. The superior performance of the proposed strategy is established by comparison with two recent LVRT control strategies. The proposed method not only injects necessary active and reactive power but also minimizes overcurrent with increased exploitation of the inverter's capacity under unbalanced grid voltage sag.
One of the greatest challenges for widespread utilization of solar energy is the low conversion efficiency, motivating the needs of developing more innovative approaches to improve the design of ...solar energy conversion equipment. Solar cell is the fundamental component of a photovoltaic (PV) system. Solar cell's precise modelling and estimation of its parameters are of paramount importance for the simulation, design, and control of PV system to achieve optimal performances. It is nontrivial to estimate the unknown parameters of solar cell due to the nonlinearity and multimodality of search space. Conventional optimization methods tend to suffer from numerous drawbacks such as a tendency to be trapped in some local optima when solving this challenging problem. This paper aims to investigate the performance of eight state-of-the-art metaheuristic algorithms (MAs) to solve the solar cell parameter estimation problem on four case studies constituting of four different types of PV systems: R.T.C. France solar cell, LSM20 PV module, Solarex MSX-60 PV module, and SS2018P PV module. These four cell/modules are built using different technologies. The simulation results clearly indicate that the Coot-Bird Optimization technique obtains the minimum RMSE values of 1.0264E-05 and 1.8694E-03 for the R.T.C. France solar cell and the LSM20 PV module, respectively, while the wild horse optimizer outperforms in the case of the Solarex MSX-60 and SS2018 PV modules and gives the lowest value of RMSE as 2.6961E-03 and 4.7571E-05, respectively. Furthermore, the performances of all eight selected MAs are assessed by employing two non-parametric tests known as Friedman ranking and Wilcoxon rank-sum test. A full description is also provided, enabling the readers to understand the capability of each selected MA in improving the solar cell modelling that can enhance its energy conversion efficiency. Referring to the results obtained, some thoughts and suggestions for further improvements are provided in the conclusion section.
The rapid increase in the penetration of photovoltaic (PV) power plants results in an increased risk of grid failure, primarily due to the intermittent nature of the plant. To overcome this problem, ...the flexible power point tracking (FPPT) algorithm has been proposed in the literature over the maximum power point tracking (MPPT) algorithm. These algorithms regulate the PV power to a certain value instead of continuously monitoring the maximum power point (MPP). The proposed work carries out a detailed comparative study of various constant power generation (CPG) control strategies. The control strategies are categorized in terms of current-, voltage-, and power-based tracking capabilities. The comparative analysis of various reported CPG/FPPT techniques was carried out. This analysis was based on some key performance indices, such as the type of control strategy, irradiance pattern, variation in G, region of operation, speed of tracking, steady-state power oscillations, drift severity scenario, partial shading scenario, implementation complexity, stability, fast dynamic response, robustness, reactive power, cost, and tracking efficiency. Among existing FPPT algorithms, model-based control has a superior performance in terms of tracking speed and low steady-state power oscillations, with a maximum tracking efficiency of 98.57%.
An enhanced version of the moth flame optimization algorithm is proposed in this paper for rapid and precise parameter extraction of solar cells. The proposed OBLVMFO algorithm’s novelty lies ...primarily in the improved search strategies, where two modifications are proposed to maintain a proper balance between exploration and exploitation. Firstly, an opposition-based learning mechanism is employed to initialize the search population for the purpose of enhancing the global search. Secondly, Lévy flight distribution is used to prevent the stagnation of solutions in local minima. The implementation of intelligent rules such as OBL and Lévy flight distribution significantly improves the performance of the standard MFO. The developed OBLVMFO performed adequately and is reliable in terms of RMSE compared to other methodologies such as MFO, ALO, SCA, MRFO, and WOA. The best optimized value of RMSE achieved by OBLVMFO is 6.060E−04, 1.3600E−05, and 7.0001E−06 for STE 4/100 (polycrystalline), LSM 20 (monocrystalline), and SS2018P (polycrystalline) PV modules, respectively. The experiments performed on the benchmark test function revealed that the OBLVMFO has a 61% faster convergence speed than the standard version of MFO, which improves solution accuracy. In addition to this, two non-parametric tests: Friedman ranking and Wilcoxon rank sum are performed for the validation.
•A novel OBLVMFO algorithm is proposed for parameter extraction of solar cell models.•Two modifications are anticipated to overcome the limitations of original MFO algorithm.•The performance of OBLVMFO is tested on standard benchmark functions and practical measured datasets.•The proposed OBLVMFO algorithm is 61% faster than standard version of MFO algorithm.
A directional overcurrent relay is commonly used to protect the power distribution networks of a distributed system. The selection of the appropriate settings for the relays is an important component ...of the protection strategies used to isolate the faulty parts of the system. The rapid growth of distributed generation (DG) systems present new challenges to these protection schemes. The effect of solar photovoltaic power plants on relay coordination is studied initially in this research work. A protection strategy was formulated to guarantee that the increased penetration of solar photovoltaic (PV) plants does not affect the relay coordination time. This paper addresses these issues associated with a high penetration of DG through the use of a hybrid protection scheme. The protection strategy is divided into two parts. The first part is based on an optimal fault current limiter value estimated with respect to constraints and the optimal time multiplier setting, and then the coordination time interval is estimated with respect to constraint in Part II. The results of these analyses show that a hybrid protection scheme can effectively handle the complexity of distributed generation (DG) and dynamic relay coordination problems. In this research, three optimization algorithms have been used for calculating the estimated value of impedance fault current limiter (Zfcl) and time multiplier setting (TMS). The response time of hybrid protection schemes is very important. If the computational time of their proposed algorithms is less than their actual computational time, then their response time to address the issue is also less. The performance in all algorithms was identified to arrive at a conclusion that the grey wolf optimized algorithm (GWO) algorithm can substantially reduce the computational time needed to implement hybrid protection algorithms. The GWO algorithm takes a computational time of 0.946 s, achieving its feasible solution in less than 1 s.
Maximum power point tracking (MPPT) through an effective control strategy increases the efficiency of solar panels under rapidly changing atmospheric conditions. Due to the nonlinearity of the I–V ...characteristics of the PV module, the Sliding Mode Controller (SMC) is considered one of the commonly used control approaches for MPPT in the literature. This paper proposed a Backstepping SMC (BSMC) method that ensures system stability using Lyapunov criteria. A fuzzy inference system replaces the saturation function, and a modified SMC is used for MPPT to ensure smooth behavior. The proposed Fuzzy BSMC (FBSMC) parameters are optimized using a Particle Swarm Optimization (PSO) approach. The proposed controller is tested through various case studies on account of MPP’s dependence on temperature and solar radiation. The controller performance is assessed in partial shading conditions as well. The simulation results show that less settling time, a small error, and enhanced power extraction capability are achieved by applying the PSO-based FBSMC approach compared to the conventional BSMC- and ABC-based PI control presented in previous research in different scenarios. Moreover, the proposed approach provides faster adaptation to temperature and solar radiation variation, ensuring faster convergence to the MPP. Finally, the robustness of the proposed controller is validated by providing variation within the system components. The result of the proposed controller clearly indicates the lowest value of RMSE measured between PV voltage and the reference voltage, as well as the RMSE between PV power and maximum power. The results also show that the proposed MPPT controller exhibits the highest dynamic efficiency and mean power.
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 (<inline-formula> <tex-math notation="LaTeX">6.83\times 10 ^{-4} </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">2.06\times 10 ^{-4} </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">4.48\times 10 ^{-6} </tex-math></inline-formula>) 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 multilevel inverters (MLI), output voltage waveform consists of dominant low order harmonics, which needs to be minimized. Simultaneously, good control over the fundamental voltage for desirable ...operation is needed. In this paper, an atom search optimization (ASO) based selective harmonic elimination (SHE) method is proposed for a variable dc bus based reduced component count (RCC) MLI. ASO is a population-based metaheuristic algorithm, which mathematically models the motion of atoms in nature to accurately determine the optimum firing angle of the switches by solving SHE fitness function. The proposed ASO SHE method outperforms recent metaheuristics based SHE methods such as bee algorithm, imperialistic colonial algorithm (ICA), firefly, particle swarm optimization (PSO), and teaching learning-based optimization (TLBO) in solving SHE problem for 11-level multilevel inverter. Detailed simulation case studies are presented to effectively demonstrate the performance of the proposed ASO SHE method on a stand-alone photovoltaic (PV) based RCC MLI subjected to sudden changes in irradiance, load and dc-link capacitor voltage. The experimental results on a PV based variable dc bus multilevel inverter validate the excellent performance of ASO SHE method in minimizing the total harmonic distortion (THD) and dominant order harmonics under sudden change in operating conditions.
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