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In a grid-connected photovoltaic system, the quality of energy injected by the photovoltaic system into the grid is directly linked to the topology of the inverter used and to the efficiency of its control technique. This paper addresses this problem for a two-level grid-connected photovoltaic inverter operating under low irradiance conditions. The aim is to reduce the harmonic distortion on the electrical network and therefore improve its power quality. To achieve this goal, a control strategy was set up considering the nonlinearity of the dynamic system, and the high dimension of the system model. Thus, a nonlinear controller designed using the backstepping technique is proposed. The effectiveness of this control strategy was evaluated by simulation in MATLAB/Simulink. Results show that the proposed control technique significantly improves the power quality of the grid-connected photovoltaic system by minimizing the current harmonic distortion rates in low irradiance conditions. The current harmonic distortion rates obtained for solar irradiance of 1000 W/m2, 750 W/m2, 500 W/m2, and 250 W/m2 are 0.48%, 0.78%, 1.22%, and 2.16%, respectively. The power factor is 0.988, and the DC bus voltage is maintained at its reference voltage of 600 V with a very low response time during the transient phases. A comparison of our simulation results with those found in the literature on other control techniques such as proportional-integral-derivative (PID) and synergetic controls shows the efficiency, superiority, and satisfactory performance of the proposed control scheme to minimize harmonic distortion under low irradiance conditions. The robustness and better dynamic performance of the proposed backstepping controller under varying irradiance conditions have also been shown. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid economic development, the development speed of all walks of life has entered a new level, and the power industry has also developed rapidly. Driven by market demand, China's power transmission range and power transmission capacity will enter a new level. At the same time, the problems brought about by the development of the power system are equally severe. Due to the large load density in individual areas, the detection of short-circuit current must be improved as an important issue. The purpose of this paper is to study how to improve the practical model of short-circuit current calculation of photovoltaic power plants, so that it can be well applied to the current high-density current detection in China. Therefore, this paper improves the recursive least squares (RLS) algorithm and applies it to the practical model of short-circuit current calculation of photovoltaic power plants and describes the improvement process of the algorithm in detail. At the same time, this paper designs relevant experiments and analysis to count the data of the improved RLS algorithm in the short-circuit current calculation of the actual photovoltaic power station and combines the data of this part to test and analyze the ability of the algorithm. The experimental results in this paper show that the improved RLS algorithm has a very good improvement in the calculation accuracy of the short-circuit current calculation of photovoltaic power plants in the actual model calculation. At the same time, the calculation efficiency is also improved, and the current tracking effect is also improved by 7%. [ABSTRACT FROM AUTHOR]

The photovoltaic(PV) system's efficiency can be improved with the aid of effective solar PV cell modeling. However, flawed solar cell parameters affect PV cell modeling incorrectly. The manufacturers are usually not providing all the necessary data for the exact modeling of PV cells. It is therefore necessary to efficiently predict the parameters of the PV cell. The literature discusses various optimization algorithms, but the suboptimal outcomes are obtained by most of the algorithms due to their convergence toward local minima. This paper introduces a new stochastic optimization algorithm for the estimation of solar PV cell parameters. Therefore, for the evaluation of the solar cell, a new novel chaotic algorithm is introduced in this paper called the chaotic tunicate swarm algorithm (CTSA). The proposed algorithm has a new function called an outstanding mathematical model of adaptive weights to stimulate negative and positive inputs from the spreading wave in order to find the best way to connect food with an excellent exploitation tendency and exploratory ability. For the double‐diode model, the computation time of the chaotic variants (CTSA1‐1.1885, CTSA2‐1.2502, CTSA3‐1.2962, CTSA4‐1.1204, and CTSA5‐1.2214) is better than the rest of the compared algorithms (particle swarm optimization [PSO]‐17.4532, tunicate swarm algorithm (TSA)‐2.5278, Harris hawks optimization [HHO]‐10.8942, ant lion optimizer [ALO]‐15.0214, and atom search optimization [ASO]‐9.5214). For the triple‐diode model, the computation time of the chaotic variants (CTSA1‐1.2658, CTSA2‐1.3520, CTSA3‐1.3056, CTSA4‐1.2290, and CTSA5‐1.3859) is better than the rest of the compared algorithms (PSO‐18.5421, TSA‐3.2568, HHO‐9.8754, ALO‐13.5841, and ASO‐11.8412). The superiority of proposed algorithm is established using ranking test, statistical error analysis, and sensitivity temperature variation. [ABSTRACT FROM AUTHOR]

Current interharmonics caused by gird‐connected PV systems have been noticed. From previous research, there is a trade‐off between the interharmonic emission and tracking performance. In cyber‐physical co‐regulation systems, there also exists a trade‐off between the cyber resource consumption and system performance. Motivated by this coincidence, a cyber‐physical co‐regulation based P&O MPPT algorithm is proposed for a three‐phase single‐stage grid‐connected PV system to mitigate the interharmonic emission in this paper. In this algorithm, a cyber‐physical model is built in which the sampling frequency is seen as the cyber state, and the step size is considered as the physical state. Based on the model, cyber and physical control laws are proposed in which the cyber and physical states are used to regulate each other. To apply the control laws, guidelines for designing control coefficients are also proposed based on the limits on the cyber and physical states to avoid chaotic behaviors caused by P&O MPPT characteristics. Compared with another mitigation algorithm without considering the cyber‐physical characteristics, the proposed algorithm can reduce the peak amplitude of the interharmonics by about 50% with a 1.35% cyber resource utilization savings and a 0.35% power efficiency improvement. [ABSTRACT FROM AUTHOR]

This paper discusses and analyses the work done in developing a Nature‐inspired optimization algorithm in a solar PV system for tracking the global maximum power point (GMPP) in partial shaded condition (PSC). Partial shading is a situation where PV panels connected in series do not receive the same solar radiation, thus exhibiting different I‐V characteristics for the individual panel. Under PSC, solar PV system output exhibits multiple local maximum power point (MPP) and a unique global MPP on the Power‐Voltage curve. Conventional maximum power point tracking (MPPT) techniques such as Perturb and Observe (P&O), incremental conductance (IC), and Hill Climbing (HC) method can only track MPP under uniform insolation conditions. It may fail to track GMPP in case of PSC and get trapped in local MPP, thereby causing loss of power that could have been tapped if the operation would have been at GMPP. To overcome the problem posed by PSC, researchers are applying the nature‐inspired optimization algorithm for tracking GMPP under partial shading conditions with fairly good results. Nature‐inspired optimization algorithm offers the advantage of solving multivariable nonlinear objective functions with constraints by exploiting the search space and can converge quickly to GMPP. Some of the most recent and famous nature‐inspired algorithms have been discussed and compared here. Simulation and hardware results of some of them are also taken in order to compare them effectively. Simulation results show that the Most Valuable Player Algorithm (MVPA) takes the least time to track the MPP in all the cases, and the MPP tracked is also comparable to the maximum while PSO with differentially perturbed velocity (PSO_DV) takes the maximum time to track the MPP in all cases. [ABSTRACT FROM AUTHOR]

Background: Tracking the maximum power point (MPPT) of photovoltaic (PV) systems is essential to increase the utilization efficiency. One of the most widely used algorithms is perturb and observe (P&O) technique. Methods: In this paper, tuning algorithms based on genetic algorithm (GA) and cuckoo search (CS) are proposed to provide optimal controller parameters for P&O MPPT. This optimal tuning is crucial in P&O MPPT applications of PV systems where it is difficult to tune the controllers using conventional tuning techniques to attain good performance because of the wide variations in the system parameters and its nonlinearity. The proposed tuning algorithms are used to overcome the drawbacks of the classical P&O MPPT under rapidly changing atmospheric conditions such as oscillation around the maximum power point (MPP) and low convergence speed. Results: The robustness of the algorithms is confirmed against fast varying insolation proving its ability to track the MPP in case of random and fast changing atmospheric conditions. An experimental set up of a 1 kW PV system is carried out and implemented using dSPACE DS1103 controller board and E4360 Keysight modular solar array emulator to validate the performance of the proposed optimal tuning algorithms. The measured results are in a close agreement with the simulation and the analytical system. Conclusion: Experimental and simulation results demonstrate a superior improvement in the transient and steady‐state performances and in the maximum power tracking efficiency. The steady‐state and the transient efficiencies of the presented work are 99.95% and 94.02% which are almost 17.59% and 10.61%, respectively superior to the classical P&O one and are 14.66% and 1.52%, respectively as compared to the manual tuning approach. This proposed optimal tuning approaches can be used to enhance the performance of any other controllers. [ABSTRACT FROM AUTHOR]

Summary: The performance of the Photovoltaic (PV) system is highly affected by fluctuating climatic conditions. Due to these changing climatic conditions, the irradiance diminishes, and it results in the non‐linearity in voltage‐power (P‐V) curves of the PV system. Therefore, it is one of the reasons for the occurrence of the multiple peaks on P‐V curves instead of a single peak. Therefore, it is challenging to track the global maximum power point tracking (MPPT) under these circumstances with traditional MPPT techniques viz. perturb and observe (P&O) and fuzzy logic control (FLC). Concerning global MPP tracking, this paper proposes a novel MPPT method based on a Rao‐1 optimization algorithm. The proposed algorithm is used to overcome the drawbacks of the classical P&O, FLC, and PSO based MPPT techniques under partical shading conditions (PSCs) and rapidly changing atmospheric conditions viz. slow convergence speed and oscillation around the MPP. The proposed MPPT tracks the global MPP under any abnormal conditions like PSCs. To show the efficiency of the proposed MPPT a comparative assessment is performed with well‐established MPPT methods in the literature. The simulation results witnessed that the proposed MPPT is more superior in improving the PV system performance under normal as well as under PSCs. The proposed MPPT over the PSO technique decreases the tracking time by 72.7% for pattern 1, 29.8% for pattern 2, 66.7% for pattern 3, 61.9% for pattern 5, 27.8% for pattern 6, and improves the tracking efficiency in all shading patterns. [ABSTRACT FROM AUTHOR]

Summary: Enhancing distribution system operation accomplished with the integration of renewable energy resources (RERs) has several technical, economical, and environmental dimensions. In this regard, this paper presents an optimal integration procedure of Distributed Generation (DG) based on Photovoltaic panel (PV) and Distribution Static Compensator (DSTATCOM) in Electrical Distribution System (EDS). The proposed procedure is formulated as a multi‐objective function (MOF). The considered objectives that reflect the technical, economic, and environmental issues, are Active Power Loss Level (APLL), Short Circuit Level (SCL), Voltage Deviation Level (VDL), Net Saving Level (NSL), and Environmental Pollution Reduction Level (EPRL). The proposed procedure investigates several hybrid optimization methods that combine the firefly algorithm (FA) with various acceleration coefficients PSO algorithms to improve the overall solution quality of the hybrid algorithms compared with the individual algorithms. To prove the capability of the proposed procedure, four different cases are tested on IEEE 33‐bus and 69‐bus EDSs. Added to that, the proposed algorithms are extended to practical Algerian EDS in Adrar City 205‐bus. Results obtained by the hybrid FA‐SCAC‐PSO algorithm showed that the simultaneous allocation of multiple DG and DSTATCOM in all standard and practical test systems significantly reduces the loss and enhances the voltage profile. An energy‐efficient analysis to proceed for different cases studied based on the best hybrid FA‐SCAC‐PSO algorithm to reach the best value of MOF compared to other algorithms, moreover the capability to achieve the optimal allocation of DG and DSTATCOM by maintaining the voltages profile within the permissible limit, whatever the variation of load. Significant technical economic and environmental achievements are found for different case studies especially in the existence of DGs and DSTATCOM devices. [ABSTRACT FROM AUTHOR]

Summary Partial shading conditions generate multiple peaks in the P‐V curve of photovoltaic (PV) arrays. Smart MPPT optimization techniques should be used to capture the global peak (GP) and avoid being trapped in one of the local peaks (LPs). The tracking of the GP should be fast and reliable to enhance the stability and increase the generated efficiency of the PV systems. Bat algorithm (BA) is one of the fastest swarm optimization techniques. The BA control parameters (BA‐CPs) have substantial effects on their performance. This paper introduced a nested BA strategy called BA‐BA strategy to determine the optimal values of control parameters of BA for the lowest convergence time and failure convergence rate to be used in the online MPPT of PV systems. The inner BA loop used the BA as an MPPT of the PV system, meanwhile, the outer BA loop used the inner BA loop as a fitness function to determine the optimal BA‐CPs for minimum convergence time and failure rate. Ten benchmark BA strategies, particle swarm optimization (PSO), and cuckoo search (CS) algorithm have been used to compare their results with the results obtained from the BA‐BA strategy. The results of the BA‐BA strategy reduced the convergence time of 250% of the time associated with the best benchmark BA strategy, 518%, and 395% as compared to the PSO, and CS algorithm, respectively. The simulation and experimental results obtained from the BA‐BA strategy showed its superior for determining the optimal control parameters for BA in MPPT of PV systems or any other applications. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a series‐parallel photovoltaic (PV)‐storage independent microgrid (MG), where low‐voltage distributed generation units could be connected to power grid conveniently. Moreover, it provides a promising way to form medium voltage MG. For this proposed structure, a decentralized control is proposed. The PV units are controlled by a maximum power point tracking (MPPT)‐based droop control, where MPPT of the PV units is achieved. Storage units are regulated via a "droop" controller dependent its reactive power polarity and take the responsibility of keeping the power supply‐demand balance of the MG. Since all these controllers make decisions only based on their local information, the proposed control is reliable and cost‐effective. Moreover, the small‐signal stability of the system is proved. And simulation results are presented to verify the effectiveness. [ABSTRACT FROM AUTHOR]