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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]

Summary: Identifying maximum penetration level (MPL) of Solar Photo‐Voltaic Generation (SPVG) in the distribution system is crucial for system planners and network operators to keep the system parameters within the acceptable limits. This paper provides a systematic and extensive procedure for evaluation of MPL of SPVG and its enhancement methods in distribution systems. More importantly, it illustrates a novel algorithm for affixing the penetration cap of Solar PV. The methodology initially derives a new stability index for locating SPVGs in the system, including both active and reactive power support from SPVGs. The study shows the utility of redefining MPL of SPVGs by considering the reactive power injection through solar inverters also. Deviating from the practice of considering the conventional constraints of voltage rise and maximum loading for MPL estimation, the paper considers loss minimization as the major constraint. The algorithm has potential to identify distributed integration scenario of SPVGs compared to the limited number of placements in the extant algorithms. This paper provides a novel algorithm to evaluate of maximum penetration level (MPL) of solar photo voltaic generation (SPVG) and its enhancement methods in distribution systems. The utility of redefining the MPL of SPVG by considering the reactive power injection through solar inverter is illustrated. The algorithm has distinct advantages over the extant algorithms in identifying a distributed integration scenario. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an auto‐tuned Incremental Conductance (Inc) maximum power point tracking (MPPT) algorithm for a photovoltaic (PV) system. Proposed MPPT algorithm is adaptive in nature in terms of both MPPT perturbation frequency and step‐size based on the change in PV voltage. To verify the functionality of the proposed adaptive frequency adaptive step‐size Inc MPPT method, a boost converter based PV setup is developed and the algorithm is implemented in Arduino Mega‐2560 micro‐controller. An extensive comparative result analysis of the proposed technique is done with the conventional fixed frequency variable step‐size Inc MPPT algorithms which are based on power‐voltage slope and change in power. The proposed change in PV voltage based adaptive frequency adaptive step‐size algorithm improves both steady‐state and dynamic performance compared to the conventional fixed frequency variable step‐size Inc algorithm. The steady‐state oscillation is low with a faster tracking response. [ABSTRACT FROM AUTHOR]

Summary: It is known that the demand for renewable energy systems during recent years has increased significantly, especially for wind and photovoltaic systems. The continuous improvement of the efficiency and performance of these systems is important to their growth, particularly in terms of harvesting the maximum power. Many types of methods and techniques are available for tracking the maximum power point, the most important of which is the concept of artificial intelligence. Artificial intelligence (eg, neural networks and fuzzy neural networks) has great potential to handle complex systems or those that have non‐linear parameters and are influenced by environmental conditions. In this paper, artificial intelligence is used to predict the optimal reference values for wind turbines and photovoltaic panels used in maximum power point tracking (MPPT). Neural networks were used in the wind turbine system, while fuzzy neural networks were used in the photovoltaic systems. The networks were trained in real‐time using an incremental training mode based on the back‐propagation algorithm. The proposed system was designed practically based on the microcontroller and has been tested, and the results are extracted in real‐time. The results have proven their effectiveness and conformity with the required optimal values. [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]