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The photovoltaic (PV) systems must work at the maximum power point (MPP) to derive the highest possible power with the higher performance during a change in operating conditions. The primary objective is to implement a novel hybrid tracking algorithm to extract the maximum output power from the solar PV panel or array under partial shading conditio...
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... photovoltaic array comprises multiple PV strings connected in parallel, each of which is constructed from different PV modules linked to each array, as shown in Figure 2. Each of the photovoltaic modules comprises several parallel and series-connected PV cells. ...
Context 2
... effect on the PV array due to the PSCs is shown in Figure 4, and it is observed that voltage-power characteristic of the PV array exhibits multiple local peak (LP) points during the shading condition and one global peak (GP) point. The most referred PV array configuration, such as 4S (four series-connected panels) and 2S2P (two series-connected panels are connected in parallel), as shown in Figure 2 are considered to validate the performance of the proposed hybrid algorithm. The hybrid algorithm is suitable for other PV array configurations such as honey-comb, total-cross-tied, and bridge-linked. ...
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Partial shading (PS) is a common issue in photovoltaic systems (PVs), and it can significantly reduce the system's output power. This paper presents the advanced particle swarm optimization (APSO) algorithm. APSO is designed to alleviate the challenges posed by PS in PVs in from where of effectiveness and stability speed so that it works to achieve...
![Characteristic of a PV cell [14]](publication/352467064/figure/fig1/AS:11431281179427478@1691187282635/Characteristic-of-a-PV-cell-14_Q320.jpg)
Abstract A new Lyapunov‐based adaptive controller (LBAC) for a single stage three‐phase grid‐connected PV system (GCPVS) is proposed here. The uncertainties in solar irradiation affects the performance of the GCPVS. Further, ageing in the DC‐link capacitor affects the PV voltage tracking. To achieve an improved performance of the GCPVS under both h...
Citations
... By drawing inspiration from diverse fields, metaheuristic algorithms provide a powerful and versatile approach to parameter estimation in PV models. For instance, the algorithms such as genetic algorithm (GA) [39], differential evolutionary (DE) [2], particle swarm optimizer (PSO) [101], ant colony optimization (ACO) [22], artificial bee colony (ABC) algorithm [18], firefly algorithm (FA) [55], bat algorithm (BA) [20], cuckoo search (CS) [42], whale optimization algorithm (WOA) [64], grey wolf optimization (GWO) algorithm [37,94], artificial humming bird optimization algorithm [10], flower pollination algorithm (FPA) [5], krill herd algorithm (KHA) [15], salp swarm algorithm (SSA) [1,73], social spider optimization (SSO) [63], water cycle algorithm (WCA) [86], spiral optimization algorithm (SOA) [13], dolphin swarm algorithm (DSA) [102], dragonfly algorithm (DA) [59], quantum-based avian navigation optimizer [9], grasshopper optimization algorithm (GOA) [23], winddriven optimization (WDO) [57], moth flame algorithm [91,92], slime mould algorithm (SMA) [47], squirrel search algorithm (SQA) [36], Newton-Raphson-based optimizer [96], shuffled frog leaping algorithm (SFLA) [30], sparrow search algorithm [25], butterfly optimization algorithm (BOA) [54], marine predator algorithm (MPA) [38], multiple particle collision algorithm (MPCA) [56], antlion optimizer (ALO) [103], resistance-capacitance optimization [84], fruit fly optimization algorithm (FOA) [32], crow search algorithm (CSA) [82], chicken swarm optimization algorithm (CSOA) [58], war strategy optimization (WSO) algorithm [11], elephant herding optimization (EHO) [35], harmony search (HS) algorithm [7], mountain gazelle optimization (MGO) [17], white shark optimizer [50], exponential distribution algorithm [68], and Cheetah Algorithm (CA) [4] are used for many real-world engineering design problems, including parameter estimation of various PV models. ...
This paper proposes a unique method for estimating three-diode photovoltaic (PV) model parameters that uses an enhanced Newton–Raphson (NR) method and the selective opposition-based grey wolf optimization (GWO) algorithm with variable weights. For PV systems to operate more effectively, it is essential that these characteristics be estimated accurately. The GWO algorithm and the NR method are used to their full potential in the suggested method to overcome the drawbacks of conventional approaches. The selective opposition mechanism improves the GWO algorithm's exploration and exploitation capabilities, making it possible to search the parameter space efficiently. Using variable weights modifies the impact of various search operators, enhancing convergence rate and solution quality. An enhanced NR (ENR) approach is added to the optimization process to increase accuracy further. The ENR technique is used to revise estimated parameter values from initial guesses iteratively. A real-world PV system dataset used in experiments shows that the suggested solution performs better than state-of-the-art methods. The outcomes demonstrate better robustness, convergence speed, and accuracy. The suggested method provides an effective and efficient method for extracting the unknown parameters of the three-diode photovoltaic model. The research findings can help with photovoltaic energy generation system design and optimization.
... In order to discover the maximum power position of a PV panel, artificial intelligence (AI) based methods using genetic algorithm (GA), (ANN), and PSO (particle swarm optimization) are employed as solutions in the MPPT controller. (6,7) Amid this search, a particularly interesting path is emerging: the integration of artificial neural networks into photovoltaic control frameworks. ANNs represent a paradigm shift in the way we think about control mechanisms. ...
Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels in response to environmental changes. The performance of the ANN controller was compared with traditional MPPT approaches using simulation in Simulink/Matlab. The results of the simulation showed that the ANN controller performed better than the traditional MPPT techniques, highlighting the effectiveness of this method for dynamically changing solar panel performance. The ANN particularly demonstrates higher precision and adaptability when environmental conditions vary. The strategy consistently achieves and maintains the maximum power point, enhancing overall energy harvesting efficiency. The integration of an ANN controller marks a significant advance in solar energy control. The study highlights the superiority of the ANN controller through rigorous simulations, demonstrating increased accuracy and adaptability. This approach not only proves effective, but also has the potential to outperform other MPPT strategies in terms of stability and responsiveness.
... Genetic algorithm (GA) [83], particle swarm optimization (PSO) [10,11,71], differential evolutionary (DE) algorithm [26], artificial bee colony (ABC) algorithm [25], firefly algorithm (FA) [24], harmony search algorithm (HSA) [14], bacterial foraging algorithm (BFA) [67], imperialist competitive algorithm (ICA) [65], grey wolf optimizer (GWO) [43,49,52,79], whale optimizer (WO) [53], salp swarm algorithm (SSA) [48], moth flame optimizer (MFO) [19,36], equilibrium optimizer (EO) [16,44], marine predator algorithm (MPA) [56], gradient-based optimizer (GBO) [42,47], Runge-Kutta optimizer (RKO) [12], reptile search algorithm (RSA) [1], hunger games search optimizer (HGSO) [13], beluga whale optimizer [55], political optimizer (PO) [51], Aquila optimizer [2], JAYA algorithm [45], RAO algorithm [41], resistance-capacitance optimizer [58], etc., are some of these algorithms. In order to solve a DEED problem, the authors [21] combined BFA, PSO, and DE and reduced the stagnation caused by the incorporation of PSO-DE operators in BFA. ...
This paper presents improved single- and multi-objective algorithms based on the original moth flame optimizer (MFO) to tackle the dynamic economic emission dispatch (DEED) problem that affects power systems operations. The DEED problem is a multi-objective optimization problem that is strongly constrained, multi-dimensional, nonlinear, and non-convex. It comprises several optimization criteria, many of which are in direct opposition to one another; therefore, no one solution is optimal with regard to all of those criteria. Firstly, an enhanced flame generation strategy is incorporated into the MFO algorithm to improve performance. Then, the improved MFO is combined with the crowding distance mechanism and non-dominated sorting framework to enhance the convergence rate and the quality of the results. This helps improve the convergence pace. Firstly, the proposed multi-objective moth flame optimizer (MOMFO) algorithm is validated using 15 ZDT and UF benchmark multi-objective test functions. Then, the nonlinear DEED problem is also solved by determining the feasible optimal solution using the MOMFO algorithm. The implementation of the MOMFO on 10-unit systems and the IEEE 30-bus test system is being done to display the ability to solve a nonlinear, non-convex, and constrained DEED optimization problem. The DEED problem is solved using the MOMFO algorithm and other state-of-the-art algorithms, such as the non-dominated sorting genetic algorithm-II (NSGA-II), the multi-objective teaching–learning-based optimization (MOTLBO) algorithm, and multi-objective reptile search algorithm (MORSA). The selection of the control parameters of the MOMFO can be decided from the algorithm’s findings on different IEEE bus systems. This study also introduces a new technique for incorporating loss predictions using artificial neural networks into the DEED model. During each phase of the dispatch time, the trained neural network can make only a single forecast of the transmission loss. The performance of MOMFO is compared with NSGA-II, MOTLBO, and MORSA, and the results obtained for both benchmarks and DEED proved the superiority of the proposed algorithm in solving the DEED of the power systems.
... A hybrid technique that combines adaptive particle swarm optimization (PSO) and cuckoo search was proposed by Xu et al. [8] to solve the issue of premature convergence of traditional particle swarm. Premkumar et al. [9] put forth the innovative Salp Swarm Algorithm, which discovered the initial global peak operating point and was used by the P&O algorithm in the final step to achieve a faster convergence rate. An enhanced PSO method was put forth by Premkumar et al. [10] with the goal of capturing the global maximum power point (GMPP) faster, more precisely, and with less chattering of the power curve. ...
The greatest amount of electricity that is accessible must always be extracted in order to operate photovoltaic (PV) systems effectively. Determining the maximum available power is a time-varying challenge since environmental factors like irradiation, temperature, and shading can change fast. Maximum power point tracking (MPPT) strategies are suggested in order to extract the maximum possible power and track the ideal power point under these varied environmental conditions. The use of MPPT to extract the most power is essential for creating effective PV systems. Because it is clean and pollution-free, solar energy has gained a lot of interest. However, the solar array cannot operate uniformly at the maximum power point due to the partially shadowed state, resulting in a significant power loss. These MPPT approaches have a number of drawbacks and limitations, especially when there is partial shadowing brought on by uneven environmental circumstances. An overview of various maximum power point tracking (MPPT) methods for photovoltaic (PV) systems is given in this paper. This thorough analysis of MPPT techniques seeks to give electricity companies and researchers a resource and direction for choosing the optimum MPPT technique for typical operating and partially shaded PV systems based on efficiency and financial viability.
... A hybrid technique that combines adaptive particle swarm optimization (PSO) and cuckoo search was proposed by Xu et al. [8] to solve the issue of premature convergence of traditional particle swarm. Premkumar et al. [9] put forth the innovative Salp Swarm Algorithm, which discovered the initial global peak operating point and was used by the P&O algorithm in the final step to achieve a faster convergence rate. An enhanced PSO method was put forth by Premkumar et al. [10] with the goal of capturing the global maximum power point (GMPP) faster, more precisely, and with less chattering of the power curve. ...
The greatest amount of electricity that is accessible must always be extracted in order to operate photovoltaic (PV) systems effectively. Determining the maximum available power is a time-varying challenge since environmental factors like irradiation, temperature, and shading can change fast. Maximum power point tracking (MPPT) strategies are suggested in order to extract the maximum possible power and track the ideal power point under these varied environmental conditions. The use of MPPT to extract the most power is essential for creating effective PV systems. Because it is clean and pollution-free, solar energy has gained a lot of interest. However, the solar array cannot operate uniformly at the maximum power point due to the partially shadowed state, resulting in a significant power loss. These MPPT approaches have a number of drawbacks and limitations, especially when there is partial shadowing brought on by uneven environmental circumstances. An overview of various maximum power point tracking (MPPT) methods for photovoltaic (PV) systems is given in this paper. This thorough analysis of MPPT techniques seeks to give electricity companies and researchers a resource and direction for choosing the optimum MPPT technique for typical operating and partially shaded PV systems based on efficiency and financial viability.
... The optimal weight is mentioned as a leader, and the remaining weight is followers. The follower weights are guided by the leader's weight [32]. The best value of weight is assumed as the leader and the remaining weights as followers. ...
Renewable energy technologies provide clean and abundant energy that can be self-renewed from natural sources; more support from the public to replace fossil fuels with various renewable energy sources to protect the environment. Although solar energy has less impact on the environment than other renewable sources, the output efficiency is lower due to the different weather conditions. So to overcome that, the MPPT controller is used for tracking peak power and better efficiency. Some conventional methods in MPPT controllers provide less tracking efficiency, and steady-state oscillations occur in maximum power tracking due to the sudden variations in solar irradiance. Thus, in this work salp swarm optimized (SSO) based Elman recurrent neural network (ERNN) controller is proposed to track the maximum power form PV with high efficiency. The weight parameter of ERNN layer is optimized with the help of SSO, which solve the complex problems and give maximum efficiency. The proposed method is performed in MATLAB/Simulink environment, which differs from existing plans and gives a better output efficiency. Using this proposed controller, the system can achieve high tracking efficiency of 99.74% compared to conventional processes.
... finding solutions, their effectiveness is highly reliant on the appropriate selection of control variables. 12 Few instance of metaheuristic methods, including, Differential Evolutionary (DE) algorithms and its variants, 22,23 Genetic Algorithm (GA) and its variants, 24,25 Particle Swarm Optimizer (PSO) and its variants, 26,27 Artificial Bee Colony (ABC), 28 Ant Colony Optimization (ACO), 29 Water Cycle Algorithm (WCA), 30 Cuckoo Search Algorithm (CSA) and its variants, 31,32 Grey Wolf Optimizer (GWO), 33 Whale Optimizer (WO), 34 Firefly Optimizer (FFO), 35 Flower Pollination Algorithm (FPA), 36 Wind Driven Optimization (WDO), 37 Crow Search Algorithm (CrSA), 38 Jaya algorithm and its variants, 39,40 Shuffled Frog Leaping Algorithm (SFLA), 41 Symbiotic Organisms Search (SOS), 42 Salp Swarm Algorithm (SSA), [43][44][45] Emperor Penguin Algorithm (EPA), 46 Spotted Hyena Algorithm (SHA), 47 Ant Lion Optimizer (ALO), 48 Marine Predator Algorithm (MPA), 49,50 Equilibrium Optimizer (EO), 51,52 Teaching-Learning-Based Optimization (TLBO) algorithm, 53 Fireworks Algorithm (FA), 54 Slime Mould Optimization (SMA), 55,56 Runge-Kutta Optimizer (RKO), 57 Hunger Games Search Optimization Algorithm (HGSO), 14,58 Gradient-Based Optimizer (GBO), [59][60][61] Tuna Swarm Optimizer (TSO), 62 Atom Search Optimizer (ASO), 63 Arithmetic Optimization Algorithm (AOA), 64 Jumping Spider Algorithm (JSA), 65 Plasma Generation Optimization (PGO), 66 Generalized Normal Distribution Optimization (GNDO) algorithm, 67 African Vulture Algorithm (AVA), 68 Thermal Exchange Optimization (TEO), 69 Turbulent Water Flow Optimization Algorithm (TWFOA), 70 etc. and improvement techniques, such as Nelder-Mead simplex methods, 71 Levy flight mechanism, 72 Brownian random walk strategy, 73 opposition-based learning methods, 74 chaotic-based methods, 75 and adaptive methods are very much useful for engineering optimization problems including parameter estimation problem of the PV systems. ...
The photovoltaic (PV) system stands out as a viable energy source due to its environmental friendliness and cleanliness. The conversion rate at which solar power generation is still relatively low due to limitations imposed by advances in PV technology. For PV systems, an appropriate model with precise internal parameters is considerably more crucial to increase conversion efficiency further. Different PV mathematical models, such as single‐diode, two‐diode, and three‐diode, are available to model the PV system. Investigators are interested in assessing the accurate PV model parameters through the experimental voltage–current (I–V) samples or using the manufacturer's specifications. At the same time, the difficulty is in accurately assessing and developing a more trustworthy PV model with well‐optimized parameters. To address the parameter estimation of various solar PV models, in this article, a new bio‐inspired algorithm called Brownian random walk‐based Sand Cat Swarm Optimization Algorithm (SCSOA) named Boosted SCSOA (BSCSOA) is proposed and developed. Along with the Brownian random strategy, chaotic tent drift is also used to enhance the exploration and exploitation of SCSOA, and the proposed BSCSOA is applied to different models to estimate their parameters accurately. The effectiveness of the suggested BSCSOA is compared with other well‐known algorithms, including the basic SCSOA, in terms of statistical measures and fitness values. The obtained results demonstrated the superiority of the BSCSOA over the other algorithms for all PV models of the cell and module.
... A conventional SSA has the advantage of simple upgrade functionality, bu large oscillations at the output and is unable to perform fast tracking [29]. Th authors in [115] proposed a hybrid technique in which an SSA is integrated wit O to track the MPP. To track the MPP under UEC, the P and O is executed. ...
... A conventional SSA has the advantage of simple upgrade functionality, but it creates large oscillations at the output and is unable to perform fast tracking [29]. Therefore, the authors in [115] proposed a hybrid technique in which an SSA is integrated with the P and O to track the MPP. To track the MPP under UEC, the P and O is executed. ...
To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: a combination of conventional algorithms, a combination of soft computing algorithms, and a combination of conventional and soft computing algorithms are discussed in detail. Particularly, about 90 hybrid MPPT techniques are presented, and their key specifications, such as accuracy, speed, cost, complexity, etc., are summarized. Along with these specifications, numerous other parameters, such as the PV panel’s location, season, tilt, orientation, etc., are also discussed, which makes its selection easier according to the requirements. This research work is organized in such a manner that it provides a valuable path for energy engineers and researchers to select an appropriate MPPT technique based on the projects’ limitations and objectives.
... To solve this problem, different algorithms such as particle swarm optimization (PSO) algorithm, firefly algorithm, bat algorithm, shuffled frog leap algorithm, artificial fish swarm algorithm, and ant colony optimization algorithm has been proposed (Access et al. 2021;Akwasi and Xie 2022;Duku et al. 2022;Eltamaly, Farh, and Al-Saud. 2019;Liao et al. 2020;Mao et al. 2016;Pilakkat and Kanthalakshm 2019;Premkumar et al. , 2021Premkumar et al. , 2022Premkumar and Sumithira 2018;Priyadarshi et al. 2019;Sridhar, Dash, and Vishnuram 2017;Yetayew, Jyothsna, and Kusuma 2016). From the literature, different algorithms have been used. ...
The generation of power from solar energy by using Photovoltaic (PV) systems to convert the irradiation of the sun into electricity has been adopted over the past years. However, the PV system’s P–V and I–V characteristics become unstable when solar irradiation and temperature change. In this paper, the incremental conductance (INC) has been improved using signals to measure the current and voltage from the PV systems directly which quickly changes with the environmental conditions, and the conventional particle swarm optimization (PSO) is modified so that under multiple shaded peak PV array curves with fast-changing solar irradiance and temperature, more power is extracted at a faster rate without any tracking failure at high-speed tracking of both individual maximum power point (IMPP) and global maximum power point (GMPP) under varying solar irradiance and temperature at a longer distance to enhance the power generated. The individual and global coefficients are also improved to change with multiple shaded peak PV array curves with fast-changing solar irradiance and temperature. DC-DC converter converts DC power from one circuit to another and DC-AC inverter converts DC power to AC power. Simulation was carried out in MATLAB Simulink with different solar irradiance and temperature whereby the conventional INC and PSO were compared with the proposed INC and PSO. An experiment was carried out for a whole day from 8 am to 5 pm to test the validity of the proposed algorithm and compared it with the conventional INC and PSO by using the solar irradiance and temperature received. From both the simulation and experimental results, the proposed INC and PSO performed better by attaining high power and tracking speed with stable output results than the conventional INC and PSO.
... PSC is quite frequent for residential solar systems because of the presence of buildings, trees, clouds, and other obstructions. The approach of treating GMPP tracking as an optimization issue has prompted the development of a new aspect in MPPT studies [9], [10]. Evolutionary algorithms have emerged as the leading trendsetters for the MPP tracking problem under PSCs. ...
... It is desirable to examine the characteristics of the PV panel under PSCs and STC situations. For the PV cell to conduct during PSC, the photovoltaic module voltage must meet Eq. 6, and the diode begins to conduct when Eq. 6 is satisfied [10], [18]. ...
Since the performance of the Photo-Voltaic (PV) arrays is dependent on irradiation and temperature, the PV output power fluctuates with the ambient temperature and the solar irradiance. The achievement of the Maximum Power Point (MPP) under various shading patterns is, as a result, an important aspect in the enhancement of PV systems' overall performance. Because existing methodologies, such as perturb & observe, incremental conductance, etc., are likely to fail, it is necessary to develop an improved Maximum Power Point Tracking (MPPT) method to distinguish between the Global MPP (GMPP) and the Local MPP (LMPP). The characteristics of a PV array under shading conditions include several LMPPs and a single GMPP. To improve the MPPT method of shaded photovoltaic systems, this paper introduces an improved Particle Swarm Optimization (PSO) algorithm with Time-Varying Acceleration Coefficients (PSO-TVAC) that is fast and more efficient than the PSO. Firstly, the PSO-TVAC algorithm is mathematically modeled and applied to the MPPT application for solar PV systems. The primary objective of this paper is to analyze the performance of the PSO-TVAC for the MPPT application. Three shading patterns are considered to study the effectiveness of the PSO-TVAC in handling the MPPT application. The results and discussions prove that PSO-TV AC can be an alternative tool for MPPT application for PV systems.
