A novel modified ant colony optimization based maximum power point tracking controller for photovoltaic systems

Phanden, Rakesh Kumar; Sharma, Lalit Kumar; Chhabra, Jatinder; Demir, Halil İbrahim

doi:10.1016/j.matpr.2020.06.020

Cited by 25 publications

(14 citation statements)

References 12 publications

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“…So, if the parameter P 3 is larger than or equal to rand 3 , the strategy is to attract a variety of vultures to the source of food, resulting in competitive behavior. Therefore, the vulture's position can be updated using Equation (17).…”

Section: E Phase 5: Exploitation (Second Stage)mentioning

confidence: 99%

“…Recently, considerable research has revealed a strong desire in bioinspired MPPTs, which have outperformed intelligent methods such as the PSO [12], artificial-bee colony [13], grey-wolf-optimization technique [14], firefly algorithm [15], salp-swarm optimization [16], ant-colony optimization [17], and cuckoo-search algorithm [18] in various environmental conditions. Moreover, several research works have incorporated these methods into hybrid ones to improve their performance and eliminate their drawbacks, specifically, by integrating the optimization ability of various searching mechanisms into an incorporated form of at least two methods, to recover the limitations of one method via the performance of another [19].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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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Section: E Phase 5: Exploitation (Second Stage)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Furthermore, the DMPP techniques should give the advantages of both soft computing and conventional DMPP algorithms [14]. The optimization methods for MPPT in PV systems include Artificial Bee Colony (ABC) [15], Ant Colony Optimization (ACO) [16], Genetic Algorithm (GA) [17], Differential Evaluation (DE) [18], Grey Wolf Optimization (GFO) [19], and Cuckoo's Search (CS) [20]. The PV system's efficiencies have been rated based on reaction speed, accuracy, efficiency, partial shading, and performance under quick response.…”

Section: Introductionmentioning

confidence: 99%

Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions

et al. 2022

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Photovoltaic (PV) solar energy is a very promising renewable energy technology, as solar PV systems are less efficient because of climate conditions, temperature, and irradiance change. So, to resolve this problem, two PV topologies are used, i.e., centralized and distributed PV systems. The centralized technique is quicker than the distributed technique in terms of convergence speed and a faster power tracking approach. In the event of uniform irradiance, the centralized system also has the benefit of supplying superior energy, but in PS scenarios, a huge amount of energy is lost. However, the distributed approach requires current and voltage measurements at each panel, resulting in a massive data set. Nevertheless, in the event of shading circumstances, the distributed technique is highly effective because a modular level power electronics (MLPE) converter is used. While in a centralized PV system, there is only a single DC-DC converter for the whole PV system. In this research work, a DFO-based DC-DC converter is designed for modular level, with an ability to perform a rapid shutdown of the module under fire hazard conditions, troubleshooting, and monitoring of a module in a very efficient way. The robustness of the proposed MPPT DFO algorithm is tested with different techniques such as Cuckoo Search (CS), Fruit Fly Optimization (FFO), Particle swarm optimization (PSO), Incremental conductance (InC), and Perturb and observe(P&O) techniques. The proposed technique shows better results in terms of MPPT efficiency, dynamic responsiveness, and harmonics. Furthermore, the result of MLPE and the centralized system is verified by using the Helioscope with different inverter companies like SMA, Tigo, Enphase, Solar edge, and Huawei. The results prove that MLPE is a better option in the case of shading region for attaining the maximum power point.

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“…Fuzzy Logic Control (FLC) is also used to overcome the uncertainty of PV array caused by varying solar irradiance in [22]. Numerous meta-heuristic algorithms were used for MPPT as a solution for the PS such as in [23] where ACO was used with different control parameters as a convergence method.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Optimal PV Array Configurations and MPPT under Partial Shading with Fast Dynamical Change of Hybrid Load

et al. 2022

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The characteristics of photovoltaic (PV) are directly affected by partial shading (PS) conditions due to the non-uniform irradiance. The PV system can be compromised based on the shading pattern as well as the shading area. Thus, the need for a solution that can deal with non-uniform irradiance has increased significantly. Consequently, this paper proposes a thorough analysis of the impact of PS patterns on different PV array configurations such as SP, TCT, and BL. The five optimization algorithms PSO, DA, MLS-SPA, IGWO, and BWO, were used to tune the variable step of the conventional P&O technique to extract the maximum power point. The proposed PV array is 4×4 with a fixed location, yet changing electrical connections. The main objective and novelty of this paper is to locate the Global Maximum Power Point (GMPP) of a PV array while the occurrence of different PSC with fast change of hybrid load e.g., (resistive and pump load). The results showed the superior performance of the IGWO algorithm regarding the maximum power tracking and disturbance rejection.

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A novel modified ant colony optimization based maximum power point tracking controller for photovoltaic systems

Cited by 25 publications

References 12 publications

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

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

Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions

Comparative Study of Optimal PV Array Configurations and MPPT under Partial Shading with Fast Dynamical Change of Hybrid Load

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