A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions

Sarwar, Sajid; Hafeez, Muhammad Annas; Javed, Muhammad Yaqoob; Asghar, Aamer Bilal; Ejsmont, Krzysztof

doi:10.3390/en15051880

Cited by 35 publications

(18 citation statements)

References 16 publications

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“…PS, on the other hand, produces multiple peaks on the curve, putting the efficiency of these traditional algorithms at risk. For PV power systems, bio-inspired techniques can efficiently find GM [55][56][57]. Power losses are a big issue under varying irradiance circumstances, and they can be mitigated with MPPT approaches because PS affects the system process performance greatly.…”

Section: Shading In Pv Systemmentioning

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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Section: Shading In Pv Systemmentioning

confidence: 99%

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

et al. 2022

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“…Generally, the shading phenomenon is unavoidable in PV generation [10,13]. The shadows of buildings, trees, and clouds lead to a non-uniform irradiance penetrating the PV array [13,14].…”

Section: Introductionmentioning

confidence: 99%

“…Generally, the shading phenomenon is unavoidable in PV generation [10,13]. The shadows of buildings, trees, and clouds lead to a non-uniform irradiance penetrating the PV array [13,14]. Once partial shading occurs, the conventional MPPT control is unable to distinguish the accurate operating point of the PV system [11,13,15].…”

Section: Introductionmentioning

confidence: 99%

“…The shadows of buildings, trees, and clouds lead to a non-uniform irradiance penetrating the PV array [13,14]. Once partial shading occurs, the conventional MPPT control is unable to distinguish the accurate operating point of the PV system [11,13,15]. This idea has gained significant interest in addressing the these disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…In the conventional method, the next movement is based on the previous location, and no memory is employed; thus this method fails to identify the optimum point under shading conditions [20,21]. Therefore, soft computing techniques using particle swarm optimization (PSO) [17,18], cuckoo search [22] differential evaluation (DE) [23,24], fuzzy logic control [25], ant colony optimization (ACO) [26], artificial bee colony (ABC) [21,27], musical chairs algorithm [28], salp swarm algorithm (SSA) [29][30][31], horse herd optimization algorithm [13], and the grasshopper optimization algorithm (GOA) [32] are used in the search for the global point. These techniques are more preferable due to their superiority in handling extreme conditions, such as shading and the dynamic change of irradiance [33].…”

Section: Introductionmentioning

confidence: 99%

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Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking

et al. 2022

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This paper presents a modified Levy particle swarm optimization (MLPSO) to improve the capability of maximum power point tracking (MPPT) under various partial shading conditions. This method is aimed primarily at resolving the tendency to trap at the local optimum particularly during shading conditions. By applying a Levy search to the particle swarm optimization (PSO), the randomness of the step size is not limited to a specific value, allowing for full exploration throughout the power-voltage (P-V) curve. Therefore, the problem such as immature convergence or being trapped at a local maximum power point can be avoided. The proposed method comes with great advantages in terms of consistent solutions over various environmental changes with a small number of particles. To verify the effectiveness of the proposed idea, the algorithm was tested on a boost converter of a photovoltaic (PV) energy system. Both simulation and experimental results showed that the proposed algorithm has a high efficiency and fast-tracking speed compared to the conventional HC and PSO algorithm under various shading conditions. Based on the results, it was found that the proposed algorithm successfully converges most rapidly to the global maximum power point (GMPP) and that the tracking of GMPP under complex partial shading is guaranteed. Furthermore, the average efficiency for all test conditions was 99% with a tracking speed of 1.5 s to 3.0 s and an average output steady-state oscillation of 0.89%.

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Nature Inspired Optimizers and Their Importance for AI: An Inclusive Analysis

Haldorai,

Murugan

et al. 2024

EAI/Springer Innovations in Communication and Computing

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A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions

Cited by 35 publications

References 16 publications

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

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

Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking

Nature Inspired Optimizers and Their Importance for AI: An Inclusive Analysis

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