“…Not only can trees and buildings cast shadows, but they cast several kinds of shadows. Therefore, due to this effect, a series-connected solar array cannot receive the same amount of incident irradiance [18]. The output power generation of the Photovoltaic modules drops because of shade on the PV array.…”
Solar energy offers several environmental, economic, and energy security advantages. Parasitic parameters and shading on solar panels can reduce efficiency. This paper presents a bio-inspired Enhanced Slime Mold (ESM) algorithm search strategy to find the optimal power point by simulating the behaviour of slime molds in a virtual environment. In a solar panel, proposed ESM provides not only for parameter extraction but also serves as Maximum Power Point Tracking (MPPT) during Partial Shading Conditions (PSC). Proposed ESM dynamic behaviour is examined under solar irradiation and various temperature conditions. The effectiveness of proposed technique has been validated by extracting parameters from conventional polycrystalline and monocrystalline modules in the form of a 5S-5P arrangement. In the instance of MPPT operation, the proposed ESM algorithm is compared with Ant Bee Colony and Perturb& Observe (ABC-PO) to determine its efficacy. Moreover, during extraction of unknown parameters of solar cell ESM is compared with existing optimization algorithms such as Artificial Bee Swarm Optimization (ABC SO), Genetic Algorithm (GA), Covariant Matrix (CM), Ant Bee Colony (ABC), and Advanced Particle Swarm Optimization (APSO). In this connection, proposed ESM algorithm is superior to above-mentioned algorithms due to high accuracy, a smaller number of computations, and minimum computational time.
“…Not only can trees and buildings cast shadows, but they cast several kinds of shadows. Therefore, due to this effect, a series-connected solar array cannot receive the same amount of incident irradiance [18]. The output power generation of the Photovoltaic modules drops because of shade on the PV array.…”
Solar energy offers several environmental, economic, and energy security advantages. Parasitic parameters and shading on solar panels can reduce efficiency. This paper presents a bio-inspired Enhanced Slime Mold (ESM) algorithm search strategy to find the optimal power point by simulating the behaviour of slime molds in a virtual environment. In a solar panel, proposed ESM provides not only for parameter extraction but also serves as Maximum Power Point Tracking (MPPT) during Partial Shading Conditions (PSC). Proposed ESM dynamic behaviour is examined under solar irradiation and various temperature conditions. The effectiveness of proposed technique has been validated by extracting parameters from conventional polycrystalline and monocrystalline modules in the form of a 5S-5P arrangement. In the instance of MPPT operation, the proposed ESM algorithm is compared with Ant Bee Colony and Perturb& Observe (ABC-PO) to determine its efficacy. Moreover, during extraction of unknown parameters of solar cell ESM is compared with existing optimization algorithms such as Artificial Bee Swarm Optimization (ABC SO), Genetic Algorithm (GA), Covariant Matrix (CM), Ant Bee Colony (ABC), and Advanced Particle Swarm Optimization (APSO). In this connection, proposed ESM algorithm is superior to above-mentioned algorithms due to high accuracy, a smaller number of computations, and minimum computational time.
“…As a new source of energy from a wide range, solar energy is recognized by most countries [1][2][3]. China has one of the highest proportions of solar energy resources.…”
Photovoltaic (PV) power has become a crucial solution to the escalating energy crisis. Among the various implementations, Rooftop PV power generation systems (RPVPGS) are predominant in PV buildings. However, RPVPGS will face challenges such as reduced output power due to array fault or shading, leading to fluctuations in Building-Integrated PV (BIPV) power generation. This paper attempts to solve this problem by proposing a novel multivariate reconfiguration method based on the improved northern goshawk optimization algorithm (INGO). The aim is to find the optimal state of RPVPGS under various conditions. In this paper, extensive simulations were conducted on the experimental platform to assess the feasibility and effectiveness of the proposed method. It is worth noting that INGO outperforms existing technologies such as Arrow SoDuku and Zig-zag for the evaluation metrics mentioned in the article. Furthermore, rigorous simulation experiments were conducted on the semi-physical platform to validate the proposed approach. The power enhancement percentage deviation was between +0.1% to +0.2%. These results unequivocally demonstrate that the INGO-based multivariate reconfiguration method accurately reconfigures RPVPGS, ensuring the efficiency and stability of BIPV systems.
“…The dynamic reconfiguration technique (DRT) calls for a large number of hardware structures, a supervising reconfiguration algorithm to determine the ideal configuration, sensors, and switch matrices to connect PV modules, which increases control complexity and decreases system reliability 25 . In contrast to DRT, static reconfiguration technique (SRT) does not dynamically change the electrical interconnections 26 – 28 . Figure 1 MPPT categories.…”
Partial shading of solar panels diminishes their operating efficiency and energy synthesized as it disrupts the uniform absorption of sunlight. To tackle the issue of partial shading in photovoltaic (PV) systems, this article puts forward a comprehensive control strategy that takes into account a range of contributing factors. The proposed control approach is based on using multi-string PV system configuration in place of a central-type PV inverter for all PV modules with a single DC-DC converter. This adaptation enhances overall efficiency across varying radiation levels. Also, the proposed technique minimizes the overall system cost by reducing the required sensors number by utilizing a radiation estimation strategy. The converter switching strategy is synthesized considering direct duty-cycle control method to establish the maximum power point (MPP) location on the P–V curve. The direct duty-cycle tracking approach simplifies the control system and improves the system’s response during sudden partial shading restrictions. To validate the effectiveness of the suggested MPPT method, two system configurations were constructed using MATLAB/SIMULINK software and assessed under various partial shading scenarios. Additionally, a multi-string system was subjected to real irradiance conditions. The sensor-less MPPT algorithm proposed achieved an impressive system efficiency of 99.81% with a peak-to-peak ripple voltage of 1.3V. This solution offers clear advantages over alternative approaches by reducing tracking time and enhancing system efficiency. The system findings undoubtedly support the theoretical scrutiny of the intended technique.
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