Generalized regression neural network and fitness dependent optimization: Application to energy harvesting of centralized TEG systems

Mirza, Adeel Feroz; Haider, Syed Kamran; Ahmed, Abbas; Rehman, Ateeq Ur; Shafiq, Muhammad; Bajaj, Mohit; Zawbaa, Hossam M.; Szczepankowski, Pawel; Kamel, Salah

doi:10.1016/j.egyr.2022.05.003

Cited by 6 publications

(6 citation statements)

References 34 publications

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“…Tracking Performance: tracking performance refers to how accurately the algorithm can track the optimal solution. [20] and [22] along with the proposed algorithm perform better than [17,19] and [21] in this regard.…”

Section: Comparative Analysismentioning

confidence: 92%

“…Computation time: computation time refers to the time taken by the algorithm to compute the optimal solution. MWOA has the lowest computation time compared to all compared algorithms except [17,18], and [20] algorithm has a higher computation time than MWOA.…”

Section: Comparative Analysismentioning

confidence: 95%

“…Most of the authors used Jaya, Whale, and PSO algorithms for MPPT tracking. In [17][18][19][20][21], and [22] authors describes the PI controller, Fuzzy logic controller, estimation-based extremum-seeking control algorithm, improved generalized regression neural network and fitness-dependent optimization (GRNNFDO), student psychology based optimization (SPBO), and adaptive jaya (Ajaya) algorithm used for MPPT technique respectively. A comparative analysis of these algorithms based on different parameters is given below:…”

Section: Comparative Analysismentioning

confidence: 99%

“…Convergence time: convergence time refers to the time required for the algorithm to reach the optimal solution. [19] and [21] algorithm has a longer convergence time compared to [18,20,22] and MWOA. In [22] presented algorithm has the fastest convergence time among all algorithms.…”

Section: Comparative Analysismentioning

confidence: 99%

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Optimal control for a photovoltaic integrated grid system using PSO and modified whale optimization to enhance power quality

Tiwari

Agarwal

2023

Eng. Res. Express

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The paper proposes a modified & optimized controller for PV-integrated grid-connected PV systems. As the solar power output depends on the environmental conditions, i.e., irradiation and atmospheric temperature, the maximum power from the photovoltaic (PV) module is extracted using perturb and observe (P & O) algorithm. With a variety of controllers, such as conventional proportional and integral (PI) controllers, particle swarm optimization (PSO), and the modified Whale optimization algorithm (MWOA) method based on optimum control, the power as compared to extracted from PV panels using MWOA-based controllers. A complete simulation and implementation of a single-phase grid-connected inverter are shown to verify the proposed PV controller. The power flow control and grid synchronization are also presented when the above controllers act as MPPT controllers in the proposed system. The performance of proposed system is evaluated and compared among conventional and optimal controllers under a MATLAB-Simulink environment.

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Section: Comparative Analysismentioning

confidence: 92%

Section: Comparative Analysismentioning

confidence: 95%

Section: Comparative Analysismentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

See 2 more Smart Citations

Optimal control for a photovoltaic integrated grid system using PSO and modified whale optimization to enhance power quality

Tiwari

Agarwal

2023

Eng. Res. Express

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“…The experiments were done in the capital city of a tropical, fast-developing country (Dhaka, Bangladesh), where using solar power is quite significant considering the poor economic condition of many people (Thentral et al, 2022) (Mirza et al, 2022). Therefore, the cost-effectiveness of a realistic MPPT approach is vital for real-life implementation and our method can track the maximum power point without adding to any significant installment cost.…”

Section: Conclusion and Scope Of The Future Workmentioning

confidence: 99%

Implementation of an MPPT technique of a solar module with supervised machine learning

et al. 2022

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Automated calibration of a maximum power point tracking (MPPT) algorithm for the photovoltaic (PV) system is pivotal for harnessing the maximum possible energy from solar power. However, most existing calibration methods of such an MPPT system are cumbersome and vary greatly with the environmental condition. Hence, an automated pipeline capable of performing suitable adjustments is highly desirable. We proposed a method using supervised machine learning (ML) in a solar PV system for MPPT analysis. For this purpose, an overall schematic diagram of a PV system is designed and simulated to create a dataset in MATLAB/Simulink. Thus, by analyzing the output characteristics of a solar cell, an improved MPPT algorithm on the basis of a neural network (NN) method is put forward to track the maximum power point (MPP) of solar cell modules. Moreover, we implemented the algorithm in a hardware setup and verified the theoretical result with the empirical data. Typically, the performance accuracy of the NN models is around 97∼98%. But our proposed model shows an even higher efficiency (99.8% approximately) without adding to any extra computational cost.

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Techno-economic-environmental optimization of hybrid photovoltaic-thermoelectric generator systems based on data-driven approach

Yang,

Xie,

Shu

et al. 2024

Applied Thermal Engineering

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Generalized regression neural network and fitness dependent optimization: Application to energy harvesting of centralized TEG systems

Cited by 6 publications

References 34 publications

Optimal control for a photovoltaic integrated grid system using PSO and modified whale optimization to enhance power quality

Optimal control for a photovoltaic integrated grid system using PSO and modified whale optimization to enhance power quality

Implementation of an MPPT technique of a solar module with supervised machine learning

Techno-economic-environmental optimization of hybrid photovoltaic-thermoelectric generator systems based on data-driven approach

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