Analysis of a Hybrid Wind/Photovoltaic Energy System Controlled by Brain Emotional Learning-Based Intelligent Controller

Albalawi, Hani; EL-Shimy, M.; AbdelMeguid, Hosam; Kassem, Ahmed M.; Zaid, Sherif A.

doi:10.3390/su14084775

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…To address these concerns, this research aims at mitigating the intermittent nature of RESs. The conventional sources of electricity, however, are insufficient and have numerous environmental issues [39,40]. Thus, RESs for electricity have attracted a lot of attention.…”

Section: Nomenclature/abbreviationmentioning

confidence: 99%

Mitigating Uncertainty Problems of Renewable Energy Resources Through Efficient Integration of Hybrid Solar PV/Wind Systems Into Power Networks

Ahmed,

Mirsaeidi,

Koondhar

et al. 2024

IEEE Access

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In recent years, there has been a proliferation of studies focusing on sustainable energy sources aimed at delivering reliable power with reduced greenhouse gas emissions. Among these endeavors, electrification of urban areas through incorporation of renewable energy sources (RESs) stands out as a pivotal application of RES. Increasingly, the integration of RESs like PV and wind into the power grid is being pursued as an alternative to traditional centralized power stations, offering a means to ensure continuous power supply during peak demand periods while simultaneously managing unit costs, thereby providing a systematic and readily accessible power supply to consumers. This paper presents a comprehensive examination of hybrid wind and PV with focus on achieving consistent DC bus-bar voltage through integration with microcontroller system via buck-boost converter. Additionally, hybrid system's output voltage is tailored to accommodate necessary battery connections. However, given influence of environmental variables like fluctuations in solar irradiation and wind velocity on power output of PV and wind seamless integration of these sources becomes paramount. To address this challenge MATLAB model is developed to simulate grid integration of hybrid wind and PV, enabling a detailed analysis of integrated grid's performance. Simulation results are then proposed with those obtained from conventional fossil fuelbased power generation methods. In this research underscores importance of integrating RES into existing power grids and highlights efficacy of hybrid wind and PV in meeting this objective. Findings presented herein contribute to advancing discourse on sustainable energy solutions, offering valuable insights for policymakers, industry stakeholders, and researchers.

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Section: Nomenclature/abbreviationmentioning

confidence: 99%

Mitigating Uncertainty Problems of Renewable Energy Resources Through Efficient Integration of Hybrid Solar PV/Wind Systems Into Power Networks

Ahmed,

Mirsaeidi,

Koondhar

et al. 2024

IEEE Access

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“…A computer model of the limbic system's cognitive processes related to emotion processing was created by the BELBIC control system [32]. Figure 6a shows the model of the suggested BELBIC structure.…”

Section: Proposed Belbic Controllermentioning

confidence: 99%

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Zaid,

Albalawi,

Alatwi

et al. 2024

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Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

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“…As the cost to maintain and operate wind turbines drops and their dependability improves, the number of wind farms grows exponentially. Yet, the reliability of wind farms is impacted by the intermittent and variable nature of wind energy (Albalawi et al, 2022;Chen et al, 2022). Therefore, it is becoming increasingly important that wind energy be predictable (Zeng et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Wind speed forecasting using optimized bidirectional LSTM based on dipper throated and genetic optimization algorithms

et al. 2023

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Accurate forecasting of wind speed is crucial for power systems stability. Many machine learning models have been developed to forecast wind speed accurately. However, the accuracy of these models still needs more improvements to achieve more accurate results. In this paper, an optimized model is proposed for boosting the accuracy of the prediction accuracy of wind speed. The optimization is performed in terms of a new optimization algorithm based on dipper-throated optimization (DTO) and genetic algorithm (GA), which is referred to as (GADTO). The proposed optimization algorithm is used to optimize the bidrectional long short-term memory (BiLSTM) forecasting model parameters. To verify the effectiveness of the proposed methodology, a benchmark dataset freely available on Kaggle is employed in the conducted experiments. The dataset is first preprocessed to be prepared for further processing. In addition, feature selection is applied to select the significant features in the dataset using the binary version of the proposed GADTO algorithm. The selected features are utilized to learn the optimization algorithm to select the best configuration of the BiLSTM forecasting model. The optimized BiLSTM is used to predict the future values of the wind speed, and the resulting predictions are analyzed using a set of evaluation criteria. Moreover, a statistical test is performed to study the statistical difference of the proposed approach compared to other approaches in terms of the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. The results of these tests confirmed the proposed approach’s statistical difference and its robustness in forecasting the wind speed with an average root mean square error (RMSE) of 0.00046, which outperforms the performance of the other recent methods.

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Analysis of a Hybrid Wind/Photovoltaic Energy System Controlled by Brain Emotional Learning-Based Intelligent Controller

Cited by 11 publications

References 34 publications

Mitigating Uncertainty Problems of Renewable Energy Resources Through Efficient Integration of Hybrid Solar PV/Wind Systems Into Power Networks

Mitigating Uncertainty Problems of Renewable Energy Resources Through Efficient Integration of Hybrid Solar PV/Wind Systems Into Power Networks

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Wind speed forecasting using optimized bidirectional LSTM based on dipper throated and genetic optimization algorithms

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