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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems PELS (Procedia of Engineering and Life Science)
Nadweh, Safwan
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Using Grey Wolf Optimization Algorithm and Whale Optimization Algorithm for Optimal Sizing of Grid-Connected Bifacial PV Systems Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21777

Abstract

The shift towards renewable energies is driven by the shortage of fossil fuels for electricity generation and the associated harmful impacts. Grid-connected PV systems are a reliable and effective choice for power production across different uses, making them a key player in the global renewable energy landscape. Consequently, the careful selection of components for these systems is a crucial and widely studied aspect in this area of research. This paper introduced using gray wolf optimization algorithm GWO whale optimization algorithm WOA for determining the optimal number of grid - connected bifacial photovoltaic PV systems in Babylon Hilla. The considered factors included available space, desired energy production, radiation, dihedral factor, budget constraints, and grid connectivity requirements. The mathematical formulation of the problem and implementation details of the algorithms are presented. In addition, two cases studied are performed one for a residential area, and the other for a single house. The results demonstrated the efficiency and effectiveness of both algorithms in identifying optimal solutions for determining the size of systems in the area under study. However, the WOA surpassed the GWO in meeting the optimization criteria. The proper selection of these systems resulted in higher power generation, lower costs, improved energy management, and the advancement of sustainable solar energy solutions.
Using Imperialist Competitive Algorithm Powered Optimization of Bifacial Solar Systems for Enhanced Energy Production and Storage Efficiency Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.; Abdulhasan, Mahmood Jamal
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22100

Abstract

Interest in renewable energy has grown due to increased environmental awareness and concern about climate change. Among the various renewable energy technologies, grid-connected bifacial PV systems are particularly important due to their higher efficiency compared to conventional systems. However, maximizing energy harvesting and storage efficiency remains a challenge for these systems, requiring the use of an efficient charge controller and an appropriate battery. The process of setting charge controller parameters and selecting the best storage technology is complex and requires a thorough study of various operating conditions. The main research contribution of this paper is the development of an efficient optimization methodology to increase the energy production and storage efficiency of the studied systems using optimization algorithms. The imperialist competitive algorithm (ICA) is used in the system design to improve performance through optimal adjustment of charge controller parameters and selection of appropriate storage technology. This decision was based on factors such as energy production from PV panels, energy consumption from loads, and energy storage in batteries. Performance is also evaluated using both the flower pollination algorithm (FPA) and Gray Wolf optimization (GWO) algorithms. The study evaluated system performance by analyzing energy production, storage efficiency, and cost effectiveness. The results showed that the ICA algorithm is effective in improving energy production and storage, resulting in higher energy output, better battery efficiency, and lower system costs. In addition, lithium-ion batteries were identified as the best storage technology. This research demonstrates the potential of the ICA approach to increase efficiency and reduce costs in the PV systems.
Using Active Filter Controlled by Imperialist Competitive Algorithm ICA for Harmonic Mitigation in Grid-Connected PV Systems Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.; Moubayed, Nazih
International Journal of Robotics and Control Systems Vol 4, No 2 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i2.1365

Abstract

Solar energy has been gaining momentum recently, with a focus on maximizing its investment potential due to its reputation as the most sustainable and efficient energy source. This shift towards solar power could potentially reduce the reliance on oil-based fuels in the future. As a result of the integration of photovoltaic (PV) energy sources into the grid, the reliability of power distribution and maintaining its quality in these systems has become increasingly important. The presence of non-linear loads in these grids causes distortion of both voltage and current waves on the grid side, so it is necessary to implement effective reduction techniques to reduce the distortions in these waves. The research contribution is TO introduce the integration of an active filter on the dc side of grid-connected PV systems, along with a control circuit for the filter switches. The control switches were operated using a Sinusoidal Pulse Width Modulation (SPWM) control scheme, while the controller parameters were tuned using the Imperialist Competitive Algorithm (ICA). The proposed system was simulated in the MATLAB/Simulink environment with variations in solar radiation and temperature. The simulation results demonstrated a reduction in the total harmonic distortion factor (THD) for voltage and current waveforms on the grid side, which are within the permissible limits. This confirms the effectiveness of the proposed filter and the efficiency of the control strategy and algorithm for parameter adjustment.
Artificial Intelligence for a Circular Economy of Renewable Energy Infrastructure: A Comprehensive Review of AI-driven Solutions for Recycling, Repurposing, and Environmental Lifecycle Management of Solar Panels and Wind Turbines: Kecerdasan Buatan untuk Ekonomi Sirkular Infrastruktur Energi Terbarukan: Tinjauan Komprehensif tentang Solusi Berbasis Kecerdasan Buatan untuk Daur Ulang, Penggunaan Kembali, dan Pengelolaan Siklus Hidup Lingkungan Panel Surya dan Turbin Angin Abdulhasan, Mahmood Jamal; Nadweh, Safwan; Khader, Aya Haider; Shayyish, Yaqoub Shamal
Procedia of Engineering and Life Science Vol. 8 No. 2 (2025): Proceedings of the 8th Seminar Nasional Sains 2025
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v8i2.2932

Abstract

General Background: The rapid global deployment of solar photovoltaic and wind energy systems is central to climate change mitigation but generates a growing end-of-life waste challenge. Specific Background: By 2050, cumulative waste from solar panels and wind turbine blades is projected to reach tens of millions of tons, while current linear recycling systems face technical inefficiencies and economic constraints. Knowledge Gap: There is a lack of scalable and economically viable circular management solutions capable of addressing complex composite materials and lifecycle optimization in renewable energy infrastructure. Aims: This study systematically evaluates Artificial Intelligence applications across the lifecycle of solar panels and wind turbines to assess their role in enabling circular economy strategies. Results: Based on a systematic review of 496 publications and quantitative synthesis, AI-driven solutions demonstrate 35.8% carbon emission reduction per recycled solar panel, 33% improvement in material recovery rates, 43.8% gains in disassembly efficiency, and 62.5 kg CO2 savings per logistics operation. Novelty: The study develops an integrated analytical framework linking Machine Learning, Computer Vision, Robotics, Digital Twins, and lifecycle assessment within renewable energy circularity. Implications: The findings support AI-enabled reverse logistics, Digital Product Passports, and policy-informed lifecycle management as foundational mechanisms for sustainable renewable energy systems. Keywords: Artificial Intelligence, Circular Economy, Renewable Energy Systems, Lifecycle Assessment, Waste Management Key Findings Highlights: Carbon savings of 35.8% achieved through intelligent recycling workflows Material recovery improvements reached up to 33% across PV components Logistics routing reduced transport-related CO2 by 62.5 kg per delivery
Co-Authors Abdulhasan, Mahmood Jamal Amoud, Hassan Ghazaly, Nouby M. Hadi, Husam Ali Kassem, Abdallah Khader, Aya Haider Moubayed, Nazih Shayyish, Yaqoub Shamal