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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Hashim, Norazlan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Metaheuristics-based maximum power point tracking for PV systems: a review Jamhari, Muhammad Khairul Azman Mohd; Hashim, Norazlan; Othman, Muhammad Murtadha; Abidin, Ahmad Farid Bin
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i4.pp2495-2513

Abstract

Over the years, numerous maximum power point tracking (MPPT) methods have been developed to extract the maximum available power from PV arrays. They are generally categorized as conventional or metaheuristic methods. The most employed conventional methods include perturb and observe (P&O), hill climbing (HC), and incremental conductance (INC), due to their simplicity and ease of implementation. However, under partial shading condition (PSC), none of them can effectively locate a global maximum power point (GMPP) out of many local maximum power points (LMPPs). This results in significant power loss during PSC, prompting the development of various metaheuristic-based MPPT methods to address the problem. This paper reviews 38 existing metaheuristic-based MPPTs and 27 metaheuristic methods that have not yet been applied to any MPPT operation up to date. Metaphorically, these methods are divided into four categories: (i) evolutionary-based, (ii) physics-based, (iii) swarm-based, and (iv) human-based. The different MPPTs are compared in terms of complexity, converter topology, and PSC tracking capability. This paper is intended to serve as a one-stop resource for any researcher, practitioner, or advanced student seeking to develop a new metaheuristic-based MPPT method.
Enhancing engineering education in electric drive systems through integrated computer simulation modules Baharom, Rahimi; Hashim, Norazlan; Hannoon, Naeem M. S.; Rahman, Nor Farahaida Abdul
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i1.pp45-54

Abstract

The integration of computer simulation modules in electric drive courses plays a pivotal role in modern engineering education by offering students hands-on experience and fostering a deeper understanding of theoretical concepts. This study highlights the significance of enhancing engineering education through an innovative simulation module designed to analyze electric drive systems. The module enables the specification of suitable converters and machines for speed and position control systems while focusing on the steady-state operations of AC and DC drives. Through simulation exercises, students explore converter circuit topologies, control strategies, and the two-quadrant operations of electric machines using fully controlled two-pulse bridge circuits, encompassing motoring and braking modes in the first and fourth quadrants. The proposed module demonstrates its effectiveness in bridging theory and practice, evidenced by significant improvements in students' comprehension of circuit configurations and control algorithms. The approach enhances critical thinking, problem-solving skills, and the ability to relate theoretical knowledge to practical applications. Future research will focus on extending the module's capabilities to incorporate additional quadrants of operation and advanced control strategies. By integrating such tools into the curriculum, educators can better prepare students for the evolving demands of engineering careers.
Simulation and verification of improved particle swarm optimization for maximum power point tracking in photovoltaic systems under dynamic environmental conditions Mohd Jamhari, Muhammad Khairul Azman; Hashim, Norazlan; Baharom, Rahimi; Othman, Muhammad Murtadha
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i1.pp608-621

Abstract

This paper introduces an improved particle swarm optimization (iPSO) algorithm designed for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The proposed algorithm incorporates a novel reinitialization mechanism that dynamically detects and adapts to environmental changes. Additionally, an exponentially decreasing inertia weight is utilized to balance exploration and exploitation, ensuring rapid convergence to the global maximum power point (GMPP). A deterministic initialization strategy is employed to uniformly distribute particles across the search space, thereby increasing the likelihood of identifying the GMPP. The iPSO algorithm is thoroughly evaluated using a MATLAB/Simulink simulation and validated with real-time hardware, including a boost DC-DC converter, dSPACE, and a Chroma PV simulator. Comparative analysis with conventional PSO and PSO-reinit algorithms under various irradiance patterns demonstrates that the iPSO consistently outperforms in terms of convergence speed and MPPT efficiency. The study highlights the robustness of the iPSO algorithm in bridging theoretical models with practical applications.
Optimal population size of particle swarm optimization for photovoltaic systems under partial shading condition Hashim, Norazlan; Nik Ismail, Nik Fasdi; Johari, Dalina; Musirin, Ismail; Rahman, Azhan Ab.
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4599-4613

Abstract

Particle swarm optimization (PSO) is the most widely used soft computing algorithm in photovoltaic systems to address partial shading conditions (PSC). The success of the PSO run heavily depends on the initial population size (NP). A higher NP increases the probability of a global peak (GP) solution, but at the expense of a longer convergence time. To find the optimal value of NP, a trade-off is typically made between a high success rate and a reasonable convergence time. The most used trade-off method is a trial-and-error approach that lacks explicit guidelines and empirical evidence from detailed analysis, which can affect data reproducibility when different systems are used. Hence, this study proposes an empirical trade-off method based on the performance index (PI) indicator, which takes into account the weighted importance of success rate and convergence time. Furthermore, the impact of NP on achieving a successful PSO was empirically investigated, with the PSO tested with 16 different NPs ranging from 3 to 50, and 10,000 independent runs on various PSC problems. Overall, this study found that the best NP to use was 25, which had the best average PI value of 0.9373 for solving all PSC problems under consideration.
Co-Authors Abidin, Ahmad Farid Bin Baharom, Rahimi Hannoon, Naeem M. S. Jamhari, Muhammad Khairul Azman Mohd Johari, Dalina Mohd Jamhari, Muhammad Khairul Azman Musirin, Ismail Nik Ismail, Nik Fasdi Othman, Muhammad Murtadha Rahman, Azhan Ab. Rahman, Nor Farahaida Abdul