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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Azmi, Azralmukmin
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Control & Systems Engineering Electrical & Electronics Engineering

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Experimental study on modified GOA-MPPT for PV system under mismatch conditions Muhammad, Nur Afida; Tajuddin, Mohammad Faridun Naim; Azmi, Azralmukmin; Jamaludin, Mohd Nasrul Izzani; Ayob, Shahrin Md; Sutikno, Tole
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i1.pp611-622

Abstract

This paper presents a modified grasshopper optimization algorithm (GOA) tailored for optimizing the power extraction capability of a solar photovoltaic (PV) system. The algorithm`s focus is on addressing one of the issues associated with mismatch loss (MML), particularly the mismatch (MM) in solar irradiance conditions, to attain maximum output power. The core strategy of the GOA involves optimizing the duty cycles of the converter to achieve the maximum power point (MPP) for the PV system. The PV system configuration comprises three PV modules connected in series and a SEPIC converter. To facilitate efficient maximum power point tracking (MPPT), the paper proposes using the GOA as a controlling mechanism. The study employs a comparative approach, contrasting the performance of the proposed system against established algorithms, such as PSO and GWO. The results of these evaluations exhibit the superior performance of the proposed GOA when compared to other optimization techniques. The GOA exhibits exceptional MPPT tracking characteristics, characterized by rapid tracking speed, heightened efficiency, and minimal oscillations within the PV system. Consequently, the GOA effectively addresses one of the MML issues.
Optimizing battery energy storage sizing in microgrids using manta ray foraging optimization algorithm Yatim, Yazhar; Tajuddin, Mohammad Faridun Naim; Sulaiman, Shahril Irwan; Azmi, Azralmukmin; Ayob, Shahrin Md; Sutikno, Tole
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2535-2544

Abstract

The integration of renewable energy sources (RES) into microgrids (MGs) is becoming increasingly important as the world strives to transition towards more sustainable and eco-friendly energy systems. Unfortunately, integrating RES such as solar and wind power into MGs poses challenges due to their intermittent nature. The batteries need to be integrated into the MG system to overcome these challenges and ensure a stable and reliable power supply. However, the size of the battery presents another challenge as it affects the total operation cost of the MG system. Manta ray foraging optimization (MRFO) is used as an optimization technique to minimize the total operation cost of the MG system while ensuring optimum battery capacity. This algorithm is compared with the particle swarm optimization (PSO), differential evolution (DE), and the sine cosine algorithm (SCA). As a result, the proposed technique achieved a better solution than the existing algorithms.
Adaptive intelligent PSO-Based MPPT technique for PV systems under dynamic irradiance and partial shading conditions Islam, Muhammad Gul E.; Tajuddin, Mohammad Faridun Naim; Azmi, Azralmukmin; Hasanah, Rini Nur; Ayob, Shahrin Md.; Sutikno, Tole
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2841-2859

Abstract

This research introduces an adaptive improved particle swarm optimization (AIPSO) approach for maximum power point tracking (MPPT) approach designed to enhance energy harvesting from photovoltaic (PV) systems under dynamic irradiance conditions. The proposed AIPSO algorithm addresses the challenges associated with traditional MPPT methods, particularly in scenarios characterized by fluctuating solar irradiance, such as step changes and partial shading. By incorporating a robust reinitialization strategy along with updated velocity and position equations, the algorithm demonstrates superior performance in terms of convergence accuracy, tracking speed, and tracking efficiency. This modification enables the algorithm to effectively escape local maxima and explore a wider search space, leading to improved convergence and optimal power point tracking. Furthermore, the adaptive nature of the PSO enhances the algorithm’s ability to respond to real-time changes in environmental conditions, making it particularly suitable for large- scale PV systems subjected to varying atmospheric factors. Here, “adaptive” denotes coefficient scheduling (C3) and a re-initialization trigger that responds to irradiance regime changes; “intelligent” denotes robust regime shift detection and safe duty ratio clamping. Across uniform, step change, and partial shading conditions, the proposed AIPSO achieves fast reconvergence and high tracking efficiency with negligible steady state oscillations, as summarized in the results. Building on this contribution, future research will focus on evaluating its scalability across different PV architectures and large-scale grid integration with real hardware setup.
Co-Authors Ayob, Shahrin Md Ayob, Shahrin Md. Islam, Muhammad Gul E. Jamaludin, Mohd Nasrul Izzani Muhammad, Nur Afida Rini Nur Hasanah Sulaiman, Shahril Irwan Tajuddin, Mohammad Faridun Naim Tole Sutikno Yatim, Yazhar