Azralmukmin Azmi
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Preferred name
Azralmukmin Azmi
Official Name
Azralmukmin, Azmi
Alternative Name
Azmi, Azralmukmin
Azmi, A.
Azmi, Azralmuknin
Main Affiliation
Scopus Author ID
39360914400
Researcher ID
R-1532-2019

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Fundamental study on the impacts of water-cooling and accumulated dust on photovoltaic module performance

2022-12-01 , Alwesabi F.A.A. , Aziz A.S. , Mohamad Shukor Abdul Rahim , Mohammad Faridun Naim Tajuddin , Mohd Fayzul Mohammed , Azralmukmin Azmi , Satterlee C. , Ayob S.M. , Sutikno T.

Photovoltaic (PV) modules have been becoming well-spread recently as alternative clean energy sources to traditional energy sources due to their efficiency and sustainability benefits. This paper applied various water temperatures and artificial dust levels to a couple of monocrystalline PV modules under outdoor conditions to observe their performance. Two different IV tracers were connected separately to each module for comparison purposes. Two temperature sensors were installed at the back of the panels to observe the cell temperatures. Besides, a temperature sensor was specified for ambient readings. Water flowed through an adjustable water-flow sensor to cool the overheated PV module using specific mass flow rates. The results indicate that the efficiency of the PV module starts to reduce when the panel temperature begins to surpass 49.1°C. It was discovered that cooling the PV module increases its efficiency from 0.97 percent at the lowest rate to 4.70 percent at the highest rate. Furthermore, accumulated dust on the PV module top surface can be reduced up to 3-fold under 110 g/m2 of dust, and up to 29.30% under 10 g/m2 of 100% of its generated energy. Improvement techniques and future work on PV module performance are also discussed.

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A comparative study on DG placement using marine predator and Osprey algorithms to enhance loss reduction index in the distribution system

2025-06 , Siti Rafidah Abdul Rahim , Azralmukmin Azmi , Syahrul Ashikin Azmi , Muhamad Hatta Hussain , Syazwan Ahmad Sabri , Ismail Musirin

The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. MPA simulates marine predators' foraging strategies through Lévy and Brownian movements, while OOA models the hunting and survival tactics of ospreys, known for their remarkable fishing skills. Effective placement and sizing of DG units are crucial for minimizing network losses and ensuring cost efficiency. Improper configurations can lead to overcompensation or undercompensation in the network, increasing operational costs. Different DG technologies, such as photovoltaic (PV), wind, microturbines, and generators, vary significantly in cost and performance, highlighting the importance of selecting the right models and designs. This study compares MPA and OOA in optimizing the placement of multiple DGs with two types of power injection which are active and reactive power. Simulations on the IEEE 69-bus reliability test system, conducted using MATLAB, demonstrated MPA’s superiority, achieving a 69% reduction in active power losses compared to OOA’s 61%, highlighting its potential for more efficient DG placement in power distribution systems. The proposed approach incorporates a DG model encompassing multiple technologies to ensure economic feasibility and improve overall system performance.

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Arcing fault diagnosis using first peak arrival of EM radiation signal

2021-06-11 , Melaty binti Amirruddin , Mohd Rafi Adzman , Nur Adyani Mohd Affendi , Azralmukmin Azmi , Muhd Hafizi Idris , Hana Abdull Halim , Muzamir Isa , Halim S.A.

The objective of this study was to diagnose the arcing fault signals based on the first peak of arrival method using antenna to assess its use as potential arcing fault detection in power system network. Square patch antenna and circle patch antenna were employed for detection on artificial arcing in real environment. First peak of arcing signal arrival was measured through an analysis over a range of time and amplitude signals detected. For accurate results, Discrete Wavelet Transform (DWT) denoising technique was applied to the arcing signals detected as denoising tools. Analysis of first peak of signal arrival time and amplitude were carried out using MATLAB software to measure the changes in signals detected caused by di different placements of antenna. The results revealed that the first peak of signal arrival time, amplitude, type of antenna used and placement of the antenna around arcing source point all reflect the signals measurement.

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Modeling, experimental investigation and real-time control of active water cooling system for photovoltaic module

2024-01-01 , Mohamad Shukor Abdul Rahim , Mohammad Faridun Naim Tajuddin , Mohd Sazli Saad , Hasanuzzaman M. , Azralmukmin Azmi , Mohd Fayzul Mohammed

Photovoltaic (PV) cells are integral in harnessing solar energy, yet their performance is hindered by excessive heat generation, impacting efficiency and sustainability. Addressing the challenge of efficiency loss in photovoltaic (PV) cells due to overheating, this study focuses on optimizing active water cooling control for PV modules. The aim is to develop a dynamic, sustainable model and integrate a PID controller tuned by Sine Cosine Algorithm (SCA), targeting optimal operating temperatures. This study introduces a dynamic model and a closed-loop control system to manage PV cell temperature, investigating the correlation between water flow and temperature regulation. Experimental data is gathered using a pseudo-random binary sequence (PRBS) as an excitation signal, forming the foundation of an Auto Regressive eXogenous (ARX) model. The closed-loop system incorporates a PID controller and tuned using the Sine Cosine Algorithm (SCA) to optimize performance. The resulting model is rigorously validated through experimental investigation, demonstrating its precision in capturing the system’s dynamics. Moreover, the implementation of a controller-based cooling system substantiates the model’s practical efficacy. The research demonstrates significant improvements when implementing a controller-based water-cooling system for photovoltaic (PV) modules. Compared to the baseline scenario without cooling, the system achieves a 34.5% reduction in average PV temperature (from 59.2°C to 38.9°C) and a 9.46% increase in average power output (from 196.7W to 215.3W). Moreover, this system utilizes only 248.8 liters of water, marking a substantial 64% decrease in water consumption compared to traditional free-flow cooling methods, which use 790.9 liters. The research demonstrates that the controller-based cooling approach is a sustainable option, delivering power output comparable to the free-flow method, yet significantly lowering water consumption. This research signifies a turning point for sustainability, offering an efficient and water-conscious approach for enhancing PV system performance, a crucial step toward a greener and more environmentally responsible energy future.

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Improved hill climbing algorithm with fast scanning technique under dynamic irradiance conditions in photovoltaic system

2020 , Ali Jawad Khadhim Alrubaie , Mohammad Faridun Naim Tajuddin , Tekai Eddine Khalil Zidane , Azralmukmin Azmi

The perturb and observe (P&O) algorithm is an easy and effective method used for tracking maximum power point. However, this technique suffers from deviation when irradiation changes suddenly. Moreover, the impact of this deviation is high when the insolation variation is rapid. This error is due to the incorrect decision taken by the conventional P&O method throughout the first step-change in the duty cycle during the increase in irradiation. The proposed P&O is a modified conventional P&O that focuses on using additional dI parameter with variable step size ΔDn. In this manner, the conventional P&O algorithm is allowed to identify the source of deviation caused by rapid irradiance changes. The efficiency of the proposed P&O is assessed using simulation in MATLAB/Simulink. Results show that the proposed P&O effectively tracks maximum power and prevents deviations in rapidly changing climate conditions within a short time, which is lesser than the conventional P&O method. In addition, the proposed P&O has a rapid dynamic response. A DC–DC boost converter is utilized in this work to validate the proposed P&O algorithm.

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Experimental study on modified GOA-MPPT for PV system under mismatch conditions

2024 , Nur Afida Muhammad , Mohammad Faridun Naim Tajuddin , Azralmukmin Azmi , Mohd Nasrul Izzani Jamaludin , Shahrin Md Ayob , Tole Sutikno

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.

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Optimal sizing of a fixed-tilt ground-mounted grid-connected photovoltaic system with bifacial modules using Harris Hawks Optimization

2024-08-15 , Nor Syafiqah Syahirah Mohamed , Shahril Irwan Sulaiman , Siti Rafidah Abdul Rahim , Azralmukmin Azmi

This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm's stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables − albedo values (25 %, 50 %, and 75 %), tilt angles (10°, 25°, and 35°), and module elevations (0.5 m, 1.5 m, and 2 m) − was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system's Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations.

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Integration of Multiple Distributed Generation Sources in Radial Distribution System Using a Hybrid Evolutionary Programming-Firefly Algorithm

2024-02-29 , Nik Hasmadi Nik Hassan , Siti Rafidah Abdul Rahim , Muhamad Hatta Hussain , Syahrul Ashikin Azmi , Azralmukmin Azmi , Ismail Musirin , Sazwan Ishak

This paper presents an approach for the optimal integration of multiple distributed generation (DG) sources in a radial distribution system. The integration of DG sources poses various challenges such as can lead to higher power losses caused by reverse power flow, voltage exceeding secure limits, voltage stability, power quality, and economic operation. To address these challenges, a hybrid algorithm is proposed which combines the benefits of both Evolutionary Programming and Firefly Algorithm. The proposed hybrid Evolutionary - Firefly Algorithm is employed for the determination of the optimal size of the DG sources. The objective of the proposed algorithm is to minimize the total system power losses and improve the voltage profile. The algorithm considers various constraints including the DG capacity limits and voltage limits. A comprehensive case study is conducted on a radial distribution system to demonstrate the effectiveness of the proposed approach. The simulation results show that the hybrid algorithm can find the optimal size and location of DG sources while achieving the desired system performance. The integration of multiple DG sources leads to a significant reduction in power losses and improved voltage profile. Furthermore, the proposed approach provides a flexible framework for the optimal integration of DG sources in radial distribution systems, allowing for the accommodation of different types and capacities of DG sources. The proposed technique is tested on the IEEE Reliability Test systems, specifically the IEEE 69-bus. The combination of DG at bus 61 and bus 27 yields a loss reduction index of 94%.

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Optimal Allocation and Sizing of Multi DG Units including Different Load Model Using Evolutionary Programming

2021-06-11 , Mohd Fayzul Mohammed , Siti Rafidah Abdul Rahim , Azralmukmin Azmi , Wan Zulmajdi Wan Zanudin , Muhamad Hatta Hussain , Nurul Huda H. , Aliman O.

This paper presents the optimal allocation and sizing of multi distributed generation (DG) units including different load models using evolutionary programming (EP) in solving power system optimization problem. This paper also studies on the effect of multi DG placement in different load model. To optimize the power distribution system, multi DG units were used to reduce losses power distribution system. By using EP, the optimal allocation and sizing of multi-DG was determined in order to obtain maximum benefits from its installation. The propose technique was tested into IEEE 69-bus distribution system. The result shows the placement of DG can reduce power loss 89% to 98%. The placement of multi-DG unit has better performance compare to single DG.

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An Effective Salp Swarm Based MPPT for Photovoltaic Systems under Dynamic and Partial Shading Conditions

2021-01-01 , Jamaludin M.N.I. , Mohammad Faridun Naim Tajuddin , Ahmed J. , Azralmukmin Azmi , Syahrul Ashikin Azmi , Nur Hafizah Ghazali , Babu T.S. , Alhelou H.H.

This study proposes a duty cycle-based direct search method that capitalizes on a bioinspired optimization algorithm known as the salp swarm algorithm (SSA). The goal is to improve the tracking capability of the maximum power point (MPP) controller for optimum power extraction from a photovoltaic system under dynamic environmental conditions. The performance of the proposed SSA is tested under a transition between uniform irradiances and a transition between partial shading (PS) conditions with a focus on convergence speed, fast and accurate tracking, reduce high initial exploration oscillation, and low steady-state oscillation at MPP. Simulation results demonstrate the superiority of the proposed SSA algorithm in terms of tracking performance. The performance of the SSA method is better than the conventional (hill-climbing) and among other popular metaheuristic methods. Further validation of the SSA performance is conducted via experimental studies involving a DC-DC buck-boost converter driven by TMS320F28335 DSP on the Texas Instruments Experimenter Kit platform. Hardware results show that the proposed SSA method aligns with the simulation in terms of fast-tracking, convergence speed, and satisfactory accuracy under PS and dynamic conditions. The proposed SSA method tracks maximum power with high efficiency through its superficial structures and concepts, as well as its easy implementation. Moreover, the SSA maintains a steady-state oscillation at a minimum level to improve the overall energy yield. It thus compensates for the shortcomings of other existing methods.

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