Browsing by Author "Suriza Ahmad Zabidi"

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Islanded microgrid is identified as a cost-effective solution to provide electricity to the off-grid areas which are far from the main electricity grid. However, intermittent nature of renewable energy source (RES) and unpredictable nature of load demand can cause an imbalance in the islanded microgrid. In islanded microgrid, components such as battery energy storage system (BESS) and diesel generator can compensate this problem. Diesel generator, especially, can provide auxiliary energy and act as the main grid in the islanded microgrid. Nonetheless, the inefficient utilisation of diesel generator can lead to unnecessary operation cost, shorten the life span and most importantly, contributing to the environmental pollution. Hence, energy management system (EMS) is employed to operate and manage the microgrid efficiently. Model predictive control (MPC) which has grown in popularity in recent years will be integrated with the EMS. The focus of this research is to reduce the use of diesel generator through efficient microgrid management utilising MPC-based EMS. It is proposed that load curtailment and greenhouse gas emissions cost function are included in the EMS to reduce the cost of the diesel generator operation. Two test cases are designed. The first test case compares the performance MPC-based EMS with and without load curtailment to evaluate the diesel generator cost savings. The second test case will observe the impact of the cost functions especially diesel fuel consumption cost and greenhouse gas emission cost influence the cost saving of diesel generator. The MPC-based EMS is simulated using MATLAB software. The optimisation problem of the MPC-based EMS is formulated using mixed integer quadratic programming (MIQP) and is solved using CPLEX. The results shows that the reduction of diesel fuel cost and consumption by 52.21% in MPC-based EMS utilising load curtailment compared to without load curtailment. In the second test case, it is found the second order polynomial diesel fuel consumption cost function is sufficed in reducing fuel cost by 34% compared to combining both diesel fuel consumption cost function and greenhouse gas emission cost function. In contrast, diesel fuel cost and consumption increased dramatically by 178%, despite a trivial improvement in greenhouse gas emission cost. Hence, it can be concluded that MPC-based EMS with load curtailment is able to improve the cost saving of the diesel generator. In addition, second order polynomial diesel fuel consumption cost function is sufficed to drive down the usage of diesel generator. However, load curtailment, particularly demand side management strategies, must be enhanced for islanded or standalone microgrids where incentive-based method cannot be applied.

Passive cell-balancing remains attractive for cost-sensitive battery management systems (BMS), but its fixed-threshold control wastes energy and accelerates component ageing. Many cost-sensitive applications rely on passive shunt-resistor balancing for lithium-ion battery packs because it is simple, reliable and low-cost, which typically uses open circuit voltage (OCV) as a proxy of state of charge (SOC), and this does not provide enough accuracy, mainly in lithium-ion cells where the voltage remains nearly the same for a wide range of SOC levels. In these aspects, despite large differences in SOC, the voltage variations may be too small for the BMS to measure accurately. For those reasons, the cells may remain persistently imbalanced during the balancing process, which can result in them not being fully used, a higher risk of overcharging and faster wear from heat produced. This study aims to investigate recent advancements in passive balancing and machine learning for SOC estimation, develop an enhanced passive balancing BMS architecture that integrates machine learning, and evaluate the proposed system against conventional OCV-based approaches in terms of accuracy, efficiency, and performance. An enhanced passive balancing architecture has been developed that couples a low-cost switched-shunt network with a long-short-term-memory (LSTM) SOC estimator trained on multi-temperature public datasets. The data-driven predictor supplies real-time SOC values to a hysteresis controller, enabling balancing decisions that are informed by cell dynamics rather than voltage alone. A three-cell lithium-ion in 3S1P arrangement was modelled in MATLAB/Simulink and validated against a hardware prototype configured for conventional OCV-based estimation that was used for the balancing process. Performance metrics such as balancing time, switching frequency, power dissipation and thermal rise were recorded for both strategies, and such findings were discovered in this study. The ML-assisted approach reduced balancing time to 80% SOC by 29% (2.88 hours vs. 4.05 hours), decreased average switching frequency by 97% (10.2 mHz vs. 333.8 mHz), and lowered total power dissipation by 81% (0.422 W vs. 2.22 W). The LSTM-based SOC estimator maintained high predictive accuracy across a temperature range of −10°C to 25°C with a root mean square error (RMSE) of 0.025, ensuring reliable SOC guidance under typical operating conditions. Thermal analysis revealed minimal temperature rise, with cell temperatures remaining within 0.3–0.5°C of ambient during the final charging phase. These results show that machine learning enables passive balancing circuits to match the efficiency of much more expensive active balancers, while maintaining simplicity, low cost, and safety of the battery cell. The proposed framework extends battery life, reduces thermal management costs, lowers total power loss, and increases efficiency.

In present world, optical fiber is the most emerging and prominent high speed medium of communication. However, optical communication can also be established without fiber and it is known as Free Space Optics (FSO). FSO has attracted a lot of attentions for a variety of applications in telecommunications field. FSO is a line of sight technology allowing optical connectivity without requiring fiber cable or security spectrum licenses. Compare to fiber optic, FSO provides lower cost and shorter time of deployment. Whereas comparing to microwave, FSO offers higher speed, broader and unlimited bandwidth with unlicensed spectrum. Since the transmission of FSO is through atmosphere, there is a variety of detrimental features of the atmospheric channel that may lead to serious signal fading and even complete loss of signal altogether. For FSO signal, the most important obstruction is local weather condition such as fog, snow, cloud, haze and rain. In temperate region, numerous studies and experiments have been conducted and the most limiting factors are found fog and snow. In tropical region with the absence of fog and snow, rain and haze are considered to be the limiting factors. ITU-R has recommended FSO attenuation models which are based on data acquired mainly from temperate climate. Hence to investigate those prediction models using tropical data measurement is required urgently to implement FSO in tropics. Therefore, the focus of this research is to propose new specific rain attenuation parameters that best represent tropical weather condition and to investigate the link availability. A FSO link with 850nm wavelength and 6mW transmit power was installed at International Islamic University Malaysia with 800m link distance. A real time rain intensity and visibility measurement system were installed synchronously with FSO link. Laser power was monitored concurrently with rain intensity and haze. From the concurrent measurement, the effects of rain and haze on FSO link were investigated. Based on one year measurement, modified regression coefficients for ITU-R specific rain attenuation model have been proposed. Preliminary analysis on the effect of haze on FSO and availability of FSO link in tropical climate has been done based on measured data. From the observation, link availability of 99.99% can be achieved for a link distance of 1.25km and fade margin of 25dB. The outcome of this research is expected to give a foundation for the design and development of a long range FSO link under tropical weather condition.

The community reporting system is a system that allows the user to report any misconduct to the relevant authority, easily and hustle free. In this thesis, the focus is on a road violation reporting system android app. In Malaysia, there are two types of reporting system, a manual reporting system and a website-based reporting system. These systems are fully authorized by the government body called JP J ( Jabatan Pengangkutan Jalanraya) or RTD (Road Transport Department). The problem with the existing manual system is time-consuming and to many platforms, thus, the report database will not be organized and well-managed. The system makes the users unwilling to make reports to the authority due to any road violation that they have seen. The objective of this project is to design a road violation reporting system using an android system. It is cost-effective, easy to use and more portable than the current reporting system. Also, to provide more services and implement the reporting system app in real time. The scope of this project is on application layer based on the design and the implementation of the reporting system. As the outcome, this community reporting app provides a user with a secure reporting system app and also the authority can manage the report quickly. The reporting app also provides more services than the current system and able to work on real-time. The development of the system is expected to enhance the reporting system and betterment for the community as well as the authority as a whole.