Sheikh Izzal Azid

Battery storage systems (BSS) are critical for maintaining stability and flexibility in renewable energy microgrids. However, the generation and load profiles introduce challenges for optimal energy management. This study presents a deep reinforcement learning (DRL) approach to real-time battery scheduling in a grid-connected microgrid. The control problem is modeled as a Markov decision process (MDP), and a Deep Q-Network (DQN) agent is trained to output optimal charging and discharging actions based on system states. Results indicate that the proposed DQN-based EMS achieves superior performance in balancing economic cost, energy self-sufficiency, and battery health compared to conventional rule-based strategy. This study contributes to advancing reinforcement learning-based approaches for real-time battery scheduling in microgrid energy systems.

This research improves the output of the integrating processes. The technique regulates a double-integrating process with time delay and non-minimal phase while handling high parametric uncertainties and load disturbances. A Smith predictor (SP) architecture based on a fractional-order internal model controller is developed (FOSPIMC). The gain and phase margins are applied to tune the fractional-order parameter. The fractional filter time constant is calculated using the intended performance limitation. Numerical studies and comparisons reveal better performance with a hybrid structure.

This paper proposes a fuzzy logic-based control approach to manage the thermal and fluidic coupling of a Proton Exchange Membrane Fuel Cell (PEMFC) with a Metal Hydride Tank (MHT). The strategy is based on managing the operating temperature of the PEMFC and the hydrogen pressure in the tank. Two PEMFC power modes (static and dynamic) were used to study the performance of this type of control. The main components of the system under study, i.e. the FC, the MHT, and the heat redistribution circuit, were modeled in a Matlab/Simulink environment. The fuzzy logic control used for the proposed system demonstrated good performance under static operating conditions.

Unmanned aerial vehicles (UAVs) have been employed for a variety of inspection and monitoring tasks, including agricultural applications and search and rescue (SAR) in remote areas. However, traditional monitoring methods tend to focus on optimizing one aspect. This study aims to propose a complete framework by integrating advanced methods to provide a robust and accurate path coverage solution. The combination of edge detection and area decomposition with a pathfinding algorithm can improve the overall performance. An effective edge detection model is developed that simultaneously detects the boundary and segments the area of interest (AOI) from the aerial land images and provides precise area mapping of the area. An intuitive grid decomposition with grid-to-graph mapping improves the flexibility of the area decomposition and ensures maximal coverage and safe operation routes for the UAVs. Finally, a robust modified simulated annealing (MSA) algorithm is introduced to determine the shortest path coverage route. The performance of the proposed methodology is tested on aerial imagery. Area decomposition ensures that there are no gaps in the AOI during the coverage planning. The MSA algorithm obtains the minimum length cost, charge consumption cost, and minimum number of turns to cover the area. It is shown that the integration of these techniques enhances the performance of the coverage path planning (CPP). A comparison of the proposed approach with benchmark algorithms further demonstrates its effectiveness. This study contributes to creating a complete CPP application for UAVs, which may assist with precision agriculture as well as safe and secure rescue operations.

This paper proposes a novel sensorless control strategy for permanent magnet vernier machines (PMSMs) in high-power propulsion applications. Integrating fractional-order proportional-integral (PI) control with a fast terminal sliding mode observer (FTSMO), the approach enhances wide-speed sensorless control. The fractional-order PI controller mitigates torque ripples, while the FTSMO eliminates the need for a phase-locked loop (PLL), reducing computational load and design complexities. An adaptation law facilitates direct speed estimation, and a unique terminal sliding surface improves reaching phase dynamics, enhancing convergence rates and estimation precision. Validated through MATLAB simulations on a 5 MW high propulsion PMSM, the proposed method demonstrates effective sensorless control, emphasizing its potential for high-power propulsion application.

The article introduces the design of an online disturbance compensator based on machine learning for quadrotor aircraft. The article presents the state-space models for the quadrotor, which encompass wind disturbances. The machine learning algorithm estimates unmeasurable states, which are linear and angular velocities, and constructs the unknown disturbances. These disturbances are then fed to the controller to compensate for disturbance and deviation in trajectory by varying the rotor speeds of the quadrotor aircraft. To present the simplicity of the proposed system, a simple PD controller is employed to manage the nonlinear modelled quadrotor. For the online training and validation purposes, the Parrot Mambo drone is utilized. The results are provided to demonstrate the effectiveness and advantages of the proposed controller.

This paper investigates the design and heat transfer optimization of a finned multi-tubular metal hydride tank for hydrogen storage. Metal hydrides are promising candidates for hydrogen storage due to their high volumetric capacity and safety, but their low thermal conductivity poses challenges in heat management during hydrogen absorption and desorption. The study explores the integration of finned tubes into the tank design to enhance heat transfer. Numerical modeling of a cylindrical tank with radial symmetry and multiple finned tubes was conducted, focusing on heat flux improvements and overall system performance. The results demonstrate that incorporating fins significantly increases the heat transfer surface area, improving thermal conductivity, absorption, and desorption kinetics. This leads to faster and more efficient hydrogen storage, reducing the time for charging and discharging processes. The findings highlight the potential of finned designs in advancing hydrogen storage technologies by addressing key thermal management challenges.

The growth of utility-scale solar power plants, which can now have more than a million PV modules, has created the need for automated monitoring and inspection technologies. Unmanned aerial vehicle (UAVs, or “drones”) have become widely used, especially for thermal infrared imaging, due to their falling cost, increasing technical capability, and ability to survey large areas quickly. However drones have certain limitations: they do not see the underside of PV arrays or balance-of-system equipment, it is difficult to hold a steady position for long- or repeated-exposure imaging, and operating permits can be onerous. In such cases unmanned ground vehicles (UGVs, or “robots”) can be advantageous for PV plant inspection. This paper reviews robot movement mechanisms (wheels, tracks and legs), types of PV faults for which they are suited, and their current status of use in commercial solar farms. Further, it examines typical obstacles to robot navigation in Australian solar farms. Key conclusions are that robots are likely to complement rather than replace drones for PV inspection, and are especially valuable for reducing fire risk by detecting hot-spots in electrical components.

Smart sensors and automated manufacturing processes can enable quality-assured production of geopolymer concrete from recovered materials and reduce the carbon footprint of housing, civil and energy infrastructure works and marina construction materials. Geopolymer is a new cementitious binder with lower carbon footprint than Ordinary Portland Cement (OPC) and the development of geopolymer from waste-derived materials contributes to Circular Economy. However, unlike OPC which is made from limestone and clay where the quality and consistency of these finite virgin materials extracted from the natural environment can be assured, the quality and consistency of waste-derived materials like coal flyash, mine tailings, mineral processing residues, metal slags and other industrial byproducts cannot be assured. Moreover, once these feedstock materials are used to manufacture geopolymer concrete the use of hazardous alkali-activating chemicals is required. Therefore, the use of advanced monitoring and control is applied at three levels: 1) assessment of the waste materials at their source (eg tailings storage facilities) with RPA (drones), 2) monitoring of the waste materials during pre-treatment to become feedstock with advanced sensors (eg X-Ray Fluorescence XRF) and 3) robotics and automation for the chemical handling plant, mixing of the feedstock materials into the reactor vessel and casting of the resultant concrete into the moulds.

Smart agricultural drones for crop spraying are becoming popular worldwide. Research institutions, commercial companies, and government agencies are investigating and promoting the use of technologies in the agricultural industry. This study presents a smart agriculture drone integrated with Internet of Things technologies that use machine learning techniques such as TensorFlow Lite with an EfficientDetLite1 model to identify objects from a custom dataset trained on three crop classes, namely, pineapple, papaya, and cabbage species, achieving an inference time of 91 ms. The system’s operation is characterised by its adaptability, offering two spray modes, with spray modes A and B corresponding to a 100% spray capacity and a 50% spray capacity based on real-time data, embodying the potential of Internet of Things for real-time monitoring and autonomous decision-making. The drone is operated with an X500 development kit and has a payload of 1.5 kg with a flight time of 25 min, travelling at a velocity of 7.5 m/s at a height of 2.5 m. The drone system aims to improve sustainable farming practices by optimising pesticide application and improving crop health monitoring.